Pub Date : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00066
J. Viji Cripsy, T. Divya
People who have never smoked can get lung cancer, but smokers have a higher risk than non-smokers. Any aspect of the respiratory system can be affected by lung cancer, which can start anywhere in the lungs, Different classification methods are used for lung cancer prediction. This article uses five different classification algorithms to predict lung cancer in patients using Kaggle dataset. Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes methods are used, Based on the carefully identified correct and incorrect cases, the quality of the result was measured using the evaluation technique and the WEKA tool. The experimental results showed that Logistic Regression performed best (91.90%), followed by Naive Bayes (90.29%), Bayesian Network (88.34%), j48 (86.08%) and Random Forest (90.93%).
{"title":"Lung Cancer Disease Prediction and Classification based on Feature Selection method using Bayesian Network, Logistic Regression, J48, Random Forest, and Naïve Bayes Algorithms","authors":"J. Viji Cripsy, T. Divya","doi":"10.1109/ICSMDI57622.2023.00066","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00066","url":null,"abstract":"People who have never smoked can get lung cancer, but smokers have a higher risk than non-smokers. Any aspect of the respiratory system can be affected by lung cancer, which can start anywhere in the lungs, Different classification methods are used for lung cancer prediction. This article uses five different classification algorithms to predict lung cancer in patients using Kaggle dataset. Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes methods are used, Based on the carefully identified correct and incorrect cases, the quality of the result was measured using the evaluation technique and the WEKA tool. The experimental results showed that Logistic Regression performed best (91.90%), followed by Naive Bayes (90.29%), Bayesian Network (88.34%), j48 (86.08%) and Random Forest (90.93%).","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113081","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00103
Deeksha Pal, Nimrat Kaur, Richa Motwani, A. Mane, Pragati Pal
This paper proposes a voice-controlled robotic system that uses Bluetooth to follow human commands. The voice commands are given to an android app built using MIT App Inventor. These commands are then sent to the Bluetooth module which then sends them to the controller interfaced with it. This interfacing was done using Universal Asynchronous Receiver-Transmitter (UART) Protocol. After processing the commands, the microcontroller controls the movement of the robot in different directions. An open-source hardware and software is used in the proposed research work. Further, the proposed model can be implemented by almost every student for educational and understanding purposes as it is both economical and easy-to-use. This study considers the domain of Natural Language Processing (NLP) as well as communication using Bluetooth, both of which have high possibilities in future based on the technological advancement.
{"title":"Voice-Controlled Robot using Arduino and Bluetooth","authors":"Deeksha Pal, Nimrat Kaur, Richa Motwani, A. Mane, Pragati Pal","doi":"10.1109/ICSMDI57622.2023.00103","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00103","url":null,"abstract":"This paper proposes a voice-controlled robotic system that uses Bluetooth to follow human commands. The voice commands are given to an android app built using MIT App Inventor. These commands are then sent to the Bluetooth module which then sends them to the controller interfaced with it. This interfacing was done using Universal Asynchronous Receiver-Transmitter (UART) Protocol. After processing the commands, the microcontroller controls the movement of the robot in different directions. An open-source hardware and software is used in the proposed research work. Further, the proposed model can be implemented by almost every student for educational and understanding purposes as it is both economical and easy-to-use. This study considers the domain of Natural Language Processing (NLP) as well as communication using Bluetooth, both of which have high possibilities in future based on the technological advancement.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130569300","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00049
Manmeet Kaur, A. Kaimal, Jasminder Kaur Sandhu, Rakesh Sahu
Today's growing research topics include security in cloud computing. Since cloud storage provides easy access to the data whenever you need it. Many companies are switching from Conventional data storage to cloud storage. But data security is the biggest concern that companies face while using cloud computing. In this article, a multilevel cryptography-based safety solution for cloud computing is designed. This paradigm is a combination of asymmetric & symmetric key cryptography techniques. The RSA and Data Encryption Standard (DES) are used in this proposed methodology to provide several levels of encoding and decoding at the sender & recipient side, increasing the safety of cloud storage. This paradigm increases the data security to the highest possible level as compared to the current system.
{"title":"Cloud Data Security using Hybrid Algorithm","authors":"Manmeet Kaur, A. Kaimal, Jasminder Kaur Sandhu, Rakesh Sahu","doi":"10.1109/ICSMDI57622.2023.00049","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00049","url":null,"abstract":"Today's growing research topics include security in cloud computing. Since cloud storage provides easy access to the data whenever you need it. Many companies are switching from Conventional data storage to cloud storage. But data security is the biggest concern that companies face while using cloud computing. In this article, a multilevel cryptography-based safety solution for cloud computing is designed. This paradigm is a combination of asymmetric & symmetric key cryptography techniques. The RSA and Data Encryption Standard (DES) are used in this proposed methodology to provide several levels of encoding and decoding at the sender & recipient side, increasing the safety of cloud storage. This paradigm increases the data security to the highest possible level as compared to the current system.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114851178","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00063
Aruna Chittineni, Yaswanth Sai Kotagiri, Mohit Kolli, Teja Kollipara, John Raju Modepalli, Sravan Kumar Namburi
Yoga is a practice that aims to develop an all-around personality by synchronizing the mind, body, and spirit. However, incorrect postures or techniques can cause damage. In ancient times, yoga was performed under the supervision of a teacher, but it is difficult to find a competent guru in today's fast-paced world. The goal of this project is to develop an application that can track and evaluate physical exercise, specifically yoga, through the use of human pose estimation. This application, called “A Real-Time Virtual Yoga Assistant,” uses machine learning methodologies to classify data on yoga positions in both pre-recorded and real-time videos. The research also examines various pose estimation and key point detection approaches and deep learning models used for posture classification.
{"title":"A Real-Time Virtual Yoga Assistant Using Machine Learning","authors":"Aruna Chittineni, Yaswanth Sai Kotagiri, Mohit Kolli, Teja Kollipara, John Raju Modepalli, Sravan Kumar Namburi","doi":"10.1109/ICSMDI57622.2023.00063","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00063","url":null,"abstract":"Yoga is a practice that aims to develop an all-around personality by synchronizing the mind, body, and spirit. However, incorrect postures or techniques can cause damage. In ancient times, yoga was performed under the supervision of a teacher, but it is difficult to find a competent guru in today's fast-paced world. The goal of this project is to develop an application that can track and evaluate physical exercise, specifically yoga, through the use of human pose estimation. This application, called “A Real-Time Virtual Yoga Assistant,” uses machine learning methodologies to classify data on yoga positions in both pre-recorded and real-time videos. The research also examines various pose estimation and key point detection approaches and deep learning models used for posture classification.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927273","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}
Cryptocurrencies, like Bitcoin, have become increasingly popular over the last decade. The price of Bitcoin has gone through several cycles of highs and lows. As a result, it is a widely discussed topic, especially on platforms like Twitter. Sentiment analysis is a research area of Natural Language Processing. It is used to determine whether the text is positive, negative, or neutral. Twitter tweets are more challenging to analyze when compared to other forms of text, due to the presence of irregular grammar, emoticons, and sarcasm. This study intends to analyze the effect of tweets on the stock price of Bitcoin. In order to study the effect, the sentiment associated with each tweet is calculated using VADER, and also the profession and follower count associated with verified users who tweet about bitcoin is found. Following this, a model is trained and tested using a combined dataset of tweet related data and historical bitcoin price data. It was found that the sentiment of tweets does correlate with the shift in the price of bitcoin.
{"title":"Twitter Sentiment Analysis for Bitcoin Price Prediction","authors":"Achyut Jagini, Kaushal Mahajan, Namita Aluvathingal, Vedanth Mohan, Prajwala Tr","doi":"10.1109/ICSMDI57622.2023.00015","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00015","url":null,"abstract":"Cryptocurrencies, like Bitcoin, have become increasingly popular over the last decade. The price of Bitcoin has gone through several cycles of highs and lows. As a result, it is a widely discussed topic, especially on platforms like Twitter. Sentiment analysis is a research area of Natural Language Processing. It is used to determine whether the text is positive, negative, or neutral. Twitter tweets are more challenging to analyze when compared to other forms of text, due to the presence of irregular grammar, emoticons, and sarcasm. This study intends to analyze the effect of tweets on the stock price of Bitcoin. In order to study the effect, the sentiment associated with each tweet is calculated using VADER, and also the profession and follower count associated with verified users who tweet about bitcoin is found. Following this, a model is trained and tested using a combined dataset of tweet related data and historical bitcoin price data. It was found that the sentiment of tweets does correlate with the shift in the price of bitcoin.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131005988","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00111
V. Akshaykrishnan, C. Sharanya, K. Abhinav, C. K. Aparna, P. Bindu
Identifying insects by their bite marks can assist doctors in diagnosing victims and providing appropriate treatment. In recent years, researches using Machine Learning have been actively conducted and have produced excellent results in fields such as object detection, behaviour recognition, voice recognition, and cancer detection in medical field. This study has developed a classification application that can be used on mobile phones to solve the insect classification problems. Experiments were carried out on five insect species chosen for being the most common biting insects. Detailed study was conducted on different images with the help of Random Forest and Support Vector Machine models. These models need different insect bite marks images to classify them. Random forests achieve a better performance and are usually much faster than Support Vector Machines.
{"title":"A Machine Learning based Insect Bite Classification","authors":"V. Akshaykrishnan, C. Sharanya, K. Abhinav, C. K. Aparna, P. Bindu","doi":"10.1109/ICSMDI57622.2023.00111","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00111","url":null,"abstract":"Identifying insects by their bite marks can assist doctors in diagnosing victims and providing appropriate treatment. In recent years, researches using Machine Learning have been actively conducted and have produced excellent results in fields such as object detection, behaviour recognition, voice recognition, and cancer detection in medical field. This study has developed a classification application that can be used on mobile phones to solve the insect classification problems. Experiments were carried out on five insect species chosen for being the most common biting insects. Detailed study was conducted on different images with the help of Random Forest and Support Vector Machine models. These models need different insect bite marks images to classify them. Random forests achieve a better performance and are usually much faster than Support Vector Machines.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036277","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00064
Raji C.G, Fathima Safa, Jishana P, Mohammed Adhil
Wildlife infiltration in places with high human mobility has been proven to be dangerous for both humans and animals. If people fail to recognize an approaching wild animal, it may result in a direct attack. Due to their size and style of movement, monitoring and surveillance of wild animals is challenging. Additionally, it is a significant task to identify the species that were photographed. Elephants, tigers, and monkeys pose a serious threat to humans, and it will take a very long time for them to recover. Because interactions between humans and animals can be harmful to both species, successive frame differencing makes it possible to identify moving objects in videos. By utilizing the traits, the moving objects can be identified. Interactions between humans and animals can be hazardous. The proposed system is based on digital image processing, convolutional neural network and background subtraction method.
{"title":"Early Warning System from Threat of Wild Animals using Digital Image Processing","authors":"Raji C.G, Fathima Safa, Jishana P, Mohammed Adhil","doi":"10.1109/ICSMDI57622.2023.00064","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00064","url":null,"abstract":"Wildlife infiltration in places with high human mobility has been proven to be dangerous for both humans and animals. If people fail to recognize an approaching wild animal, it may result in a direct attack. Due to their size and style of movement, monitoring and surveillance of wild animals is challenging. Additionally, it is a significant task to identify the species that were photographed. Elephants, tigers, and monkeys pose a serious threat to humans, and it will take a very long time for them to recover. Because interactions between humans and animals can be harmful to both species, successive frame differencing makes it possible to identify moving objects in videos. By utilizing the traits, the moving objects can be identified. Interactions between humans and animals can be hazardous. The proposed system is based on digital image processing, convolutional neural network and background subtraction method.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128887064","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}
Predicting Bitcoin price is a universal research area as it attains significance in predicting the market way of its rate so that, investors could procure profits. Concurrently, with the evolution of Machine Learning (ML), researchers attempted to use ML based algorithms for forecasting the Bitcoin price. However, these researches have resulted in inefficient prediction due to error rate. For alleviating such pitfalls, this study intends to forecast the Bitcoin price by comparing its deviations pre and post Covid using suitable ML algorithms. To achieve this, the study proposes Auto Regressive Integrated Moving Average (ARIMA) with Optimized Genetic Algorithm (OGA). In this case, ARIMA model is considered as it possess the innate ability in capturing standard temporal reliances which is distinct to time-series data. Further, hyperparameters are selected by GA based on the fitness function. Based on this, hyperparameter tuning is performed which assist to improvise the model performance. For determining if there exists any deviations in Bitcoin price (pre and post Covid), Augmented Dickey Fuller (ADF) test is considered. Further, comparative analysis is regarded in accordance with performance metrics to validate the performance of the proposed system which proves its effectiveness in predicting Bitcoin price.
{"title":"Prediction of Bitcoin Price using Optimized Genetic ARIMA Model and Analysis in Post and Pre Covid Eras*","authors":"Vibha Srivastava, Vijay Kumar Dwivedi, Ashutosh Kumar Singh","doi":"10.1109/ICSMDI57622.2023.00033","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00033","url":null,"abstract":"Predicting Bitcoin price is a universal research area as it attains significance in predicting the market way of its rate so that, investors could procure profits. Concurrently, with the evolution of Machine Learning (ML), researchers attempted to use ML based algorithms for forecasting the Bitcoin price. However, these researches have resulted in inefficient prediction due to error rate. For alleviating such pitfalls, this study intends to forecast the Bitcoin price by comparing its deviations pre and post Covid using suitable ML algorithms. To achieve this, the study proposes Auto Regressive Integrated Moving Average (ARIMA) with Optimized Genetic Algorithm (OGA). In this case, ARIMA model is considered as it possess the innate ability in capturing standard temporal reliances which is distinct to time-series data. Further, hyperparameters are selected by GA based on the fitness function. Based on this, hyperparameter tuning is performed which assist to improvise the model performance. For determining if there exists any deviations in Bitcoin price (pre and post Covid), Augmented Dickey Fuller (ADF) test is considered. Further, comparative analysis is regarded in accordance with performance metrics to validate the performance of the proposed system which proves its effectiveness in predicting Bitcoin price.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858873","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00017
M. Sobhana, M. Chandra, K. Rakesh, K. Vivek
Farming is a major sector of Indian economy, and one of the most efficient and beneficial ways of promoting farming is Green Marketing, which is the process of improvisation of brand perception by following positive environmental objectives. Delivery of farm products from the producers to consumers often includes the hand of middlemen, called Mediators, because of whom, there often arises a substantial decrease in profit for the producers. By incorporating the principles of green marketing, buyers can receive the farm produce, i.e., vegetables, and grains directly from the producers, thereby eliminating the hand of mediators. It is believed that this approach has a wide scope in metropolitan areas, where people seldom have time to walk to the grocery stores and buy farm produce. The proposed system is developed using Java, XML, and Firebase. The application maintains complete transparency in payments and is user-friendly.
{"title":"Sustainable Farming Community using Green Marketing","authors":"M. Sobhana, M. Chandra, K. Rakesh, K. Vivek","doi":"10.1109/ICSMDI57622.2023.00017","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00017","url":null,"abstract":"Farming is a major sector of Indian economy, and one of the most efficient and beneficial ways of promoting farming is Green Marketing, which is the process of improvisation of brand perception by following positive environmental objectives. Delivery of farm products from the producers to consumers often includes the hand of middlemen, called Mediators, because of whom, there often arises a substantial decrease in profit for the producers. By incorporating the principles of green marketing, buyers can receive the farm produce, i.e., vegetables, and grains directly from the producers, thereby eliminating the hand of mediators. It is believed that this approach has a wide scope in metropolitan areas, where people seldom have time to walk to the grocery stores and buy farm produce. The proposed system is developed using Java, XML, and Firebase. The application maintains complete transparency in payments and is user-friendly.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126181792","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00032
Himanshu Dwivedi
This paper predicts sentiments of crypto currency news articles using BERT (Bidirectional Encoder Representation) model, as there is a lack of research in crypto currency price prediction using natural language processing. The text data obtained is unlabeled and it is labelled using a parsimonious rule-based model and then BERT is used to dassify news sentiment as “Positive”, “Negative” or “Neutral” which may be helpful in reading cryptocurrency market movement.
{"title":"Cryptocurrency Sentiment Analysis using Bidirectional Transformation","authors":"Himanshu Dwivedi","doi":"10.1109/ICSMDI57622.2023.00032","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00032","url":null,"abstract":"This paper predicts sentiments of crypto currency news articles using BERT (Bidirectional Encoder Representation) model, as there is a lack of research in crypto currency price prediction using natural language processing. The text data obtained is unlabeled and it is labelled using a parsimonious rule-based model and then BERT is used to dassify news sentiment as “Positive”, “Negative” or “Neutral” which may be helpful in reading cryptocurrency market movement.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124736999","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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