Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environment. It integrates CNN for local feature extraction, RNN for global feature extraction, and attention mechanism for feature scaling. As a result, it can acquire the correct meaning of sentences. After experimenting with the hotel review dataset, it has an improvement in positive feeling classification compared with the baseline model (3%~13%), and it showed a competitive performance compared with ordinary deep learning models (~1%). On negative feeling classification, it also performed well close to other models.
{"title":"RA-CNN: A Semantic-Enhanced Method in a Multi-Semantic Environment","authors":"Zhiwei Zhan, Guoliang Liao, Xiang Ren, Guangsi Xiong, Weilin Zhou, Wenchao Jiang, Hong Xiao","doi":"10.4018/ijssci.311446","DOIUrl":"https://doi.org/10.4018/ijssci.311446","url":null,"abstract":"Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environment. It integrates CNN for local feature extraction, RNN for global feature extraction, and attention mechanism for feature scaling. As a result, it can acquire the correct meaning of sentences. After experimenting with the hotel review dataset, it has an improvement in positive feeling classification compared with the baseline model (3%~13%), and it showed a competitive performance compared with ordinary deep learning models (~1%). On negative feeling classification, it also performed well close to other models.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124274702","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}
Many software reliability algorithms have been used to predict and approximate the reliability of software. One general expectation of these traditional algorithms is to predict the fault and automatically delete the observed faults. This presumption will not be reasonable in practice and may not always exist. In this paper, the various algorithms have been used such as probabilistic neural network (PNN), generalized neural network (GRNN), linear regression, support vector machine (SVM), bagging, decision trees (DTs), and k-nearest neighbor (KNN) to measure the accuracy of various data and comparison has been done. The proposed algorithm has been used for predicting the reliability of software and the algorithms have been implemented to check the accuracy while using different machine learning (ML) techniques. Experimental studies based on actual failure evidence indicate that the proposed algorithm can more effectively explain the change in failure data and predict the software development behavior than conventional techniques.
{"title":"Fault-Tolerant Algorithm for Software Preduction Using Machine Learning Techniques","authors":"Jullius Kumar, D. Gupta, L. S. Umrao","doi":"10.4018/ijssci.309425","DOIUrl":"https://doi.org/10.4018/ijssci.309425","url":null,"abstract":"Many software reliability algorithms have been used to predict and approximate the reliability of software. One general expectation of these traditional algorithms is to predict the fault and automatically delete the observed faults. This presumption will not be reasonable in practice and may not always exist. In this paper, the various algorithms have been used such as probabilistic neural network (PNN), generalized neural network (GRNN), linear regression, support vector machine (SVM), bagging, decision trees (DTs), and k-nearest neighbor (KNN) to measure the accuracy of various data and comparison has been done. The proposed algorithm has been used for predicting the reliability of software and the algorithms have been implemented to check the accuracy while using different machine learning (ML) techniques. Experimental studies based on actual failure evidence indicate that the proposed algorithm can more effectively explain the change in failure data and predict the software development behavior than conventional techniques.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117306276","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}
Ashish Tripathi, Ashutosh Kumar Singh, Adarsh Singh, Arjun Choudhary, K. Pareek, K. Mishra
Skin disease is one of the major concerns for clinicians and researchers. Fungus, germs, allergies, and viruses are the main causes of skin diseases. There has always been unsaid competition between conventional and advanced computing-based techniques, and with these new techniques, cost of treatment is also being reduced drastically. In this paper, a deep learning-based model named eXtended Convolutional Neural Network (XCNN) has been proposed to classify three types of skin diseases (i.e., acne, rosacea, and melanoma). XCNN is easy-to-use, economic, and accurate. It will help clinicians to identify and categorize such diseases at the initial stage through automated screening. The proposed work is designed for multi-classification that takes digital images and applies XCNN to identify the type of disease. The model has been built on the dataset of the various skin disease images. It gives 95.67% accuracy in recognizing the diseases with improved recall, f1-score, and precision values compared to other state-of-the-art models.
{"title":"Analyzing Skin Disease Using XCNN (eXtended Convolutional Neural Network)","authors":"Ashish Tripathi, Ashutosh Kumar Singh, Adarsh Singh, Arjun Choudhary, K. Pareek, K. Mishra","doi":"10.4018/ijssci.309708","DOIUrl":"https://doi.org/10.4018/ijssci.309708","url":null,"abstract":"Skin disease is one of the major concerns for clinicians and researchers. Fungus, germs, allergies, and viruses are the main causes of skin diseases. There has always been unsaid competition between conventional and advanced computing-based techniques, and with these new techniques, cost of treatment is also being reduced drastically. In this paper, a deep learning-based model named eXtended Convolutional Neural Network (XCNN) has been proposed to classify three types of skin diseases (i.e., acne, rosacea, and melanoma). XCNN is easy-to-use, economic, and accurate. It will help clinicians to identify and categorize such diseases at the initial stage through automated screening. The proposed work is designed for multi-classification that takes digital images and applies XCNN to identify the type of disease. The model has been built on the dataset of the various skin disease images. It gives 95.67% accuracy in recognizing the diseases with improved recall, f1-score, and precision values compared to other state-of-the-art models.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130241570","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}
Smart cities and smart homes have a larger number of devices requiring continuous electricity. An inverter circuit is often used to cope up with power failures. Such inverters can also be used in the period of high demand for power to reduce the load on the grid. On the other hand, utilizing renewable energy is an important aspect of sustainable development. A solar-powered inverter reduces the usage of grid power and makes efficient utilization of solar energy. Further, the inverter can be integrated with microcontrollers to work on predetermined time slots to substitute the grid power. This paper describes the design of a novel solar-powered smart inverter that automatically switches the power supply from the grid to the inverter during peak hours. It is designed to suit smart home requirements up to 1 kW and a holistic design is presented. The performance of the circuit is analyzed and compared with similar works in literature to show the improvements. Simulations and hardware implementations show that the proposed system ensures an uninterrupted power supply for smart homes.
{"title":"Powering Up an IoT-Enabled Smart Home: A Solar Powered Smart Inverter for Sustainable Development","authors":"Sarath Madhu, Sooraj Padunnavalappil, Prarthana Puthenpurayil Saajlal, Vipindev Adat Vasudevan, J. Mathew","doi":"10.4018/ijssci.300362","DOIUrl":"https://doi.org/10.4018/ijssci.300362","url":null,"abstract":"Smart cities and smart homes have a larger number of devices requiring continuous electricity. An inverter circuit is often used to cope up with power failures. Such inverters can also be used in the period of high demand for power to reduce the load on the grid. On the other hand, utilizing renewable energy is an important aspect of sustainable development. A solar-powered inverter reduces the usage of grid power and makes efficient utilization of solar energy. Further, the inverter can be integrated with microcontrollers to work on predetermined time slots to substitute the grid power. This paper describes the design of a novel solar-powered smart inverter that automatically switches the power supply from the grid to the inverter during peak hours. It is designed to suit smart home requirements up to 1 kW and a holistic design is presented. The performance of the circuit is analyzed and compared with similar works in literature to show the improvements. Simulations and hardware implementations show that the proposed system ensures an uninterrupted power supply for smart homes.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126470502","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}
Smartphone use has expanded dramatically in recent years, particularly for Android-based smartphones, due to their wide availability and competitive pricing compared to non-Android devices. The significant increase in the use of Android applications has resulted in a spike in the number of malicious applications, which represent a severe danger to user privacy. In this paper, the authors proposed FWA-FS, a novel method for Android malware detection with feature selection based on the fireworks algorithm. Static analysis is used in the proposed technique to classify applications as benign or malicious. To describe applications, they employ permissions derived from APK files as feature vectors. The most important features were then chosen using the proposed FWA-FS method. Finally, to develop classification models, different machine learning algorithms were trained using specified features. According to experimental findings, the suggested strategy can greatly enhance classification performance with an average increase of 6% and 25% in accuracy for KNN and Naïve Bayes respectively.
{"title":"A New Wrapper-Based Feature Selection Technique with Fireworks Algorithm for Android Malware Detection","authors":"Mohamed Guendouz, Abdelmalek Amine","doi":"10.4018/ijssci.312554","DOIUrl":"https://doi.org/10.4018/ijssci.312554","url":null,"abstract":"Smartphone use has expanded dramatically in recent years, particularly for Android-based smartphones, due to their wide availability and competitive pricing compared to non-Android devices. The significant increase in the use of Android applications has resulted in a spike in the number of malicious applications, which represent a severe danger to user privacy. In this paper, the authors proposed FWA-FS, a novel method for Android malware detection with feature selection based on the fireworks algorithm. Static analysis is used in the proposed technique to classify applications as benign or malicious. To describe applications, they employ permissions derived from APK files as feature vectors. The most important features were then chosen using the proposed FWA-FS method. Finally, to develop classification models, different machine learning algorithms were trained using specified features. According to experimental findings, the suggested strategy can greatly enhance classification performance with an average increase of 6% and 25% in accuracy for KNN and Naïve Bayes respectively.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122353848","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}
Angelin Gladston, I. ArjunSharmaa, G. BagirathanS.S.K.
Gross domestic product is the main measure used predominantly for assessing the wealth and growth of a country. Previous works used the amount of CO2 emitted by a country in predicting the gross domestic product growth of that quarter. Though it is a valid indicator, there are many other features that can be considered while calculating the gross domestic product of a country. In this paper, an approach to predict gross domestic product utilizing many features is introduced. Macroeconomic data like unemployment rate, gold rate, foreign exchange rate, and other important data to plot the graph are used for linear regression, employing dimensionality reduction to analyze and extract only the important features and thereby increasing the effectiveness of the proposed GDP prediction. Since data has been published in different time intervals, preprocessing like interpolation, reshaping, and dimensionality reduction using PCA are carried out to make the proposed GDP prediction model more precise and accurate, and the maximum accuracy of 95% is obtained.
{"title":"Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data","authors":"Angelin Gladston, I. ArjunSharmaa, G. BagirathanS.S.K.","doi":"10.4018/ijssci.312561","DOIUrl":"https://doi.org/10.4018/ijssci.312561","url":null,"abstract":"Gross domestic product is the main measure used predominantly for assessing the wealth and growth of a country. Previous works used the amount of CO2 emitted by a country in predicting the gross domestic product growth of that quarter. Though it is a valid indicator, there are many other features that can be considered while calculating the gross domestic product of a country. In this paper, an approach to predict gross domestic product utilizing many features is introduced. Macroeconomic data like unemployment rate, gold rate, foreign exchange rate, and other important data to plot the graph are used for linear regression, employing dimensionality reduction to analyze and extract only the important features and thereby increasing the effectiveness of the proposed GDP prediction. Since data has been published in different time intervals, preprocessing like interpolation, reshaping, and dimensionality reduction using PCA are carried out to make the proposed GDP prediction model more precise and accurate, and the maximum accuracy of 95% is obtained.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128149606","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}
Sign language, often termed “dactylology,” is a mode of communication for those who are hard of hearing. With over 2.5 billion people projected to have hearing loss by 2050, there are very few efficient real-time sign language translation (SLT) applications present today despite extensive research in the domain. The main purpose of the systematic literature review is to analyze existing research in SLT systems and obtain results that will help in building an efficient and improved SLT system. A total of 125 different research articles within the time frame of 2015–2022 were identified. The study analyzes each paper against nine main research questions. The results obtained show the unique strengths and weaknesses of the different methods used, and while the reviewed papers showed significant results, there is still room for improvement in the implementations. This systematic literature review helps in identifying suitable methods to develop an efficient SLT application, identifies research gaps in this domain, and simultaneously indicates recent trends in the field of SLT systems.
{"title":"Sign Language Translation Systems: A Systematic Literature Review","authors":"Ankith Boggaram, Aaptha Boggaram, Aryan Sharma, Ashwin Srinivasa Ramanujan, R. Bharathi","doi":"10.4018/ijssci.311448","DOIUrl":"https://doi.org/10.4018/ijssci.311448","url":null,"abstract":"Sign language, often termed “dactylology,” is a mode of communication for those who are hard of hearing. With over 2.5 billion people projected to have hearing loss by 2050, there are very few efficient real-time sign language translation (SLT) applications present today despite extensive research in the domain. The main purpose of the systematic literature review is to analyze existing research in SLT systems and obtain results that will help in building an efficient and improved SLT system. A total of 125 different research articles within the time frame of 2015–2022 were identified. The study analyzes each paper against nine main research questions. The results obtained show the unique strengths and weaknesses of the different methods used, and while the reviewed papers showed significant results, there is still room for improvement in the implementations. This systematic literature review helps in identifying suitable methods to develop an efficient SLT application, identifies research gaps in this domain, and simultaneously indicates recent trends in the field of SLT systems.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130899369","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}
Since COVID-19 was released, online education has taken center stage. Educational performance analysis is a central topic in virtual classrooms and across the spectrum of academic institutions. This research analyzed students' studies in virtual learning using many machine-learning classifiers, which include LogitBoost, Logistic Regression, J48, OneR, Multilayer Perceptron, and Naive Bayes, to find the ideal one that produces the best outcomes. This research evaluates algorithms based on recall, precision, and f-measure to determine their efficacy. Accordingly, the authors try to perform a comparative analysis of the algorithms in this research by employing two distinct test models: the use of training sets and the 10 cross-fold models. The research results demonstrate that the training set model outperforms the 10 cross-fold model. The findings demonstrate that the multilayer perceptron classifier utilizing the use training set model performs much better in terms of predicting student study in virtual learning.
{"title":"Analysis of Student Study of Virtual Learning Using Machine Learning Techniques","authors":"Neha Singh, U. C. Jaiswal","doi":"10.4018/ijssci.309995","DOIUrl":"https://doi.org/10.4018/ijssci.309995","url":null,"abstract":"Since COVID-19 was released, online education has taken center stage. Educational performance analysis is a central topic in virtual classrooms and across the spectrum of academic institutions. This research analyzed students' studies in virtual learning using many machine-learning classifiers, which include LogitBoost, Logistic Regression, J48, OneR, Multilayer Perceptron, and Naive Bayes, to find the ideal one that produces the best outcomes. This research evaluates algorithms based on recall, precision, and f-measure to determine their efficacy. Accordingly, the authors try to perform a comparative analysis of the algorithms in this research by employing two distinct test models: the use of training sets and the 10 cross-fold models. The research results demonstrate that the training set model outperforms the 10 cross-fold model. The findings demonstrate that the multilayer perceptron classifier utilizing the use training set model performs much better in terms of predicting student study in virtual learning.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123250042","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}
A brain tumor is an abnormal development of cells in the brain that are either benign or malignant. Magnetic resonance imaging (MRI) is used to identify tumors. Manual evaluation of brain tumors from MRI images by a radiologist is a challenging task. Hence, this paper proposes VGG-19 Convolutional Neural Networks (CNN)-based deep learning model for the classification of brain tumors. Initially, in the proposed model, contrast stretching technique is employed for noises removal. Next, a deep neural network is employed for rich feature extract. Further, these learning features are combined with classifier models of CNN for training and validation. performance analysis of the proposed methodology and experiments have been carried out using publicly available MRI images in Figshare dataset of 3064 slices from 233 subjects. The proposed model has achieved 99.83% accuracy. Moreover, the proposed model obtained precision 96.32%, 98.26%, and 98.56%, recall of 97.82%, 98.62%, 98.87%, and specificity of 98.72%, 99.51%, and 99.43% for the Glioma, Meningioma, and Pituitary tumors respectively.
{"title":"CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images","authors":"Anil Kumar Mandle, S. Sahu, Govind P. Gupta","doi":"10.4018/ijssci.304438","DOIUrl":"https://doi.org/10.4018/ijssci.304438","url":null,"abstract":"A brain tumor is an abnormal development of cells in the brain that are either benign or malignant. Magnetic resonance imaging (MRI) is used to identify tumors. Manual evaluation of brain tumors from MRI images by a radiologist is a challenging task. Hence, this paper proposes VGG-19 Convolutional Neural Networks (CNN)-based deep learning model for the classification of brain tumors. Initially, in the proposed model, contrast stretching technique is employed for noises removal. Next, a deep neural network is employed for rich feature extract. Further, these learning features are combined with classifier models of CNN for training and validation. performance analysis of the proposed methodology and experiments have been carried out using publicly available MRI images in Figshare dataset of 3064 slices from 233 subjects. The proposed model has achieved 99.83% accuracy. Moreover, the proposed model obtained precision 96.32%, 98.26%, and 98.56%, recall of 97.82%, 98.62%, 98.87%, and specificity of 98.72%, 99.51%, and 99.43% for the Glioma, Meningioma, and Pituitary tumors respectively.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130902583","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}
The distributed publication and subscription for the Internet of Things is a model of communication between devices that is simple and powerful. In comparison with other variant problems of ME, the problem considered here is a group mutual exclusion problem. The specificity of an IoT system is that a process can be in more than one group at the same time which is not the case of the algorithms mentioned in the literature where a process request one group in advance for each request. In this paper, we define formally the notion of group. Furthermore, we propose a distributed algorithm for automatic group generation and we will show that this problem is maximal cliques’ problem. This leads us to a new kind of distributed Maximal cliques algorithm to compute the groups suitable for IoT systems. As an application, we propose an IoT-based intersection traffic light management system for vehicles.
{"title":"A Distributed Algorithm for Computing Groups in IoT Systems","authors":"Zine El Abidine Bouneb","doi":"10.4018/ijssci.300363","DOIUrl":"https://doi.org/10.4018/ijssci.300363","url":null,"abstract":"The distributed publication and subscription for the Internet of Things is a model of communication between devices that is simple and powerful. In comparison with other variant problems of ME, the problem considered here is a group mutual exclusion problem. The specificity of an IoT system is that a process can be in more than one group at the same time which is not the case of the algorithms mentioned in the literature where a process request one group in advance for each request. In this paper, we define formally the notion of group. Furthermore, we propose a distributed algorithm for automatic group generation and we will show that this problem is maximal cliques’ problem. This leads us to a new kind of distributed Maximal cliques algorithm to compute the groups suitable for IoT systems. As an application, we propose an IoT-based intersection traffic light management system for vehicles.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120911505","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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