Publisher: Hindawi   (Total: 343 journals)

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Showing 1 - 200 of 343 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 51, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 67)
Advances in Agriculture     Open Access   (Followers: 12)
Advances in Artificial Intelligence     Open Access   (Followers: 23)
Advances in Astronomy     Open Access   (Followers: 51, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 21, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 12)
Advances in Chemistry     Open Access   (Followers: 35)
Advances in Civil Engineering     Open Access   (Followers: 51, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 11, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 52)
Advances in Electronics     Open Access   (Followers: 101)
Advances in Emergency Medicine     Open Access   (Followers: 17)
Advances in Endocrinology     Open Access   (Followers: 6)
Advances in Environmental Chemistry     Open Access   (Followers: 10)
Advances in Epidemiology     Open Access   (Followers: 9)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.161, CiteScore: 1)
Advances in Geology     Open Access   (Followers: 19)
Advances in Geriatrics     Open Access   (Followers: 6)
Advances in Hematology     Open Access   (Followers: 13, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 3)
Advances in High Energy Physics     Open Access   (Followers: 26, SJR: 0.866, CiteScore: 2)
Advances in Human-Computer Interaction     Open Access   (Followers: 21, SJR: 0.186, CiteScore: 1)
Advances in Materials Science and Engineering     Open Access   (Followers: 31, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 9, SJR: 0.218, CiteScore: 1)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 24, SJR: 0.48, CiteScore: 1)
Advances in Multimedia     Open Access   (Followers: 1, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 7)
Advances in Numerical Analysis     Open Access   (Followers: 9)
Advances in Nursing     Open Access   (Followers: 37)
Advances in Operations Research     Open Access   (Followers: 13, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 9)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 11, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological and Pharmaceutical Sciences     Open Access   (Followers: 9, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 14, SJR: 0.179, CiteScore: 1)
Advances in Polymer Technology     Open Access   (Followers: 14, SJR: 0.299, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 44, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 28)
Advances in Regenerative Medicine     Open Access   (Followers: 4)
Advances in Software Engineering     Open Access   (Followers: 11)
Advances in Statistics     Open Access   (Followers: 10)
Advances in Toxicology     Open Access   (Followers: 4)
Advances in Tribology     Open Access   (Followers: 15, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 13, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 8, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 2, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 3, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 4)
Anemia     Open Access   (Followers: 6, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 15, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 20, SJR: 0.451, CiteScore: 1)
Applied Bionics and Biomechanics     Open Access   (Followers: 7, SJR: 0.288, CiteScore: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 15)
Archaea     Open Access   (Followers: 4, SJR: 0.852, CiteScore: 2)
Autism Research and Treatment     Open Access   (Followers: 36)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 9, SJR: 0.786, CiteScore: 2)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 0.437, CiteScore: 2)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 11, SJR: 0.419, CiteScore: 2)
BioMed Research Intl.     Open Access   (Followers: 5, SJR: 0.935, CiteScore: 3)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2, SJR: 0.531, CiteScore: 1)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 5, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 8, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 3, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 11, SJR: 1.237, CiteScore: 4)
Cardiovascular Therapeutics     Open Access   (Followers: 2, SJR: 1.075, CiteScore: 2)
Case Reports in Anesthesiology     Open Access   (Followers: 11)
Case Reports in Cardiology     Open Access   (Followers: 8, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 13)
Case Reports in Dentistry     Open Access   (Followers: 8, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 20)
Case Reports in Endocrinology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 4)
Case Reports in Genetics     Open Access   (Followers: 2)
Case Reports in Hematology     Open Access   (Followers: 9)
Case Reports in Hepatology     Open Access   (Followers: 2)
Case Reports in Immunology     Open Access   (Followers: 6)
Case Reports in Infectious Diseases     Open Access   (Followers: 6)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 5)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 12)
Case Reports in Oncological Medicine     Open Access   (Followers: 2, SJR: 0.204, CiteScore: 1)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 6)
Case Reports in Otolaryngology     Open Access   (Followers: 7)
Case Reports in Pathology     Open Access   (Followers: 7)
Case Reports in Pediatrics     Open Access   (Followers: 8)
Case Reports in Psychiatry     Open Access   (Followers: 18)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 12)
Case Reports in Rheumatology     Open Access   (Followers: 10)
Case Reports in Surgery     Open Access   (Followers: 12)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 12)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Child Development Research     Open Access   (Followers: 21, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Chromatography Research Intl.     Open Access   (Followers: 5)
Complexity     Hybrid Journal   (Followers: 8, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Biology J.     Open Access   (Followers: 7)
Computational Intelligence and Neuroscience     Open Access   (Followers: 15, SJR: 0.326, CiteScore: 1)
Concepts in Magnetic Resonance Part A     Open Access   (Followers: 1, SJR: 0.354, CiteScore: 1)
Concepts in Magnetic Resonance Part B, Magnetic Resonance Engineering     Open Access   (Followers: 1, SJR: 0.26, CiteScore: 1)
Conference Papers in Science     Open Access   (Followers: 2)
Contrast Media & Molecular Imaging     Open Access   (Followers: 2, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 14, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 10, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 19, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 4, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 6, SJR: 0.279, CiteScore: 1)
Disease Markers     Open Access   (Followers: 1, SJR: 0.9, CiteScore: 2)
Economics Research Intl.     Open Access   (Followers: 1)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 10, SJR: 0.298, CiteScore: 1)
Enzyme Research     Open Access   (Followers: 5, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 30, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 1, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 5, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
Heteroatom Chemistry     Open Access   (Followers: 3, SJR: 0.333, CiteScore: 1)
HPB Surgery     Open Access   (Followers: 9, SJR: 0.824, CiteScore: 2)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 1.27, CiteScore: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.627, CiteScore: 2)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 81, SJR: 0.232, CiteScore: 1)
Intl. J. of Agronomy     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 12, SJR: 0.787, CiteScore: 3)
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 13, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 3)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.511, CiteScore: 2)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.501, CiteScore: 2)
Intl. J. of Breast Cancer     Open Access   (Followers: 14, SJR: 1.025, CiteScore: 2)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 1.887, CiteScore: 4)
Intl. J. of Chemical Engineering     Open Access   (Followers: 8, SJR: 0.327, CiteScore: 1)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 10, SJR: 0.287, CiteScore: 2)
Intl. J. of Corrosion     Open Access   (Followers: 11, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 8, SJR: 0.649, CiteScore: 2)
Intl. J. of Differential Equations     Open Access   (Followers: 8, SJR: 0.191, CiteScore: 0)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.296, CiteScore: 2)
Intl. J. of Electrochemistry     Open Access   (Followers: 10)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 7)
Intl. J. of Food Science     Open Access   (Followers: 5, SJR: 0.44, CiteScore: 2)
Intl. J. of Forestry Research     Open Access   (Followers: 3, SJR: 0.373, CiteScore: 1)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.868, CiteScore: 3)
Intl. J. of Geophysics     Open Access   (Followers: 7, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 4, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 8, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 4)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.177, CiteScore: 0)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6, SJR: 0.31, CiteScore: 1)
Intl. J. of Metals     Open Access   (Followers: 7)
Intl. J. of Microbiology     Open Access   (Followers: 8, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 6, SJR: 0.136, CiteScore: 1)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.267, CiteScore: 2)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 8)
Intl. J. of Optics     Open Access   (Followers: 10, SJR: 0.231, CiteScore: 1)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
Intl. J. of Peptides     Open Access   (Followers: 2, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 3, SJR: 0.341, CiteScore: 1)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 0.583, CiteScore: 1)
Intl. J. of Polymer Science     Open Access   (Followers: 28, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 4)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 6)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 0.645, CiteScore: 2)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.193, CiteScore: 1)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, SJR: 0.279, CiteScore: 1)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.573, CiteScore: 2)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 7, SJR: 0.403, CiteScore: 2)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.782, CiteScore: 2)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Intl. Scholarly Research Notices     Open Access   (Followers: 230)

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Similar Journals
Journal Cover
Computational Intelligence and Neuroscience
Journal Prestige (SJR): 0.326
Citation Impact (citeScore): 1
Number of Followers: 15  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-5265 - ISSN (Online) 1687-5273
Published by Hindawi Homepage  [343 journals]
  • Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

    • Abstract: This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.
      PubDate: Wed, 22 Jan 2020 01:50:02 +000
  • A Dendritic Neuron Model with Adaptive Synapses Trained by Differential
           Evolution Algorithm

    • Abstract: A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution (DE) algorithm training is proposed. According to the signal transmission order, a DNM can be divided into four parts: the synaptic layer, dendritic layer, membrane layer, and somatic cell layer. It can be converted to a logic circuit that is easily implemented on hardware by removing useless synapses and dendrites after training. This logic circuit can be designed to solve complex nonlinear problems using only four basic logical devices: comparators, AND (conjunction), OR (disjunction), and NOT (negation). To obtain a faster and better solution, we adopt the most popular DE for DMAS training. We have chosen five classification datasets from the UCI Machine Learning Repository for an experiment. We analyze and discuss the experimental results in terms of the correct rate, convergence rate, ROC curve, and the cross-validation and then compare the results with a dendritic neuron model trained by the backpropagation algorithm (BP-DNM) and a neural network trained by the backpropagation algorithm (BPNN). The analysis results show that the DE-DMAS shows better performance in all aspects.
      PubDate: Fri, 17 Jan 2020 13:35:04 +000
  • Anomaly Detection in EEG Signals: A Case Study on Similarity Measure

    • Abstract: Motivation. Anomaly EEG detection is a long-standing problem in analysis of EEG signals. The basic premise of this problem is consideration of the similarity between two nonstationary EEG recordings. A well-established scheme is based on sequence matching, typically including three steps: feature extraction, similarity measure, and decision-making. Current approaches mainly focus on EEG feature extraction and decision-making, and few of them involve the similarity measure/quantification. Generally, to design an appropriate similarity metric, that is compatible with the considered problem/data, is also an important issue in the design of such detection systems. It is however impossible to directly apply those existing metrics to anomaly EEG detection without any consideration of domain specificity. Methodology. The main objective of this work is to investigate the impacts of different similarity metrics on anomaly EEG detection. A few metrics that are potentially available for the EEG analysis have been collected from other areas by a careful review of related works. The so-called power spectrum is extracted as features of EEG signals, and a null hypothesis testing is employed to make the final decision. Two indicators have been used to evaluate the detection performance. One is to reflect the level of measured similarity between two compared EEG signals, and the other is to quantify the detection accuracy. Results. Experiments were conducted on two data sets, respectively. The results demonstrate the positive impacts of different similarity metrics on anomaly EEG detection. The Hellinger distance (HD) and Bhattacharyya distance (BD) metrics show excellent performances: an accuracy of 0.9167 for our data set and an accuracy of 0.9667 for the Bern-Barcelona EEG data set. Both of HD and BD metrics are constructed based on the Bhattacharyya coefficient, implying the priority of the Bhattacharyya coefficient when dealing with the highly noisy EEG signals. In future work, we will exploit an integrated metric that combines HD and BD for the similarity measure of EEG signals.
      PubDate: Fri, 10 Jan 2020 02:20:01 +000
  • Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type
           Detection in Twitter

    • Abstract: Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.
      PubDate: Thu, 09 Jan 2020 07:05:02 +000
  • An Overview of Image Caption Generation Methods

    • Abstract: In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.
      PubDate: Thu, 09 Jan 2020 05:50:00 +000
  • Shadow Elimination Algorithm Using Color and Texture Features

    • Abstract: Shadow detection and removal in real scene images are a significant problem for target detection. This work proposes an improved shadow detection and removal algorithm for urban video surveillance. First, the foreground is detected by background subtraction and the shadow is detected by HSV color space. Using local variance and OTSU method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, we embed our algorithm into C/S framework based on the HTML5 web socket protocol. Both the experimental and actual operation results show that the proposed algorithm is efficient and robust in target detection and shadow detection and removal under different scenes.
      PubDate: Thu, 09 Jan 2020 03:50:00 +000
  • A Hyperheuristic Approach for Location-Routing Problem of Cold Chain
           Logistics considering Fuel Consumption

    • Abstract: In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company.
      PubDate: Sat, 04 Jan 2020 10:05:03 +000
  • Iterative Deep Neighborhood: A Deep Learning Model Which Involves Both
           Input Data Points and Their Neighbors

    • Abstract: Deep learning models, such as deep convolutional neural network and deep long-short term memory model, have achieved great successes in many pattern classification applications over shadow machine learning models with hand-crafted features. The main reason is the ability of deep learning models to automatically extract hierarchical features from massive data by multiple layers of neurons. However, in many other situations, existing deep learning models still cannot gain satisfying results due to the limitation of the inputs of models. The existing deep learning models only take the data instances of an input point but completely ignore the other data points in the dataset, which potentially provides critical insight for the classification of the given input. To overcome this gap, in this paper, we show that the neighboring data points besides the input data point itself can boost the deep learning model’s performance significantly and design a novel deep learning model which takes both the data instances of an input point and its neighbors’ classification responses as inputs. In addition, we develop an iterative algorithm which updates the neighbors of data points according to the deep representations output by the deep learning model and the parameters of the deep learning model alternately. The proposed algorithm, named “Iterative Deep Neighborhood (IDN),” shows its advantages over the state-of-the-art deep learning models over tasks of image classification, text sentiment analysis, property price trend prediction, etc.
      PubDate: Thu, 02 Jan 2020 09:35:01 +000
  • A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI
           Data Classification

    • Abstract: Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.
      PubDate: Tue, 31 Dec 2019 11:50:05 +000
  • Ant Colony Clustering for ROI Identification in Functional Magnetic
           Resonance Imaging

    • Abstract: Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.
      PubDate: Thu, 26 Dec 2019 04:20:03 +000
  • An Interactive Model of Target and Context for Aspect-Level Sentiment

    • Abstract: Aspect-level sentiment classification aims to identify the sentiment polarity of a review expressed toward a target. In recent years, neural network-based methods have achieved success in aspect-level sentiment classification, and these methods fall into two types: the first takes the target information into account for context modelling, and the second models the context without considering the target information. It is concluded that the former is better than the latter. However, most of the target-related models just focus on the impact of the target on context modelling, while ignoring the role of context in target modelling. In this study, we introduce an interactive neural network model named LT-T-TR, which divided a review into three parts: the left context with target phrase, the target phrase, and the right context with target phrase. And the interaction between the left/right context and the target phrase is utilized by an attention mechanism to learn the representations of the left/right context and the target phrase separately. As a result, the most important words in the left/right context or in the target phrase are captured, and the results on laptop and restaurant datasets demonstrate that our model outperforms the state-of-the-art methods.
      PubDate: Thu, 19 Dec 2019 08:50:11 +000
  • Multiscale Cooperative Differential Evolution Algorithm

    • Abstract: A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.
      PubDate: Tue, 17 Dec 2019 10:20:03 +000
  • Channel Projection-Based CCA Target Identification Method for an
           SSVEP-Based BCI System of Quadrotor Helicopter Control

    • Abstract: The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.
      PubDate: Mon, 16 Dec 2019 15:05:05 +000
  • Identification of Tomato Disease Types and Detection of Infected Areas
           Based on Deep Convolutional Neural Networks and Object Detection

    • Abstract: This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
      PubDate: Mon, 16 Dec 2019 09:20:05 +000
  • Effective and Generalizable Graph-Based Clustering for Faces in the Wild

    • Abstract: Face clustering is the task of grouping unlabeled face images according to individual identities. Several applications require this type of clustering, for instance, social media, law enforcement, and surveillance applications. In this paper, we propose an effective graph-based method for clustering faces in the wild. The proposed algorithm does not require prior knowledge of the data. This fact increases the pertinence of the proposed method near to market solutions. The experiments conducted on four well-known datasets showed that our proposal achieves state-of-the-art results, regarding the clustering performance, also showing stability for different values of the input parameter. Moreover, in these experiments, it is shown that our proposal discovers a number of identities closer to the real number existing in the data.
      PubDate: Sat, 14 Dec 2019 02:05:05 +000
  • Dragonfly Algorithm and Its Applications in Applied Science Survey

    • Abstract: One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm’s weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.
      PubDate: Fri, 06 Dec 2019 09:20:02 +000
  • A Novel Selection Approach for Genetic Algorithms for Global Optimization
           of Multimodal Continuous Functions

    • Abstract: Genetic algorithms (GAs) are stochastic-based heuristic search techniques that incorporate three primary operators: selection, crossover, and mutation. These operators are supportive in obtaining the optimal solution for constrained optimization problems. Each operator has its own benefits, but selection of chromosomes is one of the most essential operators for optimal performance of the algorithms. In this paper, an improved genetic algorithm-based novel selection scheme, i.e., stairwise selection (SWS) is presented to handle the problems of exploration (population diversity) and exploitation (selection pressure). For its global performance, we compared with several other selection schemes by using ten well-known benchmark functions under various dimensions. For a close comparison, we also examined the significance of SWS based on the statistical results. Chi-square goodness of fit test is also used to evaluate the overall performance of the selection process, i.e., mean difference between observed and expected number of offspring. Hence, the overall empirical results along with graphical representation endorse that the SWS outperformed in terms of robustness, stability, and effectiveness other competitors through authentication of performance index (PI).
      PubDate: Thu, 05 Dec 2019 10:05:06 +000
  • Deep Learning Application for Predicting Soil Organic Matter Content by
           VIS-NIR Spectroscopy

    • Abstract: Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.
      PubDate: Fri, 29 Nov 2019 07:05:13 +000
  • Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal
           Geometric Algebra and Artificial Vision

    • Abstract: In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which results in a tiresome process for simple tasks such as pick and replace an item from a surface. Here, we take a semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and then the user only has to decide on the start of the movement and the final position of the effector (goal-selection approach). Under these conditions, we implemented pick-and-place tasks with a disk as an item and two target areas placed on the table at arbitrary positions. An artificial vision (AV) algorithm was used to obtain the positions of the items expressed in the robot frame through images captured with a webcam. Then, the AV algorithm is integrated into the inverse kinematics model to perform the manipulation tasks. As proof-of-concept, different users were trained to control the pick-and-place tasks through the process-control and semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.
      PubDate: Wed, 27 Nov 2019 02:05:12 +000
  • Improved Transductive Support Vector Machine for a Small Labelled Set in
           Motor Imagery-Based Brain-Computer Interface

    • Abstract: Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework. We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature. Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions. In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set. Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable. In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used. Therefore, our algorithms can provide an alternative way to reduce the calibration time.
      PubDate: Mon, 25 Nov 2019 11:05:22 +000
  • Deep Learning with Taxonomic Loss for Plant Identification

    • Abstract: Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively. The taxonomic loss could further facilitate the fine-grained classification task with hierarchical labels.
      PubDate: Thu, 21 Nov 2019 16:05:03 +000
  • Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse

    • Abstract: An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.
      PubDate: Thu, 21 Nov 2019 12:05:19 +000
  • Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for
           Numerical Function Optimization

    • Abstract: Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.
      PubDate: Sat, 16 Nov 2019 06:05:13 +000
  • Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with

    • Abstract: Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust model. In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L2-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets. Secondly, the proposed MS-CNN framework with L2-Regularization is trained to extract robust features, in which the L2-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model. Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples. Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance. Extensive experiments demonstrated that our proposed method could not only outperform other state-of-the-art methods on the public and extended MSTAR dataset but also obtain good performance on the random small datasets.
      PubDate: Fri, 15 Nov 2019 03:05:09 +000
  • An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal
           Distribution and Direction-Based Crossover

    • Abstract: A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of participation crossover are the same. Under these circumstances, the crossover operation does not generate new individuals, and therefore does not work. To avoid this problem, the substitution operation is added after the crossover so that there is no duplication of the same individuals in the population. This improves the computational efficiency of MOIRCGA by leading it to quickly converge to the global optimal solution. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, a Combinational Mutation method is proposed with both local search and global search. The experimental results with sixteen examples show that the multi-offspring improved real-coded genetic algorithm (MOIRCGA) has fast convergence speed. As an example, the optimization model of the cantilevered beam structure is formulated, and the proposed MOIRCGA is compared to the RCGA in optimizing the parameters of the cantilevered beam structure. The optimization results show that the function value obtained with the proposed MOIRCGA is superior to that of RCGA.
      PubDate: Thu, 14 Nov 2019 06:05:08 +000
  • A Multibranch Object Detection Method for Traffic Scenes

    • Abstract: The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16 × 16, 32 × 32, and 64 × 64 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33 fps) is fast, which can meet the real-time requirements of industry.
      PubDate: Mon, 11 Nov 2019 08:05:07 +000
  • Detection of Image Seam Carving Using a Novel Pattern

    • Abstract: Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.
      PubDate: Mon, 11 Nov 2019 00:09:30 +000
  • A Competitive Multiattribute Group Decision-Making Approach for the Game
           between Manufacturers

    • Abstract: Under the competitive market environment, the game between manufacturers comes down to the competitive multiattribute group decision-making problem. In this study, the evaluation information of experts is given in the form of 2-dimension 2-tuple linguistic variables, and an approach is proposed for the competitive multiattribute group decision-making problem based on game theory and evidence theory. Firstly, based on the evidence theory, the attribute values of each situation are obtained by aggregating the 2-dimension 2-tuple linguistic evaluation information given by experts. Secondly, according to the attribute values of every situation, the evidence theory is applied for the second aggregation to obtain the overall values of every situation, and then the game matrix of competitive multiattribute group decision problem is formed. Then, according to the bivariate game matrix, the Nash equilibrium point of competitive multiattribute group decision-making problem is determined based on game theory. Finally, a practical case about the alternative selections for a duopoly problem is used to illustrate the effectiveness and applicability of the proposed approach for the competitive multiattribute group decision-making problem.
      PubDate: Mon, 11 Nov 2019 00:09:28 +000
  • Advanced Signal Processing and Adaptive Learning Methods

    • PubDate: Sun, 03 Nov 2019 00:09:43 +000
  • A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable
           Neighborhood Search

    • Abstract: Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.
      PubDate: Sun, 03 Nov 2019 00:09:41 +000
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