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Publisher: Springer-Verlag (Total: 2573 journals)

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Showing 1 - 200 of 2573 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 4)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 11, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 29, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 8, SJR: 0.752, CiteScore: 3)
Abdominal Radiology     Hybrid Journal   (Followers: 18, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 30, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 9, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 31, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (Followers: 1, SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 19, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 3, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 11, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 24, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 7, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 2, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 4, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 4, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 19, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
Adaptive Human Behavior and Physiology     Hybrid Journal  
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 28, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 19, SJR: 1.005, CiteScore: 2)
Adolescent Research Review     Hybrid Journal   (Followers: 1)
Adsorption     Hybrid Journal   (Followers: 5, SJR: 0.703, CiteScore: 2)
Advanced Composites and Hybrid Materials     Hybrid Journal  
Advanced Fiber Materials     Full-text available via subscription  
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Astronautics Science and Technology     Hybrid Journal  
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 40, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 21, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 58, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 35, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 3, SJR: 0.475, CiteScore: 2)
Advances in Neurodevelopmental Disorders     Hybrid Journal  
Advances in Polymer Science     Hybrid Journal   (Followers: 49, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 7)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 4, SJR: 0.673, CiteScore: 2)
Aerosol Science and Engineering     Hybrid Journal  
Aerospace Systems     Hybrid Journal   (Followers: 1)
Aerotecnica Missili & Spazio : J. of Aerospace Science, Technologies & Systems     Hybrid Journal  
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 11, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 21, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 7, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Open Access   (Followers: 15, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Open Access   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Open Access   (Followers: 15, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 9, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 16, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 7, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 2)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 17, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 29, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 9, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 18, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 6, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 3, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 22, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 16, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 7, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 10, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 6, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 6)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 3, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 23, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 19, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 13)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 35, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 13, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 5, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 11, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 13)
Annals of PDE     Hybrid Journal  
Annals of Regional Science     Hybrid Journal   (Followers: 9, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 11, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 15, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 9, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 3, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 44, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 19, SJR: 0.21, CiteScore: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 4, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 53, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 7, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 9, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 15, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 10, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (Followers: 1, SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 68, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 10, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 26, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 22, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 26, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 37, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 3, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 14, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 22, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 13, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 68, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 172, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 18, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 10, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 9, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 11, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 18, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 17, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 3, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.766, CiteScore: 1)
arktos : The J. of Arctic Geosciences     Hybrid Journal  
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 12, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 22, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 10, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 4, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 17, SJR: 1.185, CiteScore: 2)

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Similar Journals
Journal Cover
Applied Intelligence
Journal Prestige (SJR): 0.6
Citation Impact (citeScore): 2
Number of Followers: 15  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7497 - ISSN (Online) 0924-669X
Published by Springer-Verlag Homepage  [2573 journals]
  • Facial expression recognition sensing the complexity of testing samples
    • Abstract: Abstract Facial expression recognition has always been a challenging issue due to the inconsistencies in the complexity of samples and variability of between expression categories. Many facial expression recognition methods train a classification model and then use this model to identify all test samples, without considering the complexity of each test sample. They are inconsistent with human cognition laws such as the principle of simplicity, so that they are easily under-learned and then are difficult to identify test samples correctly. Hence, this paper proposed a new facial expression recognition method sensing the complexity of test samples, which can nicely solve the problem of the inconsistent distribution of samples complexity. It firstly divided the training data into the hard subset and the easy subset for classification according to the complexity of samples for expression recognition. Subsequently, these two subsets are applied to train two classifiers. Instead of using the same classifier to predict all test samples, our method assigned each test sample to the corresponding classifier based on the complexity of the test sample. The experimental results demonstrated the effectiveness of the proposed method and obtained a significant improvements of the recognition performance on benchmark datasets.
      PubDate: 2019-12-01
  • Feature selection for intrusion detection using new multi-objective
           estimation of distribution algorithms
    • Abstract: Abstract The manipulation of a large number of features has become a critical problem in Intrusion Detection Systems(IDS). Therefore, Feature Selection (FS) is integrated to select the significant features, in order to avoid the computational complexity, and improve the classification performance. In this paper, we present a new multi-objective feature selection algorithm MOEDAFS (Multi-Objective Estimation of Distribution Algorithms (EDA) for Feature Selection). The MOEDAFS is based on EDA and Mutual Information (MI). EDA is used to explore the search space and MI is integrated as a probabilistic model to guide the search by modeling the redundancy and relevance relations between features. Therefore, we propose four probabilistic models for MOEDAFS. MOEDAFS selects the better feature subsets (non-dominated solutions) that have a better detection accuracy and smaller number of features. MOEDAFS uses two objective functions (minimizing classification Error Rate (ER) and minimizing the Number of Features(NF)). In order to demonstrate the performance of MOEDAFS, a comparative study is designed by internal and external comparison on NSL-KDD dataset. Internal comparison is performed between the four versions of MOEDAFS. External comparison is organized against some well-known deterministic, metaheuristic, and multi-objective feature selection algorithms that have a single and Multi-solution. Experimental results demonstrate that MOEDAFS outperforms recent algorithms.
      PubDate: 2019-12-01
  • A new asynchronous reinforcement learning algorithm based on improved
           parallel PSO
    • Abstract: Abstract As an important machine learning method, reinforcement learning plays a more and more important role in practical application. In recent years, many scholars have studied parallel reinforcement learning algorithm, and achieved remarkable results in many applications. However, when using existing parallel reinforcement learning to solve problems, due to the limited search scope of agents, it often fails to reduce the running episodes of algorithms. At the same time, the traditional model-free reinforcement learning algorithm does not necessarily converge to the optimal solution, which may lead to some waste of resources in practical applications. In view of these problems, we apply Particle swarm optimization (PSO) algorithm to asynchronous reinforcement learning algorithm to search for the optimal solution. First, we propose a new asynchronous variant of PSO algorithm. Then we apply it into asynchronous reinforcement learning algorithm, and proposed a new asynchronous reinforcement learning algorithm named Sarsa algorithm based on backward Q-learning and asynchronous particle swarm optimization (APSO-BQSA). Finally, we verify the effectiveness of the asynchronous PSO and APSO-BQSA algorithm proposed in this paper through experiments.
      PubDate: 2019-12-01
  • A new hybrid feature selection based on multi-filter weights and
           multi-feature weights
    • Abstract: Abstract A traditional feature selection of filters evaluates the importance of a feature by using a particular metric, deducing unstable performances when the dataset changes. In this paper, a new hybrid feature selection (called MFHFS) based on multi-filter weights and multi-feature weights is proposed. Concretely speaking, MFHFS includes the following three stages: Firstly, all samples are normalized and discretized, and the noises and the outliers are removed based on 10-folder cross validation. Secondly, the vector of multi-filter weights and the matrix of multi-feature weights are calculated and used to combine different feature subsets obtained by the optimal filters. Finally, a Q-range based feature relevance calculation method is proposed to measure the relationship of different features and the greedy searching policy is used to filter the redundant features of the temp feature subset to obtain the final feature subset. Experiments are carried out using two typical classifiers of support vector machine and random forest on six datasets (APS, Madelon, CNAE9, Gisette, DrivFace and Amazon). When the measurements of F1macro and F1micro are used, the experimental results show that the proposed method has great improvement on classification accuracy compared to the traditional filters, and it achieves significant improvements on running speed while guaranteeing the classification accuracy compared to typical hybrid feature selections.
      PubDate: 2019-12-01
  • Robust multi-objective multi-humanoid robots task allocation based on
           novel hybrid metaheuristic algorithm
    • Abstract: Abstract Nowadays humanoid robots have generally made dramatic progress, which can form a coalition replacing human work in dangerous environments such as rescue, defense, exploration, etc. In contrast to other types of robots, humanoid robots’ similarity to human make them more suitable for performing such a wide range of tasks. Rescue applications for robots, especially for humanoid robots are exciting. In rescue operating conditions, tasks’ dependencies, tasks’ repetitive accomplishment requirements, robots’ energy consumption, and total tasks’ accomplishment time are key factors. This paper investigates a practical variant of the Multi-Robots Task Allocation (MRTA) problem for humanoid robots as multi-humanoid robots’ task allocation (MHTA) problem. In order to evaluate relevant aspects of the MHTA problem, we proposed a robust Multi-Objective Multi-Humanoid Robots Task allocation (MO-MHTA) algorithm with four objectives, namely energy consumption, total tasks’ accomplishment time, robot’s idle time and fairness were optimized simultaneously in an evolutionary framework in MO-MHTA, which address for the first time. MO-MHTA exhibits multi-objective properties in real-world applications for humanoid robots in two phases. In the first phase, the tasks are partitioned in a fair manner with a proposed constraint k-medoid (CKM) algorithm. In the second phase, a new non-dominated sorting genetic algorithm with special genetic operators is applied. Evaluations based on extensive experiments on the newly proposed benchmark instances with three robust multi-objective evolutionary algorithms (MOEAs) are applied. The proposed algorithm achieves favorable results in comparison to six other algorithms. Besides, the proposed algorithm can be seen as benchmark algorithms for real-world MO-MHTA instances.
      PubDate: 2019-12-01
  • Student-t policy in reinforcement learning to acquire global optimum of
           robot control
    • Abstract: Abstract This paper proposes an actor-critic algorithm with a policy parameterized by student-t distribution, named student-t policy, to enhance learning performance, mainly in terms of reachability on global optimum for tasks to be learned. The actor-critic algorithm is one of the policy-gradient methods in reinforcement learning, and is proved to learn the policy converging on one of the local optima. To avoid the local optima, an exploration ability to escape it and a conservative learning not to be trapped in it are deemed to be empirically effective. The conventional policy parameterized by a normal distribution, however, fundamentally lacks these abilities. The state-of-the-art methods can somewhat but not perfectly compensate for them. Conversely, heavy-tailed distribution, including student-t distribution, possesses an excellent exploration ability, which is called Lévy flight for modeling efficient feed detection of animals. Another property of the heavy tail is its robustness to outliers. Namely, conservative learning is performed to not be trapped in the local optima even when it takes extreme actions. These desired properties of the student-t policy enhance the possibility of the agents reaching the global optimum. Indeed, the student-t policy outperforms the conventional policy in four types of simulations, two of which are difficult to learn faster without sufficient exploration and the others have the local optima.
      PubDate: 2019-12-01
  • A distributed group recommendation system based on extreme gradient
           boosting and big data technologies
    • Abstract: Abstract Personalized recommendation systems have emerged as useful tools for recommending the appropriate items to individual users. However, in such situations, some items tend to be consumed by groups of users, such as tourist attractions or television programs. With this purpose in mind, Group Recommender Systems (GRSs) are tailored to help groups of users to find suitable items according to their preferences and needs. In general, these systems often confront the sparsity problem, which negatively affects their efficiency. With the increase in the number of users, items, groups, and ratings in the system. Data becomes too big to be processed efficiently by traditional systems. Thus, there is an increasing need for distributed recommendation approaches able to manage the issues related to Big Data and sparsity problem. In this paper, we propose a distributed group recommendation system, which is designed based on Apache Spark to handle large-scale data. It integrates a novel proposed recommendation method, a dimension reduction technique, with supervised and unsupervised learning for dealing efficiently with the curse of dimensionality problem, detecting the groups of users, and improving the prediction quality. Experimental results on three real-world data sets show that our proposal is significantly better than other competitors.
      PubDate: 2019-12-01
  • Multiparametric similarity measures on Pythagorean fuzzy sets with
           applications to pattern recognition
    • Abstract: Abstract Pythagorean fuzzy sets (PFSs), characterized by membership degrees and non-membership degrees, are a more effective and flexible way than intuitionistic fuzzy sets (IFSs) to capture indeterminacy. In this paper, some new diverse types of similarity measures, overcoming the blemishes of the existing similarity measures, for PFSs with multiple parameters are studied, along with their detailed proofs. The various desirable properties among the developed similarity measures and distance measures have also been derived. A comparison between the proposed and the existing similarity measures has been performed in terms of the division by zero problem, unsatisfied similarity axiom conditions, and counter-intuitive cases for showing their effectiveness and feasibility. The initiated similarity measures have been illustrated with case studies of pattern recognition, along with the effect of the different parameters on the ordering and classification of the patterns.
      PubDate: 2019-12-01
  • Brain storm optimization for feature selection using new individual
           clustering and updating mechanism
    • Abstract: Abstract Feature selection is an important preprocessing technique for data. Brain storm optimization (BSO) is one of the latest swarm intelligence algorithms, which simulates the collective behavior of human beings. However, traditional updating mechanisms in BSO limit its application in feature selection. We study a new individual clustering technology and two individual updating mechanisms in BSO for developing novel feature selection algorithms with the purpose of maximizing the classification performance. The proposed individual updating mechanisms are compared with each other. The more promising updating mechanism and the new individual clustering technology are combined into the BSO framework to form a new wrapper feature selection algorithm, called BBSOFS. Compared with existing algorithms including particle swarm optimization, firefly algorithm and BSO algorithm, experimental results on benchmark datasets show that with the help of the proposed individual clustering and updating mechanism, the proposed BBSOFS algorithm can obtain feature subsets with good classification accuracy.
      PubDate: 2019-12-01
  • An effective asynchronous framework for small scale reinforcement learning
    • Abstract: Abstract Reinforcement learning is one of the research hotspots in the field of artificial intelligence in recent years. In the past few years, deep reinforcement learning has been widely used to solve various decision-making problems. However, due to the characteristics of neural networks, it is very easy to fall into local minima when facing small scale discrete space path planning problems. Traditional reinforcement learning uses continuous updating of a single agent when algorithm executes, which leads to a slow convergence speed. Although some scholars have done some improvement work to solve these problems, there are still many shortcomings to be overcome. In order to solve the above problems, we proposed a new asynchronous tabular reinforcement learning algorithms framework in this paper, and present four new variants of asynchronous reinforcement learning algorithms. We apply these algorithms on the standard reinforcement learning environments: frozen lake problem, cliff walking problem and windy gridworld problem, and the simulation results show that these methods can solve discrete space path planning problems efficiently and well balance the exploration and exploitation.
      PubDate: 2019-12-01
  • Mining contrast sequential pattern based on subsequence time distribution
           variation with discreteness constraints
    • Abstract: Abstract Contrast sequential pattern is defined as a pattern that occurs frequently in one sequence dataset but not in the others. Contrast sequential pattern mining has been widely used in many fields, such as customer behavior analysis and medical diagnosis. Existing algorithms first require users to set a distinguishing location and then use this fixed location to identify distribution differences of different subsequences, i.e., the subsequence pattern that appears before the given distinguishing location in one sequence dataset and after the same location in another sequence dataset. However, it is difficult for users to set an appropriate location without sufficient prior knowledge. Since the distinguishing location is different for different subsequences, setting a fixed location may ignore many meaningful patterns. In addition, previous studies rarely considered the time distribution variation of subsequences and the discreteness of patterns. To solve the above problems, we propose a novel method of mining contrast sequential pattern based on subsequence time distribution variation with discreteness constraints in this paper. A suffix-tree based search algorithm, which transforms the dataset to be processed into a tree representation, is designed to mine contrast sequential pattern based on subsequence time distribution variation. Experiments are conducted on real-world time-series datasets, and the experimental results validate the superiority of our method in terms of effectiveness and efficiency when compared with other state-of-the-art methods.
      PubDate: 2019-12-01
  • Single image dehazing using gradient channel prior
    • Abstract: Abstract The dehazing techniques designed so far are not so-effective at preserving texture details, especially in case of a complex background and large haze gradient image. Therefore, the exploration of new alternatives for designing an effective prior is desirable. Thus, in this research work, Gradient profile prior (GPP) is designed to evaluate depth map from hazy images. The transmission map is also improved by utilizing Guided anisotropic diffusion and iterative learning based image filter (GADILF). The restoration model is also improved to reduce the effect of pixels saturation and color distortion from restored images. Performance analysis demonstrates that GPP can naturally restore the hazy image especially at the edges of sudden changes in the obtained depth map. Through extensive analysis, it has been found that GPP based dehazing can effectively suppress visual artefacts for hazy images and yield high-quality results as compared to the competitive dehazing techniques both quantitatively and qualitatively. Moreover, the relatively high computational speed of the proposed technique will facilitate it in real-time applications.
      PubDate: 2019-12-01
  • An unsupervised strategy for defending against multifarious reputation
    • Abstract: Abstract In electronic markets, malicious sellers often employ reviewers to carry out different types of attacks to improve their own reputations or destroy their opponents’ reputations. As such attacks may involve deception, collusion, and complex strategies, maintaining the robustness of reputation evaluation systems remains a challenging problem. From a platform manager’s view, no trader can be taken as a trustable benchmark for reference, therefore, accurate filtration of dishonest sellers and fraud reviewers and precise presentation of users’ reputations remains a challenging problem. Based on impression theory, this paper presents an unsupervised strategy, which first design a nearest neighbor search algorithm to select some typical lenient reviewers and strict reviewers. Then, based on these selected reviewers and the behavior expectation theory in impression theory, this paper adopts a classification algorithm that pre-classify sellers into honest and dishonest ones. Thirdly, another classification algorithm is designed to classify reviewers (i.e., buyers) into honest, dishonest, and uncertain ones according to their trading experiences with the pre-classified sellers. Finally, based on the ratings of various reviewers, this paper proposes a formula to estimate seller reputations. We further designed two general sets of experiments over simulated data and real data to evaluate our scheme, which demonstrate that our unsupervised scheme outperforms benchmark strategies in accurately estimating seller reputations. In particular, this strategy can robustly defend against various common attacks and unknown attacks.
      PubDate: 2019-12-01
  • Epsilon-nonparallel support vector regression
    • Abstract: Abstract In this work, a novel method called epsilon-nonparallel support vector regression (ε-NPSVR) is proposed. The reasoning behind the nonparallel support vector machine (NPSVM) method for binary classification is extended for predicting numerical outputs. Our proposal constructs two nonparallel hyperplanes in such a way that each one is closer to one of the training patterns, and as far as possible from the other. Two epsilon-insensitive tubes are also built for providing a better alignment for each hyperplane with their respective training pattern, which are obtained by shifting the regression function up and down by two fixed parameters. Our proposal shares the methodological advantages of NPSVM: A kernel-based formulation can be derived directly by applying the duality theory; each twin problem has the same structure of the SVR method, allowing the use of efficient optimization algorithms for fast training; it provides a generalized formulation for twin SVR; and it leads to better performance compared with the original TSVR. This latter advantage is confirmed by our experiments on well-known benchmark datasets for the regression task.
      PubDate: 2019-12-01
  • Discovering varying patterns of Normal and interleaved ADLs in smart homes
    • Abstract: Abstract People may do the same activity in many different ways hence, modeling and recognizing that activity based on data gathered through simple sensors like motion sensor is a complex task. In this paper, we propose an approach for activity mining and activity tracking which identifies frequent normal and interleaved activities that individuals perform. With this capability, we can track the occurrence of regular activities to monitor users and detect changes in an individual’s behavioral pattern and lifestyle. We have tested the proposed method using the datasets of Washington State University CASAS and the Massachusetts Institute of Technology (MIT) smart home projects. The obtained results show considerable improvements compared with existing methods.
      PubDate: 2019-12-01
  • Detecting facial emotions using normalized minimal feature vectors and
           semi-supervised twin support vector machines classifier
    • Abstract: Abstract In this paper, human facial emotions are detected through normalized minimal feature vectors using semi-supervised Twin Support Vector Machine (TWSVM) learning. In this study, face detection and tracking are carried out using the Constrained Local Model (CLM), which has 66 entire feature vectors. Based on Facial Animation Parameter’s (FAPs) definition, entire feature vectors are those things that visibly affect human emotion. This paper proposes the 13 minimal feature vectors that have high variance among the entire feature vectors are sufficient to identify the six basic emotions. Using the Max & Min and Z-normalization technique, two types of normalized minimal feature vectors are formed. The novelty of this study is methodological in that the normalized data of minimal feature vectors fed as input to the semi-supervised multi-class TWSVM classifier to classify the human emotions is a new contribution. The macro facial expression datasets are used by a standard database and several real-time datasets. 10-fold and hold out cross-validation is applied with the cross-database (combining standard and real-time). In the experimental result, using ‘One vs One’ and ‘One vs All’ multi-class techniques with 3 kernel functions produce a 36 trained model of each emotion and their validation parameters are calculated. The overall accuracy achieved for 10-fold cross-validation is 93.42 ± 3.25% and hold out cross-validation is 92.05 ± 3.79%. The overall performance (Precision, Recall, F1-score, Error rate and Computation Time) of the proposed model was also calculated. The performance of the proposed model and existing methods were compared and results indicate them to be more reliable than existing models.
      PubDate: 2019-12-01
  • An efficient regularized K-nearest neighbor structural twin support vector
    • Abstract: Abstract K-nearest neighbor based structural twin support vector machine (KNN-STSVM) performs better than structural twin support vector machine (S-TSVM). It applies the intra-class KNN method, and different weights are given to the samples in one class to strengthen the structural information. For the other class, the redundant constraints are deleted by the inter-class KNN method to speed up the training process. However, the empirical risk minimization principle is implemented in the KNN-STSVM, so it easily leads to over-fitting and reduces the prediction accuracy of the classifier. To enhance the generalization ability of the classifier, we propose an efficient regularized K-nearest neighbor structural twin support vector machine, called RKNN-STSVM, by introducing a regularization term into the objective function. So there are two parts in the objective function, one of which is to maximize the margin between the two parallel hyper-planes, and the other one is to minimize the training errors of two classes of samples. Therefore the structural risk minimization principle is implemented in our RKNN-STSVM. Besides, a fast DCDM algorithm is introduced to handle relatively large-scale problems more efficiently. Comprehensive experimental results on twenty-seven benchmark datasets and two popular image datasets demonstrate the efficiency of our proposed RKNN-STSVM.
      PubDate: 2019-12-01
  • ADSCNet: asymmetric depthwise separable convolution for semantic
           segmentation in real-time
    • Abstract: Abstract Semantic segmentation can be considered as a per-pixel localization and classification problem, which gives a meaningful label to each pixel in an input image. Deep convolutional neural networks have made extremely successful in semantic segmentation in recent years. However, some challenges still exist. The first challenge task is that most current networks are complex and it is hard to deploy these models on mobile devices because of the limitation of computational cost and memory. Getting more contextual information from downsampled feature maps is another challenging task. To this end, we propose an asymmetric depthwise separable convolution network (ADSCNet) which is a lightweight neural network for real-time semantic segmentation. To facilitating information propagation, Dense Dilated Convolution Connections (DDCC), which connects a set of dilated convolutional layers in a dense way, is introduced in the network. Pooling operation is inserted before ADSCNet unit to cover more contextual information in prediction. Extensive experimental results validate the superior performance of our proposed method compared with other network architectures. Our approach achieves mean intersection over union (mIOU) of 67.5% on Cityscapes dataset at 76.9 frames per second.
      PubDate: 2019-11-28
  • Automatic evolution of bi-clusters from microarray data using
           self-organized multi-objective evolutionary algorithm
    • Abstract: Abstract In the current paper, a novel approach is proposed for bi-clustering of gene expression data using the fusion of differential evolution framework and self-organizing map (SOM), named as BiClustSMEA. Variable number of gene and condition cluster centers are encoded in different solutions of the population to determine the number of bi-clusters from a dataset in an automated way. The concept of SOM is utilized in designing new genetic operators for both gene and condition clusters to reach to the optimal solution in a faster way. In order to measure the goodness of a bi-clustering solution, three bi-cluster quality measures, mean squared error, row variance, and bi-cluster size, are optimized simultaneously using differential evolution as the underlying optimization strategy. The concept of polynomial mutation is incorporated in our framework to generate highly diverse solutions which in turn helps in faster convergence. The proposed approach is applied on two real-life microarray gene expression datasets and results are compared with various state-of-the-art techniques. Results obtained clearly illustrate that our approach extracts high-quality bi-clusters as compared to other methods and also it converges much faster than other competitors. Further, the obtained results are validated using statistical significance test and biological significance test.
      PubDate: 2019-11-28
  • A novel hybrid sine cosine algorithm for global optimization and its
           application to train multilayer perceptrons
    • Abstract: Abstract The Sine Cosine Algorithm (SCA) is a recently developed efficient metaheuristic algorithm to find the solution of global optimization problems. However, in some circumstances, this algorithm suffers the problem of low exploitation, skipping of true solutions and insufficient balance between exploration and exploitation. Therefore, the present paper aims to alleviate these issues from SCA by proposing an improved variant of SCA called HSCA. The HSCA modifies the search mechanism of classical SCA by including the leading guidance and hybridizing with simulated quenching algorithm. The proposed HSCA is tested on classical benchmark set, standard and complex benchmarks sets IEEE CEC 2014 and CEC 2017 and four engineering optimization problems. In addition to these problems, the HSCA is also used to train multilayer perceptrons as a real-life application. The experimental results and analysis on benchmark problems and real-life application problems demonstrate the superiority of the HSCA as compared to other comparative optimization algorithms.
      PubDate: 2019-11-27
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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