List of Articles Neural Network

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Analysis and Evaluation of Techniques for Myocardial Infarction Based on Genetic Algorithm and Weight by SVM
  hojatallah hamidi Atefeh Daraei
  10.7508/jist.2016.02.003
  Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effe More

  Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effects of using Angioplasty as main method for diagnosing Myocardial Infarction, presenting a method for diagnosing MI before occurrence seems really important. This study aim to investigate prediction models for Myocardial Infarction, by applying a feature selection model based on Wight by SVM and genetic algorithm. In our proposed method, for improving the performance of classification algorithm, a hybrid feature selection method is applied. At first stage of this method, the features are selected based on their weights, using weight by Support Vector Machine. At second stage, the selected features, are given to genetic algorithm for final selection. After selecting appropriate features, eight classification methods, include Sequential Minimal Optimization, REPTree, Multi-layer Perceptron, Random Forest, K-Nearest Neighbors and Bayesian Network, are applied to predict occurrence of Myocardial Infarction. Finally, the best accuracy of applied classification algorithms, have achieved by Multi-layer Perceptron and Sequential Minimal Optimization. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - The Separation of Radar Clutters using Multi-Layer Perceptron
  Mohammad Akhondi Darzikolaei Ataollah Ebrahimzadeh Elahe Gholami
  10.7508/jist.2017.17.006
  Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complica More

  Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complicated issues. Therefore, in this paper aims to classify radar clutters. The novel proposed MLP-based classifier for separating radar clutters is introduced. This classifier is designed with different hidden layers and five training algorithms. These training algorithms consist of Levenberg-Marquardt, conjugate gradient, resilient back-propagation, BFGS and one step secant algorithms. Statistical distributions are established models which widely used in the performance calculations of radar clutters. Hence In this research, Rayleigh, Log normal, Weibull and K-distribution clutters are utilized as input data. Then Burg’s reflection coefficients, skewness and kurtosis are three features which applied to extract the best characteristics of input data. In the next step, the proposed classifier is tested in different conditions and the results represent that the proposed MLP-based classifier is very successful and can distinguish clutters with high accuracy. Comparing the results of proposed technique and RBF-based classifier show that proposed method is more efficient. The results of simulations prove that the validity of MLP-based method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Improvement in Accuracy and Speed of Image Semantic Segmentation via Convolution Neural Network Encoder-Decoder
  Hanieh Zamanian Hassan Farsi Sajad Mohammadzadeh
  10.7508/jist.2018.03.002
  Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predic More

  Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in proposed network, the speed and the accuracy improve compared to the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60,000 iterations, we obtain the 91% for global accuracy, which indicates improvements in the efficiency of proposed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System
  Shima Mehrabi Seyed Abolghassem Mirroshandel Hamidreza  Ahmadifar
  10.7508/jist.2019.03.005
  With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language pro More

  With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language processing researchers. Today, with improvement in processing power and the development of computational tools, efforts to improve the performance of the summarization system is continued, especially with utilizing more powerful learning algorithms such as deep learning method. In this paper, a novel multi-lingual multi-document summarization system is proposed that works based on deep learning techniques, and it is amongst the first Persian summarization system by use of deep learning. The proposed system ranks the sentences based on some predefined features and by using a deep artificial neural network. A comprehensive study about the effect of different features was also done to achieve the best possible features combination. The performance of the proposed system is evaluated on the standard baseline datasets in Persian and English. The result of evaluations demonstrates the effectiveness and success of the proposed summarization system in both languages. It can be said that the proposed method has achieve the state of the art performance in Persian and English. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - Drone Detection by Neural Network Using GLCM and SURF Features
  Tanzia  Ahmed Tanvir  Rahman Bir  Ballav Roy Jia Uddin
  10.52547/jist.9.33.15
  20.1001.1.23221437.2021.9.33.3.8
  This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extrac More

  This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extraction method using SURF and GLCM is used to detect object by Neural Network which has never been experimented before. Both are very popular ways of feature extraction. Speeded-up Robust Feature (SURF) is a blob detection algorithm which extracts the points of interest from an integral image, thus converts the image into a 2D vector. The Gray-Level Co-Occurrence Matrix (GLCM) calculates the number of occurrences of consecutive pixels in same spatial relationship and represents it in a new vector- 8 × 8 matrix of best possible attributes of an image. SURF is a popular method of feature extraction and fast matching of images, whereas, GLCM method extracts the best attributes of the images. In the proposed model, the images were processed first to fit our feature extraction methods, then the SURF method was implemented to extract the features from those images into a 2D vector. Then for our next step GLCM was implemented which extracted the best possible features out of the previous vector, into a 8 × 8 matrix. Thus, image is processed in to a 2D vector and feature extracted from the combination of both SURF and GLCM methods ensures the quality of the training dataset by not just extracting features faster (with SURF) but also extracting the best of the point of interests (with GLCM). The extracted featured related to the pattern are used in the neural network for training and testing. Pattern recognition algorithm has been used as a machine learning tool for the training and testing of the model. In the experimental evaluation, the performance of proposed model is examined by cross entropy for each instance and percentage error. For the tested drone dataset, experimental results demonstrate improved performance over the state-of-art models by exhibiting less cross entropy and percentage error. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  6 - Recognizing Transliterated English Words in Persian Texts
  Ali Hoseinmardy Saeedeh Momtazi
  10.29252/jist.8.30.84
  One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classific More

  One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classification and clustering. In this case, if the text-processing engine does not use similar/related words in the same sense, it may not be able to guide you to the appropriate result. Various statistical techniques have been proposed to bridge the vocabulary gap problem; e.g., if two words are used in similar contexts frequently, they have similar/related meanings. Synonym and similar words, however, are only one of the categories of related words that are expected to be captured by statistical approaches. Another category of related words is the pair of an original word in one language and its transliteration from another language. This kind of related words is common in non-English languages. In non-English texts, instead of using the original word from the target language, the writer may borrow the English word and only transliterate it to the target language. Since this kind of writing style is used in limited texts, the frequency of transliterated words is not as high as original words. As a result, available corpus-based techniques are not able to capture their concept. In this article, we propose two different approaches to overcome this problem: (1) using neural network-based transliteration, (2) using available tools that are used for machine translation/transliteration, such as Google Translate and Behnevis. Our experiments on a dataset, which is provided for this purpose, shows that the combination of the two approaches can detect English words with 89.39% accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  7 - Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification
  Nidhi Chandra Laxmi  Ahuja Sunil Kumar Khatri Himanshu Monga
  10.52547/jist.9.34.89
  20.1001.1.23221437.2021.9.34.2.9
  The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time a More

  The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis. Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism. Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment. The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures. A few of the use cases that follow are – sentiment analysis for NLP. GRU with RNN is being used as it would need to retain long-term dependencies. This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network. This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text. It helps specifically inform the network of long-term dependencies. We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  8 - Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)
  Mohammad Nejadeh Peyman Bayat Jalal Kheirkhah Hassan Moladoust
  10.29252/jist.8.31.197
  20.1001.1.23221437.2020.8.31.2.6
  Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the progno More

  Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  9 - Deep Learning Approach for Cardiac MRI Images
  Afshin Sandooghdar Farzin Yaghmaee
  10.52547/jist.16121.10.37.61
  20.1001.1.23221437.2022.10.37.5.5
  Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an o More

  Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an open question for scholars in the field. There are so many types of function approximators like Decision Tree, Gaussian Processes and Deep Learning, used in multi-layered Neural Networks (NNs), which should be evaluated to determine their effectiveness. Therefore, this study aimed to assess an approach based on DL techniques for modern medical imaging methods according to Magnetic Resonance Imaging (MRI) segmentation. To do so, an experiment with a random sampling approach was conducted. One hundred patient cases were used in this study for training, validation, and testing. The method used in this study was based on full automatic processing of segmentation and disease classification based on MRI images. U-Net structure was used for the segmentation process, with the use of cardiac Right Ventricular Cavity (RVC), Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and information extracted from the segmentation step. With train and using random forest classifier, and Multilayer Perceptron (MLP), the task of predicting the pathologic target class was conducted. Segmentation extracted information was in the form of comprehensive features handcrafted to reflect demonstrative clinical strategies. Our study suggests 92% test accuracy for cardiac MRI image segmentation and classification. As for the MLP ensemble, and for the random forest, test accuracy was equal to 91% and 90%, respectively. This study has implications for scholars in the field of medical image processing. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  10 - Detection of Attacks and Anomalies in the Internet of Things System using Neural Networks Based on Training with PSO Algorithms, Fuzzy PSO, Comparative PSO and Mutative PSO
  Mohammad  Nazarpour navid nezafati Sajjad  Shokouhyar
  10.52547/jist.16307.10.40.270
  20.1001.1.23221437.2022.10.40.4.0
  Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detect More

  Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore,in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  11 - Edge Detection and Identification using Deep Learning to Identify Vehicles
  Zohreh Dorrani Hassan Farsi Sajad Mohammadzadeh
  10.52547/jist.16385.10.39.201
  20.1001.1.23221437.2022.10.39.2.6
  A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract informati More

  A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  12 - Recognition of Attention Deficit/Hyperactivity Disorder (ADHD) Based on Electroencephalographic Signals Using Convolutional Neural Networks (CNNs)
  Sara Motamed Elham Askari
  10.52547/jist.16399.10.39.222
  20.1001.1.23221437.2022.10.39.3.7
  Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might More

  Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might interfere with your own learning, work, and communication with others, it could be controlled by early diagnosis and treatment. Because the automatic recognition and classification of electroencephalography (EEG) signals is challenging due to the large variation in time features and signal frequency, the present study attempts to provide an efficient method for diagnosing hyperactive patients. The proposed method is that first, the recorded brain signals of hyperactive subjects are read from the input and in order to the signals to be converted from time range to frequency range, Fast Fourier Transform (FFT) is used. Also, to select an effective feature to check hyperactive subjects from healthy ones, the peak frequency (PF) is applied. Then, to select the features, principal component analysis and without principal component analysis will be used. In the final step, convolutional neural networks (CNNs) will be utilized to calculate the recognition rate of individuals with hyperactivity. For model efficiency, this model is compared to the models of K- nearest neighbors (KNN), and multilayer perceptron (MLP). The results show that the best method is to use feature selection by principal component analysis and classification of CNNs and the recognition rate of individuals with ADHD from healthy ones is equal to 91%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  13 - Hierarchical Weighted Framework for Emotional Distress Detection using Personalized Affective Cues
  Nagesh Jadhav
  10.52547/jist.16499.10.38.89
  20.1001.1.23221437.2022.10.38.8.0
  Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distres More

  Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distress in humans. Personalized affective cues provide a non-invasive approach considering visual, vocal, and verbal cues to recognize the affective state. In this paper, we are proposing a multimodal hierarchical weighted framework to recognize emotional distress. We are utilizing negative emotions to detect the unapparent behavior of the person. To capture facial cues, we have employed hybrid models consisting of a transfer learned residual network and CNN models. Extracted facial cue features are processed and fused at decision using a weighted approach. For audio cues, we employed two different models exploiting the LSTM and CNN capabilities fusing the results at the decision level. For textual cues, we used a BERT transformer to learn extracted features. We have proposed a novel decision level adaptive hierarchical weighted algorithm to fuse the results of the different modalities. The proposed algorithm has been used to detect the emotional distress of a person. Hence, we have proposed a novel algorithm for the detection of emotional distress based on visual, verbal, and vocal cues. Experiments on multiple datasets like FER2013, JAFFE, CK+, RAVDESS, TESS, ISEAR, Emotion Stimulus dataset, and Daily-Dialog dataset demonstrates the effectiveness and usability of the proposed architecture. Experiments on the enterface'05 dataset for distress detection has demonstrated significant results. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  14 - Convolutional Neural Networks for Medical Image Segmentation and Classification: A Review
  Jenifer S Carmel Mary Belinda M J
  10.61186/jist.37936.11.44.347
  Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep le More

  Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep Convolutional Neural Networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the works exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pre-trained models and General Adversarial Networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on covid-19 detection and child bone age prediction. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  15 - Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN
  Nafiseh Sadeghi Homayoun Mahdavi-Nasab Mansoor Zeinali Hossein Pourghasem
  10.61186/jist.39680.11.44.359
  Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a giv More

  Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  16 - Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
  Motahareh Ghavidel meisam Yadollahzadeh tabari Mehdi Golsorkhtabaramiri
  10.61186/jist.41465.12.45.62
  To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimension More

  To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers. Manuscript profile