Pub Date : 2023-08-08DOI: 10.1142/s1469026823500244
Xiaojie Xu, Yun Zhang
Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments.
{"title":"Wholesale Food Price Index Forecasts with the Neural Network","authors":"Xiaojie Xu, Yun Zhang","doi":"10.1142/s1469026823500244","DOIUrl":"https://doi.org/10.1142/s1469026823500244","url":null,"abstract":"Food price forecasts in the agricultural sector have always been a vital matter to a wide variety of market participants. In this work, we approach this forecast problem for the weekly wholesale food price index in the Chinese market during a 10-year period of January 1, 2010–January 3, 2020. To facilitate the analysis, we propose the use of the nonlinear auto-regressive neural network. Technically, we investigate forecast performance, based upon the relative root mean square error (RRMSE) as the evaluation metrics, corresponding to one hundred and twenty settings that cover different algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data. Our experimental result suggests the construction of the neural network with three delays and 10 hidden neurons, which is trained through the Levenberg–Marquardt algorithm, as the forecast model. It leads to high accuracy and stabilities with the RRMSEs of 1.93% for the training phase, 2.16% for the validation phase, and 1.95% for the testing phase. Comparisons of forecast accuracy between the proposed model and some other machine learning models, as well as traditional time-series econometric models, suggest that our proposed model leads to statistically significant better performance. Our results could benefit different forecast users, such as policymakers and various market participants, in policy analysis and market assessments.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44867824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1142/s1469026823500220
Mohamed Skander Daas, Billel Kenidra, Hamza Bouanaka, S. Chikhi
With the appearance of complete mammalian genomes, comparative approaches have experienced a recent upsurge. Searching maximal exact match is among the most used tasks in sequence searching within a larger DNA sequence or database. Many exact algorithms have been designed to deal with this problem. The best improvements made by these algorithms have led to a time and space complexity of O(n) and they remain practically less effective for large sequences. Heuristic methods will therefore be a good alternative to implement. In this work, we present an efficient heuristic algorithm based on PSO metaheuristic to deal with the problem of searching the maximal exact match of small searched sequences in large ones. The time and space complexity of the designed algorithm is of O(1). The experimental results showed the efficiency of the proposed search algorithm for finding maximal exact match in large sequences when compared with two other sequence searching algorithms.
{"title":"An Efficient PSO-Based Algorithm for Finding Maximal Exact Match in Large DNA Sequences","authors":"Mohamed Skander Daas, Billel Kenidra, Hamza Bouanaka, S. Chikhi","doi":"10.1142/s1469026823500220","DOIUrl":"https://doi.org/10.1142/s1469026823500220","url":null,"abstract":"With the appearance of complete mammalian genomes, comparative approaches have experienced a recent upsurge. Searching maximal exact match is among the most used tasks in sequence searching within a larger DNA sequence or database. Many exact algorithms have been designed to deal with this problem. The best improvements made by these algorithms have led to a time and space complexity of O(n) and they remain practically less effective for large sequences. Heuristic methods will therefore be a good alternative to implement. In this work, we present an efficient heuristic algorithm based on PSO metaheuristic to deal with the problem of searching the maximal exact match of small searched sequences in large ones. The time and space complexity of the designed algorithm is of O(1). The experimental results showed the efficiency of the proposed search algorithm for finding maximal exact match in large sequences when compared with two other sequence searching algorithms.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43316522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-11DOI: 10.1142/s1469026823500207
Pang Qi, Yu Yanan, Haile Haftom Berihu
Few-shot learning can solve new learning tasks in the condition of fewer samples. However, currently, the few-shot learning algorithms mostly use the ResNet as a backbone, which leads to a large number of model parameters. To deal with the problem, a lightweight backbone named DenseAttentionNet which is based on the Convolutional Block Attention Mechanism is proposed by comparing the parameter amount and the accuracy of few-shot classification with ResNet-12. Then, based on the DenseAttentionNet, a few-shot learning algorithm called Meta-DenseAttention is presented to balance the model parameters and the classification effect. The dense connection and attention mechanism are combined to meet the requirements of fewer parameters and to achieve a good classification effect for the first time. The experimental results show that the DenseAttentionNet, not only reduces the number of parameters by 55% but also outperforms other classic backbones in the classification effect compared with the ResNet-12 benchmark. In addition, Meta-DenseAttention has an accuracy of 56.57% (5way-1shot) and 72.73% (5way-5shot) on the miniImageNet, although the number of parameters is only 3.6[Formula: see text]M. The experimental results also show that the few-shot learning algorithm proposed in this paper not only guarantees classification accuracy but also has the characteristics of lightweight.
{"title":"Research on Lightweight Few-Shot Learning Algorithm Based on Convolutional Block Attention Mechanism","authors":"Pang Qi, Yu Yanan, Haile Haftom Berihu","doi":"10.1142/s1469026823500207","DOIUrl":"https://doi.org/10.1142/s1469026823500207","url":null,"abstract":"Few-shot learning can solve new learning tasks in the condition of fewer samples. However, currently, the few-shot learning algorithms mostly use the ResNet as a backbone, which leads to a large number of model parameters. To deal with the problem, a lightweight backbone named DenseAttentionNet which is based on the Convolutional Block Attention Mechanism is proposed by comparing the parameter amount and the accuracy of few-shot classification with ResNet-12. Then, based on the DenseAttentionNet, a few-shot learning algorithm called Meta-DenseAttention is presented to balance the model parameters and the classification effect. The dense connection and attention mechanism are combined to meet the requirements of fewer parameters and to achieve a good classification effect for the first time. The experimental results show that the DenseAttentionNet, not only reduces the number of parameters by 55% but also outperforms other classic backbones in the classification effect compared with the ResNet-12 benchmark. In addition, Meta-DenseAttention has an accuracy of 56.57% (5way-1shot) and 72.73% (5way-5shot) on the miniImageNet, although the number of parameters is only 3.6[Formula: see text]M. The experimental results also show that the few-shot learning algorithm proposed in this paper not only guarantees classification accuracy but also has the characteristics of lightweight.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48160658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06DOI: 10.1142/s1469026823500116
Universities play a huge role in the cultivation of talents. Especially in the context of internationalization, the teaching of English as a common language is becoming more and more important. This paper introduced the traditional methods for evaluating the quality of English teaching, established a deep learning algorithm for evaluating the quality of English teaching with the evaluation indicators of the traditional methods combined with the convolutional neural network (CNN) algorithm, conducted simulation experiments on the CNN algorithm, and compared it with the support vector machine (SVM) algorithm. The results showed that the scores obtained by the CNN algorithm had some errors with the actual scores but were much lower than the scores obtained by the SVM algorithm, and the CNN algorithm consumed a shorter time in computing. This paper used the CNN algorithm combined with evaluation indexes constructed by the analytic hierarchy process (AHP) method to evaluate the quality of English teaching and verified the effectiveness of the CNN algorithm through a comparison with the SVM algorithm, which provides an effective reference for intelligent evaluation of English teaching quality.
{"title":"A Deep Learning Algorithm for Evaluating the Quality of English Teaching","authors":"","doi":"10.1142/s1469026823500116","DOIUrl":"https://doi.org/10.1142/s1469026823500116","url":null,"abstract":"Universities play a huge role in the cultivation of talents. Especially in the context of internationalization, the teaching of English as a common language is becoming more and more important. This paper introduced the traditional methods for evaluating the quality of English teaching, established a deep learning algorithm for evaluating the quality of English teaching with the evaluation indicators of the traditional methods combined with the convolutional neural network (CNN) algorithm, conducted simulation experiments on the CNN algorithm, and compared it with the support vector machine (SVM) algorithm. The results showed that the scores obtained by the CNN algorithm had some errors with the actual scores but were much lower than the scores obtained by the SVM algorithm, and the CNN algorithm consumed a shorter time in computing. This paper used the CNN algorithm combined with evaluation indexes constructed by the analytic hierarchy process (AHP) method to evaluate the quality of English teaching and verified the effectiveness of the CNN algorithm through a comparison with the SVM algorithm, which provides an effective reference for intelligent evaluation of English teaching quality.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43927091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06DOI: 10.1142/s1469026823500128
This paper studied music feature recognition and classification. First, the common signal features were analyzed, and the signal pre-processing method was introduced. Then, the Mel–Phon coefficient (MPC) was proposed as a feature for subsequent recognition and classification. The deep belief network (DBN) model was applied and improved by the gray wolf optimization (GWO) algorithm to get the GWO–DBN model. The experiments were conducted on GTZAN and free music archive (FMA) datasets. It was found that the best hidden-layer structure of DBN was 1440-960-480-300. Compared with machine learning methods such as decision trees, the DBN model had better classification performance in recognizing and classifying music types. The classification accuracy of the GWO–DBN model reached 75.67%. The experimental results demonstrate the reliability of the GWO–DBN model. The GWO–DBN model can be further promoted and applied in actual music research.
{"title":"Music Feature Recognition and Classification Using a Deep Learning Algorithm","authors":"","doi":"10.1142/s1469026823500128","DOIUrl":"https://doi.org/10.1142/s1469026823500128","url":null,"abstract":"This paper studied music feature recognition and classification. First, the common signal features were analyzed, and the signal pre-processing method was introduced. Then, the Mel–Phon coefficient (MPC) was proposed as a feature for subsequent recognition and classification. The deep belief network (DBN) model was applied and improved by the gray wolf optimization (GWO) algorithm to get the GWO–DBN model. The experiments were conducted on GTZAN and free music archive (FMA) datasets. It was found that the best hidden-layer structure of DBN was 1440-960-480-300. Compared with machine learning methods such as decision trees, the DBN model had better classification performance in recognizing and classifying music types. The classification accuracy of the GWO–DBN model reached 75.67%. The experimental results demonstrate the reliability of the GWO–DBN model. The GWO–DBN model can be further promoted and applied in actual music research.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49391897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1142/s1469026823500165
Cu Vinh Loc, Truong Xuan Viet, Tran Hoang Viet, Le Hoang Thao, Nguyen Hoang Viet
In recent years, with the strong and outstanding development of the Internet, the need to refer to the feedback of previous customers when shopping online is increasing. Therefore, websites are developed to allow users to share experiences, reviews, comments and feedback about the services and products of businesses and organizations. The organizations also collect user feedback about their products and services to give better directions. However, with a large amount of user feedback about certain services and products, it is difficult for users, businesses, and organizations to pay attention to them all. Thus, an automatic system is necessary to analyze the sentiment of a customer feedback. Recently, the well-known pre-trained language models for Vietnamese (PhoBERT) achieved high performance in comparison with other approaches. However, this method may not focus on the local information in the text like phrases or fragments. In this paper, we propose a Convolutional Neural Network (CNN) model based on PhoBERT for sentiment classification. The output of contextualized embeddings of the PhoBERT’s last four layers is fed into the CNN. This makes the network capable of obtaining more local information from the sentiment. Besides, the PhoBERT output is also given to the transformer encoder layers in order to employ the self-attention technique, and this also makes the model more focused on the important information of the sentiment segments. The experimental results demonstrate that the proposed approach gives competitive performance compared to the existing studies on three public datasets with the opinions of Vietnamese people.
{"title":"Pre-Trained Language Model-Based Deep Learning for Sentiment Classification of Vietnamese Feedback","authors":"Cu Vinh Loc, Truong Xuan Viet, Tran Hoang Viet, Le Hoang Thao, Nguyen Hoang Viet","doi":"10.1142/s1469026823500165","DOIUrl":"https://doi.org/10.1142/s1469026823500165","url":null,"abstract":"In recent years, with the strong and outstanding development of the Internet, the need to refer to the feedback of previous customers when shopping online is increasing. Therefore, websites are developed to allow users to share experiences, reviews, comments and feedback about the services and products of businesses and organizations. The organizations also collect user feedback about their products and services to give better directions. However, with a large amount of user feedback about certain services and products, it is difficult for users, businesses, and organizations to pay attention to them all. Thus, an automatic system is necessary to analyze the sentiment of a customer feedback. Recently, the well-known pre-trained language models for Vietnamese (PhoBERT) achieved high performance in comparison with other approaches. However, this method may not focus on the local information in the text like phrases or fragments. In this paper, we propose a Convolutional Neural Network (CNN) model based on PhoBERT for sentiment classification. The output of contextualized embeddings of the PhoBERT’s last four layers is fed into the CNN. This makes the network capable of obtaining more local information from the sentiment. Besides, the PhoBERT output is also given to the transformer encoder layers in order to employ the self-attention technique, and this also makes the model more focused on the important information of the sentiment segments. The experimental results demonstrate that the proposed approach gives competitive performance compared to the existing studies on three public datasets with the opinions of Vietnamese people.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48096819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1142/s1469026823500141
Shaopeng Yu, Liyuan Dong, Fengyuan Pang
In order to detect and identify fire accidents accurately and efficiently, an intelligent fire identification system based on neural network algorithm is designed, which can overcome the shortcomings of single information, complex wiring, poor adaptability, etc. The characteristic extraction of sensors is adopted in the information layer to solve the problems in multi-sensor fusion. The fire data are transmitted to the main controller through LoRa wireless module and fused by back propagation neural network, which is self-learning and adaptive. The output of neural network and fuzzy inference with other factors are used for decision criteria to improve the identification accuracy. The common combustibles and various interference sources are selected for fire tests. The result shows that the detection accuracy is up to 100% and the false alarm rate is lower than 0.1%, meanwhile, the system has the advantages of fast response and high detection efficiency.
{"title":"Study of Intelligent Fire Identification System Based on Back Propagation Neural Network","authors":"Shaopeng Yu, Liyuan Dong, Fengyuan Pang","doi":"10.1142/s1469026823500141","DOIUrl":"https://doi.org/10.1142/s1469026823500141","url":null,"abstract":"In order to detect and identify fire accidents accurately and efficiently, an intelligent fire identification system based on neural network algorithm is designed, which can overcome the shortcomings of single information, complex wiring, poor adaptability, etc. The characteristic extraction of sensors is adopted in the information layer to solve the problems in multi-sensor fusion. The fire data are transmitted to the main controller through LoRa wireless module and fused by back propagation neural network, which is self-learning and adaptive. The output of neural network and fuzzy inference with other factors are used for decision criteria to improve the identification accuracy. The common combustibles and various interference sources are selected for fire tests. The result shows that the detection accuracy is up to 100% and the false alarm rate is lower than 0.1%, meanwhile, the system has the advantages of fast response and high detection efficiency.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47987788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1142/s1469026823500189
Khalid A. Salman, Khalid Shaker, Sufyan T. Faraj Al-Janabi
Nowadays, images have become one of the most popular forms of communication as image editing tools have evolved. Image manipulation, particularly image colorization, has become easier, making it harder to differentiate between fake colorized images and actual images. Furthermore, the RGB space is no longer considered to be the best option for color-based detection techniques due to the high correlation between channels and its blending of luminance and chrominance information. This paper proposes a new approach for fake colorized image detection based on a novel image representation created by combining color information from three separate color spaces (HSV, Lab, and Ycbcr) and selecting the most different channels from each color space to reconstruct the image. Features from the proposed image representation are extracted based on transfer learning using the pre-trained CNNs ResNet50 model. The Support Vector Machine (SVM) approach has been used for classification purposes due to its high ability for generalization. Our experiments indicate that our proposed models outperform other state-of-the-art fake colorized image detection methods in several aspects.
{"title":"Fake Colorized Image Detection Based on Special Image Representation and Transfer Learning","authors":"Khalid A. Salman, Khalid Shaker, Sufyan T. Faraj Al-Janabi","doi":"10.1142/s1469026823500189","DOIUrl":"https://doi.org/10.1142/s1469026823500189","url":null,"abstract":"Nowadays, images have become one of the most popular forms of communication as image editing tools have evolved. Image manipulation, particularly image colorization, has become easier, making it harder to differentiate between fake colorized images and actual images. Furthermore, the RGB space is no longer considered to be the best option for color-based detection techniques due to the high correlation between channels and its blending of luminance and chrominance information. This paper proposes a new approach for fake colorized image detection based on a novel image representation created by combining color information from three separate color spaces (HSV, Lab, and Ycbcr) and selecting the most different channels from each color space to reconstruct the image. Features from the proposed image representation are extracted based on transfer learning using the pre-trained CNNs ResNet50 model. The Support Vector Machine (SVM) approach has been used for classification purposes due to its high ability for generalization. Our experiments indicate that our proposed models outperform other state-of-the-art fake colorized image detection methods in several aspects.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49606854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1142/s1469026823500190
Min Li, Bangyu Wu, Shaobo Deng, Mingzhu Lou
Tumor classification based on microarray gene expression data is easy to fall into overfitting because such data are composed of many irrelevant, redundant, and noisy genes. Traditional gene selection methods cannot achieve satisfactory classification results. In this study, we propose a novel multi-target hybrid gene selection method named RMOGA (ReliefF Multi-Objective Genetic Algorithm), which aims to select a few genes and obtain good tumor recognition accuracy. RMOGA consists of two phases. Firstly, ReliefF is used to select the top 5% subset of genes from the original datasets. Secondly, a multi-objective genetic algorithm searches for the optimal gene subset from the gene subset obtained by the ReliefF method. To verify the validity of RMOGA, we conducted extensive experiments on 11 available microarray datasets and compared the proposed method with other previous methods. Two classical classifiers including Naive Bayes and Support Vector Machine were used to measure the classification performance of all comparison methods. Experimental results show that the RMOGA algorithm can yield significantly better results than previous state-of-the-art methods in terms of classification accuracy and the number of selected genes.
{"title":"A New Multi-objective Hybrid Gene Selection Algorithm for Tumor Classification Based on Microarray Gene Expression Data","authors":"Min Li, Bangyu Wu, Shaobo Deng, Mingzhu Lou","doi":"10.1142/s1469026823500190","DOIUrl":"https://doi.org/10.1142/s1469026823500190","url":null,"abstract":"Tumor classification based on microarray gene expression data is easy to fall into overfitting because such data are composed of many irrelevant, redundant, and noisy genes. Traditional gene selection methods cannot achieve satisfactory classification results. In this study, we propose a novel multi-target hybrid gene selection method named RMOGA (ReliefF Multi-Objective Genetic Algorithm), which aims to select a few genes and obtain good tumor recognition accuracy. RMOGA consists of two phases. Firstly, ReliefF is used to select the top 5% subset of genes from the original datasets. Secondly, a multi-objective genetic algorithm searches for the optimal gene subset from the gene subset obtained by the ReliefF method. To verify the validity of RMOGA, we conducted extensive experiments on 11 available microarray datasets and compared the proposed method with other previous methods. Two classical classifiers including Naive Bayes and Support Vector Machine were used to measure the classification performance of all comparison methods. Experimental results show that the RMOGA algorithm can yield significantly better results than previous state-of-the-art methods in terms of classification accuracy and the number of selected genes.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47734994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1142/s1469026823500219
Qianying Wang, Jie Tian, Meng Li, Ming Lu
In this paper, we proposed a deep fusion model for telephone comments recognition, named CNN-BiGRU. Traditionally, the most used algorithms in text classification are Convolutional Neural Network (CNN), Long and Short Term Memory (LSTM) and Bi-Gated Recurrent Neural Network (BiGRU). For CNN, it can extract the feature form the neighbors, and a softmax layer is followed for classification. The global feature is not included in the CNN model. LSTM introduces the gate, which can capture the information before the node. BiGRU is developed from LSTM, and it can find the features in the context. So compared to LSTM, BiGRU not only includes the information before, but also can capture the following features. Thus, LSTM and BiGRU can extract the global features, but cannot capture the local features. In order to deal with this weakness, we proposed a fusion model for comments classification, which combines the CNN and BiGRU in our model. Different from other methods, CNN and BiGRU are parallelly connected. CNN model can extract the local feature, and BiGRU can find the global feature. Then we concatenate the two kinds of features and feed to recognition layer for classification. Then we use our model to classify the telephone comments; compared with the traditional machine SVM and tow deep neural models — CNN and BiGRU — our model performed better.
{"title":"Text Classification Based on CNN-BiGRU and Its Application in Telephone Comments Recognition","authors":"Qianying Wang, Jie Tian, Meng Li, Ming Lu","doi":"10.1142/s1469026823500219","DOIUrl":"https://doi.org/10.1142/s1469026823500219","url":null,"abstract":"In this paper, we proposed a deep fusion model for telephone comments recognition, named CNN-BiGRU. Traditionally, the most used algorithms in text classification are Convolutional Neural Network (CNN), Long and Short Term Memory (LSTM) and Bi-Gated Recurrent Neural Network (BiGRU). For CNN, it can extract the feature form the neighbors, and a softmax layer is followed for classification. The global feature is not included in the CNN model. LSTM introduces the gate, which can capture the information before the node. BiGRU is developed from LSTM, and it can find the features in the context. So compared to LSTM, BiGRU not only includes the information before, but also can capture the following features. Thus, LSTM and BiGRU can extract the global features, but cannot capture the local features. In order to deal with this weakness, we proposed a fusion model for comments classification, which combines the CNN and BiGRU in our model. Different from other methods, CNN and BiGRU are parallelly connected. CNN model can extract the local feature, and BiGRU can find the global feature. Then we concatenate the two kinds of features and feed to recognition layer for classification. Then we use our model to classify the telephone comments; compared with the traditional machine SVM and tow deep neural models — CNN and BiGRU — our model performed better.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43834930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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