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Publisher: Springer-Verlag (Total: 2352 journals)

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Showing 1 - 200 of 2352 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 1)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 23, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 16, 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: 27, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 28, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (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: 21, 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: 1, 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: 2, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 25, 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: 17, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 4, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 59, 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: 30, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 4, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 45, 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: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 11, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 20, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
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: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 13, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 9, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, 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: 6, 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: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, 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: 8, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 16, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 14, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5, 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: 20, 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: 17, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 32, 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: 12, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, 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: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
Annals of Regional Science     Hybrid Journal   (Followers: 8, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 14, 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: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 45, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 18, SJR: 0.21, CiteScore: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 3, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 5, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 8, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 8, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (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: 67, 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: 24, 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: 36, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, 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: 21, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 63, 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: 5, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 152, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5, 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: 17, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 9, 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: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, 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: 15, 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: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.766, CiteScore: 1)
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: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 18, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, 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: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 13, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 2, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 9)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 3, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  
Astronomy and Astrophysics Review     Hybrid Journal   (Followers: 22, SJR: 3.385, CiteScore: 5)

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Journal Cover
Applied Intelligence
Journal Prestige (SJR): 0.6
Citation Impact (citeScore): 2
Number of Followers: 13  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7497 - ISSN (Online) 0924-669X
Published by Springer-Verlag Homepage  [2352 journals]
  • User profile as a bridge in cross-domain recommender systems for sparsity
    • Authors: Ashish Kumar Sahu; Pragya Dwivedi
      Abstract: In the past two decades, recommender systems have been successfully applied in many e-commerce companies. One of the promising techniques to generate personalized recommendations is collaborative filtering. However, it suffers from sparsity problem. Alleviating this problem, cross-domain recommender systems came into existence in which transfer learning mechanism is applied to exploit the knowledge from other related domains. While applying transfer learning, some information should overlap between source and target domains. Several attempts have been made to enhance the performance of collaborative filtering with the help of other related domains in cross-domain recommender systems framework. Although exploiting the knowledge from other domains is still challenging and open problem in recommender systems. In this paper, we propose a method namely User Profile as a Bridge in Cross-domain Recommender Systems (UP-CDRSs) for transferring knowledge between domains through user profile. Firstly, we build a user profile using demographical information of a user, explicit ratings and content information of user-rated items. Thereafter, the probabilistic graphical model is employed to learn latent factors of users and items in both domains by maximizing posterior probability. At last prediction on unrated item is estimated by an inner product of corresponding latent factors of users and items. Validating of our proposed UP-CDRSs method, we conduct series of experiments on various sparsity levels using cross-domain dataset. The results demonstrate that our proposed method substantially outperforms other without and with transfer learning methods in terms of accuracy.
      PubDate: 2019-01-19
      DOI: 10.1007/s10489-018-01402-3
  • KnRVEA: A hybrid evolutionary algorithm based on knee points and reference
           vector adaptation strategies for many-objective optimization
    • Authors: Gaurav Dhiman; Vijay Kumar
      Abstract: In this paper, a many-objective evolutionary algorithm, named as a hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies (KnRVEA) is proposed. Knee point strategy is used to improve the convergence of solution vectors. In the proposed algorithm, a novel knee adaptation strategy is introduced to adjust the distribution of knee points. KnRVEA is compared with five well-known evolutionary algorithms over thirteen benchmark test functions. The results reveal that the proposed algorithm provides better results than the others in terms of Inverted Generational Distance and Hypervolume. The computational complexity of the proposed algorithm is also analyzed. The statistical testing is performed to show the statistical significance of proposed algorithm. The proposed algorithm is also applied on three real-life constrained many-objective optimization problems to demonstrate its efficiency. The experimental results show that the proposed algorithm is able to solve many-objective real-life problems.
      PubDate: 2019-01-19
      DOI: 10.1007/s10489-018-1365-1
  • Twin maximum entropy discriminations for classification
    • Authors: Xijiong Xie; Huahui Chen; Jiangbo Qian
      Abstract: Maximum entropy discrimination (MED) is an excellent classification method based on the maximum entropy and maximum margin principles, and can produce hard-margin support vector machines (SVMs) under certain condition. In this paper, we propose a novel maximum entropy discrimination classifier called twin maximum entropy discriminations (TMED) which construct two discrimination functions for two classes such that each discrimination function is closer to one of the two classes and is at least γt distance from the other. Therefore, it is more flexible and has better generalization ability than typical MED. Furthermore, it solves a pair of convex optimization problems and has the same advantages as those of non-parallel SVM (NPSVM) which is only the special case of our TMED when the priors and parameters are chosen appropriately. It also owns the inherent sparseness as MED. Experimental results confirm the effectiveness of our proposed method.
      PubDate: 2019-01-17
      DOI: 10.1007/s10489-018-01404-1
  • Effective use of convolutional neural networks and diverse deep
           supervision for better crowd counting
    • Authors: Haiying Jiang; Weidong Jin
      Abstract: In this paper, we focus on the task of estimating crowd count and high-quality crowd density maps. Among crowd counting methods, crowd density map estimation is especially promising because it preserves spatial information which makes it useful for both counting and localization (detection and tracking). Convolutional neural networks have enabled significant progress in crowd density estimation recently, but there are still open questions regarding suitable architectures. We revisit CNNs design and point out key adaptations, enabling plain a signal column CNNs to obtain high resolution and high-quality density maps on all major dense crowd counting datasets. The regular deep supervision utilizes the general ground truth to guide intermediate predictions. Instead, we build hierarchical supervisory signals with additional multi-scale labels to consider the diversities in deep neural networks. We begin by obtaining multi-scale labels based on different Gaussian kernels. These multi-scale labels can be seen as diverse representations in the supervision and can achieve high performance for better quality crowd density map estimation. Extensive experiments demonstrate that our approach achieves the state-of-the-art performance on the ShanghaiTech, UCF_CC_50 and UCSD datasets.
      PubDate: 2019-01-17
      DOI: 10.1007/s10489-018-1394-9
  • Ensemble OS-ELM based on combination weight for data stream classification
    • Authors: Haiyang Yu; Xiaoying Sun; Jian Wang
      Abstract: For online classification, how to design a self-adapted model is a challenging task. To make the model easily adaptable for the fast-changing data stream, a novel ensemble OS-ELM has been put forward. Different from traditional ensemble methods, the proposed approach provided a new self-adapted weight update algorithm. In online learning stage, both the current prediction accuracy and history record are considered. Based on suffer loss and the norm of output layer vector, an aggregate model of game theory is adopted to calculate the combination weight. This strategy fully considers the differences of individual learners. It helps the ensemble method reduce the fitting error of sequence fragment. Also, alterative hidden-layer output matrix can be calculated according to the current fragment, thus building the steady network architecture in the next chunk. So interactive parameter optimization is avoided and the automatic model is suitable for online learning. Numerical experiments are conducted on eight different kinds of UCI datasets. The results demonstrate that the proposed algorithm not only has better generalisation performance but also provides faster learning procedure.
      PubDate: 2019-01-14
      DOI: 10.1007/s10489-018-01403-2
  • Selfish herds optimization algorithm with orthogonal design and
           information update for training multi-layer perceptron neural network
    • Authors: Ruxin Zhao; Yongli Wang; Peng Hu; Hamed Jelodar; Chi Yuan; YanChao Li; Isma Masood; Mahdi Rabbani
      Abstract: Selfish herd optimization algorithm is a novel meta-heuristic optimization algorithm, which simulates the group behavior of herds when attacked by predators in nature. With the further research of algorithm, it is found that the algorithm cannot get a better global optimal solution in solving some problems. In order to improve the optimization ability of the algorithm, we propose a selfish herd optimization algorithm with orthogonal design and information update (OISHO) in this paper. Through using orthogonal design method, a more competitive candidate solution can be generated. If the candidate solution is better than the global optimal solution, it will replace the global optimal solution. At the same time, at the end of each iteration, we update the population information of the algorithm. The purpose is to increase the diversity of the population, so that the algorithm expands its search space to find better solutions. In order to verify the effectiveness of the proposed algorithm, it is used to train multi-layer perceptron (MLP) neural network. For training multi-layer perceptron neural network, this is a challenging task to present a satisfactory and effective training algorithm. We chose twenty different datasets from UCI machine learning repository as training dataset, and the experimental results are compared with SSA, GG-GSA, GSO, GOA, WOA and SOS, respectively. Experimental results show that the proposed algorithm has better optimization accuracy, convergence speed and stability compared with other algorithms for training multi-layer perceptron neural network.
      PubDate: 2019-01-14
      DOI: 10.1007/s10489-018-1373-1
  • All-in-one multicategory Ramp loss maximum margin of twin spheres support
           vector machine
    • Authors: Sijie Lu; Huiru Wang; Zhijian Zhou
      Abstract: Maximum margin of twin spheres support vector machine (MMTSSVM) is effective to deal with imbalanced data classification problems. However, it is sensitive to outliers because of the use of the Hinge loss function. To enhance the stability of MMTSSVM, we propose a Ramp loss maximum margin of twin spheres support vector machine (Ramp-MMTSSVM) in this paper. In terms of the Ramp loss function, the outliers can be given fixed loss values, which reduces the negative effect of outliers on constructing models. Since Ramp-MMTSSVM is a non-differentiable non-convex optimization problem, we adopt Concave-Convex Procedure (CCCP) approach to solve it. We also analyze the properties of parameters and verify them by one artificial experiment. Besides, we use Rest-vs.-One(RVO) strategy to extend Ramp-MMTSSVM to multi-class classification problems. The experimental results on twenty benchmark datasets indicate that no matter in binary or multi-class classification cases, our approaches both can obtain better experimental performance than the compared algorithms.
      PubDate: 2019-01-12
      DOI: 10.1007/s10489-018-1377-x
  • Trajectory similarity clustering based on multi-feature distance
    • Authors: Qingying Yu; Yonglong Luo; Chuanming Chen; Shigang Chen
      Abstract: With the development of GPS-enabled devices, wireless communication and storage technologies, trajectories representing the mobility of moving objects are accumulated at an unprecedented pace. They contain a large amount of temporal and spatial semantic information. A great deal of valuable information can be obtained by mining and analyzing the trajectory dataset. Trajectory clustering is one of the simplest and most powerful methods to obtain knowledge from trajectory data, which is based on the similarity measure between trajectories. The existing similarity measurement methods cannot fully utilize the specific features of trajectory itself when measuring the distance between trajectories. In this paper, an enhanced trajectory model is proposed and a new trajectory clustering algorithm is presented based on multi-feature trajectory similarity measure, which can maximize the similarity of trajectories in the same cluster, and can be used to better serve for applications including traffic monitoring and road congestion prediction. Both the intuitive visualization presentation and the experimental results on synthetic and real trajectory datasets show that, compared to existing methods, the proposed approach improves the accuracy and efficiency of trajectory clustering.
      PubDate: 2019-01-12
      DOI: 10.1007/s10489-018-1385-x
  • A self-organizing map based hybrid chemical reaction optimization
           algorithm for multiobjective optimization
    • Authors: Hongye Li; Lei Wang
      Abstract: Multiobjective particle swarm optimisation (MOPSO) is faced with convergence difficulties and diversity deviation, owing to combined learning orientations and premature phenomena. In MOPSO, leader selection is an important factor that can enhance the algorithm convergence rate. Inspired by this case, and aimed at balancing the convergence and diversity during the searching procedure, a self-organising map is used to construct the neighbourhood relationships among current solutions. In order to increase the population diversity, an extended chemical reaction optimisation algorithm is introduced to improve the diversity performance of the proposed algorithm. In view of the above, a self-organising map-based multiobjective hybrid particle swarm and chemical reaction optimisation algorithm (SMHPCRO) is proposed in this paper. Furthermore, the proposed algorithm is applied to 35 multiobjective test problems with all Pareto set shape and compared with 12 other multiobjective evolutionary algorithms to validate its performance. The experimental results indicate its advantages over other approaches.
      PubDate: 2019-01-11
      DOI: 10.1007/s10489-018-1358-0
  • Fast 6D object pose refinement in depth images
    • Authors: Haoruo Zhang; Qixin Cao
      Abstract: Recovering 6D object pose has gained much focus, because of its application in robotic intelligent manipulation to name but a few. This paper presents an approach for 6D object pose refinement from noisy depth images obtained from a consumer depth sensor. Compared to the state of the art aimed at the same goal, the proposed method has high precision, high robustness to partial occlusions and noise, low computation cost and fast convergence. This is achieved by using an iterative scheme that only employs Random Forest to minimize a cost function of object pose which can quantify the misalignment between the ground truth and the estimated one. The random forest in our algorithm is learnt only using synthetic depth images rendered from 3D model of the object. Several experimental results show the superior performance of the proposed approach compared to ICP-based algorithm and optimization-based algorithm, which are generally used for 6D pose refinement in depth images. Moreover, the iterative process of our algorithm can be much faster than the state of the art by only using one CPU core.
      PubDate: 2019-01-11
      DOI: 10.1007/s10489-018-1376-y
  • Unsupervised deep neuron-per-neuron hashing
    • Authors: Sanaa Chafik; Mounim A. El Yacoubi; Imane Daoudi; Hamid El Ouardi
      Abstract: Hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. A variety of hashing methods have been developed for learning an efficient binary data representation, mainly by relaxing some imposed constraints during hash function learning. Although they have achieved good accuracy-speed trade-off, the resulting binary codes may fail sometimes in adequately approximating the input data, thus significantly decreasing the search accuracy. In this paper, we present a new Unsupervised Deep Learning Hashing approach, called Deep Neuron-per-Neuron Hashing, for high dimensional data indexing. Unlike most existing hashing approaches, our method does not seek to binarize the neural network output, but rather relies directly on the continuous output to create an efficient index structure with hash tables. Given the neural network deepest layer, each table indexes separately a neuron output, capturing in this way a particular high level individual structure (feature) of the input. An efficient search is then performed by computing a cumulative collision score of a given query over all the neuron-based hash tables. Experimental comparisons to the state-of-the-art demonstrate the competitiveness of the proposed method for large datasets.
      PubDate: 2019-01-08
      DOI: 10.1007/s10489-018-1353-5
  • Variable Length IPO and its application in concurrent design and train of
           ANFIS systems
    • Authors: Amir Soltany Mahboob; Seyed Hamid Zahiri
      Abstract: In this paper, a new version of IPO called (VLIPO- Variable Length Inclined Planes System Optimization Algorithm) has been provided primarily. Then, an efficient tool for simultaneous design and training of an ANFIS (adaptive neuro-fuzzy inference system) has been proposed using the mentioned algorithm. It should be noted that till the present time, related research has been only dealing with finding type and location of membership functions or proposing a method of training such networks. Length of standard versions of heuristic algorithms has been mainly the reason for not specifying the type and location of membership and training an ANFIS network simultaneously. For the same reason, first, a new version of the IPO is introduced with such factors being variable. Then, such capability is used for specifying the type and location of membership functions and simultaneous training of an ANFIS classifier. It goes without saying that the idea of making variations in search factors could be also implemented in other heuristic methods. Some of them have been reported in the research. Therefore, the presented idea in the paper may be applied to other heuristic methods and used in the design and simultaneous training of an ANFIS. So, the results from the comparison made between implementing the proposed method and other methods of which a version with a variable length has been previously reported (PSO, ACOR, DE, and GA) have been presented in several well-known databases. The results showed the better execution of ANFIS classifier designed by VLIPO compared to other heuristic methods.
      PubDate: 2019-01-08
      DOI: 10.1007/s10489-018-1366-0
  • A radial basis probabilistic process neural network model and
           corresponding classification algorithm
    • Authors: Kun Liu; Shaohua Xu; Naidan Feng
      Abstract: A radial basis probabilistic process neuron (RBPPN) and radial basis probabilistic process neural network (RBPPNN) model are proposed to fuse a priori knowledge for application to time-varying signal pattern classification. RBPPN inputs were multi-channel time-varying signals and a generalized inner product was used to perform spatio-temporal aggregation of input signals in the kernel. Typical signal samples from various pattern subsets in the sample set were used as kernel center functions, which use morphological distribution characteristics and combination relationships to implicitly express prior knowledge for the signal category. The exponential probability function was used as the activation function to achieve kernel transformation and RBPPN probability output. The RBPPNN is composed of process signal input layers, an RBPPN hidden layer, a pattern layer, and a Softmax classifier developed through stacking. Generalized inner product operations were used to conduct probability similarity measurements of distribution characteristics between process signals. The pattern layer selectivity summed inputs from the RBPPN hidden layer to the pattern layer according to the category of the kernel center function. Its outputs were then used as inputs in the Softmax classifier. The proposed RBPPNN information processing mechanism was extended to the time domain, and through learning time-varying signal training samples, achieved extraction, expression, and information association of time-varying signal characteristics, as well as direct classification. It can improve the deficiencies of existing neural networks, such as a complete large-scale training dataset is needed, and the information processing flow is complex. In this paper, the properties of the RBPPNN are analyzed and a specific learning algorithm is presented which synthesizes dynamic time warping, dynamic C-means clustering, and the mean square error algorithm. A series of 12-lead electrocardiogram (ECG) signals were used for classification testing of heart disease diagnosis results. The ECG classification accuracy across ten disease types was 75.52% and sinus arrhythmia was identified with an accuracy of 86.75%, verifying the effectiveness of the model and algorithm.
      PubDate: 2019-01-08
      DOI: 10.1007/s10489-018-1369-x
  • Three dimensional path planning using Grey wolf optimizer for UAVs
    • Authors: Ram Kishan Dewangan; Anupam Shukla; W. Wilfred Godfrey
      Abstract: Robot path planning is essential to identify the most feasible path between a start point and goal point by avoiding any collision in the given environment. This task is an NP-hard problem and can be modeled as an optimization problem. Many researchers have proposed various deterministic and meta-heuristic algorithm to obtain better results for the path planning problem. The path planning for 3D multi-Unmanned Aerial Vehicle (UAV) is very difficult as the UAV has to find a viable path between start point and goal point with minimum complexity. This work utilizes a newly proposed methodology named ‘grey wolf optimization (GWO)’ to solve the path planning problem of three Dimensional UAV, whose task is to find the feasible trajectory while avoiding collision among obstacles and other UAVs. The performance of GWO algorithm is compared with deterministic algorithms such as Dijkstra, A* and D*, and meta-heuristic algorithms such as Intelligent BAT Algorithm (IBA), Biogeography Based Optimization (BBO), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO), Whale Optimization Algorithm (WOA) and Sine Cosine Algorithm (SCA), so as to find the optimal method. The results show that GWO algorithm outperforms the other deterministic and meta-heuristic algorithms in path planning for 3D multi-UAV.
      PubDate: 2019-01-07
      DOI: 10.1007/s10489-018-1384-y
  • Book search using social information, user profiles and query expansion
           with Pseudo Relevance Feedback
    • Authors: Ritesh Kumar; Guggilla Bhanodai; Rajendra Pamula
      Abstract: Book Search has gained astounding popularity worldwide. Nowadays, users search the items/products online. Users who have not any idea about the product they look towards the social information and user profiles. Social information is further categorized into structured information (e.g. rating and tags) and unstructured information (reviews and annotations). Consequently, how to offer the best recommendation or suggestion of items to end users is becoming a hot topic among researchers. The retrieval and recommendation of relevant documents to the users is a key issue in many domain e.g. songs, accessories, movies, books, etc. In this paper, taking social books as an example, we propose a novel Pseudo Relevance Feedback (PRF) framework for retrieving and searching for relevant documents using social information and user profiles. Especially, we have redesigned a typical distribution-based term selection strategy and transformation-based term selection strategy. Terms are selected and weighted in hope to avoid word mismatch problem and to improve retrieval of the relevant document. Finally, we develop a searching system, where Learning-to-Rank technique is used to adaptively combine the results which are obtained from various PRF strategies with user profiles and social information. Our proposed methodology is extensively evaluated on INEX/CLEF Social Book Search Track (SBS) datasets to verify the effectiveness and robustness of the proposed method. As a result, our proposed method shows the best performance (nDCG@10) on all 3-years SBS track (Suggestion Task) datasets compared to other state-of-the-art methods.
      PubDate: 2019-01-07
      DOI: 10.1007/s10489-018-1383-z
  • Principal component analysis based on block-norm minimization
    • Authors: Jian-Xun Mi; Quanwei Zhu; Jia Lu
      Abstract: Principal Component Analysis (PCA) has attracted considerable interest for years in the studies of image recognition. So far, several state-of-the-art PCA-based robust feature extraction techniques have been proposed, such as PCA-L1 and R1-PCA. Since those methods treat image by its transferred vector form, it leads to the loss of latent information carried by images and loses sight of the spatial structural details of image. To exploit these two kinds of information and improve robustness to outliers, we propose principal component analysis based on block-norm minimization (Block-PCA) which employs block-norm to measure the distance between an image and its reconstruction. Block-norm imposes L2-norm constrain on a local group of pixel blocks and uses L1-norm constrain among different groups. In the case where parts of an image are corrupted, Block-PCA can effectively depress the effect of corrupted blocks and make full use of the rest. In addition, we propose an alternative iterative algorithm to solve the Block-PCA model. Performance is evaluated on several datasets and the results are compared with those of other PCA-based methods.
      PubDate: 2019-01-03
      DOI: 10.1007/s10489-018-1382-0
  • Adaptive local learning regularized nonnegative matrix factorization for
           data clustering
    • Authors: Yongpan Sheng; Meng Wang; Tianxing Wu; Han Xu
      Abstract: Data clustering aims to group the input data instances into certain clusters according to the high similarity to each other, and it could be regarded as a fundamental and essential immediate or intermediate task that appears in areas of machine learning, pattern recognition, and information retrieval. Clustering algorithms based on graph regularized extensions have accumulated much interest for a couple of decades, and the performance of this category of approaches is largely determined by the data similarity matrix, which is usually calculated by the predefined model with carefully tuned parameters combination. However, they may lack a more flexible ability and not be optimal in practice. In this paper, we consider both discriminative information as well as the data manifold in a matrix factorization point of view, and propose an adaptive local learning regularized nonnegative matrix factorization (ALLRNMF) approach for data clustering, which assumes that similar instance pairs with a smaller distance should have a larger probability to be assigned to the probabilistic neighbors. ALLRNMF simultaneously learns the data similarity matrix under the assumption and performs the nonnegative matrix factorization. The constraint of the similarity matrix encodes both the discriminative information as well as the learned adaptive local structure and benefits the data clustering on manifold. In order to solve the optimization problem of our approach, an effective alternative optimization algorithm is proposed such that our objective function could be decomposed into several subproblems that each has an optimal solution, and its convergence is theoretically guaranteed. Experiments on real-world benchmark datasets demonstrate the superior performance of our approach against the existing clustering approaches.
      PubDate: 2019-01-03
      DOI: 10.1007/s10489-018-1380-2
  • Community-based influence maximization for viral marketing
    • Authors: Huimin Huang; Hong Shen; Zaiqiao Meng; Huajian Chang; Huaiwen He
      Abstract: Derived from the idea of word-to-mouth advertising and with applying information diffusion theory, viral marketing attracts wide research interests because of its business value. As an effective marketing strategy, viral marketing is to select a small set of initial users based on trust among close social circles of friends or families so as to maximize the spread of influence in the social network. In this paper, we propose a new community-based influence maximization method for viral marketing that integrates community detection into influence diffusion modeling, instead of performing community detection independently, to improve the performance. We first build a comprehensive latent variable model which captures community-level topic interest, item-topic relevance and community membership distribution of each user, and we propose a collapsed Gibbs sampling algorithm to train the model. Then we infer community-to-community influence strength using topic-irrelevant influence and community topic interest, and further infer user-to-user influence strength using community-to-community influence strength and community membership distribution of each user. Finally we propose a community-based heuristic algorithm to mine influential nodes that selects the influential nodes with a divide-and-conquer strategy, considering both topic-aware and community-relevant to enhance quality and improve efficiency. Extensive experiments are conducted to evaluate effectiveness and efficiency of our proposals. The results validate our ideas and show the superiority of our method compared with state-of-the-art influence maximization algorithms.
      PubDate: 2019-01-02
      DOI: 10.1007/s10489-018-1387-8
  • Taxonomy-aware collaborative denoising autoencoder for personalized
    • Authors: Chunhong Zhang; Tiantian Li; Zhibin Ren; Zheng Hu; Yang Ji
      Abstract: Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.
      PubDate: 2019-01-02
      DOI: 10.1007/s10489-018-1378-9
  • A multi-objective bat algorithm for community detection on dynamic social
    • Authors: Imane Messaoudi; Nadjet Kamel
      Abstract: Many evolutionary algorithms have been proposed to deal with the problem of community detection in social dynamic networks. Some algorithms need to fix parameters in advance; others use a random process to generate the initial population and to apply the algorithm operators. These drawbacks increase the search space and cause a high spatial and temporary complexity. To overcome these weaknesses, we propose in this paper a novel multi-objective Bat Algorithm that uses Mean Shift algorithm to generate the initial population, to obtain solutions of high quality. In our proposal, Bat Algorithm simultaneously optimizes the modularity density and the normalized mutual information of the solutions as objective functions. The operators of the algorithm are applied to the problem of community detection in social dynamic networks by giving another sense to the velocity, frequency, loudness and the pulse rate of natural Bat. The algorithm keeps the principal of the Mean Shift algorithm to generate new solution and avoid the random process by defining a new mutation operator. The algorithm does not need to the non-dominated sorted approach or the crowding distance, but it attributes a weight to each objective function. The method is tested on artificial and real dynamic networks and the experiments show satisfactory results in terms of normalized mutual information, modularity and error rate.
      PubDate: 2019-01-02
      DOI: 10.1007/s10489-018-1386-9
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