Pub Date : 2021-11-26DOI: 10.1109/iccica52458.2021.9697162
Ajinkya Yelne, Dipti Theng
Using Supervised Machine learning, our project is to analyzed and predict the stock value. As due to pandemic situation stock market trading is the most learned and become important activities to earn money as a second source of income in the people of India. The concept of predicting a stock's future worth is known as stock trading or stock prediction. Stock market is difficult to understand and to predict the value of stock. The majority of stock traders utilize various analytical techniques, as well as time series analysis, when seeking to make stock forecasts. So, we need a better tool to get out of this contemptuous situation and help the common man to make profit. In this research, we discuss a Machine Learning strategy that will be taught using publicly released stock data to build information, then using that information to make a valid prediction.For accuracy and prediction of stock Classification and Regression Algorithms are used with Kaggle dataset a machine learning technique comes under supervised learning that are Random Forest, Decision Tree, and Logistic Regression to predict stock prices for the given company previous year data, employing prices with daily trading prices. Python is the coding language used to anticipate the stock market using machine learning. Result come across that Regression model has more accuracy and can predict more accurate stock price.
{"title":"Stock Prediction and analysis Using Supervised Machine Learning Algorithms","authors":"Ajinkya Yelne, Dipti Theng","doi":"10.1109/iccica52458.2021.9697162","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697162","url":null,"abstract":"Using Supervised Machine learning, our project is to analyzed and predict the stock value. As due to pandemic situation stock market trading is the most learned and become important activities to earn money as a second source of income in the people of India. The concept of predicting a stock's future worth is known as stock trading or stock prediction. Stock market is difficult to understand and to predict the value of stock. The majority of stock traders utilize various analytical techniques, as well as time series analysis, when seeking to make stock forecasts. So, we need a better tool to get out of this contemptuous situation and help the common man to make profit. In this research, we discuss a Machine Learning strategy that will be taught using publicly released stock data to build information, then using that information to make a valid prediction.For accuracy and prediction of stock Classification and Regression Algorithms are used with Kaggle dataset a machine learning technique comes under supervised learning that are Random Forest, Decision Tree, and Logistic Regression to predict stock prices for the given company previous year data, employing prices with daily trading prices. Python is the coding language used to anticipate the stock market using machine learning. Result come across that Regression model has more accuracy and can predict more accurate stock price.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130580461","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}
Over a previous decade people have experienced an exponential boom in the usage of online resources in specific social media and microblogging internet site such as Twitter, Facebook, Instagram and YouTube. Many businesses and agencies has identified these sources as a wealthy mine of marketing information. On such platforms, massive quantities of records are produced (e.g.: 5000 tweets per 2d on twitter), this representing an chance for companies to check their social impact and people opinions towards their products, and even frequent people can additionally discover out what is a performance of a certain product or the overall performance of a particular political personality. In this project, we fetch the given number of tweets from users and classify it as Positive, Negative and Neutral by the usage of supervised machine learning approach. In this method we’re analyzing the Polarity and Subjectivity of the tweets and then later we’re using NLP to classify the raw records into records body which gets rid of the undesirable words from each of the tweets. Neutral words like ‘as, the, of’ are eliminated from the tweets. Using NLP, we get better results of the tweets, later we classify the tweets using classifying algorithms like Random Forest Classifier, Decision Tree Classifier, Logistic Regression, and Support Vector Classifier. Later it compares the result of tweets which had been analyzed before processing into NLP. We are also using Data Visualization for phrase frequencies, and for displaying a pie or bar chart of a variety of positive, negative and impartial tweets.
{"title":"Performance Prediction of Product/Person Using Real Time Twitter Tweets","authors":"Devesh Bhangale, Snehal Poojary, Sameer Ahire, Priyanka Shingane","doi":"10.1109/iccica52458.2021.9697223","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697223","url":null,"abstract":"Over a previous decade people have experienced an exponential boom in the usage of online resources in specific social media and microblogging internet site such as Twitter, Facebook, Instagram and YouTube. Many businesses and agencies has identified these sources as a wealthy mine of marketing information. On such platforms, massive quantities of records are produced (e.g.: 5000 tweets per 2d on twitter), this representing an chance for companies to check their social impact and people opinions towards their products, and even frequent people can additionally discover out what is a performance of a certain product or the overall performance of a particular political personality. In this project, we fetch the given number of tweets from users and classify it as Positive, Negative and Neutral by the usage of supervised machine learning approach. In this method we’re analyzing the Polarity and Subjectivity of the tweets and then later we’re using NLP to classify the raw records into records body which gets rid of the undesirable words from each of the tweets. Neutral words like ‘as, the, of’ are eliminated from the tweets. Using NLP, we get better results of the tweets, later we classify the tweets using classifying algorithms like Random Forest Classifier, Decision Tree Classifier, Logistic Regression, and Support Vector Classifier. Later it compares the result of tweets which had been analyzed before processing into NLP. We are also using Data Visualization for phrase frequencies, and for displaying a pie or bar chart of a variety of positive, negative and impartial tweets.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212095","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697324
P. Yadav, S. Dorle, Rahul Agrawal
The world has been shook by a rigorous pandemic covid-19 additionally it has accentuated a consequentiality on automating the health sectors from manually reading the reports to utilizing machine learning as an implement to getting the results of findings of sundry reports in an automated manner. There are many studies which have proved that the persons suffering from corona virus had optically discerned its effect on heart health. In rigorous cases it lead to cardiac apprehend proving it to be fatal for the patients. ECG (Electro cardiogram) is undertaken on patients to monitor their heart health; the ECG reports are then manually checked by medicos to conclude about heart health of a person. Cardiology is a study of heart and includes a variety of intricate diseases to be studied. This paper presents an efficient way of arrhythmia detection utilizing dataset which would be subsidiary for implementation of machine learning in this disease detection. Neural network has been utilized in the proposed work and is found to be 99% efficient thereby exhibiting a precise and tested method to further facilitate automation in this sector.
{"title":"Arrhythmia Detection on ECG Signal Using Neural Network Approach","authors":"P. Yadav, S. Dorle, Rahul Agrawal","doi":"10.1109/iccica52458.2021.9697324","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697324","url":null,"abstract":"The world has been shook by a rigorous pandemic covid-19 additionally it has accentuated a consequentiality on automating the health sectors from manually reading the reports to utilizing machine learning as an implement to getting the results of findings of sundry reports in an automated manner. There are many studies which have proved that the persons suffering from corona virus had optically discerned its effect on heart health. In rigorous cases it lead to cardiac apprehend proving it to be fatal for the patients. ECG (Electro cardiogram) is undertaken on patients to monitor their heart health; the ECG reports are then manually checked by medicos to conclude about heart health of a person. Cardiology is a study of heart and includes a variety of intricate diseases to be studied. This paper presents an efficient way of arrhythmia detection utilizing dataset which would be subsidiary for implementation of machine learning in this disease detection. Neural network has been utilized in the proposed work and is found to be 99% efficient thereby exhibiting a precise and tested method to further facilitate automation in this sector.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131288026","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697252
Megha Gupta, Akshita Jain
Nowadays, robot toys for kids are used for educational purposes as well as just having fun. We all know that kids like remote control toys more than the simple toys. It can be useful if we will try to make a mixture of their interest and their needs. Here need refers to their education. If we will do something that make education more interesting to them, then they will enjoy education and it will no more be a burden for them. So, for that we can use educational robots. Educational robots can help make the monotonous digital learning process tangible for kids and can significantly increase their interest and productivity. In addition, playing with educational robots can be a great way for kids to hone their talents and skills. It will be easy for students as well as for parents and teachers if the child will learn the things by his/her own interest. This paper examines how educational robots are gaining attraction in the education industry, as well as the benefits and drawbacks that using this intelligent technology will bring.
{"title":"Challenges of Robot Assisted Teaching in Education Domain","authors":"Megha Gupta, Akshita Jain","doi":"10.1109/iccica52458.2021.9697252","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697252","url":null,"abstract":"Nowadays, robot toys for kids are used for educational purposes as well as just having fun. We all know that kids like remote control toys more than the simple toys. It can be useful if we will try to make a mixture of their interest and their needs. Here need refers to their education. If we will do something that make education more interesting to them, then they will enjoy education and it will no more be a burden for them. So, for that we can use educational robots. Educational robots can help make the monotonous digital learning process tangible for kids and can significantly increase their interest and productivity. In addition, playing with educational robots can be a great way for kids to hone their talents and skills. It will be easy for students as well as for parents and teachers if the child will learn the things by his/her own interest. This paper examines how educational robots are gaining attraction in the education industry, as well as the benefits and drawbacks that using this intelligent technology will bring.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134306875","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697278
Neha Shivhare, Shanti Rathod, M. R. Khan
Neurodegenerative diseases, such as dementia, can impact speech, language, and the capability of communication. A recent study to improve the dementia detection accuracy studied the usage of CA (Conversation Analysis) of interviews among patients and neurologists to distinguish among progressive Neurodegenerative Memory Disorders patients & those with (non-progressive) Functional Memory Disorders (FMD). However, manual CA is costly for routine clinical use and difficult to scale. In this work, we present an early dementia detection system using speech recognition and analysis based on NLP technique and acoustic feature processing technique apply on multiple feature extraction and learning using a LSTM (Long Short-Term Memory) and GRU which remarkably captures the temporal features and long-term dependencies from historical data to prove the capabilities of sequence models over a feed-forward neural network in forecasting speech analysis related problems.
{"title":"Automatic Speech Analysis of Conversations for Dementia Detection Using LSTM and GRU","authors":"Neha Shivhare, Shanti Rathod, M. R. Khan","doi":"10.1109/iccica52458.2021.9697278","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697278","url":null,"abstract":"Neurodegenerative diseases, such as dementia, can impact speech, language, and the capability of communication. A recent study to improve the dementia detection accuracy studied the usage of CA (Conversation Analysis) of interviews among patients and neurologists to distinguish among progressive Neurodegenerative Memory Disorders patients & those with (non-progressive) Functional Memory Disorders (FMD). However, manual CA is costly for routine clinical use and difficult to scale. In this work, we present an early dementia detection system using speech recognition and analysis based on NLP technique and acoustic feature processing technique apply on multiple feature extraction and learning using a LSTM (Long Short-Term Memory) and GRU which remarkably captures the temporal features and long-term dependencies from historical data to prove the capabilities of sequence models over a feed-forward neural network in forecasting speech analysis related problems.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123435446","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}
Social Distancing is a procedure that is very effective in controlling the transmission of infectious diseases. Social distancing as the name says is the practice of keeping in distance from others physically, to reduce the spreading of diseases. This Social Distance Detection System brings an emphasis on monitoring the distance between people using technologies namely Open-CV and Deep Learning. This publication focuses on detecting people by a method called object detection and calculating the distance between them. When the distance between people becomes less than the standard value, certain indications and alerts will be displayed. This also indicates the number of Social Distancing violations.
{"title":"Model Proposal for a Yolo Objection Detection Algorithm based Social Distancing Detection System","authors":"Sudhir Sidhaarthan Balamurugan, Sanjay Santhanam, Anudeep Billa, R. Aggarwal, Nayan Varma Alluri","doi":"10.1109/iccica52458.2021.9697212","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697212","url":null,"abstract":"Social Distancing is a procedure that is very effective in controlling the transmission of infectious diseases. Social distancing as the name says is the practice of keeping in distance from others physically, to reduce the spreading of diseases. This Social Distance Detection System brings an emphasis on monitoring the distance between people using technologies namely Open-CV and Deep Learning. This publication focuses on detecting people by a method called object detection and calculating the distance between them. When the distance between people becomes less than the standard value, certain indications and alerts will be displayed. This also indicates the number of Social Distancing violations.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123696159","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697269
Ashok Kumar J, Tina Esther Trueman, E. Cambria
In recent years, the traditional way of getting news from a Television, news paper, or national newscast is gone. Today, online social media provides the fastest news content for people. This, however, brings about the problem of fake news. In fact, fake news detection is one of the challenging tasks in natural language processing to differentiate between real (or true) and fake (or false) news content. In this paper, we propose an XLNet fine-tuning model to predict fake news in a multi-class and binary class problem. Our results show that the proposed XLNet model comparatively achieves a better result than the existing state-of-the-art models.
{"title":"Fake News Detection Using XLNet Fine-Tuning Model","authors":"Ashok Kumar J, Tina Esther Trueman, E. Cambria","doi":"10.1109/iccica52458.2021.9697269","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697269","url":null,"abstract":"In recent years, the traditional way of getting news from a Television, news paper, or national newscast is gone. Today, online social media provides the fastest news content for people. This, however, brings about the problem of fake news. In fact, fake news detection is one of the challenging tasks in natural language processing to differentiate between real (or true) and fake (or false) news content. In this paper, we propose an XLNet fine-tuning model to predict fake news in a multi-class and binary class problem. Our results show that the proposed XLNet model comparatively achieves a better result than the existing state-of-the-art models.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122084764","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697124
{"title":"Glimpses of ICCICA 2021","authors":"","doi":"10.1109/iccica52458.2021.9697124","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697124","url":null,"abstract":"","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125278357","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697322
Anusha Chintam, Rajendra Kumar G, Anitha Rani J, Srilatha Yalamati, C. D
Present days deep neural networks play a crucial role in the prediction and classification of diseases. Without a doubt, DNN has a promising future in the medical area, particularly in clinical imaging. The fame of profound learning approaches is a result of their capacity to deal with a lot of information identified with the patients with reliability, accuracy in a limited ability to focus time. Nonetheless, the specialists might set aside time in breaking down and produce reports. In this work, have proposed a Deep Neural Network-based Parkinson's disease classification (DPDC). Our proposed technique is one such genuine model giving quicker and more precise outcomes for the characterization of Parkinson's sickness patients with magnificent accuracy of 94.87%. Because of the traits of the dataset of the patient, the model can be utilized for the recognizable proof of Parkinsonism's.
{"title":"Deep Neural Network-Based Classification and Diagnosis of Idiopathic Parkinsonism Disease","authors":"Anusha Chintam, Rajendra Kumar G, Anitha Rani J, Srilatha Yalamati, C. D","doi":"10.1109/iccica52458.2021.9697322","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697322","url":null,"abstract":"Present days deep neural networks play a crucial role in the prediction and classification of diseases. Without a doubt, DNN has a promising future in the medical area, particularly in clinical imaging. The fame of profound learning approaches is a result of their capacity to deal with a lot of information identified with the patients with reliability, accuracy in a limited ability to focus time. Nonetheless, the specialists might set aside time in breaking down and produce reports. In this work, have proposed a Deep Neural Network-based Parkinson's disease classification (DPDC). Our proposed technique is one such genuine model giving quicker and more precise outcomes for the characterization of Parkinson's sickness patients with magnificent accuracy of 94.87%. Because of the traits of the dataset of the patient, the model can be utilized for the recognizable proof of Parkinsonism's.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130586058","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697159
Abdulraheem Shaik, N. Durga Naga Lakshmi, C. Srinivas
It is the service sector for any nation that offers the government the maximum income. This service industry comprises a broader range of services and every individual in the nation will be affected by any bad impacts on this sector. The covid pandemic has become a threat to good health and has created fear due to the spread of the virus and a serious impact on the economy and the livelihood of people in the country. Logistics and delivery are some of the areas badly affected by covid. Because this virus is a contagious disease and very quickly spreads to neighbours. The reduction of human interference in the delivery of goods was very important. This paper presents a delivery robot that can safely and securely deliver the goodseven for the virus affected persons since this robot is a virus free agent. The key features are face recognition, obstacle detection, live streaming, GPS tracking and we achieve these features using Raspberry Pi and Node MCU. The prototype we create is for small organizations such as colleges and hospitals where objects must be transported safely and securely. This robot can also be used to safely provide patients with medicines and food. This can be extended to a greater area and can be used to replace the normal human resources delivery system.
{"title":"Delivery Robot Using GPS Technology","authors":"Abdulraheem Shaik, N. Durga Naga Lakshmi, C. Srinivas","doi":"10.1109/iccica52458.2021.9697159","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697159","url":null,"abstract":"It is the service sector for any nation that offers the government the maximum income. This service industry comprises a broader range of services and every individual in the nation will be affected by any bad impacts on this sector. The covid pandemic has become a threat to good health and has created fear due to the spread of the virus and a serious impact on the economy and the livelihood of people in the country. Logistics and delivery are some of the areas badly affected by covid. Because this virus is a contagious disease and very quickly spreads to neighbours. The reduction of human interference in the delivery of goods was very important. This paper presents a delivery robot that can safely and securely deliver the goodseven for the virus affected persons since this robot is a virus free agent. The key features are face recognition, obstacle detection, live streaming, GPS tracking and we achieve these features using Raspberry Pi and Node MCU. The prototype we create is for small organizations such as colleges and hospitals where objects must be transported safely and securely. This robot can also be used to safely provide patients with medicines and food. This can be extended to a greater area and can be used to replace the normal human resources delivery system.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646099","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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