Electric vehicles (EVs) are gaining popularity due to their improved performance and environmentally friendly nature. The effectiveness of EVs depends on the successful interface between their energy storage systems and propulsion motor. One of the key components of an EV is the motor converter, which converts the electrical energy stored in the battery into mechanical energy that powers the vehicle's propulsion system. The motor converter used in EV drive system is reviewed. Non- isolated converter for DC/DC conversion and DC/AC converter to drive the motor are stated. Despite their usefulness, EV converters have some drawbacks, large number of components, high current stress, high switching loss, slow dynamic response, and computational complexity. This review examines various EV converter configurations, highlighting their topology, features, components, operation, strengths, and weaknesses.
{"title":"Review on Converters used in Electric Vehicle Drive System","authors":"A. Anu Priya and Dr. S. Senthil Kumar","doi":"10.46501/ijmtst0901001","DOIUrl":"https://doi.org/10.46501/ijmtst0901001","url":null,"abstract":"Electric vehicles (EVs) are gaining popularity due to their improved performance and environmentally friendly nature. The\u0000effectiveness of EVs depends on the successful interface between their energy storage systems and propulsion motor. One of the\u0000key components of an EV is the motor converter, which converts the electrical energy stored in the battery into mechanical energy\u0000that powers the vehicle's propulsion system. The motor converter used in EV drive system is reviewed. Non- isolated converter for\u0000DC/DC conversion and DC/AC converter to drive the motor are stated. Despite their usefulness, EV converters have some\u0000drawbacks, large number of components, high current stress, high switching loss, slow dynamic response, and computational\u0000complexity. This review examines various EV converter configurations, highlighting their topology, features, components,\u0000operation, strengths, and weaknesses.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90462994","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 recent surge in the destructiveness of cyberweapons raises the question: will cyberweapons merely be among the most potent weapons in a country’s arsenal? Or, will they behave like nuclear weapons do in the present world order: as deterrents against interstate conflict? To answer this question, this paper first clarified exactly what gives nuclear weapons deterring ability. A list of three necessary criteria for conflict-deterring technology was generated: extreme destructiveness, ease of delivery, and resilience against a disarming first strike. Since cyberweapons fulfill these criteria, they can, in principle, deter war. Finally, the challenges to cyber deterrence were evaluated, along with recommendations for policymakers and charitable foundations concerned about international security
{"title":"Will cyberweapons deter war?","authors":"Ishan Mukherjee","doi":"10.46501/ijmtst0812021","DOIUrl":"https://doi.org/10.46501/ijmtst0812021","url":null,"abstract":"The recent surge in the destructiveness of cyberweapons raises the question: will cyberweapons merely be among the most potent\u0000weapons in a country’s arsenal? Or, will they behave like nuclear weapons do in the present world order: as deterrents against\u0000interstate conflict? To answer this question, this paper first clarified exactly what gives nuclear weapons deterring ability. A list\u0000of three necessary criteria for conflict-deterring technology was generated: extreme destructiveness, ease of delivery, and\u0000resilience against a disarming first strike. Since cyberweapons fulfill these criteria, they can, in principle, deter war. Finally, the\u0000challenges to cyber deterrence were evaluated, along with recommendations for policymakers and charitable foundations\u0000concerned about international security","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75923759","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}
,. X. Y. M. A. S. A. U. H. Md. Jewel Rana, Khan Rajib Hossain
Shape memory polymers are intelligent materials that produce shape changes under external stimulus conditions, and 4D printing is based on deformable materials and 3D printing. A comprehensive technology, shape memory polymer in deformable materials is the most widely used, and the current 4D printing shape memory polymer is in various collars. The domain has applications, especially in the biomedical field, which has excellent application value. 4D printing technology breaks through the personalized technology in traditional medicine. The bottleneck provides a new opportunity for the further development of the biomedical field. This article first reviews shape-memory polymers, 3D printing technology, and 4D printing. We will review the research progress of shape memory polymers at home and abroad and introduce examples of 4D printed shape memory polymers in biomedicine. Finally, the application prospects, existing problems, and future development directions of 4D printed shape memory polymers in the biomedical field are summarized
{"title":"Study on 4D Printing Shape Memory Polymers in the Field of Biomedical Progress","authors":",. X. Y. M. A. S. A. U. H. Md. Jewel Rana, Khan Rajib Hossain","doi":"10.46501/ijmtst0812020","DOIUrl":"https://doi.org/10.46501/ijmtst0812020","url":null,"abstract":"Shape memory polymers are intelligent materials that produce shape changes under external stimulus\u0000conditions, and 4D printing is based on deformable materials and 3D printing. A comprehensive technology,\u0000shape memory polymer in deformable materials is the most widely used, and the current 4D printing shape\u0000memory polymer is in various collars. The domain has applications, especially in the biomedical field, which\u0000has excellent application value. 4D printing technology breaks through the personalized technology in\u0000traditional medicine. The bottleneck provides a new opportunity for the further development of the biomedical\u0000field. This article first reviews shape-memory polymers, 3D printing technology, and 4D printing. We will\u0000review the research progress of shape memory polymers at home and abroad and introduce examples of 4D\u0000printed shape memory polymers in biomedicine. Finally, the application prospects, existing problems, and\u0000future development directions of 4D printed shape memory polymers in the biomedical field are\u0000summarized","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80407864","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}
This research work proposes an electric power train (EPT) with hybrid energy storage system (HESS) using an interval type 2.0 fuzzy logic controller (T2.0-FLC). EPT’s will play a vital role in present and future transportation because they do not emit harmful gases and do not rely on fuel. In this proposed work, storage devices like battery, supercapacitor, and fuel cell will be considered for electric vehicle and a novel control strategy based on interval T2.0-FLC is used. There are various types of electric motors are generally used in Electric vehicles but In our proposed research work Permanent Magnet Synchronous Motor is used. This work implemented in different cases, starting from only solar powered electric vehicle to hybrid storage-Electric Vehicle having battery, solar, supercapacitor, and fuel cell. This research study gives a detail comparative analysis of the performance of hybrid electric vehicle between Type-1 FLC& IntervalType-2.0 FLC. Also shows the edge of Interval T2.0-FLC based electric vehicle over Type-1 FLC based electric vehicle as interval T2.0 approach having better response. The entire proposed scheme implemented with the help of MATLAB software
{"title":"A Novel Control Strategy of Electric Vehicle with Hybrid Energy Storage System using Interval Type 2.0 Fuzzy Logic Controller","authors":"Yogesh Shekhar and Adeeb Uddin Ahmad","doi":"10.46501/ijmtst0809049","DOIUrl":"https://doi.org/10.46501/ijmtst0809049","url":null,"abstract":"This research work proposes an electric power train (EPT) with hybrid energy storage system (HESS) using an interval type 2.0\u0000fuzzy logic controller (T2.0-FLC). EPT’s will play a vital role in present and future transportation because they do not emit\u0000harmful gases and do not rely on fuel. In this proposed work, storage devices like battery, supercapacitor, and fuel cell will be\u0000considered for electric vehicle and a novel control strategy based on interval T2.0-FLC is used. There are various types of electric\u0000motors are generally used in Electric vehicles but In our proposed research work Permanent Magnet Synchronous Motor is used.\u0000This work implemented in different cases, starting from only solar powered electric vehicle to hybrid storage-Electric Vehicle\u0000having battery, solar, supercapacitor, and fuel cell. This research study gives a detail comparative analysis of the performance of\u0000hybrid electric vehicle between Type-1 FLC& IntervalType-2.0 FLC. Also shows the edge of Interval T2.0-FLC based electric\u0000vehicle over Type-1 FLC based electric vehicle as interval T2.0 approach having better response. The entire proposed scheme\u0000implemented with the help of MATLAB software","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90282431","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 applications in intelligent transportation systems are demanding an accurate web application-based location prediction. In this study, we satisfy this demand by designing an automated mobile user location prediction system based on the well-known traditional Auto-Regressive Integrated Moving Average (ARIMA). To increase the proposed model accuracy, make it dynamic, and reduce its execution time, the traditional ARIMA model has been modified extensively by using different combinations of design options of the model. To perform user location prediction, the proposed model depends the previous recorded user locations to predict the user future locations. To make the proposed model dynamic, it is designed to regenerate all its parameters periodically. To deal with such dynamic environment, only a specified window of the historical data is used. To reduce the regeneration of the model execution time, the model selection process is enhanced and several model selection approaches are proposed. The proposed model and the different design options are evaluated using a realistic user location dataset trace that are recorded using a WIFI embedded, as well as, using traces from a previous study called the Kaggle Dataset. To deal with any imperfection in the data used in generating the model in this study. The results show that the proposed framework can generate ARIMA models that can predict the future user locations of a user accurately and with a reasonable execution time. The results also show that the proposed model can predict the user’s location for several future steps with an acceptable accuracy.
{"title":"https://www.ijmtst.com/vol8issue09.html","authors":"Priti Mishra and Poonam Bhogale","doi":"10.46501/ijmtst0809001","DOIUrl":"https://doi.org/10.46501/ijmtst0809001","url":null,"abstract":"Many applications in intelligent transportation systems are demanding an accurate web\u0000application-based location prediction. In this study, we satisfy this demand by designing an automated\u0000mobile user location prediction system based on the well-known traditional Auto-Regressive Integrated\u0000Moving Average (ARIMA). To increase the proposed model accuracy, make it dynamic, and reduce its\u0000execution time, the traditional ARIMA model has been modified extensively by using different combinations of\u0000design options of the model. To perform user location prediction, the proposed model depends the previous\u0000recorded user locations to predict the user future locations. To make the proposed model dynamic, it is\u0000designed to regenerate all its parameters periodically. To deal with such dynamic environment, only a\u0000specified window of the historical data is used. To reduce the regeneration of the model execution time, the\u0000model selection process is enhanced and several model selection approaches are proposed.\u0000The proposed model and the different design options are evaluated using a realistic user location dataset\u0000trace that are recorded using a WIFI embedded, as well as, using traces from a previous study called the\u0000Kaggle Dataset. To deal with any imperfection in the data used in generating the model in this study. The\u0000results show that the proposed framework can generate ARIMA models that can predict the future user\u0000locations of a user accurately and with a reasonable execution time. The results also show that the proposed\u0000model can predict the user’s location for several future steps with an acceptable accuracy.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75272270","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}
We can recognize the emotion of a human by seeing their facial expression and it is an efficient way of human communication. It is the easiest way and essential technology for realizing the human and machine interaction. Facial expression recognition task can be able to classify the face images into various categories of emotions such as happy, sad, angry, fear, surprise, disgust and neutral. In this paper, we are analysing and efficiently classifying each facial image into one of the emotion category. There are numerous approaches to address and solve this problem, out of them convolutional neural network (CNN) is the best approach. Here, we are proposing a novel technique called facial emotion recognition using convolutional neural networks. It is based on the feature extractor to extract the feature and the classifier to produce the label based on the feature. The extraction of feature may be imprecise by variance of location of object and lighting condition on the image. The feature of image can be extracted without user defined feature engineering, and classifier model is integrated with feature extractor to produce the result when input is given. In this way, the CNN approach can produces a feature location invariant image classifier that achieves higher accuracy than conventional linear classifier and our model classified the emotions with 66.62 accuracy.
{"title":"Classification of Facial Expressions using Convolutional Neural Networks","authors":"","doi":"10.46501/ijmtst0710012","DOIUrl":"https://doi.org/10.46501/ijmtst0710012","url":null,"abstract":"We can recognize the emotion of a human by seeing their facial expression and it is an efficient way of human communication.\u0000It is the easiest way and essential technology for realizing the human and machine interaction. Facial expression recognition task\u0000can be able to classify the face images into various categories of emotions such as happy, sad, angry, fear, surprise, disgust and\u0000neutral. In this paper, we are analysing and efficiently classifying each facial image into one of the emotion category. There are\u0000numerous approaches to address and solve this problem, out of them convolutional neural network (CNN) is the best approach.\u0000Here, we are proposing a novel technique called facial emotion recognition using convolutional neural networks. It is based on the\u0000feature extractor to extract the feature and the classifier to produce the label based on the feature. The extraction of feature may be\u0000imprecise by variance of location of object and lighting condition on the image. The feature of image can be extracted without user\u0000defined feature engineering, and classifier model is integrated with feature extractor to produce the result when input is given. In\u0000this way, the CNN approach can produces a feature location invariant image classifier that achieves higher accuracy than\u0000conventional linear classifier and our model classified the emotions with 66.62 accuracy.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83550410","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 the past few decades, ITS have spiked an increasing research interest as a promising discipline for revolutionizing the transportation sector and solving common traffic and vehicle-related problems. ITS comprise a multitude of interconnected engineering feats that function as an entity for optimizing network-scale travel experiences from a technical, social, economic, and environmental aspect. Such optimizations necessitate the advancement of information and communication technologies, electronic sensors, control systems, and computers, which high-lights the data-driven nature of modern ITS. In this paper we design a system which uses machine learning algorithm using SVM, KNN and CNN algorithm which is a novel system which will provide intelligence to the current traffic control system present at a four-way junction. This ML technique is mainly aimed to replace the existing traffic light control system with artificial intelligence system. Nowadays most cities are equipped with CCTV cameras on the roads and the junctions, the basic idea is to collect the live video from the CCTV cameras and detect the number of vehicles on each lane and feed the data into another machine learning algorithm. according to the data of each lane changes into the light phase of the green signal. This system mainly aims to increase the traffic efficiency by increasing vehicle flow which will reduce waiting time for the vehicles. We are using HOG algorithm for feature extraction. In the implementation of the proposed architecture, we have achieved an accuracy of 86.34% for binary classification and 90.23% for multi-class classification
{"title":"Traffic Prediction for Intelligent Transportation Systems using Machine Learning","authors":"Rahul Anand and Smita Sankhe","doi":"10.46501/ijmtst0807041","DOIUrl":"https://doi.org/10.46501/ijmtst0807041","url":null,"abstract":"Over the past few decades, ITS have spiked an increasing research interest as a promising discipline for revolutionizing the\u0000transportation sector and solving common traffic and vehicle-related problems. ITS comprise a multitude of interconnected\u0000engineering feats that function as an entity for optimizing network-scale travel experiences from a technical, social, economic,\u0000and environmental aspect. Such optimizations necessitate the advancement of information and communication technologies,\u0000electronic sensors, control systems, and computers, which high-lights the data-driven nature of modern ITS.\u0000In this paper we design a system which uses machine learning algorithm using SVM, KNN and CNN algorithm which is a novel\u0000system which will provide intelligence to the current traffic control system present at a four-way junction. This ML technique is\u0000mainly aimed to replace the existing traffic light control system with artificial intelligence system. Nowadays most cities are\u0000equipped with CCTV cameras on the roads and the junctions, the basic idea is to collect the live video from the CCTV cameras and\u0000detect the number of vehicles on each lane and feed the data into another machine learning algorithm. according to the data of\u0000each lane changes into the light phase of the green signal. This system mainly aims to increase the traffic efficiency by increasing\u0000vehicle flow which will reduce waiting time for the vehicles. We are using HOG algorithm for feature extraction. In the\u0000implementation of the proposed architecture, we have achieved an accuracy of 86.34% for binary classification and 90.23% for\u0000multi-class classification","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88795717","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}
In today’s life all the organization over the globe are facing a major issue with security’s most common challenging issue of intrusion into their network. This intrusion in the network may lead to security concerns hampering the organizations integrity, confidentiality and availability. To solve this issue there are multiple tools in the market which detects the intrusion in a networkby surveillance of network activities and block the unusual activity detected. These tools and technologies monitor the network for sudden change in activity or behavior and processing them further for analyzing if unusual activity is noticed and inform the administrator about the change in behavior of network.Most of these tool uses the traditional machine learning method for intrusion classification into ‘good’ or ‘bad’ network. In this paper we propose a deep learning model whose architecture compromises of Multi-Layer Perceptron used for intrusion classification and uses GridSearchCV to automate the best model selection for the problem. Using deep learning to solve the problem of intrusion detection in an organization by classification of network has numerous advantages as deep learning performs well on large datasets, unstructured data, better self-learning capabilities, cost effective and scalable. In the implementation of the proposed architecture, we have achieved an accuracy of 98.10% for binary classification and 97.62% for multi-class classification.For hyperparameter tuning as we have used GridSearchCV and used five k-fold cross validation for evaluating the best performing model.
{"title":"Intrusion Detection System using Multi-Layer Perceptron with Grid Search CV","authors":"Ankit Kumar and Dr. Deepak Sharma","doi":"10.46501/ijmtst0807016","DOIUrl":"https://doi.org/10.46501/ijmtst0807016","url":null,"abstract":"In today’s life all the organization over the globe are facing a major issue with security’s most common challenging issue of\u0000intrusion into their network. This intrusion in the network may lead to security concerns hampering the organizations integrity,\u0000confidentiality and availability. To solve this issue there are multiple tools in the market which detects the intrusion in a\u0000networkby surveillance of network activities and block the unusual activity detected. These tools and technologies monitor the\u0000network for sudden change in activity or behavior and processing them further for analyzing if unusual activity is noticed and\u0000inform the administrator about the change in behavior of network.Most of these tool uses the traditional machine learning\u0000method for intrusion classification into ‘good’ or ‘bad’ network.\u0000In this paper we propose a deep learning model whose architecture compromises of Multi-Layer Perceptron used for intrusion\u0000classification and uses GridSearchCV to automate the best model selection for the problem. Using deep learning to solve the\u0000problem of intrusion detection in an organization by classification of network has numerous advantages as deep learning\u0000performs well on large datasets, unstructured data, better self-learning capabilities, cost effective and scalable. In the\u0000implementation of the proposed architecture, we have achieved an accuracy of 98.10% for binary classification and 97.62% for\u0000multi-class classification.For hyperparameter tuning as we have used GridSearchCV and used five k-fold cross validation for\u0000evaluating the best performing model.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91033429","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}
Different types of social media sites exist, wherein some of them are LinkedIn, Twitter, Facebook, Instagram, WhatsApp, etc. As the number of social media users increases, the opportunity for the user to express their feelings also increases. Twitter is a choice of many users as it not only allows the users to express their thoughts but to interact with official accounts (PMO, Defense Ministry) which can be seen with a verified tick on the website. In this thesis titled ‘Sentiment Analysis of COVID data extracted via Twitter’, multiple machine learning and deep learning techniques have been researched and implemented to perform sentiment analysis. Moreover, a novel approach using deep learning architecture has been proposed. It is based on a combination of Bidirectional Long Short Term (BiLSTM) neural networks and Convolution Neural Networks (CNN). Prior to implementing the algorithms, the data is acquired by using web-scraping techniques and/or public APIs pertaining to Twitter. A comparative analysis of the efficiency and performance of the proposed technique along with other existing approaches discovered during the literature review phase is also presented. KEYWORDS: Sentiment analysis, machine learning, deep learning, Natural Language Processing
{"title":"Sentiment Analysis of COVID data extracted via Twitter","authors":"Rugved Mone and Bhakti Palkar","doi":"10.46501/ijmtst0806087","DOIUrl":"https://doi.org/10.46501/ijmtst0806087","url":null,"abstract":"Different types of social media sites exist, wherein some of them are LinkedIn, Twitter, Facebook, Instagram, WhatsApp, etc.\u0000As the number of social media users increases, the opportunity for the user to express their feelings also increases. Twitter is a\u0000choice of many users as it not only allows the users to express their thoughts but to interact with official accounts (PMO, Defense\u0000Ministry) which can be seen with a verified tick on the website.\u0000In this thesis titled ‘Sentiment Analysis of COVID data extracted via Twitter’, multiple machine learning and deep learning\u0000techniques have been researched and implemented to perform sentiment analysis. Moreover, a novel approach using deep learning\u0000architecture has been proposed. It is based on a combination of Bidirectional Long Short Term (BiLSTM) neural networks and\u0000Convolution Neural Networks (CNN). Prior to implementing the algorithms, the data is acquired by using web-scraping\u0000techniques and/or public APIs pertaining to Twitter. A comparative analysis of the efficiency and performance of the proposed\u0000technique along with other existing approaches discovered during the literature review phase is also presented.\u0000KEYWORDS: Sentiment analysis, machine learning, deep learning, Natural Language Processing","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87482852","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 World Wide Web has taken a serious look at new ways for individuals to express their viewpoints and conclusions on a variety of topics, models, and concerns. Clients provide material for a variety of media, such as web gatherings, discussion groups, and weblogs, and provide a robust and open foundation for gaining clout in areas such as promoting and research. Strategy, justification research, market estimations, and a business perspective are all important considerations. Theory study eliminates derivations from publicly available data and organizes the sentiments that the author associates with a given object into one of two specified categories (positive and negative). Make a distinction between the two problems. This follows a Twitter speculation audit cycle for quickly seeking unstructured news. Furthermore, we're looking at several ways to present an itemized positive assessment on Twitter News. It also shows a parametric relationship between operations that are influenced by perceived boundaries. The qualities conveyed in them address the tweets: positive, negative, or fair. This work will in general present the defense appreciate exploring on Twitter; the qualities conveyed in them address the tweets: positive, negative, or fair. Twitter is a web-based application that integrates with a blog and a wide range of contacts, allowing users to send brief 140-character messages. It's a rapidly growing partnership with over 200 million endorsers, 100 million of whom are active clients, and a large portion of them follow Twitter on a regular basis, sending out over 250 million tweets. This study aims to perform Sentimental analysis using deep learning with bigrams and trigrams to classify the tweets accurately.
{"title":"Twitter Sentiment Analysis Using Deep Learning Techniques","authors":"S. Kasifa Farnaaz and A. Sureshbabu","doi":"10.46501/ijmtst0802035","DOIUrl":"https://doi.org/10.46501/ijmtst0802035","url":null,"abstract":"The World Wide Web has taken a serious look at new ways for individuals to express their viewpoints and conclusions on a\u0000variety of topics, models, and concerns. Clients provide material for a variety of media, such as web gatherings, discussion\u0000groups, and weblogs, and provide a robust and open foundation for gaining clout in areas such as promoting and research.\u0000Strategy, justification research, market estimations, and a business perspective are all important considerations. Theory study\u0000eliminates derivations from publicly available data and organizes the sentiments that the author associates with a given object\u0000into one of two specified categories (positive and negative). Make a distinction between the two problems. This follows a Twitter\u0000speculation audit cycle for quickly seeking unstructured news. Furthermore, we're looking at several ways to present an itemized\u0000positive assessment on Twitter News. It also shows a parametric relationship between operations that are influenced by\u0000perceived boundaries. The qualities conveyed in them address the tweets: positive, negative, or fair. This work will in general\u0000present the defense appreciate exploring on Twitter; the qualities conveyed in them address the tweets: positive, negative, or fair.\u0000Twitter is a web-based application that integrates with a blog and a wide range of contacts, allowing users to send brief\u0000140-character messages. It's a rapidly growing partnership with over 200 million endorsers, 100 million of whom are active\u0000clients, and a large portion of them follow Twitter on a regular basis, sending out over 250 million tweets. This study aims to\u0000perform Sentimental analysis using deep learning with bigrams and trigrams to classify the tweets accurately.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85365050","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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