Publisher: Springer-Verlag (Total: 2626 journals)

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Showing 1 - 200 of 2626 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 5)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 13, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 32, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 9, SJR: 0.752, CiteScore: 3)
Abdominal Radiology     Hybrid Journal   (Followers: 22, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
aBIOTECH : An Intl. J. on Plant Biotechnology and Agricultural Sciences     Hybrid Journal   (Followers: 2)
Academic Psychiatry     Full-text available via subscription   (Followers: 32, 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: 34, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 12, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (Followers: 2, 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: 3, 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: 8, 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: 13, 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: 25, 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: 9, 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: 3, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 5, 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   (Followers: 1)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 9, 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: 11)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 21, SJR: 1.005, CiteScore: 2)
Adolescent Research Review     Hybrid Journal   (Followers: 3)
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: 42, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 23, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 57, 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: 36, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 5, SJR: 0.475, CiteScore: 2)
Advances in Neurodevelopmental Disorders     Hybrid Journal   (Followers: 2)
Advances in Operator Theory     Hybrid Journal   (Followers: 4)
Advances in Polymer Science     Hybrid Journal   (Followers: 50, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Advances in Traditional Medicine     Hybrid Journal   (Followers: 4)
Adversity and Resilience Science : J. of Research and Practice     Hybrid Journal   (Followers: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 9)
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: 12, SJR: 0.825, CiteScore: 1)
Affective Science     Hybrid Journal   (Followers: 2)
African Archaeological Review     Hybrid Journal   (Followers: 20, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 3, SJR: 0.235, CiteScore: 0)
Ageing Intl.     Hybrid Journal   (Followers: 8, 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: 19, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 9, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 18, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 7, 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: 2, 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: 9, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 18, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 32, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 10, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 22, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 9, 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: 11, 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: 11, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 37, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Functional Analysis     Hybrid Journal   (Followers: 4)
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: 14, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 13, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 4, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 11, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 14)
Annals of PDE     Hybrid Journal   (Followers: 1)
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: 19, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 8, 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: 5, 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: 42, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 20, 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: 11, 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: 70, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 11, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 27, 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: 21, 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: 38, 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: 24, 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: 70, 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   (Followers: 1, 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: 184, 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: 19, 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: 10, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 12, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 19, 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: 18, 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)

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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  [2626 journals]
  • Editor’s Note: Forthcoming 30th Anniversary of Applied Intelligence
    • PubDate: 2020-08-01
  • A novel community detection method based on whale optimization algorithm
           with evolutionary population
    • Abstract: Community detection is the process of detecting communities in complex networks. Communities are important structures that can help us further study the properties of complex networks. In recent years, swarm intelligence algorithms have been applied to community detection and have achieved remarkable results. However, these existing algorithms have limited search ability and easily fall into the problem of local optima. In this paper, we propose a new community detection approach based on an improved whale optimization algorithm (WOA). The WOA is applied to a discrete symbol space in solving the community detection problem, therefore topology structure-based search strategies, adjustment and mergence policies, and evolutionary population method are designed to improve the efficiency and effectiveness of the method. Then, a whale optimization algorithm with evolutionary population for community detection (EP-WOCD) is proposed. Extensive experiments are conducted to compare the EP-WOCD with other state-of-the-art algorithms on both artificial and real-world social networks. Experimental results show that the EP-WOCD is effective and stable.
      PubDate: 2020-08-01
  • Phase enhancement model based on supervised convolutional neural network
           for coherent DOA estimation
    • Abstract: When the elevation of targets is smaller than beamwidth, the coherent multi-path signals will significantly degrade the direction of arrival (DOA) estimation accuracy of existing methods for a very-high-frequency (VHF) radar system. Through detailed theoretical analysis, we demonstrate that the phase distortion is the key factor of degrading the accuracy of DOA estimation. Hence, a novel phase enhancement model based on supervised convolutional neural network (CNN) for coherent DOA estimation is proposed to mitigate the phase distortion and improve estimation accuracy. The results of simulation experiments and real data have demonstrated the superiority of proposed method in DOA estimation accuracy and resolution compared to classic physics-driven methods. Moreover, the proposed scheme is suitable for the coherent DOA estimation compared with existing data-driven methods.
      PubDate: 2020-08-01
  • Minimum interpretation by autoencoder-based serial and enhanced mutual
           information production
    • Abstract: The present paper aims to propose an information-theoretic method for interpreting the inference mechanism of neural networks. The new method aims to interpret the inference mechanism minimally by disentangling complex information into simpler and easily interpretable information. This disentanglement of complex information can be realized by maximizing mutual information between input patterns and the corresponding neurons. However, because the use of mutual information has faced difficulty in computation, we use the well-known autoencoder to increase mutual information by re-interpreting the sparsity constraint, which is considered a device to increase mutual information. The computational procedures to increase mutual information are decomposed into the serial operation of equal use of neurons and specific responses to input patterns. The specific responses are realized by enhancing the results by the equal use of neurons. The method was applied to three data sets: the glass, office equipment, and pulsar data sets. With all three data sets, we could observe that, when the number of neurons was forced to increase, mutual information could be increased. Then, collective weights, or average collectively treated weights, showed that the method could extract the simple and linear relations between inputs and targets, making it possible to interpret the inference mechanism minimally.
      PubDate: 2020-08-01
  • Virtual machine placement based on multi-objective reinforcement learning
    • Abstract: Multi-objective virtual machine (VM) placement is a powerful tool, which can achieve different goals in data centers. It is an NP-hard problem, and various works have been proposed to solve it. However, almost all of them ignore the selection of weights. The selection of weights is difficult, but it is essential for multi-objective optimization. The inappropriate weights will cause the obtained solution set deviating from the Pareto optimal set. Fortunately, we find that this problem can be easily solved by using the Chebyshev scalarization function in multi-objective reinforcement learning (RL). In this paper, we propose a VM placement algorithm based on multi-objective RL (VMPMORL). VMPMORL is designed based on the Chebyshev scalarization function. We aim to find a Pareto approximate set to minimize energy consumption and resource wastage simultaneously. Compared with other multi-objective RL algorithms in the field of VM placement, VMPMORL not only uses the concept of the Pareto set but also solves the weight selection problem. Finally, VMPMORL is compared with some state-of-the-art algorithms in recent years. The results show that VMPMORL can achieve better performance than the approaches above.
      PubDate: 2020-08-01
  • A multi-label text classification method via dynamic semantic
           representation model and deep neural network
    • Abstract: The increment of new words and text categories requires more accurate and robust classification methods. In this paper, we propose a novel multi-label text classification method that combines dynamic semantic representation model and deep neural network (DSRM-DNN). DSRM-DNN first utilizes word embedding model and clustering algorithm to select semantic words. Then the selected words are designated as the elements of DSRM-DNN and quantified by the weighted combination of word attributes. Finally, we construct a text classifier by combining deep belief network and back-propagation neural network. During the classification process, the low-frequency words and new words are re-expressed by the existing semantic words under sparse constraint. We evaluate the performance of DSRM-DNN on RCV1-v2, Reuters-21578, EUR-Lex, and Bookmarks. Experimental results show that our method outperforms the state-of-the-art methods.
      PubDate: 2020-08-01
  • Twin support vector machine based on improved artificial fish swarm
           algorithm with application to flame recognition
    • Abstract: In this paper, a twin support vector machine (TWSVM) based on improved artificial fish swarm algorithm (IAFSA) for fire flame recognition is proposed in view of the large computation burden and slow classification speed of the traditional support vector machine (SVM). Twin support vector machine is a machine learning algorithm developing from standard support vector machine. However, twin support vector machine cannot deal with the parameter selection problem well. The difficulty of parameter selection may greatly restrict the application of TWSVM in flame recognition problem. So a novel artificial fish swarm algorithm (AFSA) is used to solve the parameter selection problem of TWSVM. In order to make up for the drawbacks of the basic AFSA, the chaotic transformation is first applied to initialize the position of artificial fish swarm since it may be non-uniformly initialized in the basic artificial fish swarm algorithm. Then, the Cauchy mutation is used to make the fish swarm jump out of the local optimal solution after continuously expanding the visual scope of the artificial fish during the foraging procedure. An adaptively step-size adjusting method is then developed to optimize the moving steps of the swarming and following behaviors in order to accelerate the convergence speed of the developed algorithm. Last, to further improve the efficiency and accuracy of the algorithm, an elimination and regeneration mechanism based on adaptive t-distribution mutation is utilized to update the artificial fish swarm at each iterative procedure. Experimental results show that the TWSVM algorithm based on improved artificial fish swarm algorithm is a more effective method to identify the flame and greatly improves the accuracy and real-time performance of the flame recognition compared with PSO-TWSVM, Grid-TWSVM, GA-TWSVM, FOA-TWSVM, GSO-TWSVM, AFSA-TWSVM and the traditional SVM.
      PubDate: 2020-08-01
  • Structured block diagonal representation for subspace clustering
    • Abstract: The aim of the subspace clustering is to segment the high-dimensional data into the corresponding subspace. The structured sparse subspace clustering and the block diagonal representation clustering are quite advanced spectral-type subspace clustering algorithms when handling to the linear subspaces. In this paper, the respective advantages of these two algorithms are fully exploited, and the structured block diagonal representation (SBDR) subspace clustering is proposed. In many classical spectral-type subspace clustering algorithms, the affinity matrix which obeys the block diagonal property can not necessarily bring satisfying clustering results. However, the k-block diagonal regularizer of the SBDR algorithm directly pursues the block diagonal matrix, and this regularizer is obviously more effective. On the other hand, the general procedure of the spectral-type subspace clustering algorithm is to get the affinity matrix firstly and next perform the spectral clustering. The SBDR algorithm considers the intrinsic relationship of the two seemingly separate steps, the subspace structure matrix obtained by the spectral clustering is used iteratively to facilitate a better initialization for the representation matrix. The experimental results on the synthetic dataset and the real dataset have demonstrated the superior performance of the proposed algorithm over other prevalent subspace clustering algorithms.
      PubDate: 2020-08-01
  • Cost-sensitive hierarchical classification for imbalance classes
    • Abstract: The hierarchical classification with an imbalance class problem is a challenge for in machine learning, and is caused by data with an uneven distribution. Learning from an imbalanced dataset can lead to performance degradation of the classifier. Cost-sensitive learning is a useful solution for handling the gap probability of majority and minority classes. This paper proposes a cost-sensitive hierarchical classification for imbalance classes (CSHCIC), constructing a cost-sensitive factor to balance the relationship between majority and minority classes. First, we divide a large hierarchical classification task into several small subclassification tasks by class hierarchy. Second, we establish a cost-sensitive factor by more precisely using the number of different samples of subclassifications. Then, we calculate the probability of every node using logistic regression. Lastly, we update the cost-sensitive factor using the flexibility factor and the number of samples. The experimental results show that the cost-sensitive hierarchical classification method achieves excellent performance on handling imbalance class datasets. The running time cost of the proposed method is smaller than most state-of-the-art methods.
      PubDate: 2020-08-01
  • Reinforcement learning with convolutional reservoir computing
    • Abstract: Recently, reinforcement learning models have achieved great success, mastering complex tasks such as Go and other games with higher scores than human players. Many of these models store considerable data on the tasks and achieve high performance by extracting visual and time-series features using convolutional neural networks (CNNs) and recurrent neural networks respectively. However, these networks have very high computational costs because they need to be trained by repeatedly using the stored data. In this study, we propose a novel practical approach called reinforcement learning with a convolutional reservoir computing (RCRC) model. The RCRC model uses a fixed random-weight CNN and a reservoir computing model to extract visual and time-series features. Using these extracted features, it decides actions with an evolution strategy method. Thereby, the RCRC model has several desirable features: (1) there is no need to train the feature extractor, (2) there is no need to store training data, (3) it can take a wide range of actions, and (4) there is only a single task-dependent weight matrix to be trained. Furthermore, we show the RCRC model can solve multiple reinforcement learning tasks with a completely identical feature extractor.
      PubDate: 2020-08-01
  • D-ANP: a multiple criteria decision making method for supplier selection
    • Abstract: Supplier selection can be regarded as a classic multiple criteria decision making (MCDM) problem. To a great extent, experts’ evaluations play a decisive role in the decision-making process. There will inevitable exist a variety of indefinite factors, which result from imprecision, uncertainty, and fuzziness due to the subjective judgment of human beings. As an effective tool to express uncertain information, the theory of D numbers performs better in comparison to other existing methods. In addition to that, analytic network process (ANP) method is applied more broadly for its advantages of flexibility, rationality and creditability than analytic hierarchy process (AHP) method. In this study, the D-ANP methodology is proposed to apply in the field of supplier selection, which is the extension of the traditional ANP method using D numbers. The validity of the presented methodology is illustrated by an application for supplier selection.
      PubDate: 2020-08-01
  • SmartIX : A database indexing agent based on reinforcement learning
    • Abstract: Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. In this paper, we develop the SmartIX architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. We train and evaluate SmartIX performance using TPC-H, a standard, and scalable database benchmark. Our empirical evaluation shows that SmartIX converges to indexing configurations with superior performance compared to standard baselines we define and other reinforcement learning methods used in related work.
      PubDate: 2020-08-01
  • Oversampling technique based on fuzzy representativeness difference for
           classifying imbalanced data
    • Abstract: Class imbalance problem poses a difficulty to learning algorithms in pattern classification. Oversampling techniques is one of the most widely used techniques to solve these problems, but the majority of them use the sample size ratio as an imbalanced standard. This paper proposes a fuzzy representativeness difference-based oversampling technique, using affinity propagation and the chromosome theory of inheritance (FRDOAC). The fuzzy representativeness difference (FRD) is adopted as a new imbalance metric, which focuses on the importance of samples rather than the number. FRDOAC firstly finds the representative samples of each class according to affinity propagation. Secondly, fuzzy representativeness of every sample is calculated by the Mahalanobis distance. Finally, synthetic positive samples are generated by the chromosome theory of inheritance until the fuzzy representativeness difference of two classes is small. A thorough experimental study on 16 benchmark datasets was performed and the results show that our method is better than other advanced imbalanced classification algorithms in terms of various evaluation metrics.
      PubDate: 2020-08-01
  • Targeted aspects oriented topic modeling for short texts
    • Abstract: Topic modeling has demonstrated its value in short text topic discovery. For this task, a common way adopted by many topic models is to perform a full analysis to find all the possible topics. However, these topic models overlook the importance of deeper topics, leading to confusing topics discovered. In practice, people always tend to find more focused topics on some special aspects (or events), rather than a set of coarse topics. Therefore, in this paper, we propose a novel method, Targeted Aspects Oriented Topic Modeling (TATM), to discover more focused topics on specific aspects in short texts. Specifically, each short text is assigned to only one targeted aspect derived from an enhanced Dirichlet Multinomial Mixture process (E-DMM). This process helps group similar words as many as possible, which achieves topic homogeneity. In addition, TATM discovers the topics for each targeted aspect from as many angles as possible by performing target-level modeling, which achieves topic completeness. Thus, TATM can make a balance between the two conflicting properties without employing any additional information or pre-trained knowledge. The extensive experiments conducted on five real-world datasets demonstrate that our proposed model can effectively discover more focused and complete topics, and it outperforms the state-of-the-art baselines.
      PubDate: 2020-08-01
  • Multi-branch cross attention model for prediction of KRAS mutation in
           rectal cancer with t2-weighted MRI
    • Abstract: The accurate identification of KRAS mutation status on medical images is critical for doctors to specify treatment options for patients with rectal cancer. Deep learning methods have recently been successfully introduced to medical diagnosis and treatment problems, although substantial challenges remain in the computer-aided diagnosis (CAD) due to the lack of large training datasets. In this paper, we propose a multi-branch cross attention model (MBCAM) to separate KRAS mutation cases from wild type cases using limited T2-weighted MRI data. Our model is built on multiple different branches generated based on our existing MRI data, which can take full advantage of the information contained in small data sets. The cross attention block (CA block) is proposed to fuse formerly independent branches to ensure that the model can learn as many common features as possible for preventing the overfitting of the model due to the limited dataset. The inter-branch loss is proposed to constrain the learning range of the model, confirming that the model can learn more general features from multi-branch data. We tested our method on the collected dataset and compared it to four previous works and five popular deep learning models using transfer learning. Our result shows that the MBCAM achieved an accuracy of 88.92% for the prediction of KRAS mutations with an AUC of 95.75%. These results are a significant improvement over those existing methods (p < 0.05).
      PubDate: 2020-08-01
  • k -PbC: an improved cluster center initialization for categorical data
    • Abstract: The performance of a partitional clustering algorithm is influenced by the initial random choice of cluster centers. Different runs of the clustering algorithm on the same data set often yield different results. This paper addresses that challenge by proposing an algorithm named k-PbC, which takes advantage of non-random initialization from the view of pattern mining to improve clustering quality. Specifically, k-PbC first performs a maximal frequent itemset mining approach to find a set of initial clusters. It then uses a kernel-based method to form cluster centers and an information-theoretic based dissimilarity measure to estimate the distance between cluster centers and data objects. An extensive experimental study was performed on various real categorical data sets to draw a comparison between k-PbC and state-of-the-art categorical clustering algorithms in terms of clustering quality. Comparative results have revealed that the proposed initialization method can enhance clustering results and k-PbC outperforms compared algorithms for both internal and external validation metrics. Graphical k-PbC algorithm for categorical data clustering
      PubDate: 2020-08-01
  • Rule-centred genetic programming (RCGP): an imperialist competitive
    • Abstract: Automatic programming is one of the challenging fields of AI to generate solutions for high-level programming problems. There are variant methodologies attempting to introduce an efficient technique which address problems of this domain. In this paper, a novel Rule-Centred Genetic Programming (RCGP) is proposed. RCGP benefits from a series of evolutionary rules to help the algorithm choose intelligent alterations in the chromosome of individuals during the evolution yet preserves its stochastic evolutionary nature. Further, a modified search strategy based on Imperialist Competitive Algorithm (ICA) is employed in RCGP that shows to be significantly effective to deal with various problems which differ in degree of complexity. The proposed method features competitive convergence both in the case of speed and accuracy as well as a simpler mechanism than the several existing GP methods. RCGP is tested on nine benchmark problems which are synthesis and real world. The obtained results indicate that RCGP outperforms recent GP methods and is capable of hybridizing with other types of evolutionary algorithms. The method shows to be competent enough to enhance the quality of automatic programming solutions in both aspects of accuracy and efficiency compared to existing methods.
      PubDate: 2020-08-01
  • Deep reinforcement learning for imbalanced classification
    • Abstract: Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning, in which we formulate the classification problem as a sequential decision-making process and solve it by a deep Q-learning network. In our model, the agent performs a classification action on one sample in each time step, and the environment evaluates the classification action and returns a reward to the agent. The reward from the minority class sample is larger, so the agent is more sensitive to the minority class. The agent finally finds an optimal classification policy in imbalanced data under the guidance of the specific reward function and beneficial simulated environment. Experiments have shown that our proposed model outperforms other imbalanced classification algorithms, and identifies more minority samples with better classification performance.
      PubDate: 2020-08-01
  • Attribute susceptibility and entropy based data anonymization to improve
           users community privacy and utility in publishing data
    • Abstract: User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users’ data publishing because some attributes may expose multiple users’ identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users’ communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users’ community can occur from the privacy preserved published data. Each item of user attributes impacts users’ community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users’ unique identifications and sensitive information inferences more easily, and their presence in published data increases users’ community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user’s community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users’ community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user’s dataset to effectively preserve users’ community privacy. Data generalization is performed adaptively by considering both user attributes’ susceptibility and entropy simultaneously. The proposed algorithm controls over-generalization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users’ community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.
      PubDate: 2020-08-01
  • Joint user mention behavior modeling for mentionee recommendation
    • Abstract: As an emerging online interaction service in Twitter-like social media systems, mention serves to significantly improve both user interaction experience and information propagation. In recent years, the problem of mentionee recommendation, i.e., recommending mentionees (mentioned users) when mentioners (mentioning users) mention others, has received considerable attention. However, the extreme sparsity of mentioner-mentionee matrix creates a severe challenge. While an increasing line of work has exploited diverse effects such as the textual content and spatio-temporal context influences to cope with this challenge, there lacks a comprehensive study of the joint effect of all these influencing factors. In light of this, we propose a joint latent-class probabilistic model, named Joint Topic-Area Model (JTAM), to tackle the mentionee recommendation problem by simultaneously learning and modeling users’ semantic interests, the spatio-temporal mentioning patterns of mentioners, the geographical distribution of mentionees, and their joint effects on users’ mention behaviors in a unified way. Moreover, to facilitate online query performance, we design an efficient query answering approach that enables fast top-k mentionee recommendation. To evaluate the performance of our method, we conduct extensive experiments on a large real-world dataset. The results demonstrate the superiority of our method in recommending mentionees in terms of both effectiveness and efficiency compared with other state-of-the-art methods.
      PubDate: 2020-08-01
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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