Pub Date : 2021-09-22DOI: 10.1109/CICN51697.2021.9574696
Shuai Lv, Lele Wu, Zeyu Li, Qi Xu, Xinxin Zhou
With the improvement of people's economic level, many families have already owned compact cars. As a convenient tool for daily travel, cars are important private property, thus the safety of car is a problem that can't be ignored. And the design in this article adopts the working mode that combines single chip microcomputer with App. Employing 52 single chip microcomputer as detection center and control core of the antitheft system, new automobile has two kinds of unlocking method: password and fingerprint. Between them, fingerprint unlocking is safe and quick, which realizes Internet of things through communication module, enabling users to receive accurate and real-time security information of the car on APP.
{"title":"Intelligent Car Security System Based on Fingerprint Identification and Internet of Things","authors":"Shuai Lv, Lele Wu, Zeyu Li, Qi Xu, Xinxin Zhou","doi":"10.1109/CICN51697.2021.9574696","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574696","url":null,"abstract":"With the improvement of people's economic level, many families have already owned compact cars. As a convenient tool for daily travel, cars are important private property, thus the safety of car is a problem that can't be ignored. And the design in this article adopts the working mode that combines single chip microcomputer with App. Employing 52 single chip microcomputer as detection center and control core of the antitheft system, new automobile has two kinds of unlocking method: password and fingerprint. Between them, fingerprint unlocking is safe and quick, which realizes Internet of things through communication module, enabling users to receive accurate and real-time security information of the car on APP.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"187 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":"128048976","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/CICN51697.2021.9574667
G. Soni
Researchers have looked at multiuser detection as a way to improve OCDMA connection performance over single-user detection. When transmitting binary data via an optical fibre, everyone uses amplitude shift keying (ASK), particularly for on/off keying modulation. The encoder applies a sequence coding to the binary data. Correlation at the end receiver makes it possible to obtain data by using a user-specific sequence code. The incoming signal is checked by the comparator against a predetermined threshold before any data recovery operations are performed. In our current research the performance investigation of OCDMA link is carried out at 1550nm Wavelength using OPTSIM Software version 8.2. The Data rate of 15Gbps is used for investigation purpose of the link. OHL and ON based techniques are used to calculate link evaluation. It has been significantly noted that the performance of ‘ON’ based link is much better than ‘OHL’ based systems.
{"title":"Analysis of OCDMA Link using OHL Based Technique at 1550nm","authors":"G. Soni","doi":"10.1109/CICN51697.2021.9574667","DOIUrl":"https://doi.org/10.1109/CICN51697.2021.9574667","url":null,"abstract":"Researchers have looked at multiuser detection as a way to improve OCDMA connection performance over single-user detection. When transmitting binary data via an optical fibre, everyone uses amplitude shift keying (ASK), particularly for on/off keying modulation. The encoder applies a sequence coding to the binary data. Correlation at the end receiver makes it possible to obtain data by using a user-specific sequence code. The incoming signal is checked by the comparator against a predetermined threshold before any data recovery operations are performed. In our current research the performance investigation of OCDMA link is carried out at 1550nm Wavelength using OPTSIM Software version 8.2. The Data rate of 15Gbps is used for investigation purpose of the link. OHL and ON based techniques are used to calculate link evaluation. It has been significantly noted that the performance of ‘ON’ based link is much better than ‘OHL’ based systems.","PeriodicalId":224313,"journal":{"name":"2021 13th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"7 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":"128979224","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}
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