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J. of Artificial Intelligence and Data Mining     Open Access   (Followers: 10)
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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]
  • A Simulated Annealing-based Throughput-aware Task Mapping Algorithm for
           Manycore Processors

    • Abstract: With the advent of having many processor cores on a single chip in many-core processors, the demand for exploiting these on-chip resources to boost the performance of applications has been increased. Task mapping is the problem of mapping the application tasks on these processor cores to achieve lower latency and better performance. Many researches are focused on minimizing the path between the tasks that demand high bandwidth for communication. Although using these methods can result in lower latency, but at the same time, it is possible to create congestion in the network which lowers the network throughput. In this paper, a throughput-aware method is proposed that uses simulated annealing for task mapping. The method is checked on several real-world applications and simulations are conducted on a cycle-accurate network on chip simulator. The results illustrate that the proposed method can achieve higher throughput while maintaining the delay in the NoC.
       
  • Automatic Control and Guidance of Mobile Robot using Machine Learning
           Methods

    • Abstract: In many applications of the robotics, the mobile robot should be guided from a source to a specific destination. The automatic control and guidance of a mobile robot is a challenge in the context of robotics. So, in current paper, this problem is studied using various machine learning methods. Controlling a mobile robot is to help it to make the right decision about changing direction according to the information read by the sensors mounted around waist of the robot. Machine learning methods are trained using 3 large datasets read by the sensors and obtained from machine learning database of UCI. The employed methods include (i) discriminators: greedy hypercube classifier and support vector machines, (ii) parametric approaches: Naive Bayes’ classifier with and without dimensionality reduction methods, (iii) semiparametric algorithms: Expectation-Maximization algorithm (EM), C-means, K-means, agglomerative clustering, (iv) nonparametric approaches for defining the density function: histogram and kernel estimators, (v) nonparametric approaches for learning: k-nearest neighbors and decision tree and (vi) Combining Multiple Learners: Boosting and Bagging. These methods are compared based on various metrics. Computational results indicate superior performance of the implemented methods compared to the previous methods using the mentioned dataset. In general, Boosting, Bagging, Unpruned Tree and Pruned Tree (θ = 10-7) have given better results compared to the existing results. Also the efficiency of the implemented decision tree is better than the other employed methods and this method improves the classification precision, TP-rate, FP- rate and MSE of the classes by 0.1%, 0.1%, 0.001% and 0.001%.
       
  • Energy-Efficient Timing Assignment of Tasks to Actors in WSANs

    • Abstract: Minimizing make-span and maximizing remaining energy are usually of chief importance in the applications of wireless sensor actor networks (WSANs). Current task assignment approaches are typically concerned with one of the timing or energy constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, an optimized and type aware task assignment approach called TATA is proposed that considers the energy consumption as well as the make-span. TATA is an optimized task assignment approach and aware of the distribution necessities of WSANs with hybrid architecture. TATA comprises of two protocols, namely a Make-span Calculation Protocol (MaSC) and an Energy Consumption Calculation Protocol (ECal). Through considering both time and energy, TATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors. A series of extensive simulation results on typical scenarios show shorter make-span and larger remaining energy in comparison to when stochastic task assignment (STA), opportunistic load balancing (OLB), and task assignment algorithm based on quasi-Newton interior point (TA-QNIP) approaches is applied.
       
  • A Transformer-based Approach for Persian Text Chunking

    • Abstract: Over the last few years, text chunking has taken a significant part in sequence labeling tasks. Although a large variety of methods have been proposed for shallow parsing in English, most proposed approaches for text chunking in Persian language are based on simple and traditional concepts. In this paper, we propose using the state-of-the-art transformer-based contextualized models, namely BERT and XLM-RoBERTa, as the major structure of our models. Conditional Random Field (CRF), the combination of Bidirectional Long Short-Term Memory (BiLSTM) and CRF, and a simple dense layer are employed after the transformer-based models to enhance the model's performance in predicting chunk labels. Moreover, we provide a new dataset for noun phrase chunking in Persian which includes annotated data of Persian news text. Our experiments reveal that XLM-RoBERTa achieves the best performance between all the architectures tried on the proposed dataset. The results also show that using a single CRF layer would yield better results than a dense layer and even the combination of BiLSTM and CRF.
       
  • A random scheme to implement m-connected k-covering wireless sensor
           networks

    • Abstract: Deploying m-connected k-covering (MK) wireless sensor networks (WSNs) is crucial for reliable packet delivery and target coverage. This paper proposes implementing random MK WSNs based on expected m-connected k-covering (EMK) WSNs. We define EMK WSNs as random WSNs mathematically expected to be both m-connected and k-covering. Deploying random EMK WSNs is conducted by deriving a relationship between m-connectivity and k-coverage, together with a lower bound for the required number of nodes. It is shown that EMK WSNs tend to be MK asymptotically. A polynomial worst-case and linear average-case complexity algorithm is presented to turn an EMK WSN into MK in non-asymptotic conditions. The m-connectivity is founded on the concept of support sets to strictly guarantee the existence of m disjoint paths between every node and the sink. The theoretical results are assessed via experiments, and several metaheuristic solutions have been benchmarked to reveal the appropriate size of the generated MK WSNs.
       
  • AgriNet: a new classifying convolutional neural network for detecting
           agricultural products’ diseases

    • Abstract: An important sector that has a significant impact on the economies of countries is the agricultural sector. Researchers are trying to improve this sector by using the latest technologies. One of the problems facing farmers in the agricultural activities is plant diseases. If a plant problem is diagnosed soon, the farmer can treat the disease more effectively. This study introduces a new deep artificial neural network called AgriNet which is suitable for recognizing some types of agricultural diseases in a plant using images from the plant leaves. The proposed network makes use of the channel shuffling technique of ShuffleNet and the channel dependencies modeling technique of SENet. One of the factors influencing the effectiveness of the proposed network architecture is how to increase the flow of information in the channels after explicitly modelling interdependencies between channels. This is in fact, an important novelty of this research work. The dataset used in this study is PlantVillage, which contains 14 types of plants in 24 groups of healthy and diseased. Our experimental results show that the proposed method outperforms the other methods in this area. AgriNet leads to accuracy and loss of 98% and 7%, respectively on the experimental data. This method increases the recognition accuracy by about 2% and reduces the loss by 8% compared to the ShuffleNetV2 method.
       
  • Q-LVS: A Q-learning-based algorithm for video streaming in peer-to-peer
           networks considering a token-based incentive mechanism

    • Abstract: Peer-to-peer video streaming has reached great attention during recent years. Video streaming in peer-to-peer networks is a good way to stream video on the Internet due to the high scalability, high video quality, and low bandwidth requirements. In this paper the issue of live video streaming in peer-to-peer networks which contain selfish peers is addressed. To encourage peers to cooperate in video distribution, tokens are used as an internal currency. Tokens are gained by peers when they accept requests from other peers to upload video chunks to them, and tokens are spent when sending requests to other peers to download video chunks from them. To handle the heterogeneity in the bandwidth of peers, the assumption has been made that the video is coded as multi-layered. For each layer the same token has been used, but priced differently per layer. Based on the available token pools, peers can request various qualities. A new token-based incentive mechanism has been proposed, which adapts the admission control policy of peers according to the dynamics of the request submission, request arrival, time to send requests, and bandwidth availability processes. Peer-to-peer requests could arrive at any time, so the continuous Markov Decision Process has been used.
       
  • A New Approach to Estimate Motion and Structure of a Moving Rigid Object
           in a 3D Space with a Single Hand-held Camera

    • Abstract: Classical SFM (Structure From Motion) algorithms are widely used to estimate the three-dimensional structure of a stationary scene with a moving camera. However, when there are moving objects in the scene, if the equation of the moving object is unknown, the approach fails. This paper first demonstrates that when the frame rate is high enough and the object movement is continuous in time, meaning that acceleration is limited, a simple linear model can be effectively used to estimate the motion. This theory is first mathematically proven in a closed-form expression and then optimized by a nonlinear function applicable for our problem. The algorithm is evaluated both on synthesized and real data from Hopkins dataset.
       
  • 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.
       
  • 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.
       
 
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