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Publisher: Springer-Verlag (Total: 2355 journals)

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Showing 1 - 200 of 2355 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: 10, SJR: 1.073, h-index: 25)
AAPS J.     Hybrid Journal   (Followers: 22, SJR: 1.192, h-index: 74)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, 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: 3, SJR: 0.447, h-index: 12)
Academic Psychiatry     Full-text available via subscription   (Followers: 23, SJR: 0.492, h-index: 32)
Academic Questions     Hybrid Journal   (Followers: 8, 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: 11, 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: 4, SJR: 0.419, h-index: 25)
Acta Diabetologica     Hybrid Journal   (Followers: 15, 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: 6)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 2, 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: 6, 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: 20, 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   (Followers: 1, SJR: 0.348, h-index: 27)
Acta Neuropathologica     Hybrid Journal   (Followers: 5, SJR: 6.61, h-index: 117)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.295, h-index: 17)
Acta Parasitologica     Hybrid Journal   (Followers: 10, 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)
Activitas Nervosa Superior     Hybrid Journal  
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 5, SJR: 0.103, h-index: 4)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 22, SJR: 0.871, h-index: 15)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 8)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 15, 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: 35, SJR: 0.959, h-index: 44)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 18, SJR: 1.255, h-index: 44)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52, SJR: 1.113, h-index: 14)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.141, h-index: 3)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 25, 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: 41, 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: 2, SJR: 0.511, h-index: 36)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 9, SJR: 0.821, h-index: 49)
African Archaeological Review     Hybrid Journal   (Followers: 16, 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: 14, 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: 11, 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: 4, 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: 8, 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: 5, 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: 15, SJR: 1.094, h-index: 87)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.864, h-index: 39)
American J. of Community Psychology     Hybrid Journal   (Followers: 26, SJR: 1.237, h-index: 83)
American J. of Criminal Justice     Hybrid Journal   (Followers: 8, SJR: 0.634, h-index: 13)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 15, 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: 22, SJR: 0.293, h-index: 13)
American Sociologist     Hybrid Journal   (Followers: 14, SJR: 0.18, h-index: 13)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.362, h-index: 83)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.21, h-index: 37)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 3, SJR: 0.665, h-index: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 5)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 31, 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: 14, 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: 11)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, 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: 6, 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: 16, SJR: 1.117, h-index: 62)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.593, h-index: 42)
Annals of Microbiology     Hybrid Journal   (Followers: 10, SJR: 0.402, h-index: 26)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 4, SJR: 0.68, h-index: 45)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 1.186, h-index: 78)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
Annals of Regional Science     Hybrid Journal   (Followers: 8, SJR: 0.405, h-index: 42)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 10, SJR: 0.553, h-index: 8)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 14, SJR: 1.902, h-index: 127)
Annals of Telecommunications     Hybrid Journal   (Followers: 8, 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: 42, SJR: 0.575, h-index: 80)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 16, SJR: 0.267, h-index: 26)
Applied Cancer Research     Open Access  
Applied Categorical Structures     Hybrid Journal   (Followers: 2, SJR: 0.361, h-index: 21)
Applied Composite Materials     Hybrid Journal   (Followers: 48, SJR: 0.705, h-index: 35)
Applied Entomology and Zoology     Partially Free   (Followers: 3, SJR: 0.554, h-index: 34)
Applied Geomatics     Hybrid Journal   (Followers: 4, SJR: 0.323, h-index: 9)
Applied Geophysics     Hybrid Journal   (Followers: 8, SJR: 0.541, h-index: 13)
Applied Intelligence     Hybrid Journal   (Followers: 11, SJR: 0.777, h-index: 43)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.358, h-index: 34)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 6, 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: 5, SJR: 0.37, h-index: 26)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 62, SJR: 1.262, h-index: 161)
Applied Physics A     Hybrid Journal   (Followers: 8, SJR: 0.535, h-index: 121)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 24, SJR: 0.983, h-index: 104)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.677, h-index: 47)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 11, SJR: 0.288, h-index: 15)
Applied Solar Energy     Hybrid Journal   (Followers: 18, SJR: 0.251, h-index: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5, 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: 32, SJR: 0.646, h-index: 44)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.764, h-index: 39)
Aquatic Sciences     Hybrid Journal   (Followers: 13, 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: 23, 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: 56, SJR: 0.804, h-index: 22)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 8, 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: 5, SJR: 0.865, h-index: 40)
Archives and Museum Informatics     Hybrid Journal   (Followers: 141)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5, SJR: 2.841, h-index: 40)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 0.9, h-index: 65)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 13, SJR: 0.846, h-index: 84)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 16, 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: 10, SJR: 1.198, h-index: 74)
Archives of Toxicology     Hybrid Journal   (Followers: 17, 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: 14, 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: 2, SJR: 0.797, h-index: 17)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.145, h-index: 8)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, 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: 8, SJR: 0.231, h-index: 14)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.247, h-index: 9)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.371, h-index: 17)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.676, h-index: 50)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 12, 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: 8, SJR: 0.41, h-index: 10)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 8)
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  

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Journal Cover Applied Intelligence
  [SJR: 0.777]   [H-I: 43]   [11 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  [2355 journals]
  • Computing all minimal hitting sets by subset recombination
    • Authors: Xiangfu Zhao; Dantong Ouyang; Liming Zhang
      Pages: 257 - 270
      Abstract: In model-based diagnosis from first principles, the efficient computation of all minimal hitting sets (MHS) as candidates for the conflict component sets of a device is a vital task. However, deriving all MHS is NP-hard. In this paper, the principle of “Divide and Conquer” is used to decompose a large family of conflict sets into many smaller sub-families. To efficiently merge the sub-MHS to give sub-families of conflict sets, the relations between the sub-MHS and sub-families of conflict sets are exploited. Based on this, a new method called Subset-Rec-MHS is proposed. In theory, our method based on sub-MHS recombination generally has lower complexity than that based on whole MHS families, as it avoids a large number of set unions and comparisons (to minimize the family of hitting sets). Compared with the direct merge of whole MHS families, the proposed approach reduces the computation time by a factor of approximately \(\frac {7}{16}\) . Experimental results on both synthetic examples and ISCAS-85 benchmark circuit conflict sets show that, in many cases, our approach offers better performance than previous algorithms.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0971-7
      Issue No: Vol. 48, No. 2 (2018)
       
  • An adaptive super-peer selection algorithm considering peers capacity
           utilizing asynchronous dynamic cellular learning automata
    • Authors: Ali Mohammad Saghiri; Mohammad Reza Meybodi
      Pages: 271 - 299
      Abstract: Super-peer networks refer to a class of peer-to-peer networks in which some peers called super-peers are in charge of managing the network. A group of super-peer selection algorithms use the capacity of the peers for the purpose of super-peer selection where the capacity of a peer is defined as a general concept that can be calculated by some properties, such as bandwidth and computational capabilities of that peer. One of the drawbacks of these algorithms is that they do not take into consideration the dynamic nature of peer-to-peer networks in the process of selecting super-peers. In this paper, an adaptive super-peer selection algorithm considering peers capacity based on an asynchronous dynamic cellular learning automaton has been proposed. The proposed cellular learning automaton uses the model of fungal growth as it happens in nature to adjust the attributes of the cells of the cellular learning automaton in order to take into consideration the dynamicity that exists in peer-to-peer networks in the process of super-peers selection. Several computer simulations have been conducted to compare the performance of the proposed super-peer selection algorithm with the performance of existing algorithms with respect to the number of super-peers, and capacity utilization. Simulation results have shown the superiority of the proposed super-peer selection algorithm over the existing algorithms.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0946-8
      Issue No: Vol. 48, No. 2 (2018)
       
  • Global-patch-hybrid template-based arbitrary object tracking with integral
           channel features
    • Authors: Mayur Rajaram Parate; Vishal R. Satpute; Kishor M. Bhurchandi
      Pages: 300 - 314
      Abstract: Arbitrary object tracking is a challenging task in computer vision, as many factors affecting the target representation must be considered. A target template based on only the global appearance or on only the local appearance is unable to capture the discriminating information required for the robust performance of a tracker. In this paper, the target appearance is represented using a hybrid of global and local appearances along with a framework to exploit the Integral Channel Features (ICF). The proposed hybrid approach achieves fusion of the conventional global and patch-based approaches for target representation to synergize the advantages of both approaches. The ICF approach under the hybrid approach integrates heterogeneous sources of information of the target and provides feature strength to the hybrid template. The use of ICF also expedites the extraction of the structural and color features from video frames as the features are collected over multiple channels. The target appearance representation is updated based on only samples with appearances similar to the target appearance using clustering and vector quantization. These factors offer the proposed algorithm robustness to occlusion, illumination changes, and in-plane rotation. Further experimentation analyzes the effects of a change in the scale of the bounding box on the tracking performance of the proposed algorithm. The proposed approach outperforms all the state-of-the-art algorithms in all considered scenarios.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0974-4
      Issue No: Vol. 48, No. 2 (2018)
       
  • Combining emerging patterns with random forest for complex activity
           recognition in smart homes
    • Authors: Hadi Tabatabaee Malazi; Mohammad Davari
      Pages: 315 - 330
      Abstract: New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12% improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73% improvement.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0976-2
      Issue No: Vol. 48, No. 2 (2018)
       
  • Research on parameter selection method for support vector machines
    • Authors: Ling Sun; Jian Bao; Yangyang Chen; Mingming Yang
      Pages: 331 - 342
      Abstract: The kernel parameter and penalty parameter C are the main factors that affect the learning performance of the support vector machine. However, there are many deficiencies in the existing kernel parameters and penalty parameters C. These methods do not have high accuracy when it comes to classifying multi-category samples, and even ignore some of the samples to conduct training, which violates the integrity of the experimental data. In contrast, this paper improves the selection method of support vector machine kernel parameters and penalty parameters in two ways. First, it obtains the kernel parameter value by optimizing the maximum separation interval between the samples. Second, it optimizes the generalization ability estimation via the influence of the non-boundary support vector on the stability of the support vector machine. The method takes full account of all the training sample data, which is applicable to most sample types, and has the characteristics of low initialization requirements and high-test accuracy. The paper finally uses multiple sets of UCI sample data sets and facial image recognition to verify the method. The experimental results show that the method is feasible, effective and stable.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0975-3
      Issue No: Vol. 48, No. 2 (2018)
       
  • Generalized and group-based generalized intuitionistic fuzzy soft sets
           with applications in decision-making
    • Authors: Harish Garg; Rishu Arora
      Pages: 343 - 356
      Abstract: Intuitionistic fuzzy soft set (IFSS) theory acts as a fundamental tool for handling the uncertainty in the data by adding a parameterizing factor during the process as compared to fuzzy and intuitionistic fuzzy set (IFS) theories. In this paper, an attempt has been made to this effect to describe the concept of generalized IFSS (GIFSS), as well as the group-based generalized intuitionistic fuzzy soft set (GGIFSS) in which the evaluation of the object is done by the group of experts rather than a single expert. Based on this information, a new weighted averaging and geometric aggregation operator has been proposed by taking the intuitionistic fuzzy parameter. Finally, a decision-making approach based on the proposed operator is being built to solve the problems under the intuitionistic fuzzy environment. An illustrative example of the selection of the optimal alternative has been given to show the developed method. Comparison analysis between the proposed and the existing operators have been performed in term of counter-intuitive cases for showing the superiority of the approach.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0981-5
      Issue No: Vol. 48, No. 2 (2018)
       
  • A k-means binarization framework applied to multidimensional knapsack
           problem
    • Authors: José García; Broderick Crawford; Ricardo Soto; Carlos Castro; Fernando Paredes
      Pages: 357 - 380
      Abstract: The multidimensional knapsack problem (MKP) is one of the widely known integer programming problems. The MKP has received significant attention from the operational research community for its large number of applications. Solving this NP-hard problem remains a very interesting challenge, especially when the number of constraints increases. In this paper we present a k-means transition ranking (KMTR) framework to solve the MKP. This framework has the property to binarize continuous population-based metaheuristics using a data mining k-means technique. In particular we binarize a Cuckoo Search and Black Hole metaheuristics. These techniques were chosen by the difference between their iteration mechanisms. We provide necessary experiments to investigate the role of key ingredients of the framework. Finally to demonstrate the efficiency of our proposal, MKP benchmark instances of the literature show that KMTR competes with the state-of-the-art algorithms.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0972-6
      Issue No: Vol. 48, No. 2 (2018)
       
  • An exploratory study of mono and multi-objective metaheuristics to
           ensemble of classifiers
    • Authors: Antonino A. Feitosa Neto; Anne M. P. Canuto
      Pages: 416 - 431
      Abstract: This paper performs an exploratory study of the use of metaheuristic optimization techniques to select important parameters (features and members) in the design of ensemble of classifiers. In order to do this, an empirical investigation, using 10 different optimization techniques applied to 23 classification problems, will be performed. Furthermore, we will analyze the performance of both mono and multi-objective versions of these techniques, using all different combinations of three objectives, classification error as well as two important diversity measures to ensembles, which are good and bad diversity measures. Additionally, the optimization techniques will also have to select members for heterogeneous ensembles, using k-NN, Decision Tree and Naive Bayes as individual classifiers and they are all combined using the majority vote technique. The main aim of this study is to define which optimization techniques obtained the best results in the context of mono and multi-objective as well as to provide a comparison with classical ensemble techniques, such as bagging, boosting and random forest. Our findings indicated that three optimization techniques, Memetic, SA and PSO, provided better performance than the other optimization techniques as well as traditional ensemble generator (bagging, boosting and random forest).
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0982-4
      Issue No: Vol. 48, No. 2 (2018)
       
  • A clustering algorithm with affine space-based boundary detection
    • Authors: Xiangli Li; Qiong Han; Baozhi Qiu
      Pages: 432 - 444
      Abstract: Clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster points before expanding to the boundary points. To achieve this, an affine space-based boundary detection algorithm was employed to divide data points into cluster boundary and internal points. A connection matrix was then formed by establishing neighbor relationships between internal and boundary points to perform clustering. Our clustering algorithm with an affine space-based boundary detection algorithm accurately detected clusters in datasets with different densities, shapes, and sizes. The algorithm excelled at dealing with high-dimensional datasets.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0979-z
      Issue No: Vol. 48, No. 2 (2018)
       
  • Improved monarch butterfly optimization for unconstrained global search
           and neural network training
    • Authors: Hossam Faris; Ibrahim Aljarah; Seyedali Mirjalili
      Pages: 445 - 464
      Abstract: This work is a seminal attempt to address the drawbacks of the recently proposed monarch butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence, which makes it less suitable for solving real-world problems. The position updating of MBO is modified to involve previous solutions in addition to the best solution obtained thus far. To prove the efficiency of the Improved MBO (IMBO), a set of 23 well-known test functions is employed. The statistical results show that IMBO benefits from high local optima avoidance and fast convergence speed which helps this algorithm to outperform basic MBO and another recent variant of this algorithm called greedy strategy and self-adaptive crossover operator MBO (GCMBO). The results of the proposed algorithm are compared with nine other approaches in the literature for verification. The comparative analysis shows that IMBO provides very competitive results and tends to outperform current algorithms. To demonstrate the applicability of IMBO at solving challenging practical problems, it is also employed to train neural networks as well. The IMBO-based trainer is tested on 15 popular classification datasets obtained from the University of California at Irvine (UCI) Machine Learning Repository. The results are compared to a variety of techniques in the literature including the original MBO and GCMBO. It is observed that IMBO improves the learning of neural networks significantly, proving the merits of this algorithm for solving challenging problems.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0967-3
      Issue No: Vol. 48, No. 2 (2018)
       
  • An evolutionary non-linear ranking algorithm for ranking scientific
           collaborations
    • Authors: Fahimeh Ghasemian; Kamran Zamanifar; Nasser Ghasem-Aghaee
      Pages: 465 - 481
      Abstract: The social capital theory motivates some researchers to apply link-based ranking algorithms (e.g. PageRank) to compute the fitness level of a scholar for collaborating with other scholars on a set of skills. These algorithms are executed on the collaboration network of scholars and assign a score to each scholar based on the scores of his/her neighbors by solving a linear system in an iterative way. In this paper, we propose a new ranking algorithm by focusing on link-aggregation function and transition matrix. The evolution strategy technique is applied to find the best aggregation function and transition matrix for computing the score of a scholar in the collaboration network which is modeled by a hypergraph. Experiments conducted on two datasets gathered from ScivalExpert and VIVO show that the new non-linear ranking algorithm acts better than the other iterative ranking approaches for ranking scientific collaborations.
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0990-4
      Issue No: Vol. 48, No. 2 (2018)
       
  • δ -equality of intuitionistic fuzzy sets: a new proximity measure and
           applications in medical diagnosis
    • Authors: Roan Thi Ngan; Mumtaz Ali; Le Hoang Son
      Pages: 499 - 525
      Abstract: Intuitionistic fuzzy set is capable of handling uncertainty with counterpart falsities which exist in nature. Proximity measure is a convenient way to demonstrate impractical significance of values of memberships in the intuitionistic fuzzy set. However, the related works of Pappis (Fuzzy Sets Syst 39(1):111–115, 1991), Hong and Hwang (Fuzzy Sets Syst 66(3):383–386, 1994), Virant (2000) and Cai (IEEE Trans Fuzzy Syst 9(5):738–750, 2001) did not model the measure in the context of the intuitionistic fuzzy set but in the Zadeh’s fuzzy set instead. In this paper, we examine this problem and propose new notions of δ-equalities for the intuitionistic fuzzy set and δ-equalities for intuitionistic fuzzy relations. Two fuzzy sets are said to be δ-equal if they are equal to an extent of δ. The applications of δ-equalities are important to fuzzy statistics and fuzzy reasoning. Several characteristics of δ-equalities that were not discussed in the previous works are also investigated. We apply the δ-equalities to the application of medical diagnosis to investigate a patient’s diseases from symptoms. The idea is using δ-equalities for intuitionistic fuzzy relations to find groups of intuitionistic fuzzified set with certain equality or similar degrees then combining them. Numerical examples are given to illustrate validity of the proposed algorithm. Further, we conduct experiments on real medical datasets to check the efficiency and applicability on real-world problems. The results obtained are also better in comparison with 10 existing diagnosis methods namely De et al. (Fuzzy Sets Syst 117:209–213, 2001), Samuel and Balamurugan (Appl Math Sci 6(35):1741–1746, 2012), Szmidt and Kacprzyk (2004), Zhang et al. (Procedia Eng 29:4336–4342, 2012), Hung and Yang (Pattern Recogn Lett 25:1603–1611, 2004), Wang and Xin (Pattern Recogn Lett 26:2063–2069, 2005), Vlachos and Sergiadis (Pattern Recogn Lett 28(2):197–206, 2007), Zhang and Jiang (Inf Sci 178(6):4184–4191, 2008), Maheshwari and Srivastava (J Appl Anal Comput 6(3):772–789, 2016) and Support Vector Machine (SVM).
      PubDate: 2018-02-01
      DOI: 10.1007/s10489-017-0986-0
      Issue No: Vol. 48, No. 2 (2018)
       
  • Editorial for the special issue: Knowledge-based systems and data science
    • Authors: Hamido Fujita; Ali Selamat
      PubDate: 2018-02-07
      DOI: 10.1007/s10489-018-1143-0
       
  • GSLDA: LDA-based group spamming detection in product reviews
    • Authors: Zhuo Wang; Songmin Gu; Xiaowei Xu
      Abstract: Online product reviews are becoming increasingly important due to their guidance function in people’s purchase decisions. As being highly subjective, online reviews are subject to opinion spamming, i.e., fraudsters write fake reviews or give unfair ratings to promote or demote target products. Although there have been much efforts in this field, the problem is still left open due to the difficulties in gathering ground-truth data. As more and more people are using Internet in everyday life, group review spamming, which involves a group of fraudsters writing hype-reviews (promote) or defaming-reviews (demote) for one or more target products, becomes the main form of review spamming. In this paper, we propose a LDA-based computing framework, namely GSLDA, for group spamming detection in product review data. As a completely unsupervised approach, GSLDA works in two phases. It first adapts LDA (Latent Dirichlet Allocation) to the product review context in order to bound the closely related group spammers into a small-sized reviewer cluster, and then it extracts high suspicious reviewer groups from each LDA-clusters. Experiments on three real-world datasets show that GSLDA can detect high quality spammer groups, outperforming many state-of-the-art baselines in terms of accuracy.
      PubDate: 2018-02-07
      DOI: 10.1007/s10489-018-1142-1
       
  • A multiobjective discrete bat algorithm for community detection in dynamic
           networks
    • Authors: Xu Zhou; Xiaohui Zhao; Yanheng Liu
      Abstract: Some evolutionary based clustering approaches for community detection in dynamic networks need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and improve the quality of detecting communities in dynamic network further, a multiobjective discrete bat algorithm (MDBA) is proposed to detect community structure in dynamic networks in this paper. In the proposed algorithm, the bat location updating strategy is designed in discrete form. In addition, turbulence operation and mutation strategy are presented to guarantee the diversity of the population. The non-dominated sorting and crowding distance mechanism are used to keep good solutions during the generation. The experimental results both on synthetic and real networks show that MDBA algorithm is competitive and will get higher accuracy and lower error rate than the compared algorithms.
      PubDate: 2018-02-07
      DOI: 10.1007/s10489-017-1135-5
       
  • Regularized multi-view least squares twin support vector machines
    • Authors: Xijiong Xie
      Abstract: Regularized least squares twin support vector machines are a new nonparallel hyperplane classifier, which can lead to simple and fast algorithms for generating binary classifiers by replacing inequality constraints with equality constraints and implementing the structural risk minimization principle in twin support vector machines. Multi-view learning is an emerging direction in machine learning which aims to exploit distinct views to improve generalization performance from multiple distinct feature sets. Experimental results demonstrate that our proposed methods are effective.
      PubDate: 2018-02-07
      DOI: 10.1007/s10489-017-1129-3
       
  • Forwarding Zone enabled PSO routing with Network lifetime maximization in
           MANET
    • Authors: Rashmi Chaudhry; Shashikala Tapaswi; Neetesh Kumar
      Abstract: Network lifetime maximization is one of the most sought after issues in Mobile Adhoc Networks (MANETs). Whereas, due to geographical routing based approaches, the packet transmission becomes more suitable in dynamic environment such as MANETs. Direct heuristics are not suitable in such scenarios to provide desired solution as the problem becomes NP-hard in dense networks, thus researchers focused to utilize meta-heuristic techniques. Particle Swarm Optimization (PSO) is one of the most effective meta-heuristic techniques to solve such problems with near optimal solution. However, meta-heuristic techniques (PSO) become slow in convergence and require more computational time when network size increases. Therefore, in this work, PSO is adaptively modified (APSO) to best fit in our scenario, and re-enforced using Forwarding Search Space (FSS) heuristic technique to overcome the PSO’s convergence and computational time related issues, significantly improves the performance of PSO. In FSS, a Forwarding Zone (FZ) is selected between source and destination such that the optimal solution lies in that area and APSO is applied for an effective routing in FZ area instead of complete network. To utilize the complementary characteristics of both (APSO and FSS), a hybrid FZ-APSO is proposed for routing in dense network with minimum delay and energy consumption in order to increase the lifetime of the network. Comparative simulation results evidenced that the proposed FZ-APSO routing algorithm significantly improved the performance of the routing in terms of energy consumption, end to end delay, computational time and network lifetime.
      PubDate: 2018-02-06
      DOI: 10.1007/s10489-017-1127-5
       
  • Ego-network probabilistic graphical model for discovering on-line
           communities
    • Authors: Fei Ding; Yi Zhuang
      Abstract: Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.
      PubDate: 2018-02-06
      DOI: 10.1007/s10489-018-1137-y
       
  • A corner point-based algorithm to solve constrained multi-objective
           optimization problems
    • Authors: Xiaobing Yu; Yiqun Lu
      Abstract: The search ability of an algorithm in terms of convergence and diversity can be improved with the help of corner points. A corner point-based algorithm (CPA) based on a differential evolution (DE) algorithm is proposed to solve constrained multi-objective optimization problems. The evolutionary algorithm consists of two stages. The first stage is to find corner points by the proposed method. The second stage is to approach the real Pareto front. A novel diversity and convergence mechanism is implemented in the second stage. The performance of the proposed algorithm is evaluated on nineteen test functions. Compared with the constrained handling techniques and latest optimization algorithms, the numerical results have indicated that the proposed algorithm is effective. At last, the algorithm is used to solve resource schedule in emergency management to further validate its effectiveness.
      PubDate: 2018-02-05
      DOI: 10.1007/s10489-017-1126-6
       
  • A novel hybrid heuristic algorithm for a new uncertain
           mean-variance-skewness portfolio selection model with real constraints
    • Authors: Wei Chen; Yun Wang; Pankaj Gupta; Mukesh Kumar Mehlawat
      Abstract: This paper discusses a portfolio selection problem under the mean-variance-skewness framework wherein the security returns are obtained through evaluation of the experts instead of historical data. By treating security returns as the uncertain variables, an uncertain mean-variance-skewness model is proposed for portfolio selection under consideration of the transaction costs, bounds on holdings, cardinality of the portfolio, and minimum transaction lots constraints. To solve the resultant portfolio selection problem, which is an NP-Complete nonlinear integer programming problem, a hybrid solution method termed the FA-GA is developed by combining features of the firefly algorithm (FA) and genetic algorithm (GA). In the proposed method, the crossover and mutation operators of the GA are integrated into the FA to strike an optimal balance between the exploration and exploitation. A numerical example of portfolio selection is given to demonstrate effectiveness of the proposed model and solution algorithm. Furthermore, a detailed performance analysis and comparison are done to establish superiority of the proposed model and solution method.
      PubDate: 2018-02-02
      DOI: 10.1007/s10489-017-1124-8
       
 
 
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