Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
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    - MATHEMATICS (714 journals)
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    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (714 journals)            First | 1 2 3 4 | Last

Showing 201 - 400 of 538 Journals sorted alphabetically
Educação Matemática Debate     Open Access  
Edumatica : Jurnal Pendidikan Matematika     Open Access  
EduMatSains     Open Access  
Electronic Journal of Differential Equations     Open Access  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 3)
Em Teia : Revista de Educação Matemática e Tecnológica Iberoamericana     Open Access  
Emergent Scientist     Open Access  
Energy for Sustainable Development     Hybrid Journal   (Followers: 13)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Entropy     Open Access   (Followers: 5)
ESAIM: Control Optimisation and Calculus of Variations     Open Access   (Followers: 2)
Euclid     Open Access  
European Journal of Applied Mathematics     Hybrid Journal  
European Journal of Combinatorics     Full-text available via subscription   (Followers: 3)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 1)
Examples and Counterexamples     Open Access  
Experimental Mathematics     Hybrid Journal   (Followers: 5)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Extracta Mathematicae     Open Access  
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 5)
Fixed Point Theory and Applications     Open Access  
Formalized Mathematics     Open Access  
Forum of Mathematics, Pi     Open Access   (Followers: 1)
Forum of Mathematics, Sigma     Open Access   (Followers: 1)
Foundations and Trends® in Econometrics     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Networking     Full-text available via subscription   (Followers: 1)
Foundations and Trends® in Stochastic Systems     Full-text available via subscription   (Followers: 1)
Foundations and Trends® in Theoretical Computer Science     Full-text available via subscription   (Followers: 1)
Foundations of Computational Mathematics     Hybrid Journal  
Fractal and Fractional     Open Access  
Fractals     Hybrid Journal   (Followers: 1)
Frontiers of Mathematics in China     Hybrid Journal  
Fuel Cells Bulletin     Full-text available via subscription   (Followers: 9)
Functional Analysis and Other Mathematics     Hybrid Journal   (Followers: 4)
Fundamental Journal of Mathematics and Applications     Open Access  
Funktsional'nyi Analiz i ego Prilozheniya     Full-text available via subscription  
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Game Theory     Open Access   (Followers: 2)
Games     Open Access   (Followers: 4)
Games and Economic Behavior     Hybrid Journal   (Followers: 25)
Gamm - Mitteilungen     Hybrid Journal  
GANIT : Journal of Bangladesh Mathematical Society     Open Access  
GEM - International Journal on Geomathematics     Hybrid Journal   (Followers: 1)
General Mathematics     Open Access  
Glasgow Mathematical Journal     Full-text available via subscription  
Global Journal of Mathematical Sciences     Full-text available via subscription  
Graphs and Combinatorics     Hybrid Journal   (Followers: 4)
Grey Systems : Theory and Application     Hybrid Journal  
Groups, Complexity, Cryptology     Open Access   (Followers: 2)
GSTF Journal of Mathematics, Statistics and Operations Research     Open Access   (Followers: 1)
Historia Mathematica     Full-text available via subscription  
Historical Methods: A Journal of Quantitative and Interdisciplinary History     Hybrid Journal   (Followers: 28)
IMA Journal of Applied Mathematics     Hybrid Journal  
IMA Journal of Numerical Analysis - advance access     Hybrid Journal  
ImmunoInformatics     Open Access   (Followers: 1)
Indagationes Mathematicae     Open Access  
Indian Journal of Pure and Applied Mathematics     Hybrid Journal   (Followers: 4)
Indonesian Journal of Combinatorics     Open Access  
Indonesian Journal of Science and Mathematics Education     Open Access   (Followers: 1)
Infinite Dimensional Analysis, Quantum Probability and Related Topics     Hybrid Journal   (Followers: 1)
Infinity Jurnal Matematika dan Aplikasinya     Open Access   (Followers: 3)
Information and Inference     Free  
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
InfraMatics     Open Access  
Insight - Non-Destructive Testing and Condition Monitoring     Full-text available via subscription   (Followers: 110)
International Electronic Journal of Algebra     Open Access  
International Journal for Numerical Methods in Engineering     Hybrid Journal   (Followers: 35)
International Journal for Numerical Methods in Fluids     Hybrid Journal   (Followers: 19)
International Journal of Advanced Mathematical Sciences     Open Access  
International Journal of Advanced Mechatronic Systems     Hybrid Journal   (Followers: 2)
International Journal of Advanced Research in Mathematics     Open Access  
International Journal of Advances in Engineering Sciences and Applied Mathematics     Hybrid Journal   (Followers: 10)
International Journal of Algebra and Computation     Hybrid Journal   (Followers: 1)
International Journal of Algebra and Statistics     Open Access   (Followers: 3)
International Journal of Applied and Computational Mathematics     Hybrid Journal  
International Journal of Applied Mathematical Research     Open Access   (Followers: 1)
International Journal of Applied Mathematics and Computer Science     Open Access   (Followers: 7)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 8)
International Journal of Applied Nonlinear Science     Hybrid Journal  
International Journal of Autonomic Computing     Hybrid Journal   (Followers: 1)
International Journal of Bifurcation and Chaos     Hybrid Journal   (Followers: 4)
International Journal of Biomathematics     Hybrid Journal   (Followers: 2)
International Journal of Computational Complexity and Intelligent Algorithms     Hybrid Journal  
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Computational Geometry and Applications     Hybrid Journal   (Followers: 2)
International Journal of Computational Intelligence and Applications     Hybrid Journal   (Followers: 2)
International Journal of Computational Methods     Hybrid Journal   (Followers: 4)
International Journal of Computer Processing Of Languages     Hybrid Journal   (Followers: 1)
International Journal of Control, Automation and Systems     Hybrid Journal   (Followers: 15)
International Journal of Dynamical Systems and Differential Equations     Hybrid Journal   (Followers: 1)
International Journal of Economics and Accounting     Hybrid Journal   (Followers: 1)
International Journal of Foundations of Computer Science     Hybrid Journal   (Followers: 3)
International Journal of Fuzzy Computation and Modelling     Hybrid Journal   (Followers: 2)
International Journal of Image and Graphics     Hybrid Journal   (Followers: 5)
International Journal of Industrial Electronics and Drives     Hybrid Journal   (Followers: 3)
International Journal of Low-Carbon Technologies     Open Access   (Followers: 1)
International Journal of Mathematical Education in Science and Technology     Hybrid Journal   (Followers: 9)
International Journal of Mathematical Modelling & Computations     Open Access   (Followers: 3)
International Journal of Mathematical Modelling and Numerical Optimisation     Hybrid Journal   (Followers: 5)
International Journal of Mathematical Sciences and Computing     Open Access  
International Journal of Mathematics     Hybrid Journal   (Followers: 4)
International Journal of Mathematics & Computation     Full-text available via subscription  
International Journal of Mathematics and Mathematical Sciences     Open Access   (Followers: 4)
International Journal of Mathematics in Operational Research     Hybrid Journal   (Followers: 2)
International Journal of Metaheuristics     Hybrid Journal   (Followers: 1)
International Journal of Modelling in Operations Management     Hybrid Journal   (Followers: 2)
International Journal of Modern Nonlinear Theory and Application     Open Access   (Followers: 1)
International Journal of Number Theory     Hybrid Journal   (Followers: 1)
International Journal of Partial Differential Equations     Open Access   (Followers: 2)
International Journal of Polymer Science     Open Access   (Followers: 25)
International Journal of Pure Mathematical Sciences     Open Access  
International Journal of Reliability, Quality and Safety Engineering     Hybrid Journal   (Followers: 14)
International Journal of Research in Undergraduate Mathematics Education     Hybrid Journal   (Followers: 4)
International Journal of Sediment Research     Full-text available via subscription   (Followers: 2)
International Journal of Shape Modeling     Hybrid Journal   (Followers: 1)
International Journal of Theoretical and Mathematical Physics     Open Access   (Followers: 13)
International Journal of Trends in Mathematics Education Research     Open Access   (Followers: 4)
International Journal of Ultra Wideband Communications and Systems     Hybrid Journal  
International Journal of Wavelets, Multiresolution and Information Processing     Hybrid Journal  
International Journal on Artificial Intelligence Tools     Hybrid Journal   (Followers: 9)
International Mathematics Research Notices     Hybrid Journal   (Followers: 1)
Internet Mathematics     Hybrid Journal   (Followers: 1)
Inventiones mathematicae     Hybrid Journal   (Followers: 2)
Inverse Problems in Science and Engineering     Hybrid Journal   (Followers: 3)
Investigations in Mathematics Learning     Hybrid Journal  
Iranian Journal of Optimization     Open Access   (Followers: 2)
Israel Journal of Mathematics     Hybrid Journal  
Ithaca : Viaggio nella Scienza     Open Access  
ITM Web of Conferences     Open Access  
Izvestiya Rossiiskoi Akademii Nauk. Seriya Matematicheskaya     Full-text available via subscription  
Jahresbericht der Deutschen Mathematiker-Vereinigung     Hybrid Journal  
Japan Journal of Industrial and Applied Mathematics     Hybrid Journal  
Japanese Journal of Mathematics     Hybrid Journal  
JIPM (Jurnal Ilmiah Pendidikan Matematika)     Open Access  
JMPM : Jurnal Matematika dan Pendidikan Matematika     Open Access  
JOHME : Journal of Holistic Mathematics Education     Open Access   (Followers: 2)
Johnson Matthey Technology Review     Open Access  
Jornal Internacional de Estudos em Educação Matemática     Open Access  
Journal d'Analyse Mathématique     Hybrid Journal   (Followers: 2)
Journal de Mathématiques Pures et Appliquées     Full-text available via subscription   (Followers: 3)
Journal for Research in Mathematics Education     Full-text available via subscription   (Followers: 28)
Journal für Mathematik-Didaktik     Hybrid Journal  
Journal of Advanced Mathematics and Applications     Full-text available via subscription   (Followers: 1)
Journal of Algebra     Full-text available via subscription   (Followers: 3)
Journal of Algebra and Its Applications     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Algorithms & Computational Technology     Open Access  
Journal of Applied Mathematics     Open Access   (Followers: 3)
Journal of Applied Mathematics and Computing     Hybrid Journal  
Journal of Applied Mathematics, Statistics and Informatics     Open Access   (Followers: 1)
Journal of Artificial Intelligence and Data Mining     Open Access   (Followers: 10)
Journal of Classification     Hybrid Journal   (Followers: 5)
Journal of Combinatorial Designs     Hybrid Journal   (Followers: 4)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Theory, Series A     Full-text available via subscription   (Followers: 5)
Journal of Combinatorial Theory, Series B     Full-text available via subscription   (Followers: 3)
Journal of Complex Analysis     Open Access   (Followers: 2)
Journal of Complex Networks     Hybrid Journal   (Followers: 1)
Journal of Complexity     Hybrid Journal   (Followers: 6)
Journal of Computational and Applied Mathematics     Hybrid Journal   (Followers: 6)
Journal of Computational Biology     Hybrid Journal   (Followers: 9)
Journal of Computational Mathematics and Data Science     Open Access  
Journal of Computational Multiphase Flows     Open Access   (Followers: 1)
Journal of Computational Physics     Hybrid Journal   (Followers: 59)
Journal of Computational Physics : X     Open Access   (Followers: 1)
Journal of Computer Engineering, System and Science (CESS)     Open Access  
Journal of Contemporary Mathematical Analysis     Hybrid Journal  
Journal of Cryptology     Hybrid Journal   (Followers: 5)
Journal of Difference Equations and Applications     Hybrid Journal  
Journal of Differential Equations     Full-text available via subscription   (Followers: 1)
Journal of Discrete Mathematics     Open Access   (Followers: 1)
Journal of Dynamics and Differential Equations     Hybrid Journal  
Journal of Engineering Mathematics     Hybrid Journal   (Followers: 2)
Journal of Evolution Equations     Hybrid Journal  
Journal of Experimental Algorithmics     Full-text available via subscription  
Journal of Flood Risk Management     Hybrid Journal   (Followers: 14)
Journal of Function Spaces     Open Access  
Journal of Functional Analysis     Full-text available via subscription   (Followers: 3)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 4)
Journal of Geological Research     Open Access   (Followers: 1)
Journal of Geovisualization and Spatial Analysis     Hybrid Journal  
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Global Research in Mathematical Archives     Open Access  
Journal of Homotopy and Related Structures     Hybrid Journal  
Journal of Honai Math     Open Access  
Journal of Humanistic Mathematics     Open Access   (Followers: 1)
Journal of Hyperbolic Differential Equations     Hybrid Journal  
Journal of Indian Council of Philosophical Research     Hybrid Journal  
Journal of Industrial Mathematics     Open Access   (Followers: 2)
Journal of Inequalities and Applications     Open Access  
Journal of Infrared, Millimeter and Terahertz Waves     Hybrid Journal   (Followers: 3)
Journal of Integrable Systems     Open Access  
Journal of Knot Theory and Its Ramifications     Hybrid Journal   (Followers: 2)
Journal of Liquid Chromatography & Related Technologies     Hybrid Journal   (Followers: 7)
Journal of Logical and Algebraic Methods in Programming     Hybrid Journal   (Followers: 1)
Journal of Manufacturing Systems     Full-text available via subscription   (Followers: 3)
Journal of Mathematical Analysis and Applications     Full-text available via subscription   (Followers: 3)
Journal of mathematical and computational science     Open Access   (Followers: 2)

  First | 1 2 3 4 | Last

Similar Journals
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Journal of Artificial Intelligence and Data Mining
Number of Followers: 10  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2322-5211 - ISSN (Online) 2322-4444
Published by Shahrood University of Technology Homepage  [1 journal]
  • Voice Activity Detection using Clustering-based Method in Spectro-Temporal
           Features Space

    • Abstract: This paper proposes a novel method for voice activity detection based on clustering in spectro-temporal domain. In the proposed algorithms, auditory model is used to extract the spectro-temporal features. Gaussian Mixture Model and WK-means clustering methods are used to decrease dimensions of the spectro-temporal space. Moreover, the energy and positions of clusters are used for voice activity detection. Silence/speech is recognized using the attributes of clusters and the updated threshold value in each frame. Having higher energy, the first cluster is used as the main speech section in computation. The efficiency of the proposed method was evaluated for silence/speech discrimination in different noisy conditions. Displacement of clusters in spectro-temporal domain was considered as the criteria to determine robustness of features. According to the results, the proposed method improved the speech/non-speech segmentation rate in comparison to temporal and spectral features in low signal to noise ratios (SNRs).
       
  • Automatic Visual Inspection System based on Image Processing and Neural
           Network for Quality Control of Sandwich Panel

    • Abstract: In this study, an automatic system based on image processing methods using features based on convolutional neural networks is proposed to detect the degree of possible dipping and buckling on the sandwich panel surface by a colour camera. The proposed method, by receiving an image of the sandwich panel, can detect the dipping and buckling of its surface with acceptable accuracy. After a panel is fully processed by the system, an image output is generated to observe the surface status of the sandwich panel so that the supervisor of the production line can better detect any potential defects at the surface of the produced panels. An accurate solution is also provided to measure the amount of available distortion (depth or height of dipping and buckling) on the sandwich panels without needing expensive and complex equipment and hardware.
       
  • A Hybrid Deep Network Representation Model for Detecting
           Researchers’ Communities

    • Abstract: Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this paper, we propose HDNR; a hybrid deep network representation model, which uses a triplet deep neural network architecture that considers both the node structure and content information for network representation. In addition, the author's writing style is also considered as a significant feature in the node content information. Inspired by the application of deep learning in natural language processing, our model utilizes a deep random walk method to exploit inter-node structures and two deep sequence prediction methods to extract nodes' content information. The embedding vectors generated in this manner were shown to have the ability of boosting each other for learning optimal node representation, detecting more informative features and ultimately a better community detection. The experimental results confirm the effectiveness of this model for network representation compared to other baseline methods.
       
  • Upgrading the Human Development Index (HDI) to control pandemic mortality
           rates: A data mining approach to COVID-19

    • Abstract: In recent years, the occurrence of various pandemics (COVID-19, SARS, etc.) and their widespread impact on human life have led researchers to focus on their pathology and epidemiology components. One of the most significant inconveniences of these epidemics is the human mortality rate, which has highly social adverse effects. This study, in addition to major attributes affecting the COVID-19 mortality rate (Health factors, people-health status, and climate) considers the social and economic components of societies. These components have been extracted from the countries’ Human Development Index (HDI) and the effect of the level of social development on the mortality rate has been investigated using ensemble data mining methods. The results indicate that the level of community education has the highest effect on the disease mortality rate. In a way, the extent of its effect is much higher than environmental factors such as air temperature, regional health factors, and community welfare. This factor is probably due to the ability of knowledge-based societies to manage the crises, their attention to health advisories, lower involvement of rumors, and consequently lower incidence of mental health problems. This study shows the impact of education on reducing the severity of the crisis in communities and opens a new window in terms of cultural and social factors in the interpretation of medical data. Furthermore, according to the results and comparing different types of single and ensemble data mining methods, the application of the ensemble method in terms of classification accuracy and prediction error has the best result.
       
  • A hybridization method of prototype generation and prototype selection for
           K-NN rule based on GSA

    • Abstract: The present study aims to overcome some defects of the K-nearest neighbor (K-NN) rule. Two important data preprocessing methods to elevate the K-NN rule are prototype selection (PS) and prototype generation (PG) techniques. Often the advantage of these techniques is investigated separately. In this paper, using the gravitational search algorithm (GSA), two hybrid schemes have been proposed in which PG and PS problems have been considered together. To evaluate the classification performance of these hybrid models, we have performed a comparative experimental study including a comparison between our proposals and some approaches previously studied in the literature using several benchmark datasets. The experimental results demonstrate that our hybrid approaches outperform most of the competitive methods.
       
  • Detecting Group Review Spammers in Social Media

    • Abstract: Nowadays, some e-advice websites and social media like e-commerce businesses, provide not only their goods but a new way that their customers can give their opinions about products. Meanwhile, there are some review spammers who try to promote or demote some specific products by writing fraud reviews. There have been several types of researches and studies toward detecting these review spammers, but most studies are based on individual review spammers and few of them studied group review spammers, nevertheless it should be mentioned that review spammers can increase their effects by cooperating and working together. More words, there have been many features introduced in order to detect review spammers and it is better to use the efficient ones. In this paper we propose a novel framework, named Network Based Group Review Spammers which tries to identify and classify group review spammers with the usage of the heterogeneous information network. In addition to eight basic features for detecting group review spammers, three efficient new features from previous studies were modified and added in order to improve detecting group review spammers. Then with the definition of Meta-path, features are ranked. Results showed that by using the importance of features and adding three new features in the suggested framework, group review spammers detection is improved on Amazon dataset.
       
  • Automatic Detection of Lung Nodules on CT Scans with a Deep Direct
           Regression Method

    • Abstract: Deep-learning-based approaches have been extensively used in detecting pulmonary nodules from computer Tomography (CT) scans. In this study, an automated end-to-end framework with a convolution network (Conv-net) has been proposed to detect lung nodules from CT images. Here, boundary regression has been performed by a direct regression method, in which the offset is predicted from a given point. The proposed framework has two outputs; a pixel-wise classification between nodule or normal and a direct regression which is used to determine the four coordinates of the nodule's bounding box. The Loss function includes two terms; one for classification and the other for regression. The performance of the proposed method is compared with YOLOv2. The evaluation has been performed using Lung-Pet-CT-DX dataset. The experimental results show that the proposed framework outperforms the YOLOv2 method. The results demonstrate that the proposed framework possesses high accuracies of nodule localization and boundary estimation.
       
  • Distributed Online Pre-Processing Framework for Big Data Sentiment
           Analytics

    • Abstract: Performing sentiment analysis on social networks big data can be helpful for various research and business projects to take useful insights from text-oriented content. In this paper, we propose a general pre-processing framework for sentiment analysis, which is devoted to adopting FastText with Recurrent Neural Network variants to prepare textual data efficiently. This framework consists of three different stages of data cleansing, tweets padding, word embedding’s extraction from FastText and conversion of tweets to these vectors, which implemented using DataFrame data structure in Apache Spark. Its main objective is to enhance the performance of online sentiment analysis in terms of pre-processing time and handle large scale data volume. In addition, we propose a distributed intelligent system for online social big data analytics. It is designed to store, process, and classify a huge amount of information in online. The proposed system adopts any word embedding libraries like FastText with different distributed deep learning models like LSTM or GRU. The results of the evaluations show that the proposed framework can significantly improve the performance of previous RDD-based methods in terms of processing time and data volume.
       
  • Clustering Methods to Analyze Social Media Posts during Coronavirus
           Pandemic in Iran

    • Abstract: During the COVID-19 crisis, we face a wide range of thoughts, feelings, and behaviors on social media that play a significant role in spreading information regarding COVID-19. Trustful information, together with hopeful messages, could be used to control people's emotions and reactions during pandemics. This study examines Iranian society's resilience in the face of the Corona crisis and provides a strategy to promote resilience in similar situations. It investigates posts and news related to the COVID-19 pandemic in Iran, to determine which messages and references have caused concern in the community, and how they could be modified' and also which references were the most trusted publishers' Social network analysis methods such as clustering have been used to analyze data. In the present work, we applied a two-stage clustering method constructed on the self-organizing map and K-means. Because of the importance of social trust in accepting messages, This work examines public trust in social posts. The results showed trust in the health-related posts was less than social-related and cultural-related posts. The trusted posts were shared on Instagram and news sites. Health and cultural posts with negative polarity affected people's trust and led to negative emotions such as fear, disgust, sadness, and anger. So, we suggest that non-political discourses be used to share topics in the field of health.
       
  • Abnormal Behavior Detection over Normal Data and Abnormal-augmented Data
           in Crowded Scenes

    • Abstract: In this article, we consider the problems of abnormal behavior detection in a high-crowded environment. One of the main issues in abnormal behavior detection is the complexity of the structure patterns between the frames. In this paper, social force and optical flow patterns are used to prepare the system for training the complexity of the structural patterns. The cycle GAN system has been used to train behavioral patterns. Two models of normal and abnormal behavioral patterns are used to evaluate the accuracy of the system detection. In the case of abnormal patterns used for training, due to the lack of this type of behavioral pattern, which is another challenge in detecting the abnormal behaviors, the geometric techniques are used to augment the patterns. If the normal behavioral patterns are used for training, there is no need to augment the patterns because the normal patterns are sufficient. Then, by using the cycle generative adversarial nets (cycle GAN), the normal and abnormal behaviors training will be considered separately. This system produces the social force and optical flow pattern for normal and abnormal behaviors on the first and second sides. We use the cycle GAN system both to train behavioral patterns and to assess the accuracy of abnormal behaviors detection. In the testing phase, if normal behavioral patterns are used for training, the cycle GAN system should not be able to reconstruct the abnormal behavioral patterns with high accuracy.
       
  • Increasing Performance of Recommender Systems by Combining Deep Learning
           and Extreme Learning Machine

    • Abstract: Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems based on precision, accuracy, F-measure, and recall. Within the suggested model of this research, the hidden biases and input weights values of the extreme learning machine algorithm are produced by the Restricted Boltzmann Machine and then clustering is performed. Also, this study employs the ELM for two approaches, clustering of training data and determine the clusters of test data. The results of the proposed method evaluated in two prediction methods by employing average and Pearson Correlation Coefficient in the MovieLens dataset. Considering the outcomes, it can be clearly said that the suggested method can overcome the problem of data sparsity and achieve higher performance in recommender systems. The results of evaluation of the proposed approach indicate a higher rate of all evaluation metrics while using the average method results in rates of precision, accuracy, recall, and F-Measure come to 80.49, 83.20, 67.84 and 73.62 respectively.
       
  • A Novel Classification and Diagnosis of Multiple Sclerosis Method using
           Artificial Neural Networks and Improved Multi-Level Adaptive Conditional
           Random Fields

    • Abstract: Due to the small size, low contrast, variable position, shape, and texture of multiple sclerosis lesions, one of the challenges of medical image processing is the automatic diagnosis and segmentation of multiple sclerosis lesions in Magnetic resonance images. Early diagnosis of these lesions in the first stages of the disease can effectively diagnose and evaluate treatment. Also, automated segmentation is a powerful tool to assist professionals in improving the accuracy of disease diagnosis. This study uses modified adaptive multi-level conditional random fields and the artificial neural network to segment and diagnose multiple sclerosis lesions. Instead of assuming model coefficients as constant, they are considered variables in multi-level statistical models. This study aimed to evaluate the probability of lesions based on the severity, texture, and adjacent areas. The proposed method is applied to 130 MR images of multiple sclerosis patients in two test stages and resulted in 98% precision. Also, the proposed method has reduced the error detection rate by correcting the lesion boundaries using the average intensity of neighborhoods, rotation invariant, and texture for very small voxels with a size of 3-5 voxels, and it has shown very few false-positive lesions. The proposed model resulted in a high sensitivity of 91% with a false positive average of 0.5.
       
  • Persian Phoneme and Syllable Recognition using Recurrent Neural Networks
           for Phonological Awareness Assessment

    • Abstract: One of the main problems in children with learning difficulties is the weakness of phonological awareness (PA) skills. In this regard, PA tests are used to evaluate this skill. Currently, this assessment is paper-based for the Persian language. To accelerate the process of the assessments and make it engaging for children, we propose a computer-based solution that is a comprehensive Persian phonological awareness assessment system implementing expressive and pointing tasks. For the expressive tasks, the solution is powered by recurrent neural network-based speech recognition systems. To this end, various recognition modules are implemented, including a phoneme recognition system for the phoneme segmentation task, a syllable recognition system for the syllable segmentation task, and a sub-word recognition system for three types of phoneme deletion tasks, including initial, middle, and final phoneme deletion. The recognition systems use bidirectional long short-term memory neural networks to construct acoustic models. To implement the recognition systems, we designed and collected Persian Kid’s Speech Corpus that is the largest in Persian for children’s speech. The accuracy rate for phoneme recognition was 85.5%, and for syllable recognition was 89.4%. The accuracy rates of the initial, middle, and final phoneme deletion were 96.76%, 98.21%, and 95.9%, respectively.
       
  • Classification of sEMG Signals for Diagnosis of Unilateral Posterior
           Crossbite in Primary Dentition using Fast Fourier Transform and Logistic
           Regression

    • Abstract: Posterior crossbite is a common malocclusion disorder in the primary dentition that strongly affects masticatory function. To the best of the author’s knowledge, for the first time, this article presents a reasonable and computationally efficient diagnostic system for detecting characteristics between children with and without unilateral posterior crossbite (UPCB) in the primary dentition from the surface electromyography (sEMG) activity of masticatory muscles. In this study, 40 children (4–6y) were selected and divided into UPCB (n = 20) and normal occlusion (NOccl; n = 20) groups. The preferred chewing side was determined using a visual spot-checking method. The chewing rate was determined as the average of two chewing cycles. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. The data of the subjects were diagnosed by the dentist. In this study, the fast Fourier transform (FFT) analysis was applied to sEMG signals recorded from subjects. The number of FFT coefficients had been selected by using Logistic Regression (LR) methodology. Then the ability of a multilayer perceptron artificial neural network (MLPANN) in the diagnosis of neuromuscular disorders in investigated. To find the best neuron weights and structures for MLPANN, particle swarm optimization (PSO) was utilized. Results showed the proficiency of the suggested diagnostic system for the classification of EMG signals. The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite.
       
  • Customer Behavior Analysis to Improve Detection of Fraudulent
           ‎Transactions using Deep Learning

    • Abstract: With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. This article discusses the challenges of detecting fraudulent banking transactions and presents solutions based on deep learning. Transactions are examined and compared with other traditional models in fraud detection. According to the results obtained, optimal performance is related to the combined model of deep convolutional networks and short-term memory, which is trained using the aggregated data received from the generative adversarial network. This paper intends to produce sensible data to address the unequal class distribution problem, which is far more effective than traditional methods. Also, it uses the strengths of the two approaches by combining deep convolutional network and Long Short Term Memory network to improve performance. Due to the inefficiency of evaluation criteria such as accuracy in this application, the measure of distance score and the equal error rate has been used to evaluate models more transparent and more precise. Traditional methods were compared to the proposed approach to evaluate the efficiency of the experiment.
       
  • A Clustering-Classification Recommender System based on Firefly Algorithm

    • Abstract: In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are of great importance, especially in today's hectic world, which search for content that would be interesting to an individual. In this research, a new two-steps recommender system is proposed based on demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naïve Bayesian Classification. The results are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results showed the effectiveness of the K-means/FA/KNN compared with other extant models.
       
  • A New Learning-based Spatiotemporal Descriptor for Online Symbol
           Recognition

    • Abstract: The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local features or sequences of them. Whereas, it has been shown that the combination of global and local features can increase the recognition accuracy. This paper addresses two mentioned topics. First, a new high discriminative local feature, called Rotation Invariant Histogram of Degrees (RIHoD), is proposed for online digitizer-pen handwriting signals. Second, a feature representation layer is proposed, which maps local features into global ones in a new space using some learning kernels. Different aspects of the proposed local feature and learned global feature are analyzed and its efficiency is evaluated in several online handwriting recognition scenarios.
       
  • Deformable 3D Shape Matching to Try on Virtual Clothes via
           Laplacian-Beltrami Descriptor

    • Abstract: Recently, significant attention has been paid to the development of virtual reality systems in several fields such as commerce. Trying on virtual clothes is becoming a solution for the online clothing industry. In this paper, we propose a method for the problem of virtual clothing using 3D point matching of a selected cloth and the customer body. For this purpose, we provide a 3D model of the customer and the selected clothes, put up on the mannequin, using a Kinect camera. As the size of the abdominal part of the customer is different from the mannequin, after pre-processing of the two captured point clouds, the 3D point cloud of the selected clothes is deformed to fit the 3D point cloud of the customer’s body. We use Laplacian-Beltrami curvature as a descriptor to find the abdominal part in the two point clouds. Then, the abdominal part of the mannequin is deformed in 3D space to fit the abdominal part of the customer. Finally, the head and neck of the customer are attached to the mannequin point.The proposed method has two main advantages over the existing methods for virtual clothing. First, no need for an expert to design a 3D model for the customer body and the selected clothes in advanced graphical software such as Unity. Second, there is no restriction for the style of the selected clothes and their texture while existing methods have such restrictions. The experimental results justify the ability of the proposed method for virtual clothing.
       
  • Text Sentiment Classification based on Separate Embedding of Aspect and
           Context

    • Abstract: Text sentiment classification in aspect level is one of the hottest research topics in the field of natural language processing. The purpose of the aspect-level sentiment analysis is to determine the polarity of the text according to a particular aspect. Recently, various methods have been developed to determine sentiment polarity of the text at the aspect level, however, these studies have not yet been able to model well complementary effects of the context and aspect in the polarization detection process. Here, we present ACTSC, a method for determining the sentiment polarity of the text based on separate embedding of aspects and context. In the first step, ACTSC deals with separate modelling of the aspects and context to extract new representation vectors. Next, by combining generative representations of aspect and context, it determines the corresponding polarity to each particular aspect using a short-term memory network and a self-attention mechanism. Experimental results in the SemEval2014 dataset in both restaurant and laptop categories show that ACTSC has been able to improve the accuracy of aspect-based sentiment classification compared to the latest proposed methods.
       
  • Automatic Shadow Direction Determination using Shadow Low Gradient
           Direction Feature in RGB VHR Remote Sensing Images

    • Abstract: Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very High Resolution (VHR) imagery provides more detailed data from objects including shadow areas. In this regard, the motivation of this paper is to propose a reliable feature, Shadow Low Gradient Direction (SLGD), to automatically determine shadow and solar illumination direction in VHR data. The proposed feature is based on inherent spatial feature of fine-resolution shadow areas. Therefore, it can facilitate shadow-based operations, especially when the solar illumination information is not available in remote sensing metadata. Shadow intensity is supposed to be dependent on two factors, including the surface material and sunlight illumination, which is analyzed by directional gradient values in low gradient magnitude areas. This feature considers the sunlight illumination and ignores the material differences. The method is fully implemented on the Google Earth Engine cloud computing platform, and is evaluated on VHR data with 0.3m resolution. Finally, SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps.
       
  • Reward and Penalty Model for Public Lighting Contracts: An Empirical Study
           in a Distribution Company

    • Abstract: Lighting continuity is one of the preferences of citizens. Public lighting management from the viewpoint of city residents improves social welfare. The quality of lamps and duration of lighting defect correction is important in lighting continuity. In this regard, reward and penalty mechanism plays an important role in contract. Selecting labor and lamps has a significant impact on risk reduction during the contract period. This research improves strategies for public lighting asset management. The lifespan of lamp that announced by manufacturers is used to calculate maintenance cost in order to provide a possibility to estimate the actual cost of high-pressure sodium luminaire in public lighting system. Guarantee period of lamps and maximum permissible lighting defect detection and correction time is used for reward and penalty mechanism. The result shows that the natural life guarantee and permissible correction time have a considerable effect in maintenance cost and city resident’s satisfaction.
       
  • Reconstruction of 3D Stack of Stars in Cardiac MRI using a Combination of
           GRASP and TV

    • Abstract: One of the most advanced non-invasive medical imaging methods is MRI that can make a good contrast between soft tissues. The main problem with this method is the time limitation in data acquisition, particularly in dynamic imaging. Radial sampling is an alternative for faster data acquisition and has several advantages compared to Cartesian sampling. Among them, robustness to motion artifacts makes this acquisition useful in cardiac imaging. Recently, CS has been used to accelerate data acquisition in dynamic MRI. Cartesian acquisition uses irregular undersampling patterns to create incoherent artifacts to meet the Incoherent sampling requirement of CS. Radial acquisition, due to its incoherent artifact, even in regular sampling, has an inherent fitness to CS reconstruction. In this study, we reconstruct the (3D) stack of stars data in cardiac imaging using the combination of the TV penalty function and the GRASP algorithm. We reduced the number of spokes from 21 to 13 and then reduced to 8 to observe the performance of the algorithm at a high acceleration factor. We compared the output images of the proposed algorithm with both GRASP and NUFFT algorithms. In all three modes (21, 13, and 8 spokes), average image similarity was increased by at least by 0.4, 0.1 compared to NUFFT, GRASP respectively. Moreover, streaking artifacts were significantly reduced. According to the results, the proposed method can be used on a clinical study for fast dynamic MRI, such as cardiac imaging with the high image quality from low- rate sampling.
       
  • Optimizing CELF Algorithm for Influence Maximization Problem in Social
           Networks

    • Abstract: The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard problem. Undoubtedly, the CELF algorithm (besides the naive greedy algorithm) has the highest effectiveness among them. Of course, the CELF algorithm is faster than the naive greedy algorithm (about 700 times). This superiority has led many researchers to make extensive use of the CELF algorithm in their innovative approaches. However, the main drawback of the CELF algorithm is the very long running time of its first iteration. Because it has to estimate the influence spread for all nodes by expensive Monte-Carlo simulations, similar to the naive greedy algorithm. In this paper, a heuristic approach is proposed, namely Optimized-CELF algorithm, to improve this drawback of the CELF algorithm by avoiding unnecessary Monte-Carlo simulations. It is found that the proposed algorithm reduces the CELF running time, and subsequently improves the time efficiency of other algorithms that employed the CELF as a base algorithm. Experimental results on the wide spectral of real datasets showed that the Optimized-CELF algorithm provided better running time gain, about 88-99% and 56-98% for k=1 and k=50, respectively, compared to the CELF algorithm without missing effectiveness.
       
  • WSAMLP: Water Strider Algorithm and Artificial Neural Network-based
           Activity Detection Method in Smart Homes

    • Abstract: One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities of the people inside them. Detecting the activities of the smart homes’ users may include the detection of activities such as making food or watching TV. Detecting the activities of residents of smart homes can tremendously help the elderly or take care of the kids or, even, promote security issues. The information collected by the sensors could be used for detecting the kind of activities; however, the main challenge is the poor precision of most of the activity detection methods. In the proposed method, for reducing the clustering error of the data mining techniques, a hybrid learning approach is presented using Water Strider Algorithm. In the proposed method, Water Strider Algorithm can be used in the feature extraction phase and exclusively extract the main features for machine learning. The analysis of the proposed method shows that it has precision of 97.63 %, accuracy of 97. 12 %, and F1 index of 97.45 %. It, in comparison with similar algorithms (such as Butterfly Optimization Algorithm, Harris Hawks Optimization Algorithm, and Black Widow Optimization Algorithm), has higher precision while detecting the users’ activities.
       
  • Question Classification in Question Answering System using Combination of
           Ensemble Classification and Feature Selection

    • Abstract: A Question Answering System (QAS) is a special form of information retrieval which consists of three parts: question processing, information retrieval, and answer selection. Determining the type of question is the most important part of QAS as it affects other following parts. This study uses effective features and ensemble classification to improve the QAS performance by increasing the accuracy of question type identification. We use the gravitational search algorithm to select the features and perform ensemble classification. The proposed system is extensively tested on different datasets using four types of experiments: (1) neither feature selection nor ensemble classification, (2) feature selection without ensemble classification, (3) ensemble classification without feature selection, and (4) feature selection with ensemble classification. These four kinds of experiments are carried out under the differential evolution algorithm and gravitational search algorithm. The experimental results show that the proposed method outperforms compared to state-of-the-art methods in previous researches.
       
 
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