Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574678
Xinxin Zhou, Zhirui Gao, Xueting Yi, Daheng Lin
Aiming at the problem of low accuracy of the Chicken Swarm Optimization Algorithm and falling into the local optimum easily, a self-adaptive dynamic distribution Chicken Swarm Optimization (DCSO) is proposed. Firstly, a dynamic weight strategy is proposed to solve the problem of reduced algorithm accuracy; Secondly, the learning factor of normal distribution is used to solve the problem that the algorithm is easy to fall into the local optimum; Finally, 16 benchmark functions are used to test the performance of the algorithm. And the experimental results show that the improved Chicken Swarm Optimization has better solution accuracy and it can jump out of the local optimum.
{"title":"Chicken Swarm Optimization Algorithm Based on Adaptive Dynamic Distribution","authors":"Xinxin Zhou, Zhirui Gao, Xueting Yi, Daheng Lin","doi":"10.1109/CICN51697.2021.9574678","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574678","url":null,"abstract":"Aiming at the problem of low accuracy of the Chicken Swarm Optimization Algorithm and falling into the local optimum easily, a self-adaptive dynamic distribution Chicken Swarm Optimization (DCSO) is proposed. Firstly, a dynamic weight strategy is proposed to solve the problem of reduced algorithm accuracy; Secondly, the learning factor of normal distribution is used to solve the problem that the algorithm is easy to fall into the local optimum; Finally, 16 benchmark functions are used to test the performance of the algorithm. And the experimental results show that the improved Chicken Swarm Optimization has better solution accuracy and it can jump out of the local optimum.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121068650","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574665
Juan Carlos Rivera Rado, C. Rodriguez
The Business Intelligence (BI) tool is a solution that allows organizations to access information that enables them to address and support the complex process of decision making with multiple criteria. We present the results of implementing Business Intelligence tools to contribute to the electrical industry in this article. This article aims to present the Business Intelligence tools that can contribute to the electrical industry, using the literature review as a methodology. As a result, 170 potential articles were obtained. From these articles, 20 were selected as they will be helpful for the development of a Business Intelligence tool to solve the problem of decision making in an electricity distribution company. The conclusion is that the Business Intelligence tools implemented in the industries offer promising proposals and benefits and can be applied in the electrical industry.
{"title":"Business Intelligence Tools Implementing in the Field of Electrical Industry","authors":"Juan Carlos Rivera Rado, C. Rodriguez","doi":"10.1109/CICN51697.2021.9574665","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574665","url":null,"abstract":"The Business Intelligence (BI) tool is a solution that allows organizations to access information that enables them to address and support the complex process of decision making with multiple criteria. We present the results of implementing Business Intelligence tools to contribute to the electrical industry in this article. This article aims to present the Business Intelligence tools that can contribute to the electrical industry, using the literature review as a methodology. As a result, 170 potential articles were obtained. From these articles, 20 were selected as they will be helpful for the development of a Business Intelligence tool to solve the problem of decision making in an electricity distribution company. The conclusion is that the Business Intelligence tools implemented in the industries offer promising proposals and benefits and can be applied in the electrical industry.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325816","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574641
Rahul Gupta, Syed Sameer, Harsha Muppavarapu, M. Enduri, Satish Anamalamudi
The impact of online reviews on restaurants has reached to unprecedented level where vast number of people are checking posted opinions/reviews prior to ordering their food deliveries. The two main concepts used in the online reviews are sentiment analysis and exploratory data analysis (EDA). The goal of sentimental analysis is to determine whether the given data is positive, negative or neutral. It can help brands to determine how their product is perceived by their clientele. Sentiment analysis, otherwise known as opinion mining, works thanks to natural language processing and machine learning algorithms, to automatically determine the emotional tone behind online conversations. Sentiment analysis mainly relies on the keywords. The overall analysis is made on the data that has been reviewed on Zomato. Most restaurants available on the applications are established ones, hence we get a good idea regarding the restaurants of Hyderabad. Exploratory data analysis (EDA) is a term for certain kinds of initial analysis and findings done with data sets, usually early in an analytical process.
{"title":"Sentiment Analysis on Zomato Reviews","authors":"Rahul Gupta, Syed Sameer, Harsha Muppavarapu, M. Enduri, Satish Anamalamudi","doi":"10.1109/CICN51697.2021.9574641","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574641","url":null,"abstract":"The impact of online reviews on restaurants has reached to unprecedented level where vast number of people are checking posted opinions/reviews prior to ordering their food deliveries. The two main concepts used in the online reviews are sentiment analysis and exploratory data analysis (EDA). The goal of sentimental analysis is to determine whether the given data is positive, negative or neutral. It can help brands to determine how their product is perceived by their clientele. Sentiment analysis, otherwise known as opinion mining, works thanks to natural language processing and machine learning algorithms, to automatically determine the emotional tone behind online conversations. Sentiment analysis mainly relies on the keywords. The overall analysis is made on the data that has been reviewed on Zomato. Most restaurants available on the applications are established ones, hence we get a good idea regarding the restaurants of Hyderabad. Exploratory data analysis (EDA) is a term for certain kinds of initial analysis and findings done with data sets, usually early in an analytical process.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116358006","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574668
S. Sridhar, Sowmya Sanagavarapu
The identification of failures and defects in industrial machines has proven to be a challenge to gauge their warranty and performance. Depreciation in industrial machines occurs due to several factors, most commonly- tool wear, strain, heat and power failure. In this paper, the development of machine learning algorithms for the prediction of machine failures is done. A synthesized dataset was used in the predictive maintenance model, that reflects real-time failures encountered in the industries. The class data imbalance hinders the performance of machine learning algorithms and this is handled by evaluating SMOTE-based oversampling techniques. By using SMOTE technique, a 7.83 % increase in the AUC score is observed, thereby improving the performance of the Random Forest classifier in distinguishing the instances of non-failure and machine failures.
{"title":"Handling Data Imbalance in Predictive Maintenance for Machines using SMOTE-based Oversampling","authors":"S. Sridhar, Sowmya Sanagavarapu","doi":"10.1109/CICN51697.2021.9574668","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574668","url":null,"abstract":"The identification of failures and defects in industrial machines has proven to be a challenge to gauge their warranty and performance. Depreciation in industrial machines occurs due to several factors, most commonly- tool wear, strain, heat and power failure. In this paper, the development of machine learning algorithms for the prediction of machine failures is done. A synthesized dataset was used in the predictive maintenance model, that reflects real-time failures encountered in the industries. The class data imbalance hinders the performance of machine learning algorithms and this is handled by evaluating SMOTE-based oversampling techniques. By using SMOTE technique, a 7.83 % increase in the AUC score is observed, thereby improving the performance of the Random Forest classifier in distinguishing the instances of non-failure and machine failures.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122303963","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 : 2021-09-22DOI: 10.1109/CICN51697.2021.9574691
Ranjana Sikarwar, H. K. Shakya, S. Singh
Social network-based applications like Facebook, Twitter, and Instagram have been used by people of all age groups and backgrounds for the last few years. It is a rich platform for sharing knowledge amongst users online. This information is shared as feelings, opinions, interests, events, or comments in large volumes and varied forms of data. Many multidisciplinary researchers have conducted studies to find out the commercial values of social media data. The reason behind this interest in research is an affluence to access data from the web, process it, and pull-out useful information from the web. Researchers have worked upon and explored the topics like information spreading, relationship analysis in groups for some or other applications. This review paper conducts a survey on community detection problem in social networks, its analysis, and a study of research done on related areas.
{"title":"A Review on Social Network Analysis Methods and Algorithms","authors":"Ranjana Sikarwar, H. K. Shakya, S. Singh","doi":"10.1109/CICN51697.2021.9574691","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574691","url":null,"abstract":"Social network-based applications like Facebook, Twitter, and Instagram have been used by people of all age groups and backgrounds for the last few years. It is a rich platform for sharing knowledge amongst users online. This information is shared as feelings, opinions, interests, events, or comments in large volumes and varied forms of data. Many multidisciplinary researchers have conducted studies to find out the commercial values of social media data. The reason behind this interest in research is an affluence to access data from the web, process it, and pull-out useful information from the web. Researchers have worked upon and explored the topics like information spreading, relationship analysis in groups for some or other applications. This review paper conducts a survey on community detection problem in social networks, its analysis, and a study of research done on related areas.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123758908","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：