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Publisher: Springer-Verlag   (Total: 2345 journals)

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Showing 1 - 200 of 2345 Journals sorted alphabetically
3D Research     Hybrid Journal   (Followers: 19, SJR: 0.214, h-index: 10)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 9, SJR: 1.073, h-index: 25)
AAPS J.     Hybrid Journal   (Followers: 20, SJR: 1.192, h-index: 74)
AAPS PharmSciTech     Hybrid Journal   (Followers: 6, SJR: 0.718, h-index: 54)
Abdominal Imaging     Hybrid Journal   (Followers: 14, SJR: 0.723, h-index: 60)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 2, SJR: 0.447, h-index: 12)
Academic Psychiatry     Full-text available via subscription   (Followers: 22, SJR: 0.492, h-index: 32)
Academic Questions     Hybrid Journal   (Followers: 7, SJR: 0.135, h-index: 6)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 26, SJR: 0.378, h-index: 30)
Acoustical Physics     Hybrid Journal   (Followers: 10, SJR: 0.355, h-index: 20)
Acoustics Australia     Hybrid Journal  
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.387, h-index: 6)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.624, h-index: 34)
Acta Biotheoretica     Hybrid Journal   (Followers: 5, SJR: 0.419, h-index: 25)
Acta Diabetologica     Hybrid Journal   (Followers: 14, SJR: 1.318, h-index: 46)
Acta Endoscopica     Hybrid Journal   (Followers: 1, SJR: 0.113, h-index: 8)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.465, h-index: 23)
Acta Geochimica     Hybrid Journal   (Followers: 3)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 1, SJR: 0.294, h-index: 13)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.818, h-index: 22)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.524, h-index: 32)
Acta Mathematica     Hybrid Journal   (Followers: 11, SJR: 8.021, h-index: 47)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.53, h-index: 29)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5, SJR: 0.406, h-index: 30)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.451, h-index: 5)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.22, h-index: 20)
Acta Mechanica     Hybrid Journal   (Followers: 19, SJR: 0.898, h-index: 52)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.426, h-index: 29)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5, SJR: 0.525, h-index: 18)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.524, h-index: 14)
Acta Neurochirurgica     Hybrid Journal   (Followers: 6, SJR: 0.833, h-index: 73)
Acta Neurologica Belgica     Hybrid Journal   (SJR: 0.348, h-index: 27)
Acta Neuropathologica     Hybrid Journal   (Followers: 3, SJR: 6.61, h-index: 117)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.295, h-index: 17)
Acta Parasitologica     Hybrid Journal   (Followers: 9, SJR: 0.581, h-index: 28)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 2, SJR: 0.551, h-index: 39)
Acta Politica     Hybrid Journal   (Followers: 13, SJR: 0.658, h-index: 20)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 5, SJR: 0.103, h-index: 4)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 20, SJR: 0.871, h-index: 15)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 7)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 14, SJR: 0.795, h-index: 40)
Adsorption     Hybrid Journal   (Followers: 4, SJR: 0.774, h-index: 52)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3, SJR: 0.319, h-index: 15)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 34, SJR: 0.959, h-index: 44)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15, SJR: 1.255, h-index: 44)
Advances in Contraception     Hybrid Journal   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53, SJR: 1.113, h-index: 14)
Advances in Gerontology     Partially Free   (Followers: 9, SJR: 0.141, h-index: 3)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 23, SJR: 1.397, h-index: 42)
Advances in Manufacturing     Hybrid Journal   (Followers: 3, SJR: 0.2, h-index: 4)
Advances in Polymer Science     Hybrid Journal   (Followers: 40, SJR: 0.637, h-index: 89)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 0.79, h-index: 44)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 7)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.882, h-index: 23)
Aerobiologia     Hybrid Journal   (Followers: 1, SJR: 0.511, h-index: 36)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 8, SJR: 0.821, h-index: 49)
African Archaeological Review     Hybrid Journal   (Followers: 14, SJR: 0.612, h-index: 24)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.248, h-index: 6)
AGE     Hybrid Journal   (Followers: 7, SJR: 1.358, h-index: 33)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.337, h-index: 10)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.529, h-index: 55)
Agricultural Research     Hybrid Journal   (Followers: 3)
Agriculture and Human Values     Hybrid Journal   (Followers: 12, SJR: 1.197, h-index: 49)
Agroforestry Systems     Hybrid Journal   (Followers: 19, SJR: 0.64, h-index: 56)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 10, SJR: 1.732, h-index: 59)
AI & Society     Hybrid Journal   (Followers: 6, SJR: 0.171, h-index: 19)
AIDS and Behavior     Hybrid Journal   (Followers: 13, SJR: 2.006, h-index: 71)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 3, SJR: 0.706, h-index: 19)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 3, SJR: 0.566, h-index: 18)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.388, h-index: 22)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 0.868, h-index: 20)
Algorithmica     Hybrid Journal   (Followers: 7, SJR: 0.898, h-index: 56)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.183, h-index: 20)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 4, SJR: 0.729, h-index: 20)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3, SJR: 1.392, h-index: 32)
AMBIO     Hybrid Journal   (Followers: 14, SJR: 1.094, h-index: 87)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 10, SJR: 0.864, h-index: 39)
American J. of Community Psychology     Hybrid Journal   (Followers: 24, SJR: 1.237, h-index: 83)
American J. of Criminal Justice     Hybrid Journal   (Followers: 7, SJR: 0.634, h-index: 13)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 11, SJR: 0.283, h-index: 3)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, SJR: 0.175, h-index: 13)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.558, h-index: 35)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.293, h-index: 13)
American Sociologist     Hybrid Journal   (Followers: 12, SJR: 0.18, h-index: 13)
Amino Acids     Hybrid Journal   (Followers: 7, SJR: 1.362, h-index: 83)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5, SJR: 0.21, h-index: 37)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 4, SJR: 0.665, h-index: 7)
Analysis in Theory and Applications     Hybrid Journal  
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 4)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 28, SJR: 1.096, h-index: 123)
Anatomical Science Intl.     Hybrid Journal   (Followers: 2, SJR: 0.301, h-index: 26)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.212, h-index: 69)
Animal Cognition     Hybrid Journal   (Followers: 16, SJR: 1.122, h-index: 55)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.156, h-index: 4)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.377, h-index: 32)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.504, h-index: 14)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.167, h-index: 26)
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 12, SJR: 2.112, h-index: 98)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 18, SJR: 1.182, h-index: 94)
Annals of Combinatorics     Hybrid Journal   (Followers: 3, SJR: 0.849, h-index: 15)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Dyslexia     Hybrid Journal   (Followers: 9, SJR: 0.857, h-index: 40)
Annals of Finance     Hybrid Journal   (Followers: 28, SJR: 0.686, h-index: 14)
Annals of Forest Science     Hybrid Journal   (Followers: 4, SJR: 0.929, h-index: 57)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.136, h-index: 23)
Annals of Hematology     Hybrid Journal   (Followers: 14, SJR: 1.117, h-index: 62)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6, SJR: 0.593, h-index: 42)
Annals of Microbiology     Hybrid Journal   (Followers: 9, SJR: 0.402, h-index: 26)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 5, SJR: 0.68, h-index: 45)
Annals of Operations Research     Hybrid Journal   (Followers: 8, SJR: 1.186, h-index: 78)
Annals of Ophthalmology     Hybrid Journal   (Followers: 9)
Annals of Regional Science     Hybrid Journal   (Followers: 7, SJR: 0.405, h-index: 42)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 10, SJR: 0.553, h-index: 8)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 11, SJR: 1.902, h-index: 127)
Annals of Telecommunications     Hybrid Journal   (Followers: 7, SJR: 0.315, h-index: 25)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 0.931, h-index: 31)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.992, h-index: 87)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.14, h-index: 57)
APOPTOSIS     Hybrid Journal   (Followers: 8, SJR: 1.554, h-index: 87)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.354, h-index: 27)
Applications of Mathematics     Hybrid Journal   (Followers: 1, SJR: 0.274, h-index: 20)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 44, SJR: 0.575, h-index: 80)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 17, SJR: 0.267, h-index: 26)
Applied Categorical Structures     Hybrid Journal   (Followers: 2, SJR: 0.361, h-index: 21)
Applied Composite Materials     Hybrid Journal   (Followers: 47, SJR: 0.705, h-index: 35)
Applied Entomology and Zoology     Partially Free   (Followers: 2, SJR: 0.554, h-index: 34)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.323, h-index: 9)
Applied Geophysics     Hybrid Journal   (Followers: 7, SJR: 0.541, h-index: 13)
Applied Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.777, h-index: 43)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 3, SJR: 0.358, h-index: 34)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4, SJR: 0.955, h-index: 33)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.275, h-index: 8)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 4, SJR: 0.37, h-index: 26)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 61, SJR: 1.262, h-index: 161)
Applied Physics A     Hybrid Journal   (Followers: 7, SJR: 0.535, h-index: 121)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 23, SJR: 0.983, h-index: 104)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 7, SJR: 0.677, h-index: 47)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 10, SJR: 0.288, h-index: 15)
Applied Solar Energy     Hybrid Journal   (Followers: 16, SJR: 0.251, h-index: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4, SJR: 0.351, h-index: 9)
Aquaculture Intl.     Hybrid Journal   (Followers: 22, SJR: 0.613, h-index: 40)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 1)
Aquatic Ecology     Hybrid Journal   (Followers: 30, SJR: 0.646, h-index: 44)
Aquatic Geochemistry     Hybrid Journal   (Followers: 3, SJR: 0.764, h-index: 39)
Aquatic Sciences     Hybrid Journal   (Followers: 12, SJR: 1.172, h-index: 53)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.345, h-index: 20)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 1, SJR: 0.417, h-index: 16)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 21, SJR: 1.056, h-index: 15)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.397, h-index: 13)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.597, h-index: 29)
Archival Science     Hybrid Journal   (Followers: 52, SJR: 0.804, h-index: 22)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.28, h-index: 15)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1, SJR: 0.946, h-index: 23)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 4.091, h-index: 66)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4, SJR: 0.865, h-index: 40)
Archives and Museum Informatics     Hybrid Journal   (Followers: 121)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4, SJR: 2.841, h-index: 40)
Archives of Dermatological Research     Hybrid Journal   (Followers: 6, SJR: 0.9, h-index: 65)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 10, SJR: 0.846, h-index: 84)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 17, SJR: 0.695, h-index: 47)
Archives of Microbiology     Hybrid Journal   (Followers: 8, SJR: 0.702, h-index: 85)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 8, SJR: 1.039, h-index: 56)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 1.092, h-index: 13)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 9, SJR: 1.198, h-index: 74)
Archives of Toxicology     Hybrid Journal   (Followers: 16, SJR: 1.595, h-index: 76)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 1.086, h-index: 90)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 13, SJR: 1.264, h-index: 50)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 1.2, h-index: 42)
ArgoSpine News & J.     Hybrid Journal   (SJR: 0.102, h-index: 3)
Argumentation     Hybrid Journal   (Followers: 5, SJR: 0.295, h-index: 18)
Arid Ecosystems     Hybrid Journal   (Followers: 3)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.948, h-index: 22)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 1, SJR: 0.797, h-index: 17)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.145, h-index: 8)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 9, SJR: 0.288, h-index: 25)
Artificial Intelligence Review     Hybrid Journal   (Followers: 14, SJR: 0.948, h-index: 48)
Artificial Life and Robotics     Hybrid Journal   (Followers: 7, SJR: 0.231, h-index: 14)
Asia Europe J.     Hybrid Journal   (Followers: 4, SJR: 0.247, h-index: 9)
Asia Pacific Education Review     Hybrid Journal   (Followers: 9, SJR: 0.371, h-index: 17)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 12, SJR: 1.676, h-index: 50)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 11, SJR: 0.353, h-index: 13)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 2, SJR: 0.19, h-index: 15)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 20, SJR: 1.006, h-index: 14)
Asian Business & Management     Hybrid Journal   (Followers: 7, SJR: 0.41, h-index: 10)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 7)
Asian J. of Criminology     Hybrid Journal   (Followers: 5, SJR: 0.263, h-index: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2, SJR: 0.681, h-index: 15)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.195, h-index: 5)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  
Astronomy and Astrophysics Review     Hybrid Journal   (Followers: 21, SJR: 4.511, h-index: 44)
Astronomy Letters     Hybrid Journal   (Followers: 20, SJR: 0.58, h-index: 30)

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Journal Cover Applied Intelligence
  [SJR: 0.777]   [H-I: 43]   [13 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1573-7497 - ISSN (Online) 0924-669X
   Published by Springer-Verlag Homepage  [2345 journals]
  • Link prediction based on sampling in complex networks
    • Authors: Caiyan Dai; Ling Chen; Bin Li
      Pages: 1 - 12
      Abstract: The link prediction problem has received extensive attention in fields such as sociology, anthropology, information science, and computer science. In many practical applications, we only need to predict the potential links between the vertices of interest, instead of predicting all of the links in a complex network. In this paper, we propose a fast similarity based approach for predicting the links related to a given node. We construct a path set connected to the given node by a random walk. The similarity score is computed within a small sub-graph formed by the path set connected to the given node, which significantly reduces the computation time. By choosing the appropriate number of sampled paths, we can restrict the error of the estimated similarities within a given threshold. Our experimental results on a number of real networks indicate that the algorithm proposed in this paper can obtain accurate results in less time than existing methods.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0872-1
      Issue No: Vol. 47, No. 1 (2017)
  • Q-CAR: an intelligent solution for joint QoS multicast routing and channel
           assignment in multichannel multiradio wireless mesh networks
    • Authors: Dibakar Chakraborty; Khumbar Debbarma
      Pages: 13 - 27
      Abstract: Multicast routing improves the efficiency of a network by effectively utilizing the available network bandwidth. In multichannel multiradio wireless mesh networks the channel allocation strategy plays a vital role along with multicast tree construction. However, the multicast routing problem in multichannel multiradio wireless mesh networks is proven to be NP-hard. With this paper, we propose a Quality of Service Channel Assignment and multicast Routing (Q-CAR) algorithm. The proposed algorithm jointly solves the channel assignment and multicast tree construction problem by intelligent computational methods. We use a slightly modified differential evolution approach for assigning channels to links. We design a genetic algorithm based multicast tree construction strategy which determines a delay, jitter bounded low cost multicast tree. Moreover, we define a multi objective fitness function for the tree construction algorithm which optimizes interference as well as tree cost. Finally, we compare the performance of Q-CAR with QoS Multicast Routing and Channel Assignment(QoS-MRCA) and intelligent Quality of service multicast routing and Channel Assignment(i-QCA) algorithm in multichannel multiradio wireless mesh network (simulated) environments. Our experimental results distinctly show the outstanding performance of the proposed algorithm.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0871-2
      Issue No: Vol. 47, No. 1 (2017)
  • A new hybrid constructive neural network method for impacting and its
           application on tungsten price prediction
    • Authors: Hou Muzhou; Liu Taohua; Yang Yunlei; Zhu Hao; Liu Hongjuan; Yuan Xiugui; Liu Xinge
      Pages: 28 - 43
      Abstract: To accurately predict the price of tungsten with an optimal architecture of neural networks (NNs) and better generalization performance, based on poor generalization and overfitting of a predictor such as a NNs, this paper presents a new hybrid constructive neural network method (HCNNM) to repair the impacting value in the original data in the same manner as the jumping points of a function. A series of theorems was proven that show a function with m jumping discontinuity points (or impacting points) can be approximated with the simplest NNs and a constructive decay Radial basis function (RBF) NNs, and a function with m jumping discontinuity points can be constructively approximated by hybrid constructive NNs. The hybrid networks have an optimal architecture and generalize well. Additionally, a practical problem regarding Tungsten prices from 1900 to 2014 is presented with some impacting points to more accurately approximate the sample data set and forecast future prices with the HCNNM, and some performance measures, such as the training time, testing RMSE and neurons, are compared with traditional algorithms (BP, SVM, ELM and Deep Learning) through many numerical experiments that fully verify the superiority, correctness and validity of the theory.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0882-z
      Issue No: Vol. 47, No. 1 (2017)
  • Nonrigid 3D shape retrieval using deep auto-encoders
    • Authors: Hamed Ghodrati; A. Ben Hamza
      Pages: 44 - 61
      Abstract: The soaring popularity of deep learning in a wide variety of fields ranging from computer vision and speech recognition to self-driving vehicles has sparked a flurry of research interest from both academia and industry. In this paper, we propose a deep learning approach to 3D shape retrieval using a multi-level feature learning paradigm. Low-level features are first extracted from a 3D shape using spectral graph wavelets. Then, mid-level features are generated via the bag-of-features model by employing locality-constrained linear coding as a feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid matching in a bid to effectively measure the spatial relationship between each pair of the bag-of-feature descriptors. Finally, high-level shape features are learned by applying a deep auto-encoder on mid-level features. Extensive experiments on SHREC-2014 and SHREC-2015 datasets demonstrate the much better performance of the proposed framework in comparison with state-of-the-art methods.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0880-1
      Issue No: Vol. 47, No. 1 (2017)
  • Improving human-robot interaction based on joint attention
    • Authors: Evren Dağlarlı; Sare Funda Dağlarlı; Gülay Öke Günel; Hatice Köse
      Pages: 62 - 82
      Abstract: The current study proposes a novel cognitive architecture for a computational model of the limbic system, inspired by human brain activity, which improves interactions between a humanoid robot and preschool children using joint attention during turn-taking gameplay. Using human-robot interaction (HRI), this framework may be useful for ameliorating problems related to attracting and maintaining attention levels of children suffering from attention deficit hyperactivity disorder (ADHD). In the proposed framework, computational models including the amygdala, hypothalamus, hippocampus, and basal ganglia are used to simulate a range of cognitive processes such as emotional responses, episodic memory formation, and selection of appropriate behavioral responses. In the currently proposed model limbic system, we applied reinforcement and unsupervised learning-based adaptation processes to a dynamic neural field model, resulting in a system that was capable of observing and controlling the physical and cognitive processes of a humanoid robot. Several interaction scenarios were tested to evaluate the performance of the model. Finally, we compared the results of our methodology with a neural mass model.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0876-x
      Issue No: Vol. 47, No. 1 (2017)
  • Temporal probabilistic measure for link prediction in collaborative
    • Authors: T. Jaya Lakshmi; S. Durga Bhavani
      Pages: 83 - 95
      Abstract: Link prediction addresses the problem of finding potential links that may form in the future. Existing state of art techniques exploit network topology for computing probability of future link formation. We are interested in using Graphical models for link prediction. Graphical models use higher order topological information underlying a graph for computing Co-occurrence probability of the nodes pertaining to missing links. Time information associated with the links plays a major role in future link formation. There have been a few measures like Time-score, Link-score and T_Flow, which utilize temporal information for link prediction. In this work, Time-score is innovatively incorporated into the graphical model framework, yielding a novel measure called Temporal Co-occurrence Probability (TCOP) for link prediction. The new measure is evaluated on four standard benchmark data sets : DBLP, Condmat, HiePh-collab and HiePh-cite network. In the case of DBLP network, TCOP improves AUROC by 12 % over neighborhood based measures and 5 % over existing temporal measures. Further, when combined in a supervised framework, TCOP gives 93 % accuracy. In the case of three other networks, TCOP achieves a significant improvement of 5 % on an average over existing temporal measures and an average of 9 % improvement over neighborhood based measures. We suggest an extension to link prediction problem called Long-term link prediction, and carry out a preliminary investigation. We find TCOP proves to be effective for long-term link prediction.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0883-y
      Issue No: Vol. 47, No. 1 (2017)
  • Tree-based localized fuzzy twin support vector clustering with square loss
    • Authors: Reshma Rastogi; Pooja Saigal
      Pages: 96 - 113
      Abstract: Twin support vector machine (TWSVM) is an efficient supervised learning algorithm, proposed for the classification problems. Motivated by its success, we propose Tree-based localized fuzzy twin support vector clustering (Tree-TWSVC). Tree-TWSVC is a novel clustering algorithm that builds the cluster model as a binary tree, where each node comprises of proposed TWSVM-based classifier, termed as localized fuzzy TWSVM (LF-TWSVM). The proposed clustering algorithm Tree-TWSVC has efficient learning time, achieved due to the tree structure and the formulation that leads to solving a series of systems of linear equations. Tree-TWSVC delivers good clustering accuracy because of the square loss function and it uses nearest neighbour graph based initialization method. The proposed algorithm restricts the cluster hyperplane from extending indefinitely by using cluster prototype, which further improves its accuracy. It can efficiently handle large datasets and outperforms other TWSVM-based clustering methods. In this work, we propose two implementations of Tree-TWSVC: Binary Tree-TWSVC and One-against-all Tree-TWSVC. To prove the efficacy of the proposed method, experiments are performed on a number of benchmark UCI datasets. We have also given the application of Tree-TWSVC as an image segmentation tool.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0886-8
      Issue No: Vol. 47, No. 1 (2017)
  • Efficient algorithm for mining high average-utility itemsets in
           incremental transaction databases
    • Authors: Donggyu Kim; Unil Yun
      Pages: 114 - 131
      Abstract: In this paper, we present a novel algorithm for efficiently mining high average-utility itemsets (HAUIs) from incremental databases, in which their volumes can be expanded dynamically. The previous algorithms have inefficiencies in that they must scan a given database multiple times so as to generate candidate itemsets and determine valid itemsets level by level. The reason is that they follow the basic framework of an Apriori-like approach. This drawback can cause critical problems in processing incremental databases because scanning a database becomes a tougher task as the size of the database is increased. In contrast, the algorithm proposed in this paper builds a compact tree structure maintaining all necessary information in order to avoid such excessive database scanning during its mining process. The previous algorithms suffer from the huge generation of unnecessary candidate itemsets at each level accompanied by the naive combination based candidate generation manner of an Apriori-like approach, which generates candidate itemsets with (k+1)-lengths by simply joining itemsets with k-lengths. On the other hand, our algorithm employs the pattern growth approach, which allows the algorithm to generate a set of only essential candidate itemsets. In order for our algorithm to constantly preserve the compactness of its tree structure during the entire incremental mining process, a restructuring technique is exploited. In the performance evaluation, we show that our algorithm is faster and consumes less memory space than competitors.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0890-z
      Issue No: Vol. 47, No. 1 (2017)
  • Distance and distance induced intuitionistic entropy of generalized
           intuitionistic fuzzy soft sets
    • Authors: Ganeshsree Selvachandran; P. K. Maji; Raghad Qasim Faisal; Abdul Razak Salleh
      Pages: 132 - 147
      Abstract: This paper examines the generalized intuitionistic fuzzy soft set (GIFSS) model which is an intuitively straightforward extension of the intuitionistic fuzzy soft set (IFSS) model. This concept which arises from IFSSs, is generalized by including a moderator’s opinion regarding the validity of the information at hand, thus making it highly suitable for use in decision-making problems that involve uncertain, vague and/or unreliable data. In this paper, we introduce the tools that measure the distance, similarity and the degree of fuzziness of GIFSSs. The axiomatic definitions of the distance measure is introduced and subsequently used to define the similarity measure and intuitionistic entropy induced by this distance measure. Some of the algebraic properties of these measures are also verified. The well-known Hamming, normalized Hamming, Euclidean and normalized Euclidean distances are generalized to make them compatible with the concept of GIFSSs. Subsequently, some relations among these information measures are proposed and verified. These results indicate how these measures are related and how they can be deduced from one another. Finally, we demonstrate the application of the information measure between GIFSSs by applying it to a case study related to the moderation of school-based assessment components of students in externally accredited academic programs.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0884-x
      Issue No: Vol. 47, No. 1 (2017)
  • Spatial locality-preserving feature coding for image classification
    • Authors: Qi-Hai Zhu; Zhe-Zheng Wang; Xiao-Jiao Mao; Yu-Bin Yang
      Pages: 148 - 157
      Abstract: The state-of-the-art image classification models, generally including feature coding and pooling, have been widely adopted to generate discriminative and robust image representations. However, the coding schemes available in these models only preserve salient features which results in information loss in the process of generating final image representations. To address this issue, we propose a novel spatial locality-preserving feature coding strategy which selects representative codebook atoms based on their density distribution to retain the structure of features more completely and make representations more descriptive. In the codebook learning stage, we propose an effective approximated K-means with cluster closures to initialize the codebook and independently adjust the center of each cluster of the dense regions. Afterwards, in the coding stage, we first define the concept of “density” to describe the spatial relationship among the code atoms and the features. Then, the responses of local features are adaptively encoded. Finally, in the pooling stage, a locality-preserving pooling strategy is utilized to aggregate the encoded response vectors into a statistical vector for representing the whole image or all the regions of interest. We carry out image classification experiments on three commonly used benchmark datasets including 15-Scene, Caltech-101, and Caltech-256. The experimental results demonstrate that, comparing with the state-of-the-art Bag-of-Words (BoW) based methods, our approach achieves the best classification accuracy on these benchmarked datasets.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0887-7
      Issue No: Vol. 47, No. 1 (2017)
  • D-Brane: a diplomacy playing agent for automated negotiations research
    • Authors: Dave de Jonge; Carles Sierra
      Pages: 158 - 177
      Abstract: Existing work on Automated Negotiations commonly assumes the negotiators’ utility functions have explicit closed-form expressions, and can be calculated quickly. In many real-world applications however, the calculation of utility can be a complex, time-consuming problem and utility functions cannot always be expressed in terms of simple formulas. The game of Diplomacy forms an ideal test bed for research on Automated Negotiations in such domains where utility is hard to calculate. Unfortunately, developing a full Diplomacy player is a hard task, which requires more than just the implementation of a negotiation algorithm. The performance of such a player may highly depend on the underlying strategy rather than just its negotiation skills. Therefore, we introduce a new Diplomacy playing agent, called D-Brane, which has won the first international Computer Diplomacy Challenge. It is built up in a modular fashion, disconnecting its negotiation algorithm from its game-playing strategy, to allow future researchers to build their own negotiation algorithms on top of its strategic module. This will allow them to easily compare the performance of different negotiation algorithms. We show that D-Brane strongly outplays a number of previously developed Diplomacy players, even when it does not apply negotiations. Furthermore, we explain the negotiation algorithm applied by D-Brane, and present a number of additional tools, bundled together in the new BANDANA framework, that will make development of Diplomacy-playing agents easier.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-017-0919-y
      Issue No: Vol. 47, No. 1 (2017)
  • How an epileptic EEG segment, used as reference, can influence a
           cross-correlation classifier?
    • Authors: Jefferson Tales Oliva; João Luís Garcia Rosa
      Pages: 178 - 196
      Abstract: Several neurological disorders, such as epilepsy, can be diagnosed by electroencephalogram (EEG). Data mining supported by machine learning (ML) techniques can be used to find patterns and to build classifiers for the data. In order to make it possible, data should be represented in an appropriate format, e.g. attribute-value table, which can be built by feature extraction approaches, such as the cross-correlation (CC) method, which uses one signal as reference and correlates it with other signals. However, the reference is commonly selected randomly and, to the best of our knowledge, no studies have been conducted to evaluate whether this choice can affect the ML method performance. Thereby, this work aims to verify whether the choice of an epileptic EEG segment as reference can affect the performance of classifiers built from data. Also, a CC with artificial reference (CCAR) method is proposed in order to reduce possible consequences of the random selection of a signal as reference. Two experimental evaluations were conducted in a set of 200 EEG segments to induce classifiers using ML algorithms, such as J48, 1NN, naive Bayes, BP-MLP, and SMO. In the first study, each epileptic EEG segment was selected as reference to apply CC and ML methods. The evaluation found extremely significant difference, evidencing that the choice of an EEG segment as reference can influence the performance of ML methods. In the second study, the CCAR method was performed, in which statistical tests, only in comparisons involving the SMO classifier, showed not-so-good results.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0891-y
      Issue No: Vol. 47, No. 1 (2017)
  • GAEMTBD: Genetic algorithm based entity matching techniques for
           bibliographic databases
    • Authors: Sumit Mishra; Sriparna Saha; Samrat Mondal
      Pages: 197 - 230
      Abstract: Entity matching is to map the records in a database to their corresponding entities. It is a well-known problem in the field of database and artificial intelligence. In digital libraries such as DBLP, ArnetMiner, Google Scholar, Scopus, Web of Science, AllMusic, IMDB, etc., some of the attributes may evolve over time, i.e., they change their values at different instants of time. For example, affiliation and email-id of an author in bibliographic databases which maintain publication details of various authors like DBLP, ArnetMiner, etc. may change their values. A taxpayer can change his or her address over time. Sometimes people change their surnames due to marriage. When a database contains records of these natures and the number of records grows beyond a limit, then it becomes really challenging to identify which records belong to which entity due to the lack of a proper key. In the current paper, the problem of automatic partitioning of records is posed as an optimization problem. Thereafter, a genetic algorithm based automatic technique is proposed to solve the entity matching problem. The proposed approach is able to automatically determine the number of partitions available in a bibliographic dataset. A comparative analysis with the two existing systems – DBLP and ArnetMiner, over sixteen bibliographic datasets proves the efficacy of the proposed approach.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0874-z
      Issue No: Vol. 47, No. 1 (2017)
  • Multi-search differential evolution algorithm
    • Authors: Xiangtao Li; Shijing Ma; Jiehua Hu
      Pages: 231 - 256
      Abstract: The differential evolution algorithm (DE) has been shown to be a very simple and effective evolutionary algorithm. Recently, DE has been successfully used for the numerical optimization. In this paper, first, based on the fitness value of each individual, the population is partitioned into three subpopulations with different size. Then, a dynamically adjusting method is used to change the three subpopulation group sizes based on the previous successful rate of different mutation strategies. Second, inspired by the “DE/current to pbest/1”, three mutation strategies including “DE/current to cbest/1”, “DE/current to rbest/1” and “DE/current to fbest/1” are proposed to take on the responsibility for either exploitation or exploration. Finally, a novel effective parameter adaptation method is designed to automatically tune the parameter F and CR in DE algorithm. In order to validate the effectiveness of MSDE, it is tested on ten benchmark functions chosen from literature. Compared with some evolution algorithms from literature, MSDE performs better in most of the benchmark problems.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0885-9
      Issue No: Vol. 47, No. 1 (2017)
  • Fixing inconsistencies of fuzzy spatiotemporal XML data
    • Authors: Luyi Bai; Zhulei Shao; Zhuo Lin; Shaohui Cheng
      Pages: 257 - 275
      Abstract: Fuzzy spatiotemporal data models have been used to support spatial and temporal knowledge representation and reasoning in the presence of fuzziness. In the meantime, XML is expected to become the next generation standard language for exchanging data over the Internet, which will become a trend to represent fuzzy spatiotemporal data based on XML. However, fuzzy spatiotemporal XML documents may contain inconsistencies violating predefined spatial and temporal constraints, which cause the data inconsistency problems. Although those consistency problems in XML documents have been widely studied, their studies only take the general data into account, and the studies on consistencies of fuzzy spatiotemporal data are still open issues. In this paper we put forward solutions to the problems of inconsistencies in fuzzy spatiotemporal XML documents. We also analyze inconsistent states which are named discontinuity overlap or cycle of the temporal labels of some incoming edges. Then, we put forward the corresponding approaches to checking and fixing fuzzy spatiotemporal XML documents according to the inconsistent states. Finally, the experimental results show that our proposed algorithms can fix inconsistencies of fuzzy spatiotemporal XML documents significantly.
      PubDate: 2017-07-01
      DOI: 10.1007/s10489-016-0888-6
      Issue No: Vol. 47, No. 1 (2017)
  • A niche-elimination operation based NSGA-III algorithm for many-objective
    • Authors: Xiaojun Bi; Chao Wang
      Abstract: Decomposition-based multi-objective evolutionary algorithms have been found to be very promising for many-objective optimization. The recently presented non-dominated sorting genetic algorithm III (NSGA-III) employs the decomposition idea to efficiently promote the population diversity. However, due to the low selection pressure of the Pareto-dominance relation the convergence of NSGA-III could still be improved. For this purpose, an improved NSGA-III algorithm based on niche-elimination operation (we call it NSGA-III-NE) is proposed. In the proposed algorithm, an adaptive penalty distance (APD) function is presented to consider the importance of convergence and diversity in the different stages of the evolutionary process. Moreover, the niche-elimination operation is designed by exploiting the niching technique and the worse-elimination strategy. The niching technique identifies the most crowded subregion, and the worse-elimination strategy finds and further eliminates the worst individual. The proposed NSGA-III-NE is tested on a number of well-known benchmark problems with up to fifteen objectives and shows the competitive performance compared with five state-of-the-art decomposition-based algorithms. Additionally, a vector angle based selection strategy is also proposed for handling irregular Pareto fronts.
      PubDate: 2017-06-20
      DOI: 10.1007/s10489-017-0958-4
  • Batch-normalized Mlpconv-wise supervised pre-training network in network
    • Authors: Xiaomeng Han; Qun Dai
      Abstract: Deep multi-layered neural networks have nonlinear levels that allow them to represent highly varying nonlinear functions compactly. In this paper, we propose a new deep architecture with enhanced model discrimination ability that we refer to as mlpconv-wise supervised pre-training network in network (MPNIN). The process of information abstraction is facilitated within the receptive fields for MPNIN. The proposed architecture uses the framework of the recently developed NIN structure, which slides a universal approximator, such as a multilayer perceptron with rectifier units, across an image to extract features. However, the random initialization of NIN can produce poor solutions to gradient-based optimization. We use mlpconv-wise supervised pre-training to remedy this defect because this pre-training technique may contribute to overcoming the difficulties of training deep networks by better initializing the weights in all the layers. Moreover, batch normalization is applied to reduce internal covariate shift by pre-conditioning the model. Empirical investigations are conducted on the Mixed National Institute of Standards and Technology (MNIST), the Canadian Institute for Advanced Research (CIFAR-10), CIFAR-100, the Street View House Numbers (SVHN), the US Postal (USPS), Columbia University Image Library (COIL20), COIL100 and Olivetti Research Ltd (ORL) datasets, and the results verify the effectiveness of the proposed MPNIN architecture.
      PubDate: 2017-06-20
      DOI: 10.1007/s10489-017-0968-2
  • Successes and challenges in developing a hybrid approach to sentiment
    • Authors: Orestes Appel; Francisco Chiclana; Jenny Carter; Hamido Fujita
      Abstract: This article covers some success and learning experiences attained during the developing of a hybrid approach to Sentiment Analysis (SA) based on a Sentiment Lexicon, Semantic Rules, Negation Handling, Ambiguity Management and Linguistic Variables. The proposed hybrid method is presented and applied to two selected datasets: Movie Review and Sentiment Twitter datasets. The achieved results are compared against those obtained when Naïve Bayes (NB) and Maximum Entropy (ME) supervised machine learning classification methods are used for the same datasets. The proposed hybrid system attained higher accuracy and precision scores than NB and ME, which shows its superiority when applied to the SA problem at the sentence level. Finally, an alternative strategy to calculating the orientation polarity and polarity intensity in one step instead of the two steps method used in the hybrid approach is explored. The analysis of the yielded mixed results achieved with this alternative approach shows its potential as an aid in the computation of semantic orientations and produced some lessons learnt in developing a more effective mechanism to calculating the orientation polarity and polarity intensity.
      PubDate: 2017-06-20
      DOI: 10.1007/s10489-017-0966-4
  • An adaptive bi-flight cuckoo search with variable nests for continuous
           dynamic optimization problems
    • Authors: Javidan Kazemi Kordestani; Hossein Abedi Firouzjaee; Mohammad Reza Meybodi
      Abstract: This paper presents an adaptive bi-flight cuckoo search algorithm for continuous dynamic optimization problems. Unlike the standard cuckoo search which relies on Levy flight, the proposed method uses two types of flight that are chosen adaptively by a learning automaton to control the global and local search ability of the method during the run. Furthermore, a variable nest scheme and a new cuckoo addition mechanism are introduced. A greedy local search method is also integrated to refine the best found solution. An extensive set of experiments is conducted on a variety of dynamic environments generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the effectiveness of the proposed approach.
      PubDate: 2017-06-19
      DOI: 10.1007/s10489-017-0963-7
  • Change-aware community detection approach for dynamic social networks
    • Authors: M. E. Samie; A. Hamzeh
      Abstract: Community mining is one of the most popular issues in social network analysis. Although various changes may occur in a dynamic social network, they can be classified into two categories, gradual changes and abrupt changes. Many researchers have attempted to propose a method to discover communities in dynamic social networks with various changes more accurately. Most of them have assumed that changes in dynamic social networks occur gradually. This presumption for the dynamic social network in which abrupt changes may occur misleads the problem. Few methods have tried to detect abrupt changes, but they used the statistical approach which has such disadvantages as the need for a lot of snapshots. In this paper, we propose a novel method to detect the type of changes using the least information of social networks and then, apply it to a new community detection framework named change-aware model. The experimental results on different benchmark and real-life datasets confirmed that the new method and framework have improved the performance of community detection algorithms.
      PubDate: 2017-06-19
      DOI: 10.1007/s10489-017-0934-z
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