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Publisher: Elsevier   (Total: 3158 journals)

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Showing 1 - 200 of 3157 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 36, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 24, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 97, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 27, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 37, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 423, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 28, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 284, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 8)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 17, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 11, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 171, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 12, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 15, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 24, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 32, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 4)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 13)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 28, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 26, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 20, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 11)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 7)
Advances in Drug Research     Full-text available via subscription   (Followers: 25)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 29, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 8)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 47, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 60, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 17)
Advances in Genetics     Full-text available via subscription   (Followers: 19, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 10, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 24, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 10, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 7, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 23)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 4)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Organ Biology     Full-text available via subscription   (Followers: 2)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 25, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 12)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 10)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 19)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 65)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 409, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 12, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 34, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 19)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 48, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 359, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 472, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 4)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 12, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 11)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 10, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 10, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 52, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 57, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 60, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 44, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 11)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 13, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 29, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 48)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 229, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 66, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 29, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 29, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 63, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 19, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription   (Followers: 1)
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 43, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 196, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 12, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 14)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 207, SJR: 1.58, CiteScore: 3)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 5, SJR: 0.937, CiteScore: 2)
Animal Reproduction Science     Hybrid Journal   (Followers: 7, SJR: 0.704, CiteScore: 2)

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Similar Journals
Journal Cover
Journal Prestige (SJR): 1.073
Citation Impact (citeScore): 4
Number of Followers: 7  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0925-2312
Published by Elsevier Homepage  [3158 journals]
  • A Varying-Gain Recurrent Neural-Network with Super Exponential Convergence
           Rate for Solving Nonlinear Time-Varying Systems
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Zhijun Zhang, Xiaolu Yang, Xianzhi Deng, Lingao Li In order to solve a nonlinear time-varying system, a novel varying-gain recurrent neural network (termed as VG-RNN) is proposed and analyzed. To achieve a fast convergent performance, a vector-based unbounded error function is first defined. Second, a varying-gain neural dynamic approach is employed to design the recurrent neural network formula. Being different from the traditional constant-gain recurrent neural networks with fixed design parameters such as the gradient-based neural network (termed as GNN) and the zeroing neural network (termed as ZNN), the gain coefficient of the proposed VG-RNN is time-varying, which can change with time evolves. Otherwise, compared to the previous numerical methods on solving nonlinear time-varying systems, the solution obtained by VG-RNN is more precise. Third, rigorous mathematics analysis proves the super exponential convergence and accuracy of the proposed VG-RNN. Numerical experiments demonstrate the high accuracy, effectiveness and superiority of the VG-RNN compared with the conventional neural networks for solving nonlinear time-varying systems. Furthermore, we hope to apply the theory proposed in this paper to practical nonlinear time-varying automatic control systems, such as robots with nonlinear time-varying systems.
  • Underwater salient object detection by combining 2D and 3D visual features
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Zhe Chen, Hongmin Gao, Zhen Zhang, Helen Zhou, Xun Wang, Yan Tian Automatic salient object detection over images is an important step for object detection and recognition. Many existing salient object detection methods are performed excellently on ground, but, it remains a challenge to detect salient objects in water. Different from common scenes on ground, challenges to underwater salient object detection are posed by poor underwater image quality, uncontrolled underwater objects and environments. Till now, most of existing methods are hindered from adaptability to difficult underwater environments, due to the strong light attenuation and scattering effects. To this end, we propose a novel underwater salient object detection method by combining 2D and 3D visual features. For feature combination, a novel detection model is established by mathematically stimulating the biological vision mechanism of aquatic animals. This model, apart from the 2D visual features i.e. the color and intensity, extracts 3D depth features to arouse the depth sensitivity in the three-dimensional space. These 2D and 3D visual features are combined by our biologically inspirited model, generating comprehensive salient object detection results. Here, aiming to correctly estimate 3D depth features from underwater images, a regional method is used to respectively extract 3D depth features in two regions, namely the artificial light and natural light regions. Evaluations show the diverse and comprehensive performances of various features for underwater salient object detection. High accuracy of our proposed method is demonstrated by comparing to state-of-the-art saliency detection methods on public underwater benchmarks acquired in diverse underwater environments.
  • Speeding up k-Nearest Neighbors Classifier for Large-Scale
           Multi-Label Learning on GPUs
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Przemysław Skryjomski, Bartosz Krawczyk, Alberto Cano Multi-label classification is one of the most dynamically growing fields of machine learning, due to its numerous real-life applications in solving problems that can be described by multiple labels at the same time. While most of works in this field focus on proposing novel and accurate classification algorithms, the issue of the computational complexity on growing dataset sizes is somehow marginalized. Owning to the ever-increasing capabilities of data capturing, we are faced with the problem of large-scale data mining that forces learners to be not only highly accurate, but also fast and scalable on high-dimensional spaces of instances, features, and labels. In this paper, we propose a highly efficient parallel approach for computing the multi-label k-Nearest Neighbor classifier on GPUs. While this method is highly effective due to its accuracy and simplicity, its computational complexity makes it prohibitive for large-scale data. We propose a four-step implementation that takes an advantage of the GPU architecture, allowing for an efficient execution of the multi-label k-Nearest Neighbors classifier without any loss of accuracy. Experiments carried out on a number of real and artificial benchmarks show that we are able to achieve speedups up to 200 times when compared to a sequential CPU execution, while efficiently scaling up to varying number of instances and features.
  • Coordinated Behavior of Cooperative Agents Using Deep Reinforcement
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Elhadji Amadou Oury Diallo, Ayumi Sugiyama, Toshiharu Sugawara In this work, we focus on an environment where multiple agents with complementary capabilities cooperate to generate non-conflicting joint actions that achieve a specific target. The central problem addressed is how several agents can collectively learn to coordinate their actions such that they complete a given task together without conflicts. However, sequential decision-making under uncertainty is one of the most challenging issues for intelligent cooperative systems. To address this, we propose a multi-agent concurrent framework where agents learn coordinated behaviors in order to divide their areas of responsibility. The proposed framework is an extension of some recent deep reinforcement learning algorithms such as DQN, double DQN, and dueling network architectures. Then, we investigate how the learned behaviors change according to the dynamics of the environment, reward scheme, and network structures. Next, we show how agents behave and choose their actions such that the resulting joint actions are optimal. We finally show that our method can lead to stable solutions in our specific environment.
  • Quantum probabilistic associative memory architecture
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Fernando M de Paula Neto, Adenilton J da Silva, Wilson R de Oliveira, Teresa B. Ludermir We present a quantum probabilistic associative memory using the inverse of quantum Fourier transform and Grover’s algorithm to recover existing or similar patterns in the memory. The content of the memory is created using a generator of a superposition state representing a given set of patterns. We discuss the architecture of the proposed memory including the storing, recovering and processing of similarity tolerance of the input query. The associative memory can extrapolate and recover similar stored patterns. The system is unitary and runs in O(n) steps, where n is the number of qubits of the patterns.
  • Neural Network-Based Stochastic Adaptive Attitude Control for Generic
           Hypersonic Vehicles with Full State Constraints
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Xiaofeng Zhang, Kang Chen, Wenxing Fu, Hanqiao Huang Stochastic uncertainties are often encountered and usually regarded as one of the most challenges in the flight control for generic hypersonic vehicles (HSVs). Unfortunately, most of the existing control results still have limitations in handling the problem of stochastic multiple uncertainties. In this paper, we focus on the adaptive control design for the generic HSVs subjected to stochastic multiple uncertainties and full state constraints. By introducing a one to one nonlinear mapping, the HSV system with full state constraints is transformed into a novel nonlinear multivariable system. Additionally, the obstacle caused by unknown time-varying disturbances and stochastic uncertainties can also be effectively circumvented with the fusion of a smooth function and the adaptive bound estimation. Moreover, several Radial basis function (RBF) neural networks (NNs) are used to approximate unknown nonlinear continuous functions. With a stochastic Lyapunov process, all the signals in the closed-loop system are proved to be semi-globally uniformly ultimately bounded and states constraints be finally satisfied. Simulation results have demonstrated a superior performance of the proposed control scheme in comparison to the other state-of-art work.
  • A Sequential Deep Learning Application for Recognising Human Activities in
           Smart Homes
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Daniele Liciotti, Michele Bernardini, Luca Romeo, Emanuele Frontoni The recent advancement and development of computer electronic devices has led to the adoption of smart home sensing systems, stimulating the demand for associated products and services. Accordingly, the increasingly large amount of data calls the machine learning (ML) field for automatic recognition of human behaviour. In this work, different deep learning (DL) models that learn to classify human activities were proposed. In particular, the long short-term memory (LSTM) was applied for modelling spatio-temporal sequences acquired by smart home sensors. Experimental results performed on the Center for Advanced Studies in Adaptive Systems datasets show that the proposed LSTM-based approaches outperform existing DL and ML methods, giving superior results compared to the existing literature.
  • Weakly Supervised Precise Segmentation for Historical Document Images
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Zecheng Xie, Yaoxiong Huang, Lianwen Jin, Yulian Liu, Yuanzhi Zhu, Liangcai Gao, Xiaode Zhang With the passing of history, precious cultural heritage was left behind to tell ancient stories, especially those in the form of written documents. In this paper, a weakly supervised segmentation system with recognition-guided information on attention area, is proposed for high-precision historical document segmentation under strict intersection-over-union (IoU) requirements. We formulate the character segmentation problem from Bayesian decision theory perspective and propose boundary box segmentation (BBS), recognition-guided BBS (Rg-BBS), and recognition-guided attention BBS (Rg-ABBS), progressively, to search for the segmentation path. Furthermore, a novel judgment gate mechanism is proposed to train a high-performance character recognizer in an incremental weakly supervised learning manner. The proposed Rg-ABBS method is shown to substantially reduce time consumption while maintaining sufficiently high precision of the segmentation result by incorporating both character recognition knowledge and line-level annotation. Experiments show that the proposed Rg-ABBS system significantly outperforms traditional segmentation methods as well as deep-learning-based instance segmentation and detection methods under strict IoU requirements.
  • Deep quality-related feature extraction for soft sensing modeling: a deep
           learning approach with hybrid VW-SAE
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Xiaofeng Yuan, Chen Ou, Yalin Wang, Chunhua Yang, Weihua Gui Soft sensors have been extensively used to predict difficult-to-measure quality variables for effective modeling, control and optimization of industrial processes. To construct accurate soft sensors, it is significant to carry out feature extraction for massive high-dimensional process data. Recently, deep learning has been introduced for feature representation in process data modeling. However, most of them cannot capture deep quality-related features for output prediction. In this paper, a hybrid variable-wise weighted stacked autoencoder (HVW-SAE) is developed to learn quality-related features for soft sensor modeling. By measuring the linear Pearson and nonlinear Spearman correlations for variables at the input layer with the quality variable at each encoder, a corresponding weighted reconstruction objective function is designed to successively pretrain the deep networks. With the constraint of preferential reconstruction for more quality-related variables, it can ensure that the learned features contain more information for quality prediction. Finally, the effectiveness of the proposed HVW-SAE based soft sensor method is validated on an industrial debutanizer column process.
  • Interactive Resource Recommendation with Optimization by Tag Association
           and Significance Analysis
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Qing Xie, Yajie Zhu, Feng Xiong, Lin Li, Zhifeng Bao, Yongjian Liu Along with the fast growing web-based applications, the recommender system is now attracting much attention due to its core function that matches the target users’ interest with the potential resources from the massive online information. Since the recommender system is a user centric application, in this work, we propose a recommendation framework based on user interaction, so as to explore the user’s real-time interest from the instant feedback. Naturally, we utilize the tag information assigned to different resources as the medium for user interaction. During the interaction, the most effective tags will be provided for users to choose, and the chosen tag words will be considered as the personalized preference and utilized to dynamically adjust the recommendation list during the process. However, the interaction procedure may cause the problem of potential false dismissal during the candidate filtering. In this work, we propose to analyze the association between different tags, and utilize the tag co-occurrence to refine the recommendation candidate, so as to avoid false dismissal. To generate the recommendation list from the filtered candidates, we design the representation of user and resource characteristics based on tag information and user historical behavior. We distinguish the significance of each tag word for the corresponding resource item, so as to precisely describe the item feature. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can achieve more accurate predictions and higher recommendation efficiency.
  • Minimal Learning Parameters-Based Adaptive Neural Control for Vehicle
           Active Suspensions with Input Saturation
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Yanqi Zhang, Yanjun Liu, Lei Liu In this paper, a neural network (NN) control method is developed for nonlinear quarter vehicle active suspension systems (VASSs) which have the features of parameter uncertainties, input saturation and road disturbance, whose aim is to ensure safe driving and improve the ride comfort. The NNs are employed to approximate unkown nonlinear functions that the forming reason is uncertainties by caused varied sprung mass. When the output of actuator goes beyond its maximums, an NN control scheme combined with anti-saturation is proposed to handle this problem. Furthermore, an NN controller with the minimal learning parameters is constructed to ensure that the number of adaptive learning parameters and the burden computation are largely reduced for VASSs. Meanwhile, the control objectives of VASSs are proved based on the stability analysis. Finally, the effectiveness of designed scheme is demonstrated by an example.
  • Shoot High-Quality Color Images Using Dual-Lens System with Monochrome and
           Color Cameras
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Xuan Dong, Weixin Li In the dual-lens system with monochrome and color cameras, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Due to occlusions, the colorization will inevitably fail in some cases. Thus, evaluating the colorization quality is also of great importance. We solve both problems in this paper. For colorization, we propose a gray-color correspondence prior, i.e. in local regions, if two patches are similar in the gray channel, it is very often that the two pixels centered at these two patches have similar colors. Based on this prior, a deep learning based and coarse-to-fine colorization method is proposed. For evaluating the colorization quality, we propose a symmetry colorization based evaluation method. Experimental results show that our method could largely outperform the state-of-the-art methods and is also efficient in computation.
  • Hierarchical Temporal Memory and Recurrent Neural Networks for Time Series
           Prediction:An Empirical Validation and Reduction to Multilayer Perceptrons
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Jakob Struye, Steven Latré Recurrent Neural Networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) are often deployed as neural network-based predictors for time series data. Recently, Hierarchical Temporal Memory (HTM), a machine learning technology attempting to simulate the human brain’s neocortex, has been proposed as another approach to time series data prediction. While HTM has gained a lot of attention, little is known about the actual performance compared to the more common RNNs. The only performance comparison between the two, performed at the company behind HTM, shows they perform similarly. In this article, we present a more in-depth performance comparison, involving more extensive hyperparameter tuning and evaluation on more scenarios. Surprisingly, our results show that both LSTM and GRUs can outperform HTM by over 30% at lower runtime. Furthermore, we show that HTM requires explicitly timestamped data to recognize daily and weekly patterns, while LSTM only needs the raw sequential data to predict such time series accurately. Finally, our experiments indicate that the temporally aware components of all considered predictors contribute nothing to the prediction accuracy. We further strengthen this claim by presenting equally or better performing Multilayer Perceptrons conceptually similar to the HTM and LSTM, disregarding their temporal aspects.
  • M -tensors&rft.title=Neurocomputing&rft.issn=0925-2312&">Neural networks based approach solving multi-linear systems with
           M -tensors
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Xuezhong Wang, Maolin Che, Yimin Wei In this paper, we propose continuous time neural network and modified continuous time neural networks for solving a multi-linear system with M-tensors. Theoretically, we prove that the presented neural networks are stable in the sense of Lyapunov stability theory. Numerical simulations are presented to show the effectiveness of the proposed neural networks.
  • Developing Enhanced Conversational Agents for Social Virtual Worlds
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): David Griol, Araceli Sanchis, José Manuel Molina, Zoraida Callejas In this paper, we present a methodology for the development of embodied conversational agents for social virtual worlds. The agents provide multimodal communication with their users in which speech interaction is included. Our proposal combines different techniques related to Artificial Intelligence, Natural Language Processing, Affective Computing, and User Modeling. A statistical methodology has been developed to model the system conversational behavior, which is learned from an initial corpus and improved with the knowledge acquired from the successive interactions. In addition, the selection of the next system response is adapted considering information stored into user’s profiles and also the emotional contents detected in the user’s utterances. Our proposal has been evaluated with the successful development of an embodied conversational agent which has been placed in the Second Life social virtual world. The avatar includes the different models and interacts with the users who inhabit the virtual world in order to provide academic information. The experimental results show that the agent’s conversational behavior adapts successfully to the specific characteristics of users interacting in such environments.
  • Neural and statistical predictors for time to readmission in emergency
           departments: a case study
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Asier Garmendia, Manuel Graña, Jose Manuel Lopez-Guede, Sebastian Rios The prediction of readmissions in the healthcare system, i.e. patients that are discharged and come back in a short interval of time, has taken great importance as readmissions have been taken as a measure of the system quality of service. Most studies in the literature follow a classification approach predicting the occurrence of the readmission event, however in this paper we are concerned with the prediction of the time until readmission, which can be studied in the framework of survival analysis. We report the performance of several neural and statistical prediction models on a large real dataset, finding approaches (weighted k-NN and regression tree based rule system) which provide a smooth approximation of the observed survival function, thus encouraging further research in this direction.
  • An adaptive fractional-order BP neural network based on extremal
           optimization for handwritten digits recognition
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Min-Rong Chen, Bi-Peng Chen, Guo-Qiang Zeng, Kang-Di Lu, Ping Chu The optimal generation of initial connection weight parameters and dynamic updating strategies of connection weights are critical for adjusting the performance of back-propagation (BP) neural networks. This paper presents an adaptive fractional-order BP neural network abbreviated as PEO-FOBP for handwritten digit recognition problems by combining a competitive evolutionary algorithm called population extremal optimization and a fractional-order gradient descent learning mechanism. Population extremal optimization is introduced to optimize a large number of initial connection weight parameters and fractional-order gradient descent learning mechanism is designed to update these connection weight parameters adaptively during the evolutionary process of fractional-order BP neural network. The extensive experimental results for a well-known MNIST handwritten digits dataset have demonstrated that the proposed PEO-FOBP outperforms the original fractional-order BP neural network and the traditional integer-order BP neural network in terms of training and testing accuracies.
  • Parallel Boosted Clustering
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Yazhou Ren, Uday Kamath, Carlotta Domeniconi, Zenglin Xu Scalability of clustering algorithms is a critical issue in real world clustering applications. Usually, data sampling and parallelization are two common ways to address the scalability issue. Despite their wide utilization in a number of clustering algorithms, they suffer from several major drawbacks. For example, most data sampling can often lead to biased solutions due to its inability in accurately capturing the distribution of the entire data set. On the other hand, the performance of parallelization highly depends on the original clustering routines which are not parallel algorithms in nature, such that customizing each algorithm to be parallel may hurt the clustering performance. To alleviate these problems, we propose a general two-step framework for scalable clustering in this work, where the first step is to obtain skeleton structure of data and the second step is to obtain the final clustering. Concretely, data are first partitioned and located across a two-dimensional grid, and then local clustering algorithms are iteratively applied on the cells of the grid, each providing a set of intermediate core points. These core points represent the dense or central regions of data, which can be centers, modes and means for centroid-based, density-based and probability-based clustering, respectively. Finally, these core points are further used to obtain the final clustering. The proposed framework enjoys several benefits: (1) the local clustering on partitioned cells are conducted in parallel and thus can lead to high speed-up; (2) the clustering on the representative core points can be more robust; (3) the framework can be easily applied to other basic clustering methods and thus achieves a general scalable solution. Theoretical analysis is provided and extensive experimental results have demonstrated the effectiveness and efficiency of the proposed framework.
  • Deep Successor Feature learning for Text Generation
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Cong Xu, Qing Li, Dezheng Zhang, Yonghong Xie, Xisheng Li In this paper we present an approach to training neural network to generate sequences using successor feature learning from reinforcement learning. The model can be thought as two components, an MLE-based token generator and an estimator that predicts the future value of whole sentence. As we know, reinforcement learning has been applied to dealing with the exposure bias problem of generating sequences. Compared with other RL algorithm, successor feature(SF) can learn robust value function provided observations and reward by decomposing the value function into two components - a reward predictor and a successor map. The encoder-decoder framework with SF enables the decoder to generate outputs that receive more future reward, which means that the model pays attention on not only the current word but also the rest words. We demonstrate that the approach improves performance on two translation tasks.
  • A Data Mining Method Based on Unsupervised Learning and Spatiotempporal
           Analysis for Sheath Current Monitoring
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Y. Wang, H. Ye, T. Zhang, H. Zhang Sheath current is one of the key indicators of underground power cable conditions. Considering the limitations of existing model-based methods for sheath current monitoring and difficulty in handling the increasing amount of unlabeled sheath current data accumulated by cable monitoring systems, we propose a data mining method based on unsupervised learning and spatiotemporal analysis of sheath currents for underground power cable monitoring. Tests based on real historical data demonstrate that the proposed method can effectively reveal unknown inherent patterns in unlabeled sheath current data.
  • AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for
           Medical Image Segmentation
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Maria Baldeon-Calisto, Susana K. Lai-Yuen Adapting an existing convolutional neural network architecture to a specific dataset for medical image segmentation remains a challenging task that requires extensive expertise and time to fine-tune the hyperparameters. Hyperparameter optimization approaches that automate the search have been proposed but have mainly focused on optimizing the segmentation performance. However, optimizing the network size is also important to prevent unnecessary and costly computational operations. In this paper, we present a multiobjective adaptive convolutional neural network (AdaResU-Net) for medical image segmentation that is able to automatically adapt to new datasets while minimizing the size of the network. The proposed AdaResU-Net is comprised of a fixed architecture that combines the structure of the state-of-the-art U-Net with a residual learning framework for more efficient training. Then, a multiobjective evolutionary algorithm (MEA) is proposed to evolve the AdaResU-Net networks with different hyperparameters to optimize both segmentation accuracy and model size. The presented model was tested on two publically available medical image datasets and compared with the U-Net. Results show that the AdaResU-Net achieves better segmentation performance with less than 30% the number of trainable parameters. Additionally, the MEA algorithm generated configurations that are smaller and better than configurations generated with a Bayesian optimization approach.
  • Fine-tuning Pre-trained Convolutional Neural Networks for Gastric
           Precancerous Disease Classification on Magnification Narrow-band Imaging
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Xiaoqi Liu, Chengliang Wang, Jianying Bai, Guobin Liao Gastric cancer(GC) is the fourth leading cause of cancer death worldwide. To prevent the occurrence of advanced GCs, there is a need for immediate detection and treatment of gastric precancerous and early cancerous lesions. Magnification endoscopy with narrow-band imaging (M-NBI) system as an advanced diagnostic imaging technology is widely used in evaluating gastric lesion types, which can interpret gastric lesion characteristics by enhancing contrasts between vessels and mucosal surfaces. Based on microvascular morphologies presented on M-NBI images, physicians can manually diagnose gastric lesions; but this is a tough work for unexperienced doctors and it is lacking of objectivity. In this study, we propose a transfer learning framework by fine-tuning pre-trained convolutional neural networks (CNNs) to classify gastric M-NBI images into three classes: chronic gastritis (CGT), low grade neoplasia (LGN) and early gastric cancer (EGC). The method we choose is used to compare with three kinds of traditional handcraft texture feature extraction methods and CNN models trained directly by our dataset. Results show that the performance of fine-tuned CNNs outperforms traditional handcraft features and trained CNNs. Experiments also illustrate that ResNet50 can achieve 0.96 accuracy, 0.92, 0.91 and 0.99 f1-scores for classifying M-NBI images into CGT, LGN and EGC. In conclusion, the proposed framework is suit for multi-classification tasks of gastric M-NBI images.
  • Deep recursive up-down sampling networks for single image super-resolution
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Zhen Li, Qilei Li, Wei Wu, Jinglei Yang, Zuoyong Li, Xiaomin Yang Single image super-resolution (SISR) technology can reconstruct a high-resolution (HR) image from the corresponding low-resolution (LR) image. The emergence of deep learning pushes SISR to a new level. The successful application of the recursive network motivates us to explore a more efficient SISR method. In this paper, we propose the deep recursive up-down sampling networks (DRUDN) for SISR. In DRUDN, an original LR image is directly fed without extra interpolation. Then, we use the sophisticated recursive up-down sampling blocks (RUDB) to learn the complex mapping between the LR image and the HR image. At the reconstruction part, the feature map is up-scaled to the ideal size by a de-convolutional layer. Extensive experiments demonstrate that DRUDN outperforms the state-of-the-art methods in both subjective effects and objective evaluation.
  • Integrating pixels and segments: a deep-learning method inspired by the
           informational diversity of the visual pathways
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Xue-song Tang, Dawei Li, Mingbo Zhao, Kuangrong Hao, Hui Wei Visual cortex is able to process information in multiple pathways and integrate various forms of representations. This paper proposed a bio-inspired method that utilizes the line-segment-based representation to perform a dedicated channel for the geometric feature learning process. The extracted geometric information can be integrated with the original pixel-based information and implemented on both the convolutional neural networks (SegCNN) and the stacked autoencoders (SegSAE). Segment-based operations such as segConvolve and segPooling are designed to further process the extracted geometric features. The proposed models are verified on the MNIST dataset, Caltech 101 dataset and QuickDraw dataset for image classification. According to the experimental results, the proposed models can facilitate the classification accuracies especially when the sizes of the training set are limited. Particularly, the method based on multiple representations is found to be effective for classifying the hand-drawn sketches.
  • A new robust output tracking control for discrete-time switched
           constrained-input systems with uncertainty via a critic-only iteration
           learning method
    • Abstract: Publication date: Available online 25 April 2019Source: NeurocomputingAuthor(s): Kun Zhang, Huaguang Zhang, Yuling Liang, Yinlei Wen In this paper, the control objective is driving the output of a discrete-time switched constrained system with uncertainty to track a desired output of reference by an optimal manner. A new augmented switched system with discounted cost function is constructed based on the switched and reference dynamics, which converts the complex tracking problem to a stabilizing robust control optimization problem. Combining the two stage optimization and iteration learning technique, the overall optimal hybrid policy is first achieved for the constrained switched tracking control. Instead of the general critic-actor structure, only critic neural network (NN) is applied in the algorithm to simply the architecture and manner of implementation. As the main computational burden or load in iteration learning process comes from the information transmissions of tuning NNs, the designed critic-only structure can reduce computational load with less transmissions. Then the convergence of the iteration learning process is demonstrated by theorems and the tracking objective is achieved as the output tracking errors get converged to zero. Finally, the proposed robust tracking control scheme for constrained-input switched systems is applied in the simulation, and the tracking results proved the effectiveness and applicability of the designed method.
  • Robust Self-tuning Spectral Clustering
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Guoqiu Wen Clustering, as an effective data analysis technique, is widely used in industrial application and science research. In this paper, we have proposed a novel spectral clustering method to solve three problems of spectral clustering, i.e., cluster-initialization, cluster-specification, and noise-robustness. To do this, we first learn a high-quality affinity matrix, and then capture an inherent clustering pattern in real feature space and projected subspace, and finally extract connected components to conduct clustering. By comparing to five comparison methods in six real data sets, our proposed method has achieved a competitive clustering performance in terms of four evaluation metrics.
  • A Data-Efficient Deep Learning Approach for Deployable Multimodal Social
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Heriberto Cuayáhuitl The deep supervised and reinforcement learning paradigms (among others) have the potential to endow interactive multimodal social robots with the ability of acquiring skills autonomously. But it is still not very clear yet how they can be best deployed in real world applications. As a step in this direction, we propose a deep learning-based approach for efficiently training a humanoid robot to play multimodal games—and use the game of ‘Noughts & Crosses’ with two variants as a case study. Its minimum requirements for learning to perceive and interact are based on a few hundred example images, a few example multimodal dialogues and physical demonstrations of robot manipulation, and automatic simulations. In addition, we propose novel algorithms for robust visual game tracking and for competitive policy learning with high winning rates, which substantially outperform DQN-based baselines. While an automatic evaluation shows evidence that the proposed approach can be easily extended to new games with competitive robot behaviours, a human evaluation with 130 humans playing with the Pepper robot confirms that highly accurate visual perception is required for successful game play.
  • Multilayer Probability Extreme Learning Machine for Device-Free
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Jie Zhang, Wendong Xiao, Yanjiao Li, Sen Zhang, Zhiqiang Zhang Device-free localization (DFL) is becoming one of the new techniques in wireless localization field, due to its advantage that the target to be localized does not need to attach any electronic device. One of the key issues of DFL is how to characterize the influence of the target on the wireless links, such that the target’s location can be accurately estimated by analyzing the changes of the signals of the links. Most of the existing related research works usually extract the useful information from the links through manual approaches, which are labor-intensive and time-consuming. Deep learning approaches have attempted to automatically extract the useful information from the links, but the training of the conventional deep learning approaches are time-consuming, because a large number of parameters need to be fine-tuned multiple times. Motivated by the fast learning speed and excellent generalization performance of extreme learning machine (ELM), which is an emerging training approach for generalized single hidden layer feedforward neural networks (SLFNs), this paper proposes a novel hierarchical ELM based on deep learning theory, named multilayer probability ELM (MP-ELM), for automatically extracting the useful information from the links, and implementing fast and accurate DFL. The proposed MP-ELM is stacked by ELM autoencoders, so it also keeps the very fast learning speed of ELM. In addition, considering the uncertainty and redundant links existing in DFL, MP-ELM outputs the probabilistic estimation of the target’s location instead of the deterministic output. The validity of the proposed MP-ELM-based DFL is evaluated both in the indoor and the outdoor environments, respectively. Experimental results demonstrate that the proposed MP-ELM can obtain better performance compared with classic ELM, multilayer ELM (ML-ELM), hierarchical ELM (H-ELM), deep belief network (DBN), and deep Boltzmann machine (DBM).
  • Training Binary Neural Networks With Knowledge Transfer
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Sam Leroux, Bert Vankeirsbilck, Tim Verbelen, Pieter Simoens, Bart Dhoedt Binary Neural Networks (BNNs) use binary values for both weights and activations instead of 32 bit floating point numbers typically used in deep neural networks. This reduces the memory footprint by a factor of 32 and allows a very efficient implementation in hardware. BNNs are trained using regular gradient descent but are harder to optimise, take longer to train and generally require a more careful tuning of hyperparameters such as the learning rate decay schedule than floating point versions. We propose to use Knowledge Transfer techniques to make it easier to train BNNs. Knowledge transfer is a general technique that tries to transfer the knowledge stored in a large network (the teacher) to a smaller (student) network. In our case the teacher is a network trained with floating point weights and activations while the student is a BNN. We apply different Knowledge Transfer techniques to the task of training a BNN. We introduce a novel similarity based Knowledge Transfer algorithm and show that this technique results in a higher test accuracy on different benchmark datasets compared to training the BNN from scratch.
  • A Very Deep Two-stream Network for Crowd Type Recognition
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Xinlei Wei, Junping Du, Zhe Xue, Meiyu Liang, Yue Geng, Xin Xu, JangMyung Lee Crowd type identification is a crucial task in the emergency alert. In this paper, to solve accurate identification of crowd type, the crowd type description triad C-BMO   and a novel crowd type recognition network(CTRN): very deep two-stream network architecture are proposed respectively. The very deep two-stream network architecture is based on the static map and motion map in the video. To early warn the emergency, the reasoning rules of the emergency alert are proposed based on joining the crowd type and the crowd characteristics. To verify the proposed method, the crowd type dataset is collected, and we experiment with the proposed plan on the crowd type dataset. The experimental results demonstrate that the proposed model is competitive compared with the state-of-the-art techniques.
  • Fine-Grained Image Analysis via Progressive Feature Learning
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Yichao Yan, Bingbing Ni, Huawei Wei, Xiaokang Yang Due to large intra-class variation and inter-class ambiguity, fine-grained object recognition has been a challenging task for decades. A good approach should be able to: 1) discover discriminative local details and 2) align and aggregate these local discriminative patch-level features in an effective manner to facilitate object-level classification. Toward this end, we develop a two-stage framework for fine-grained recognition. In our framework, the first stage aims to discover and align local discriminative features, and the second stage aims to aggregate these features to get classification results. In the first stage, we propose two methods to sequentially discover informative regions. In the second stage, we progressively feed these discovered and aligned regions into a recurrent neural network to obtain object-level representation. Extensive experiments are conducted on three fine-grained image benchmarks, and the results verify the effectiveness of our proposed framework.
  • Data Augmentation in Fault Diagnosis Based on the Wasserstein Generative
           Adversarial Network with Gradient Penalty
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Xin Gao, Fang Deng, Xianghu Yue Fault detection and diagnosis in industrial process is an extremely essential part to keep away from undesired events and ensure the safety of operators and facilities. In the last few decades various data based machine learning algorithms have been widely studied to monitor machine condition and detect process faults. However, the faulty datasets in industrial process are hard to acquire. Thus low-data of faulty data or imbalanced data distributions are common to see in industrial processes, resulting in the difficulty to accurately identify different faults for many algorithms. Therefore, in this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based data augmentation approaches are researched to generate data samples to supplement low-data input set in fault diagnosis field and help improve the fault diagnosis accuracies. To verify its efficient, various classifiers are used and three industrial benchmark datasets are involved to evaluate the performance of GAN based data augmentation ability. The results show the fault diagnosis accuracies for classifiers are increased in all datasets after employing the GAN-based data augmentation techniques.
  • Coupled Ensembles of Neural Networks
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Anuvabh Dutt, Denis Pellerin, Georges Quénot We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters while at the same time improving the performance. The use of branches brings an additional form of regularization. In addition to splitting the parameters into parallel branches, we propose a tighter coupling of these branches by averaging their log-probabilities. The tighter coupling favours the learning of better representations, even at the level of the individual branches, as compared to when each branch is trained independently. We refer to this branched architecture as “coupled ensembles”. The approach is generic and can be applied to almost any neural network architecture. With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain error rates of 2.92%, 15.68% and 1.50% on CIFAR-10, CIFAR-100 and SVHN respectively. For the same parameter budget, DenseNet-BC has an error rate of 3.46%, 17.18%, and 1.8% respectively. With ensembles of coupled ensembles, of DenseNet-BC networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and 1.42% respectively on these tasks.
  • Nash Q-Learning based Equilibrium Transfer for Integrated Energy
           Management Game with We-Energy
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Lingxiao Yang, Qiuye Sun, Dazhong Ma, Qinglai Wei This paper proposes an innovative energy interacting unit (“We-Energy”) with the characteristic of full duplex trading mode. In order to manage all the We-Energies in an optimal way, a new integrated energy management framework based on a noncooperative game is performed so as to allocate the energy demands of each WE such that the benefit of each WE can be maximized. To overcome the impact of the randomness and inaccurate information of renewable energy sources, Nash Q-learning algorithm is applied for computation of game equilibrium under the unknown environment. The novelty of the proposed algorithms is related to the incorporation of the continuous action space into the discrete adaptive action set and combined the equilibrium transfer to improve the efficiency of the algorithm. Simulation studies of modified IMS confirm that it has a better performance with the desired equilibrium strategy and convergence speed.
  • Automatic detection of anatomical landmarks in brain MR scanning using
           multi-task deep neural networks
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Xulei Yang, Wai Teng Tang, Gabriel Tjio, Si Yong Yeo, Yi Su This work involves the development of a computer method to perform automatic graphical prescription in brain MR scanning. The approach is based on multi-task deep neural networks that perform automatic detection of the anterior commissures (AC) and posterior commissures (PC) in the sagittal images, and automatic determination of the symmetry line in the axial images. The proposed multi-task learning architecture solves the tasks of point and angle detection simultaneously by exploiting the commonalities and differences across these tasks. This results in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training individual models separately. After deriving the AC-PC line and symmetry line on the sagittal image and axial image, respectively, the corresponding scan coverage is then determined by using an image processing approach. Based on a study using a small-sized MR brain image dataset, three benefits are observed: Firstly, our proposed approach was able to perform the task well despite limited availability of training data. This is an advantage in view of the fact that training of single task models will typically encounter difficulty in convergence using a small training set. Secondly, it achieves better performance for landmarks detection in terms of smaller position and angulation shifts between the predicted results and the ground truth. Lastly, the inference can be performed faster since the two tasks are solved simultaneously using only one model.
  • Analysis of Tuberculosis Severity Levels From CT Pulmonary Images Based on
           Enhanced Residual Deep Learning Architecture
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Xiaohong W. Gao, Carl James-Reynolds, Ed Currie This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15±1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively.
  • Multi-label Transfer Learning for the Early Diagnosis of Breast Cancer
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Hiba Chougrad, Hamid Zouaki, Omar Alheyane Early diagnosis of breast cancer, when it is small and has not spread, can make the disease easier to treat which increases the patient's chances of survival. The recent proposed methods for the early diagnosis of breast cancer, and while showing great success in achieving this goal, rely on one of the indicators in the mammogram to diagnose the patient's condition. Whether it is identifying differences in shapes and patterns of the findings (i.e. masses, calcifications...etc.) or assessing the breast density as a risk indicator, these Computer-aided Diagnosis (CAD) systems by using single-label classification, fail to exploit the intrinsic useful correlation information among data from correlated domains.Rather than learning to identify the disease based on one of the indicators, we propose the joint learning of the tasks using multi-label image classification. Furthermore, we introduce a new fine-tuning strategy for using transfer learning, that takes advantage of the end-to-end image representation learning when adapting the pre-trained Convolutional Neural Network (CNN) to the new task. We also propose a customized label decision scheme, adapted to this problem, which estimates the optimal confidence for each visual concept. We demonstrate the effectiveness of our approach on four benchmark datasets, CBIS-DDSM, BCDR, INBreast and MIAS, obtaining better results compared to other commonly used baselines.
  • The Random Neural Network with Deep Learning Clusters in Smart Search
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Will Serrano, Erol Gelenbe This paper proposes a Neurocomputing application that reorders the Web results obtained from different Web Search Engines emulating the way our brain takes decisions. The proposed application is based on the Random Neural Network with Deep Learning Clusters that evaluates and adapts Web result relevance by associating independently each Deep Learning Cluster to a specific Web Search Engine. In addition, this paper presents a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster. The performance of the proposed Management Cluster is evaluated when included as an additional layer to the Deep Learning Clusters. On average; the proposed Deep Learning cluster structure improves Smart Search performance.
  • A Framework for Hierarchical Division of Retinal Vascular Networks
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Linfang Yu, Zhen Qin, Tianming Zhuang, Yi Ding, Zhiguang Qin, Kim-Kwang Raymond Choo Human retinal vascular network plays an important role in ophthalmology diagnosis. For example, in the diagnosis of ophthalmology, the severity of the disease has a direct correlation with the lesion location, in the sense that the closer the lesion area is to the optic disk, the higher will be the severity of the disease. If a framework is able to provide the hierarchical structure of the retinal vascular network, then the severity of disease can be quantified by leveraging the hierarchical characteristics of vessels in the vicinity of the lesion location. Thus, in this paper, an executable framework is recommended for the hierarchical division of the retinal vascular networks. Specifically, a supervised method based on deep neural network is used for retinal blood vessel segmentation. A graph-based method is also applied to generate vascular trees from the segmented retinal vessels. As part of our proposed approach, we present two algorithms: the potential landmark detection algorithm (PLDA) is used to identify the bifurcations and crossings; and the adaptive hierarchical classification algorithm (AHCA) is used in the hierarchical characteristics classification of vascular bifurcations. By classifying the hierarchical characteristics of vascular bifurcation, the hierarchical characteristics of the vessel segments containing these bifurcations are identified. Thus, the hierarchical division of retinal vascular network is realized. When applied to two publicly available datasets, DRIVE and STARE, the proposed framework achieves an accurate rate of 98.99% and sensitivity rate of 92.17%.
  • Correlational Convolutional LSTM for Human Action Recognition
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Mahshid Majd, Reza Safabakhsh In light of recent exponential growth of video data, the need for automated video processing has increased substantially. To learn the intrinsic structure of video data, many representation approaches have been proposed, focusing on learning the spatial features and time dependencies, while, motion features are hand-crafted and left out of the learning process. In this work, we present an extended version of the LSTM units named C2LSTM in which the motion data are perceived as well as the spatial features and temporal dependencies. We leverage convolution and correlation operators to credit both the spatial and motion structure of the video data. Furthermore, a deep network is designed for human action recognition using the proposed units. The network is evaluated on the two well-known benchmarks, UCF101 and HMDB51. The results confirm the potency of C2LSTM to capture motion as well as spatial features and time dependencies.
  • Data-driven and deep learning-based detection and diagnosis of incipient
           faults with application to electrical traction systems
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Hongtian Chen, Bin Jiang, Tianyi Zhang, Ningyun Lu Incipient faults in electrical drive systems will evolve into faults or failures as time goes on. Successful detection and diagnosis of incipient faults can not only improve the safety and reliability but also provide optimal maintenance instructions for electrical drive systems. In this paper, an integration strategy of data-driven and deep learning-based method is proposed to deal with incipient faults. The salient advantages of the proposed method can be summarized as: 1) The moving average technique is firstly introduced into the canonical correlation analysis (CCA) framework, which makes the new residual signals more sensitive to incipient faults than the traditional CCA-based method; 2) Based on the defined residual signals, the new test statistics cooperating closely with Kullback-Leibler divergence (KLD) are proposed from the probability viewpoint, which can greatly improve the fault detectability; 3) It is of high computational efficiency because the estimation of probability density functions of residual signals is skilly avoided; 4) Based on the new developed test statistics, the fault matrices are defined and regraded as the input of convolutional neural network (CNN) whose feature extraction ability is highly improved compared with the traditional method, which helps to accurately diagnose of incipient faults; 5) The proposed method can be implemented without any priori knowledge on system information. Theoretical analysis and three sets of experiments on a practical electrical drive system demonstrate the effectiveness of the proposed method.
  • Deep Learning Neural Networks: Methods, Systems, and Applications
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Qinglai Wei, Nikola Kasabov, Marios Polycarpou, Zhigang Zeng
  • Exploiting Deep Convolutional Neural Networks for a Neural-based Learning
           Classifier System
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Ji-Yoon Kim, Sung-Bae Cho Classification is a key factor in accuracy, simplicity, and expressiveness, and it is difficult to optimize all of these factors at the same time. The learning classifier system (LCS) is a suitable technique for addressing an adaptive classification problem. It is a combination of fast approximation and evolutionary optimization techniques. A neural-based learning classifier system (N-LCS) includes an architecture for maintaining expressiveness by incorporating neural networks into a supervised classifier system, which is also an LCS specializing in classification studies. In recent years, studies using deep artificial neural networks have been actively conducted. In particular, deep convolutional neural networks (CNN) provide a powerful representation in an extremely fundamental method and demonstrates the high performance in various domains. In this paper, we exploit various deep CNN architectures in convolutional neural-based learning classifier systems (CN-LCS) combining the CNN and LCS to explore the possibility of a CN-LCS. By using various CNNs as an action of a classifier in an N-LCS, better classification accuracy can be obtained and classifier can be optimized. Experimental results show that our models achieve the higher performance than N-LCS for database intrusion detection as well as two other datasets, and extract effective features from deep representation by projecting data samples learned by several deep CNN models into the feature space.
  • A novel Time Series Forecasting Model with Deep Learning
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Zhipeng Shen, Yuanming Zhang, Jiawei Lu, Jun Xu, Gang Xiao Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series datasets.
  • Asymptotically stable critic designs for approximate optimal stabilization
           of nonlinear systems subject to mismatched external disturbances
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Bo Zhao, Guang Shi, Ding Wang This paper addresses approximate optimal stabilization problems for nonlinear systems in the presence of mismatched external disturbances via asymptotically stable critic designs. By establishing the nonlinear disturbance observer, the corresponding information is utilized to construct the online updated cost function, which reflects the real-time disturbances, regulation and control simultaneously. With the help of the proper cost function, the Hamilton-Jacobi-Bellman equation is solved by employing a critic neural network, whose weight vector is guaranteed to be asymptotically stable with nested tuning laws. The approximate optimal control is derived to guarantee the closed-loop system to be ultimately uniformly bounded based on the Lyapunov stability theorem. The effectiveness of the developed stabilization scheme is verified via simulations of two numerical examples.
  • Non-contact Heart Rate Detection under Non-cooperative Face Shake
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Hongwei Yue, Xiaorong Li, Ken Cai, Huazhou Chen, Shufen Liang, Tianlei Wang, Wenhua Huang Video-based non-contact heart rate detection can be easily affected by factors such as face shake and shooting environment; thus, effectively extracting the blood volume pulse signal is difficult. Therefore, a video-based face-shake-resistant heart rate detection method was proposed in this paper to mediate this problem. First, the face region that was selected through the multi-task convolution neural networks was used to correct the tilt angle and obtain the face image sequence. The face image sequence possessed approximately the same skin color information. Afterward, empirical mode decomposition and permutation entropy were combined, and the initial position of the signal was determined according to the randomness of the intrinsic mode function component to denoise and reconstruct the blood volume pulse signal. Finally, spectral analysis was implemented for the reconstructed signal to compute the heart rate value. The experimental results showed that the proposed method was highly consistent with the measurement result of the pulse oximeter; moreover, the proposed method showed good stability and accuracy for human heart rate detection.
  • A Fully Convolutional Network Feature Descriptor: Application to Left
           Ventricle Motion Estimation Based on Graph Matching in Short-Axis MRI
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Junhao Wu, Ziyu Gan, Wei Guo, Xuan Yang, Adan Lin Cardiac diseases cause abnormal motion dynamics over the cardiac cycle, and can, therefore, be diagnosed by analyzing the myocardial motion of the left ventricle (LV). In this paper, a feature point descriptor using fully convolutional neural networks (FCNs) is proposed and applied to cardiac motion estimation based on graph matching. A fully convolutional network is trained to predict endocardial contours and extract features of points from short-axis cine magnetic resonance (MR) images. An LV graph is constructed using the extracted point features, and a convex graph matching cost function is defined to estimate the point correspondence between images in two given phases. The sparsity and double stochastic constraints are introduced into the cost function, which is optimized iteratively by the alternating direction method of multipliers (ADMM). Finally, the transformation using compact supported radial basis functions with sparsity constraint is employed to estimate the dense displacement field between two cardiac images in two phases based on the correspondence relationship. The performance of the proposed method was evaluated on two public cardiac databases, and the experimental results show that the FCN feature descriptor outperforms traditional feature descriptors in estimating the correspondence between endocardial contours of the LV. For LV motion estimation, the proposed method provides more accurate motion fields than existing graph matching algorithms.
  • Neural-network-based Tracking Control for a Class of Time-Delay Nonlinear
           Systems with Unmodeled Dynamics
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Huanqing Wang, Yuchun Zou, Peter X. Liu, Xudong Zhao, Jialei Bao, Yucheng Zhou This paper is concerned with the tracking control problem of a class of non-strict-feedback nonlinear systems with unmodeled dynamics and time-delay. In the backstepping procedure, a dynamic signal is designed to handle the unmodeled dynamics and the Lyapunov-Krasovskii functions are applied to compensate for the effect of time delay. Meanwhile, a neural network-based approximator is used to approximate the unknown nonlinear functions in the system. It is proved by the theoretical analysis that the presented controller guarantees the semi-global boundedness of all signals in the closed-loop systems, and the output tracking error eventually converges to a small area around zero. Simulation results are presented to illustrate the validity of the proposed approach.
  • Local Differential Privacy for Social Network Publishing
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Peng Liu, YuanXin Xu, Quan Jiang, Yuwei Tang, Yameng Guo, Li-e Wang, Xianxian Li Social networks often contain sensitive information. Releasing social network data could possibly seriously jeopardize individual privacy. Therefore, we need to protect privacy when publish social network data. However, the current differential privacy for social network data publishing seriously influences the structure of the social network. We propose a local differential privacy model for social network publishing that preserves community structure information. The model generates the synthetic social network data as published versions under the structural constraints of the edge probability reconstruction. We theoretically prove that the local differential privacy model satisfies the definition of differential privacy. We evaluate the efficacy of the proposed method using three real-life social network datasets and show that our method effectively preserves network structural properties, while ensuring a strong degree of privacy.
  • Hybrid methodology based on Bayesian Optimization and GA-PARSIMONY to
           search for parsimony models by combining hyperparameter optimization and
           feature selection
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): F.J. Martinez-de-Pison, R. Gonzalez-Sendino, A. Aldama, J. Ferreiro, E. Fraile This article presents a hybrid methodology that combines Bayesian optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimony models. The proposal is designed to reduce the sizeable computational effort associated with the use of GA-PARSIMONY alone. The method begins with BO to obtain favorable initial model parameters. Then, with these parameters, a constrained GA-PARSIMONY is implemented to generate accurate parsimony models by using feature reduction, data transformation and parsimonious model selection. Experiments with extreme gradient boosting machines (XGBoost) and ten UCI databases demonstrated that the hybrid methodology obtains models analogous to those of GA-PARSIMONY while achieving significant reductions in elapsed time in eight out of ten datasets.
  • Super-resolution Reconstruction of Single Anisotropic 3D MR Images Using
           Residual Convolutional Neural Network
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Jinglong Du, Zhongshi He, Lulu Wang, Ali Gholipour, Zexun Zhou, Dingding Chen, Yuanyuan Jia High-resolution (HR) magnetic resonance (MR) imaging is an important diagnostic technique in clinical practice. However, hardware limitations and time constraints often result in the acquisition of anisotropic MR images. It is highly desirable but very challenging to enhance image spatial resolution in medical image analysis for disease diagnosis. Recently, studies have shown that deep convolutional neural networks (CNN) can significantly boost the performance of MR image super-resolution (SR) reconstruction. In this paper, we present a novel CNN-based anisotropic MR image reconstruction method based on residual learning with long and short skip connections. The proposed network can effectively alleviate the vanishing gradient problem of deep networks and learn to restore high-frequency details of MR images. To reduce computational complexity and memory usage, the proposed network utilizes cross-plane self-similarity of 3D T1-weighted (T1w) MR images. Based on experiments on simulated and clinical brain MR images, we demonstrate that the proposed network can significantly improve the spatial resolution of anisotropic MR images with high computational efficiency. The network trained on T1w MR images is able to effectively reconstruct both SR T1w and T2-weighted (T2w) images, exploiting image features for multi-modality reconstruction. Moreover, the experimental results show that the proposed method outperforms classical interpolation methods, non-local means method (NLM), and sparse coding based algorithm in terms of peak signal-to-noise-ratio, structural similarity image index, intensity profile, and small structures. The proposed method can be efficiently applied to SR reconstruction of thick-slice MR images in the out-of-planes views for radiological assessment and post-acquisition processing.
  • Deep Multi-Center Learning for Face Alignment
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Zhiwen Shao, Hengliang Zhu, Xin Tan, Yangyang Hao, Lizhuang Ma Facial landmarks are highly correlated with each other since a certain landmark can be estimated by its neighboring landmarks. Most of the existing deep learning methods only use one fully-connected layer called shape prediction layer to estimate the locations of facial landmarks. In this paper, we propose a novel deep learning framework named Multi-Center Learning with multiple shape prediction layers for face alignment. In particular, each shape prediction layer emphasizes on the detection of a certain cluster of semantically relevant landmarks respectively. Challenging landmarks are focused firstly, and each cluster of landmarks is further optimized respectively. Moreover, to reduce the model complexity, we propose a model assembling method to integrate multiple shape prediction layers into one shape prediction layer. Extensive experiments demonstrate that our method is effective for handling complex occlusions and appearance variations with real-time performance. The code for our method is available at
  • Dynamic MRI Reconstruction Exploiting Blind Compressed Sensing Combined
           Transform Learning Regularization
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Ning He, Ruolin Wang, Yixue Wang The goal of dynamic magnetic resonance imaging (dynamic MRI) is to visualize tissue properties and their local changes over time that are traceable in the MR signal. Compressed sensing enables the accurate recovery of images from highly under-sampled measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing (BCS), where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly under-sampled measurements. Specifically, in our model, the patches of the under-sampled images are approximately sparse in a transform domain. Transform learning that combines wavelet and gradient sparsity is considered as regularization in our model for dynamic MR images. The original complex problem is decomposed into several simpler subproblems, then each of the subproblems is efficiently solved with a variable splitting iterative scheme. The results of numerous experiments show that the proposed algorithm outperforms the state-of-the-art compressed sensing MRI algorithms and yields better reconstructions results.
  • Scalp EEG epileptogenic zone recognition and localization based on
           Long-term Recurrent Convolutional Network
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Weixia Liang, Haijun Pei, Qingling Cai, Yonghua Wang The scalp electroencephalogram (EEG), a non-invasive measure of brain's electrical activity, is commonly used ancillary test to aide in the diagnosis of epilepsy. Usually, neurologists employ direct visual inspection to identify epileptiform abnormalities. Therefore, electroencephalograms have been an essential integral to the researches which aim to automatically detect epilepsy. However, it is difficult because seizure manifestations on scalp EEG are extremely variable between patients, even the same patient. In addition, scalp EEG is usually composed of large number of noise signals which might cover the real features of seizure. To this challenge, we construct an 18-layer Long-Term recurrent convolutional network (LRCN) to automatic epileptogenic zone recognition and localization on scalp EEG. As far as we know, we are the first to train a deep learning classifier to identify seizures through the EEG images, just like neurologists direct visual inspection to identify epileptiform abnormalities. Furthermore, unlike the traditionally methods extracted features from channels manually, which neglected the association of brain's epileptiform abnormalities electrical transmission, seizures is considered as a continuous brain's abnormal electrical activity in our algorithm, from produce at one or several channels, transmission between channels, to flat again after seizures. The method was evaluated in 23 patients with a total of 198 seizures. The classifier shows reasonably good results, with 84% for sensitivity, 99% for specificity, and 99% for accuracy. False Positive Rate per hours exceeds significantly previous results obtained on cross-patient classifiers, with 0.2/h.
  • Real-time Moisture Control in Sintering Process Using Offline-online NARX
           Neural Networks
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Yushan Jiang, Ning Yang, Qingqi Yao, Zhaoxia Wu, Wei Jin Sinter is the main raw material for blast furnace iron making. To provide high quality sinter, the moisture content of the mixture in the sintering need to be in the best range. However, most of the sintering is still artificial water adding which leads to a great variation in the moisture content of the mixture. The present work proposes a sintering parameter identification model using a nonlinear autoregressive model with exogenous (NARX). By exploiting the real-time and historical performing data, we set up a mixture adding water model involved the water and the major mixtures among sintering. Then, a combination of offline deep supervisor learning and online self-learning NARX algorithm is proposed. Finally, in the experimental stage, the results suggest the proposed method can effectively predict the moisture with an acceptable degree of accuracy.
  • Deep Learning-based Visual Ensemble Method for High-speed Railway Catenary
           Clevis Fracture Detection
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Ye Han, Zhigang Liu, Yang Lyu, Kai Liu, Changjiang Li, Wenxuan Zhang This paper proposes an automatic visual inspection method for the fracture detection of clevises in the catenary systems of high-speed railways, using images of catenaries captured by an inspection vehicle. First, the clevises are extracted from the catenary image using a convolutional neural network based algorithm, known as the faster region-based convolutional neural network. Because the structure of catenary systems does not have many variations and the contextual information near a catenary fitting may have strong correlation with its category, the architecture of the original faster region-based convolutional neural network is modified to make use of the contextual information of the regions of interest in the images for object recognition. A crack detection process is then used to recognize the fractures of clevises. To detect the cracks, the edge map of the clevis sub-image is generated using a region-scalable fitting model. Areas where the cracks are most likely to occur are projected from a standard clevis image to the clevis sub-image by shape context matching and affine transformation matrix computation. The cracks are then recognized by calculating the wavelet entropy inside these areas followed by morphological filtering. Experimental results show that the modified faster region-based convolutional neural network architecture achieves better results in clevis extraction than the original architecture as well as some other state-of-art object detection models. The detection is not affected by the scaling, texture and grayscale changes of the clevises caused by the variation of shooting distance, shooting angle and illumination variations. The fractures of the clevises can be accurately and reliably detected using the fracture detection method proposed in this paper and the performance of this visual inspection method meets the strict requirements for catenary system maintenance.
  • A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Jordi de la Torre, Aida Valls, Domenec Puig In this paper we present a diabetic retinopathy deep learning interpretable classifier. On one hand, it classifies retina images into different levels of severity with good performance. On the other hand, this classifier is able of explaining the classification results by assigning a score for each point in the hidden and input spaces. These scores indicate the pixel contribution to the final classification. To obtain these scores, we propose a new pixel-wise score propagation model that for every neuron, divides the observed output score into two components. With this method, the generated visual maps can be easily interpreted by an ophthalmologist in order to find the underlying statistical regularities that help to the diagnosis of this eye disease.
  • Software Defect Prediction via Cost-sensitive Siamese Parallel
           Fully-connected Neural Networks
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Linchang Zhao, Zhaowei Shang, Ling Zhao, Taiping Zhang, Yuan Yan Tang Software defect prediction (SDP) has caused widespread concern among software engineering researchers, which aims to erect a software defect predictor according to historical data. However, it is still difficult to develop an effective SDP model on high-dimensional and limited data. In this study, a novel SDP model for this problem is proposed, called Siamese parallel fully-connected networks (SPFCNN), which combines the advantages of Siamese networks and deep learning into a unified method. And training this model is administered by AdamW algorithm for finding the best weights. The minimum value of a singular formula is the target of training for SPFCNN model. Significantly, we extensively compared SPFCNN method with the state-of-the-art SDP approaches using six openly available datasets from the NASA repository. Six indexes are used to evaluate the performance of the proposed method. Experimental results showed that the SPFCNN method contributes to significantly higher performance compared with benchmarked SDP approaches, indicating that a cost-sensitive neural network could be developed successfully for SDP.
  • Deep Learning To Detect Android Malware via Opcode Sequences
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Abdurrahman Pektaş, Tankut Acarman A large number of Android malware samples can be deployed as the variants of the previously known samples. In consequence, a classification system capable of supporting a large set of samples is required to secure Android platform. Although a large set of variants requires scalability for automatic detection and classification, it also presents a significant advantage about a richer dataset at the stage of discovering underlying malicious activities and extracting representative features. Deep Neural Networks are built by a complex structure of layers whose parameters can be tuned and trained in order to enhance classification statistical metric results. Emerging parallelization computing tools and processors reduce computation time.In this paper, we propose a deep learning Android malware detection method using features extracted from instruction call graphs. The presented method examines all possible execution paths and the balanced dataset improves deep neural learning benign execution paths versus malicious paths. Since there is not a publicly available model for Android malware detection, we train deep networks from scratch. Then, we apply a grid search method to seek the optimal parameters of the network and to discover the combination of the hyper-parameters, which maximizes the statistical metric values. To validate the effectiveness of the proposed method, we evaluate with a balanced dataset constituted by 24,650 malicious and 25,000 benign samples. We evaluate the deep network architecture with respect to different parameters and compare the statistical metric values including runtime with respect to baseline classifiers. Our experimental results show that the presented malware detection is reached at 91.42% level in accuracy and 91.91% in F-measure, respectively.
  • Domain Adaptation with SBADA-GAN and Mean Teacher
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Chengjian Feng, Zhaoshui He, Jiawei Wang, Qinzhuang Lin, Zhouping Zhu, Jun Lv, Shengli Xie Unsupervised domain adaptation has received much attention in the past decade, and various methods have been developed for this problem, e.g., SBADA-GAN (Symmetric Bidirectional ADAptive Generative Adversarial Network) and Mean Teacher, which achieved state-of-the-art results. However, their performance is not perfect when the source and target distributions are not well matched. By combining SBADA-GAN with Mean Teacher, we propose a powerful model for unsupervised domain adaptation, where we use Mean Teacher to replace the target classifier of SBADA-GAN and develop a bidirectional class cycle-consistency strategy to preserve the class identity of the transformed images. Furthermore, we apply gradient penalty and spectral normalization to improve the stability of the training process. The proposed method was demonstrated on six unsupervised domain adaptation benchmarks with significant and consistent improvements over the state-of-the-art methods.
  • A new hybrid deep signal processing approach for bearing fault diagnosis
           using vibration signals
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Miao He, David He Signal processing is an important task for machine fault diagnosis. Over the recent years, many deep learning based signal processing methods have been developed for bearing fault diagnosis. However, these methods are facing some major problems when they are applied to machine fault diagnosis. In this paper, a new hybrid deep signal processing method for bearing fault diagnosis is presented. The presented method incorporates vibration analysis techniques into deep learning to form a deep learning structure embedded with time synchronous resampling mechanism. Data collected from real bearing test rig are used to validate and demonstrate the effectiveness of the presented method.
  • Robust Brain Extraction Tool for CT Head Images
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Zeynettin Akkus, Petro Kostandy, Kenneth A. Philbrick, Bradley J. EricksonABSTRACTExtracting brain parenchyma from computed tomography (CT) images of the head is an important prerequisite step in a number of image processing applications, as it improves the computational speed and accuracy of quantitative analyses and image co-registration. In this study, we present a robust method based on fully convolutional neural networks (CNN) to remove non-brain tissues from head CT scans in a computationally efficient manner. The method includes an encoding part, which has sequential convolutional filters that produce feature representation of the input image in low dimensional space, and a decoding part, which consists of convolutional filters that reconstruct the input image from the reduced representation. We trained several CNN models on 122 volumetric head CT scans and tested our models on 22 withheld volumetric CT head scans based on two experts’ manual brain segmentation. The performance of our best CNN model on the test set is: Dice Coefficient=0.998±0.001 (mean ± standard deviation), recall=0.999±0.001, precision=0.998±0.001, and accuracy=1. Our method extracts complete volumetric brain from head CT images in about 2s which is substantially faster than currently available methods. To the best of our knowledge, this is the first study using CNN to perform brain extraction from CT images. In conclusion, the proposed approach based on CNN provides accurate extraction of brain tissue from head CT images in a computationally efficient manner.
  • A Deep Learning based Multitask Model for Network-wide Traffic Speed
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Kunpeng Zhang, Liang Zheng, Zijian Liu, Ning Jia This paper proposes a deep learning based multitask learning (MTL) model to predict network-wide traffic speed, and introduces two methods to improve the prediction performance. The nonlinear Granger causality analysis is used to detect the spatiotemporal causal relationship among various links so as to select the most informative features for the MTL model. Bayesian optimization is employed to tune the hyperparameters of the MTL model with limited computational costs. Numerical experiments are carried out with taxis’ GPS data in an urban road network of Changsha, China, and some conclusions are drawn as follows. The deep learning based MTL model outperforms four deep learning based single task learning (STL) models (i.e., Gated Recurrent Units network, Long Short-term Memory network, Convolutional Gated Recurrent Units network and Temporal Convolutional Network) and three other classic models (i.e., Support Vector Machine, k-Nearest Neighbors and Evolving Fuzzy Neural Network). The nonlinear Granger causality test provides a reliable guide to select the informative features from network-wide links for the MTL model. Compared with two other optimization approaches (i.e., grid search and random search), Bayesian optimization yields a better tuning performance for the MTL model in the prediction accuracy under the budgeted computation cost. In summary, the deep learning based MTL model with nonlinear Granger causality analysis and Bayesian optimization promises the accurate and efficient traffic speed prediction for a large-scale network.
  • Deep Learning clusters in the Cognitive Packet Network
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Will Serrano, Erol Gelenbe The Cognitive Packet Network (CPN) bases its routing decisions and flow control on the Random Neural Network (RNN) Reinforcement Learning algorithm; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The proposed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.
  • Deep Learning for Variational Multimodality Tumor Segmentation in PET/CT
    • Abstract: Publication date: Available online 24 April 2019Source: NeurocomputingAuthor(s): Laquan Li, Xiangming Zhao, Wei Lu, Shan Tan Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire functional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality information for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the probability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the probability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: 1). Only a few training samples were needed for training the designed network to produce the probability map; 2). The proposed method can be applied to small datasets, normally seen in clinic research; 3). The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multimodality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); 4). The proposed method had a good performance for tumor segmentation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ± 0.05, sensitivity (SE) of 0.86 ± 0.07, positive predictive value (PPV) of 0.87 ± 0.10, volume error (VE) of 0.16 ± 0.12, and classification error (CE) of 0.30 ± 0.12.
  • A Hierarchical Meta-model for Multi-Class Mental Task Based Brain-Computer
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Akshansh Gupta, R.K. Agrawal, Jyoti Singh Kirar, Baljeet Kaur, Weiping Ding, Chin-Teng Lin, Andreu Perez Javier, Mukesh Prasad In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM) classifier is used for multi mental task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class mental task classification. Experimental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class mental task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.
  • Learning longitudinal classification-regression model for infant
           hippocampus segmentation
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Yanrong Guo, Zhengwang Wu, Dinggang Shen Hippocampus plays an important role in the memory and spatial navigation function of human brain. Study on its growth and change during first year of life would assist the investigation of early brain development as well as the biomarker for neurological disorders. With the help of Magnetic Resonance (MR) imaging techniques, infant brain at different development stage can be acquired with multiple imaging modalities. In this situation, the longitudinal segmentation of infant hippocampus is highly demanded and feasible for the clinical studies regarding to the hippocampal volume changes. However, since the brain structures undergo dynamic appearance, structural changes and various tissue contrast during the first year of life, substantial challenges will be imposed for ensuring the robustness and accuracy of automatic hippocampus segmentation algorithms. In addition, most of the existing hippocampus segmentation methods generally handle each brain development stage independently without considering the potential longitudinal consistency among different stages. In view of the above factors, we propose a longitudinal classification-regression model for segmenting hippocampus in infant brain MRIs. Generally, our model proceeds on a per-timepoint basis, guided by the output of latter timepoint towards the infant hippocampus in the previous timepoint. The key ingredient of our method is a combination of longitudinal context, static context and appearance learning strategies under the classification-regression forest architecture. Specifically, the longitudinal context is borrowed from the mask of prior-timepoint estimation and the static context is from the current-timepoint estimation. Furthermore, we implement the proposed model in a multi-scale and iterative manner to improve the efficiency and effectiveness. The proposed method is evaluated on segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that our method achieves better performance in segmentation accuracy over the state-of-the-art classification and regression random forest model.
  • Self-Paced Multi-Task Clustering
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Yazhou Ren, Xiaofan Que, Dezhong Yao, Zenglin Xu Multi-task clustering (MTC) has attracted a lot of research attentions in machine learning due to its ability in utilizing the relationship among different tasks. Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data. To alleviate these problems, we propose a novel self-paced multi-task clustering (SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train a series of MTC models with increasing complexity, thus highly decreases the risk of trapping into poor local optima. Furthermore, to reduce the negative influence of outliers and noisy data, we design a soft version of SPMTC to further improve the clustering performance. The corresponding SPMTC framework can be easily solved by an alternating optimization method. The proposed model is guaranteed to converge and experiments on real data sets have demonstrated its promising results compared with state-of-the-art multi-task clustering methods.
  • Image classification with an RGB-channel nonsubsampled contourlet
           transform and a convolutional neural network
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Lingling Fang, Hanyu Zhang, Jiaxin Zhou, Xianghai Wang In this paper, an efficient image classification method is proposed that is based on the nonsubsampled contourlet transform (NSCT) of RGB-channel images and the convolutional neural network (CNN). First, the NSCT-based coefficients of natural RGB-channel images are extracted, which are capable of capturing the statistical properties of each channel. In addition, the proposed feature descriptor is equipped with the mean-max-pooling strategy according to the characteristics of the correlated coefficients. Then, the CNN is concatenated to exaggerate the discriminative parts of the primary features. With these advantages, the proposed RGB-channel NSCT-CNN should, in general, improve the corresponding CNN-based image classification methods. Using the Food-101 and SUN Datasets, the proposed method achieves state-of-the-art classification results that are also significant for object detection. In addition, the proposed method can achieve better or comparable accuracy compared to other related methods based on these two datasets.
  • Question-Led Object Attention for Visual Question Answering
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Lianli Gao, Liangfu Cao, Xing Xu, Jie Shao, Jingkuan Song Question plays a leading role for Visual Question Answering (VQA) because it specifies the particular visual objects or conjures vivid visual that the machine should attend. However, existing approaches predominantly predict the answer by utilizing the question and the whole image without considering the leading role of the question. Also, recent object spatial inference is usually conducted on pixel level instead of object level. Therefore, we propose a novel but simple framework, namely Question-Led Object Attention (QLOB), to improve the VQA performance by exploring question semantics, fine-grained object information, and the relationship between those two modalities. First, we extract sentence semantics by a question model, and utilize the efficient object detection network to obtain a global visual feature and local features from top r object region proposals. Second, our QLOB attention mechanism selects those question-related object regions. Third, we optimize question model and QLOB attention by a softmax classifier to predict the final answer. Extensive experimental results on three public VQA datasets demonstrate that our QLOB outperforms the state-of-the-arts.
  • Adaptive dynamic programming based event-triggered control for unknown
           continuous-time nonlinear systems with input constraints
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Shan Xue, Biao Luo, Derong Liu, Yueheng Li An adaptive dynamic programming (ADP) based event-triggered control method is established in this paper to solve the optimal control problem of unknown continuous-time nonlinear systems with input constraints. First, the unknown system is identified using two neural networks (NNs). Second, the threshold for event-triggering condition is designed, which guarantees the system stability. Then, a critic NN is employed to approximate the value function and the closed-loop system is proved to be uniformly ultimately bounded. Finally, the simulation results demonstrate the feasibility of the developed ADP method. The main contributions of this paper are that the developed ADP-based event-triggered control method avoids the Zeno phenomenon effectively and updates the weights of three NNs simultaneously in the process of implementation. Meanwhile, an improved critic NN weight updating criterion is adopted, which does not require an initial admissible control.
  • Cost-Sensitive KNN Classification
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Shichao Zhang KNN (K Nearest Neighbors) classification is one of top-10 data mining algorithms. It is significant to extend KNN classifiers sensitive to costs for imbalanced data classification applications. This paper designs two efficient cost-sensitive KNN classification models, referred to Direct-CS-KNN classifier and Distance-CS-KNN classifier. The two CS-KNN classifiers are further improved with extant strategies, such as smoothing, minimum-cost k-value selection, feature selection and ensemble selection. We evaluate our methods with real data sets, to show that our CS-KNN classifiers can significantly reduce misclassification cost.
  • Fuzzy Knowledge Based Performance Analysis on Big Data
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Neha Bharill, Aruna Tiwari, Aayushi Malviya, Om Prakash Patel, Akahansh Gupta, Deepak Puthal, Amit Saxena, Mukesh Prasad Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM) algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM) called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM.
  • Deep Understanding of Big Multimedia Data
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Ruili Wang, Jian Weng, Xiaofeng Zhu
  • Two Birds with One Stone: Transforming and Generating Facial Images with
           Iterative GAN
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Dan Ma, Bin Liu, Zhao Kang, Jiayu Zhou, Jianke Zhu, Zenglin Xu Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications. Although a number of generative models have been developed to tackle this problem, there is still much room for further improvement. In paticular, the current solutions usually ignore the perceptual information of images, which we argue that it benefits the output of a high-quality image while preserving the identity information, especially in facial attributes learning area. To this end, we propose to train GAN iteratively via regularizing the min-max process with an integrated loss, which includes not only the per-pixel loss but also the perceptual loss. In contrast to the existing methods only deal with either image generation or transformation, our proposed iterative architecture can achieve both of them. Experiments on the multi-label facial dataset CelebA demonstrate that the proposed model has excellent performance on recognizing multiple attributes, generating a high-quality image, and transforming image with controllable attributes.
  • CcNet: A Cross-connected Convolutional Network for Segmenting Retinal
           Vessels Using Multi-scale Features
    • Abstract: Publication date: Available online 23 April 2019Source: NeurocomputingAuthor(s): Shouting Feng, Zhongshuo Zhuo, Daru Pan, Qi Tian Retinal vessel segmentation (RVS) helps the diagnosis of diabetic retinopathy, which can cause visual impairment and even blindness. Some problems are hindering the application of automatic RVS, including accuracy, robustness and segmentation speed. In this paper, we propose a cross-connected convolutional neural network (CcNet) for the automatic segmentation of retinal vessel trees. In the CcNet, convolutional layers extract the features and predict the pixel classes according to those learned features. The CcNet is trained and tested with full green channel images directly. The cross connections between primary path and secondary path fuse the multi-level features. The experimental results on two publicly available datasets (DRIVE: Sn = 0.7625, Acc = 0.9528; STARE: Sn = 0.7709, Acc = 0.9633) are higher than those of most state-of-the-art methods. In the cross-training phase, CcNte’s accuracy fluctuations (△Accs) on DRIVE and STARE are 0.0042 and 0.007, respectively, which are relatively small compared with those of published methods. In addition, our algorithm has faster computing speed (0.063 s) than those listed algorithms using a GPU (graphics processing unit). These results reveal that our algorithm has potential in practical applications due to promising segmentation performances including advanced specificity, accuracy, robustness and fast processing speed.
  • Boosting the performance of over-sampling algorithms through
           under-sampling the minority class
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Romero F.A.B. de Morais, Germano C. Vasconcelos Over-sampling algorithms are the most adopted approach to balance class distribution in imbalanced data problems, through random replication or synthesis of new examples in the minority class. Current over-sampling algorithms, however, usually use all available examples in the minority class to synthesise new instances, which may include noisy or outlier data. This work proposes k-INOS, a new algorithm to prevent over-sampling algorithms from being contaminated by noisy examples in the minority class. k-INOS is based on the concept of neighbourhood of influence and works as a wrapper around any over-sampling algorithm. A comprehensive experimentation was conducted to test k-INOS in 50 benchmark data sets, 8 over-sampling methods and 5 classifiers, with performance measured according to 7 metrics and Wilcoxon signed-ranks test. Results showed, particularly for weak classifiers (but not only), k-INOS significantly improved the performance of over-sampling algorithms in most performance metrics. Further investigations also allowed to identify conditions where k-INOS is likely to increase performance, according to features and rates measured from the data sets. The extensive experimentation framework evidenced k-INOS as an efficient algorithm to be applied prior to over-sampling methods.
  • Adaptive online extreme learning machine by regulating forgetting factor
           by concept drift map
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Hualong Yu, Geoffrey I. Webb In online-learning, the data is incrementally received and the distributions from which it is drawn may keep changing over time. This phenomenon is widely known as concept drift. Such changes may affect the generalization of a learned model to future data. This problem may be exacerbated by the form of the drift itself changing over time. Quantitative measures to describe and analyze the concept drift have been proposed in previous work. A description composed from these measures is called a concept drift map. We believe that these maps could be useful for guiding how much knowledge in the old model should be forgotten. Therefore, this paper presents an adaptive online learning model that uses a concept drift map to regulate the forgetting factor of an extreme learning machine. Specifically, when a batch of new instances are labeled, the distribution of each class on each attribute is firstly estimated, and then it is compared with the distribution estimated in the previous batch to calculate the magnitude of concept drift, which is further used to regulate the forgetting factor and to update the learning model. Therefore, the novelty of this paper lies in that a quantitative distance metric between two distributions constructed on continuous attribute space is presented to construct concept drift map which can be further associated with the forgetting factor to make the learning model adapt the concept drift. Experimental results on several benchmark stream data sets show the proposed model is generally superior to several previous algorithms when classifying a variety of data streams subject to drift, indicating its effectiveness and feasibility.
  • Pre-processing approaches for imbalanced distributions in regression
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Paula Branco, Luis Torgo, Rita P. Ribeiro Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS  method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.
  • A dynamic linear model for heteroscedastic LDA under class imbalance
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Kojo Sarfo Gyamfi, James Brusey, Andrew Hunt, Elena Gaura Linear Discriminant Analysis (LDA) yields the optimal Bayes classifier for binary classification for normally distributed classes with equal covariance. To improve the performance of LDA, heteroscedastic LDA (HLDA) that removes the equal covariance assumption has been developed. In this paper, we show using first and second-order optimality conditions that the existing approaches either have no principled computational procedure for optimal parameter selection, or underperform in terms of the accuracy of classification and the area under the receiver operating characteristics curve (AUC) under class imbalance. Using the same optimality conditions, we then derive a dynamic Bayes optimal linear classifier for heteroscedastic LDA that is optimised via an efficient iterative procedure, which is robust against class imbalance. Experimental work is conducted on two artificial and eight real-world datasets. Our results show that the proposed algorithm compares favourably with the existing heteroscedastic LDA procedures as well as the linear support vector machine (SVM) in terms of the error rate, but is superior to all the algorithms in terms of the AUC under class imbalance. The fast training time of the proposed algorithm also encourages its use for large-data applications that show high incidence of class imbalance, such as in human activity recognition.
  • Radial-Based oversampling for noisy imbalanced data classification
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Michał Koziarski, Bartosz Krawczyk, Michał Woźniak Imbalanced data classification remains a focus of intense research, mostly due to the prevalence of data imbalance in various real-life application domains. A disproportion among objects from different classes may significantly affect the performance of standard classification models. The first problem is the high imbalance ratios that pose a serious learning difficulty and require usage of dedicated methods, capable of alleviating this issue. The second important problem which may appear is noise, which may be accompanying the training data and causing strong deterioration of the classifier performance or increase the time required for its training. Therefore, the desirable classification model should be robust to both skewed data distributions and noise. One of the most popular approaches for handling imbalanced data is oversampling of the minority objects in their neighborhood. In this work we will criticize this approach and propose a novel strategy for dealing with imbalanced data, with particular focus on the noise presence. We propose Radial-Based Oversampling (RBO) method, which can find regions in which the synthetic objects from minority class should be generated on the basis of the imbalance distribution estimation with radial basis functions. Results of experiments, carried out on a representative set of benchmark datasets, confirm that the proposed guided synthetic oversampling algorithm offers an interesting alternative to popular state-of-the-art solutions for imbalanced data preprocessing.
  • Exploratory study on Class Imbalance and solutions for Network Traffic
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Santiago Egea Gómez, Luis Hernández-Callejo, Belén Carro Martínez, Antonio J. Sánchez-Esguevillas Network Traffic Classification is a fundamental component in network management, and the fast-paced advances in Machine Learning have motivated the application of learning techniques to identify network traffic. The intrinsic features of Internet networks lead to imbalanced class distributions when datasets are conformed, phenomena called Class Imbalance and that is attaching an increasing attention in many research fields. In spite of performance losses due to Class Imbalance, this issue has not been thoroughly studied in Network Traffic Classification and some previous works are limited to few solutions and/or assumed misleading methodological approaches. In this article, we deal with Class Imbalance in Network Traffic Classification, studying the presence of this phenomenon and analyzing a wide number of solutions in two different Internet environments: a lab network and a high-speed backbone. Namely, we experimented with 21 data-level algorithms, six ensemble methods and one cost-level approach. Throughout the experiments performed, we have applied the most recent methodological aspects for imbalanced problems, such as: DOB-SCV validation approach or the performance metrics assumed. And last but not least, the strategies to tune parameters and our algorithm implementations to adapt binary methods to multiclass problems are presented and shared with the research community, including two ensemble techniques used for the first time in Machine Learning to the best of our knowledge. Our experimental results reveal that some techniques mitigated Class Imbalance with interesting benefit for traffic classification models. More specifically, some algorithms reached increases greater than 8% in overall accuracy and greater than 4% in AUC-ROC for the most challenging network scenario.
  • Learning in the presence of class imbalance and concept drift
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao
  • Covariate shift estimation based adaptive ensemble learning for handling
           non-stationarity in motor imagery related EEG-based brain-computer
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Haider Raza, Dheeraj Rathee, Shang-Ming Zhou, Hubert Cecotti, Girijesh Prasad The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.
  • An empirical study to investigate oversampling methods for improving
           software defect prediction using imbalanced data
    • Abstract: Publication date: 28 May 2019Source: Neurocomputing, Volume 343Author(s): Ruchika Malhotra, Shine Kamal Software defect prediction is important to identify defects in the early phases of software development life cycle. This early identification and thereby removal of software defects is crucial to yield a cost-effective and good quality software product. Though, previous studies have successfully used machine learning techniques for software defect prediction, these techniques yield biased results when applied on imbalanced data sets. An imbalanced data set has non-uniform class distribution with very few instances of a specific class as compared to that of the other class. Use of imbalanced datasets leads to off-target predictions of the minority class, which is generally considered to be more important than the majority class. Thus, handling imbalanced data effectively is crucial for successful development of a competent defect prediction model. This study evaluates the effectiveness of machine learning classifiers for software defect prediction on twelve imbalanced NASA datasets by application of sampling methods and cost sensitive classifiers. We investigate five existing oversampling methods, which replicate the instances of minority class and also propose a new method SPIDER3 by suggesting modifications in SPIDER2 oversampling method. Furthermore, the work evaluates the performance of MetaCost learners for cost sensitive learning on imbalanced datasets. The results show improvement in the prediction capability of machine learning classifiers with the use of oversampling methods. Furthermore, the proposed SPIDER3 method shows promising results.
  • Twin Neural Networks for the classification of large unbalanced datasets
    • Abstract: Publication date: Available online 8 February 2019Source: NeurocomputingAuthor(s): Jayadeva, Himanshu Pant, Mayank Sharma, Sumit Soman Twin Support Vector Machines (TWSVMs) have emerged as an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets. The objective functions of the networks in the Twin NN are designed to realize the idea of the Twin SVM with non-parallel decision boudaries for the respective classes, while also being able to reduce model complexity. The Twin NN optimizes the feature map, allowing for better discrimination between classes. The paper also discusses an extension of the Twin NN for multiclass datasets. This architecture trains as many neural networks as the number of classes, and has the additional advantage that it does not have any hyper-parameter which requires tuning. Results presented in the paper demonstrate that the Twin NN generalizes well and scales well on large unbalanced datasets.
  • An adaptive immune algorithm for service-oriented agricultural Internet of
    • Abstract: Publication date: Available online 7 February 2019Source: NeurocomputingAuthor(s): Zhen Yang, Yongsheng Ding, Kuangrong Hao, Xin Cai Internet of Things (IoT) has the characteristics of multi-source, multi-type and unequal amount of task. Considering multiple requests at stochastic moment in the agricultural IoT service, an optimization model of minimum service cost is built in this paper, and an adaptive immune algorithm with endocrine regulation (AIE) is proposed to solve the problem of dynamic service for multiple requests. Based on this model, two service providing strategies are put forward, i.e. single service and collaborative service. The former means that one request task is completed by one service provider, while the latter means that one request task is collaboratively completed by multiple service providers. The service cost and service time are respectively evaluated under two service providing strategies. The simulation results verify the feasibility and effectiveness of the proposed AIE through comparison to the immune algorithm without endocrine regulation. Meanwhile, it is demonstrated that the proposed AIE can obtain more excellent performance than genetic algorithm and particle swarm optimization.
  • Neural-network-based learning algorithms for cooperative games of
           discrete-time multi-player systems with control constraints via adaptive
           dynamic programming
    • Abstract: Publication date: Available online 7 February 2019Source: NeurocomputingAuthor(s): He Jiang, Huaguang Zhang, Xiangpeng Xie, Ji Han Adaptive dynamic programming (ADP), an important branch of reinforcement learning, is a powerful tool in solving various optimal control problems. However, the cooperative game issues of discrete-time multi-player systems with control constraints have rarely been investigated in this field. In order to address this issue, a novel policy iteration (PI) algorithm is proposed based on ADP technique, and its associated convergence analysis is also studied in this brief paper. For the proposed PI algorithm, an online neural network (NN) implementation scheme with multiple-network structure is presented. In the online NN-based learning algorithm, critic network, constrained actor networks and unconstrained actor networks are employed to approximate the value function, constrained and unconstrained control policies, respectively, and the NN weight updating laws are designed based on the gradient descent method. Finally, a numerical simulation example is illustrated to show the effectiveness.
  • Editorial: Neural learning in life system and energy system
    • Abstract: Publication date: Available online 7 February 2019Source: NeurocomputingAuthor(s): Chen Peng, Dong Yue, Dajun Du, Huiyu Zhou, Aolei Yang
  • Contract-based approach to provide electric vehicles with charging service
           in heterogeneous networks
    • Abstract: Publication date: Available online 7 February 2019Source: NeurocomputingAuthor(s): Huwei Chen, Zhou Su, Yilong Hui, Hui Hui, Dongfeng Fang Recently, mobile charging stations (MCSs) have attracted more attentions compared with fixed charging stations (FCSs). Electric vehicles (EVs) can be easily provided with charging service through MCSs. However, most of existing approaches cannot be properly used to design the optimal pricing strategy for MCSs, leading to the inefficiency of power in MCSs. Thus, it is necessary to study new incentive mechanisms to improve MCSs’ profits. In this paper, we propose a contract-based scheme to maximize MCSs’ profits in the heterogeneous networks. Considering the power trading between EV users and MCSs, we develop the utility function with EV users’ types. Aiming to maximize MCSs’ profits, we formulate this problem as an optimization problem under the complete and incomplete information of EV users, respectively. Through the theoretical analysis, we prove the existence of optimal contract items, which also ensure the feasibility of EV users. Then optimal solutions can be achieved based on our proposed algorithm. Numerical and simulation results validate the effectiveness of our proposal.
  • Optimal sensor placement based on relaxation sequential algorithm
    • Abstract: Publication date: Available online 7 February 2019Source: NeurocomputingAuthor(s): Hong Yin, Kangli Dong, An Pan, Zhenrui Peng, Zhaoyuan Jiang, Shaoyuan Li Aiming at the large tension of the solution obtained by sequential algorithm for optimal sensor placement problem, a novel relaxation sequential algorithm is proposed by introducing the idea of edge relaxation operation of Dijkstra’s algorithm into sequential algorithm. An initial solution set is generated by sequential algorithm, and improved by relaxation till the relaxation operation terminates. The proposed algorithm takes modal assurance criterion (MAC) matrix as the objective fitness function. A truss structure and a rigid-framed arch bridge are applied as examples to verify the effectiveness of the new algorithm for optimal sensor placement. The result indicates that the relaxation sequential algorithm requires fewer sensors and can reach better maximum off-diagonal element of MAC matrix in OSP problem.
  • A novel online detection method of data injection attack against dynamic
           state estimation in smart grid
    • Abstract: Publication date: Available online 6 February 2019Source: NeurocomputingAuthor(s): Rui Chen, Xue Li, Huixin Zhong, Minrui Fei Dynamic state estimation is usually employed to provide real-time and effective supervision for the smart grid (SG) operation. However, dynamic state estimators have been recently found vulnerable to data injection attack, which are misled without posing any anomalies to bad data detection (BDD). To improve the robustness of the SG, it is firstly necessary to find the system vulnerability by developing an imperfect data injection attack strategy with minimum attack residual increment. In this attack strategy, these targeted state variables are chosen by a designed search approach, and their values are then determined by solving an optimal problem based on particle swarm optimization (PSO) algorithm. Considering the characters of traditional chi-square detection method and history statistical information of state variables without being attacked, a new online chi-square detection method associated with two kinds of state estimates is proposed to make up for the system vulnerability. Numerical simulations confirm the feasibility and effectiveness of the proposed method.
  • Compressed binary discernibility matrix based incremental attribute
           reduction algorithm for group dynamic data
    • Abstract: Publication date: Available online 6 February 2019Source: NeurocomputingAuthor(s): Fumin Ma, Mianwei Ding, Tengfei Zhang, Jie Cao The datasets in real-world applications often vary dynamically over time. Moreover, datasets often expand by introducing a group of data in many cases rather than a single object one by one. The traditional incremental attribute reduction approaches for a single dynamic object may not be applied to such cases. Focusing on this issue, a compressed binary discernibility matrix is introduced and an incremental attribute reduction algorithm for group dynamic data is developed. The single dynamic object and the group dynamic objects are both considered in this algorithm. According to the dynamic data is a single object or a group of objects, different branches can be chosen to update the compressed binary discernibility matrix. Thereafter, the incremental reduction result can be obtained based on the updated compressed binary discernibility matrix. The validity of this algorithm is demonstrated by simulation and experimental analysis.
  • Cost-sensitive support vector machines
    • Abstract: Publication date: Available online 5 February 2019Source: NeurocomputingAuthor(s): Arya Iranmehr, Hamed Masnadi-Shirazi, Nuno Vasconcelos Many machine learning applications involve imbalance class prior probabilities, multi-class classification with many classes (often addressed by one-versus-rest strategy), or “cost-sensitive” classification. In such domains, each class (or in some cases, each sample) requires special treatment. In this paper, we use a constructive procedure to extend SVM’s standard loss function to optimize the classifier with respect to class imbalance or class costs. By drawing connections between risk minimization and probability elicitation, we show that the resulting classifier guarantees Bayes consistency. We further analyze the primal and the dual objective functions and derive the objective function in a regularized risk minimization framework. Finally, we extend the classifier to the with cost-sensitive learning with example dependent costs. We perform experimental analysis on class imbalance, cost-sensitive learning with given class and example costs and show that proposed algorithm provides superior generalization performance, compared to conventional methods.
  • Adaptive deep dynamic programming for integrated frequency control of
           multi-area multi-microgrid systems
    • Abstract: Publication date: Available online 14 February 2019Source: NeurocomputingAuthor(s): Linfei Yin, Tao Yu, Bo Yang, Xiaoshun Zhang To reduce the frequency deviation of a multi-area multi-microgrid system, a framework of integrated frequency control is designed in this paper, which can replace load frequency control (LFC) and generation command dispatch (GCD).Then an adaptive deep dynamic programming (ADDP) scheme is proposed for the integrated frequency control. The ADDP contains three deep neural networks, i.e., deep prediction neural network, deep critic neural network and deep action neural network. Deep prediction neural network is applied to predict the next state of the multi-area multi-microgrid system from the previous states and the previous actions. Deep critic neural network is employed in the evaluations of the performance of the deep action neural network. Deep action neural network is introduced to simultaneously provide generation commands for all the LFC units in the multi-area multi-microgrid system. The ADDP is compared with other 157 algorithms under six case studies, i.e., basic situation, plug-and-play, communication failure, all-day long disturbance, time-varying topology and parameters varying. The other 157 algorithms consist of adaptive dynamic programming and 156 combined algorithms, which combined with 12 control algorithms for the controller of LFC and 13 optimization algorithms for GCD. Simulation results verify the effectiveness and superiority of the ADDP for integrated frequency control of a multi-area multi-microgrid system.
  • Using continuous Hopfield neural network for solving a new optimization
           architecture model of probabilistic self organizing map
    • Abstract: Publication date: Available online 12 February 2019Source: NeurocomputingAuthor(s): Nour-eddine Joudar, Zakariae En-naimani, Mohamed Ettaouil Probabilistic models have proven their strength to model many natural phenomena as close as possible to reality, in particular, the probabilistic self organizing map (PRSOM) that belongs to the unsupervised learning models. It allows to provide an estimation of the probability density function through the likelihood maximization. This latter function depends on several parameters given by the model architecture. In this context, the aim of this paper is to deal with the architecture choice problem that consists in determining the optimal number of components needed for a better performance. In this paper, we propose a new optimization model that describes the problem above. We present the architecture of PRSOM in a mathematical system of a non linear objective function with mixed variables under linear and quadratic constraints. Due to the complexity of the resolution, we suggest the continuous Hopfield neural network (CHN) that we support by a deep stability analysis. Performance of the proposed model is demonstrated through the dataset clustering.
  • Parameter estimation of Hammerstein–Wiener nonlinear system with noise
           using special test signals
    • Abstract: Publication date: Available online 10 February 2019Source: NeurocomputingAuthor(s): Feng Li, Li Jia In this paper, an identification procedure of Hammerstein–Wiener nonlinear system with process noise using special test signals is presented. Special test signals that contain separable signal and uniformly random multi-step signal are employed to separate the identification problems of the linear dynamic part and the output static nonlinear part from that of the input static nonlinear part, and then correlation analysis method is applied to estimate the parameters of the output static nonlinear part and linear dynamic part. Moreover, a filter is embedded to form extended Hammerstein–Wiener system to calculate the noise correlation functions by the information of zeros and poles of the extended system, further an error compensation term which consists of the noise correlation functions is added to least square estimation to compensate the error caused by process noise. Therefore, the unbiased estimation of model parameters can be derived by error compensation based recursive least square method. Simulation results demonstrate that proposed approach can effectively identify parameters of Hammerstein–Wiener nonlinear system in the presence of process noise.
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Heriot-Watt University
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