India is an agro-based economy, agriculture is the backbone of India economy. The biggest challenged faced by the farmers is to come out of their economic crisis they are facing, Sericulture is one of the best ways to earn a good income, it provides self-employment and also better returns on investment. There are many developments that can be done to the existing silkworm rearing techniques. This paper provides better solutions and developments to the existing systems by using various electrical components. It helps automate the facility by monitoring temperature and humidity. Different stages of growth of cocoon requires different temperature and humidity values hence this can be done with the help of micro-controllers. This paper idea could be carried out both manually and automatically. This proposed system will help farmers economically so that they do not have to spend much time on sericulture and can focus on other agricultural activities but still earn a good income.
{"title":"A Modern Approach To Conventional Silk Farming","authors":"Arya Veer Krishna, Burhanuddin Udaipurwala, Krisha Chhadwa, Amaan Khan, Jayashree Khanapuri, Tilottama Dhake","doi":"10.1109/ICAST55766.2022.10039591","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039591","url":null,"abstract":"India is an agro-based economy, agriculture is the backbone of India economy. The biggest challenged faced by the farmers is to come out of their economic crisis they are facing, Sericulture is one of the best ways to earn a good income, it provides self-employment and also better returns on investment. There are many developments that can be done to the existing silkworm rearing techniques. This paper provides better solutions and developments to the existing systems by using various electrical components. It helps automate the facility by monitoring temperature and humidity. Different stages of growth of cocoon requires different temperature and humidity values hence this can be done with the help of micro-controllers. This paper idea could be carried out both manually and automatically. This proposed system will help farmers economically so that they do not have to spend much time on sericulture and can focus on other agricultural activities but still earn a good income.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123750479","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039566
Tilottama Dhake, Namrata Ansari
Dental disease is a significant problem in humans and deep learning is increasingly being used in the field of dentistry. The purpose of this literature review is to identify dental problems such as tooth identification, caries, treated teeth, dental implants, and endodontic treatment using deep learning approaches in dental image analysis which help dentists in their decision-making process. Dental radiographs are essential for the diagnosis and detection of dental issues. The study focuses on the development and use of several image segmentation/ classification algorithms in the extraction of regions of interest from dental radiographs. To predict different forms of impacted teeth, a convolutional neural network is trained, validated, and tested using dental images with labelled images datasets. Our research suggests that Hybrid models such as CNN-SVM, CNN-KNN or CNN-LSTM or K-mean can be trained over mixed data sets to produce excellent results whereas compared to other image segmentation algorithms, UNet architecture performs better at segmenting dental Xray images.
{"title":"A Survey on Dental Disease Detection Based on Deep Learning Algorithm Performance using Various Radiographs","authors":"Tilottama Dhake, Namrata Ansari","doi":"10.1109/ICAST55766.2022.10039566","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039566","url":null,"abstract":"Dental disease is a significant problem in humans and deep learning is increasingly being used in the field of dentistry. The purpose of this literature review is to identify dental problems such as tooth identification, caries, treated teeth, dental implants, and endodontic treatment using deep learning approaches in dental image analysis which help dentists in their decision-making process. Dental radiographs are essential for the diagnosis and detection of dental issues. The study focuses on the development and use of several image segmentation/ classification algorithms in the extraction of regions of interest from dental radiographs. To predict different forms of impacted teeth, a convolutional neural network is trained, validated, and tested using dental images with labelled images datasets. Our research suggests that Hybrid models such as CNN-SVM, CNN-KNN or CNN-LSTM or K-mean can be trained over mixed data sets to produce excellent results whereas compared to other image segmentation algorithms, UNet architecture performs better at segmenting dental Xray images.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115082790","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 allure of traveling as a hobby has grown significantly throughout time. To enjoy the trip as much as possible and to make the most of the limited time while traveling, one must prepare and conduct adequate research before traveling to a place. Travelers currently use the technique of leaving the planning of the trip to travel companies. Travel agencies frequently follow a fixed set of travel itineraries in order to maximize profits, but these plans are not tailored to the demands of the customers. The existing travel recommendation systems on the market today have some restrictions, such as the fact that they don't account for traffic conditions or the distance between the hotel and the most popular attractions. The suggested system takes into account a number of variables, including the age of the tourist, their interests, the weather at the time of the journey, and the traffic in the cities at the time. It will make suggestions for hotels, restaurants, and other activities a visitor can partake in during his stay by applying sentiment analysis and geo-tagging.
{"title":"An Approach Travel Recommendation System and Route Optimizer using AI","authors":"Prachiti Bapat, Ruchira Jadhav, Vedant Mishra, Aarti Sahitya","doi":"10.1109/ICAST55766.2022.10039531","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039531","url":null,"abstract":"The allure of traveling as a hobby has grown significantly throughout time. To enjoy the trip as much as possible and to make the most of the limited time while traveling, one must prepare and conduct adequate research before traveling to a place. Travelers currently use the technique of leaving the planning of the trip to travel companies. Travel agencies frequently follow a fixed set of travel itineraries in order to maximize profits, but these plans are not tailored to the demands of the customers. The existing travel recommendation systems on the market today have some restrictions, such as the fact that they don't account for traffic conditions or the distance between the hotel and the most popular attractions. The suggested system takes into account a number of variables, including the age of the tourist, their interests, the weather at the time of the journey, and the traffic in the cities at the time. It will make suggestions for hotels, restaurants, and other activities a visitor can partake in during his stay by applying sentiment analysis and geo-tagging.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116016853","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039507
Payal Varangaonkar, S. Rode
A landslide is a condition in which a huge amount of rock particles slide or break off down a slope, resulting in great natural and physical loss in addition to the lives of many people. In large parts of the world, massive damage is caused by landslides. The utility of remotely sensed images is used for landslide detection, mapping, prediction, and assessment round the world. This systematic analysis might also make contributions to better expertise the considerable use of remotely sensed records and spatial evaluation techniques to conduct landslide research at more than a few scales. The machine learning algorithms in particular ANN and SVM are used as soft computing techniques for landslide prediction. The accuracy obtained from SVM is 91.78% and with ANN 93.38%. In India landslide is famous phenomena of Himalayan location, Western Ghats and southern Nilgiris Mountains. Such losses must be avoided if right perception tool is available that would notify about the event in boost. With the use of proposed soft computing techniques this paper projects unique landslide prediction techniques with cognizance on western India.
{"title":"Research on Efficient Landslide Prediction Approaches using Machine Learning Techniques","authors":"Payal Varangaonkar, S. Rode","doi":"10.1109/ICAST55766.2022.10039507","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039507","url":null,"abstract":"A landslide is a condition in which a huge amount of rock particles slide or break off down a slope, resulting in great natural and physical loss in addition to the lives of many people. In large parts of the world, massive damage is caused by landslides. The utility of remotely sensed images is used for landslide detection, mapping, prediction, and assessment round the world. This systematic analysis might also make contributions to better expertise the considerable use of remotely sensed records and spatial evaluation techniques to conduct landslide research at more than a few scales. The machine learning algorithms in particular ANN and SVM are used as soft computing techniques for landslide prediction. The accuracy obtained from SVM is 91.78% and with ANN 93.38%. In India landslide is famous phenomena of Himalayan location, Western Ghats and southern Nilgiris Mountains. Such losses must be avoided if right perception tool is available that would notify about the event in boost. With the use of proposed soft computing techniques this paper projects unique landslide prediction techniques with cognizance on western India.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"425 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116091049","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039656
Jamal, Jahidul Hasan Antor, Rajneesh Kumar, P. Rani
Breast cancer is one cancer that is becoming more prevalent every day. It's becoming worse due to a lack of detection. Lowering the death rate may be possible with quick detection. Based on the Wisconsin Breast Cancer dataset, this study suggests a machine learning-based strategy for identifying breast cancer. There were five distinct machine learning algorithms tested. Logistic Regression has given 94.73% accuracy, Decision Tree has 92.98% accuracy, Random Forest has 98.24% accuracy, and Support Vector Machine (SVM) has 96.49% accuracy. Random Forest has given the highest accuracy which is 98.24 %.
{"title":"Breast Cancer Prediction Using Machine Learning Classifiers","authors":"Jamal, Jahidul Hasan Antor, Rajneesh Kumar, P. Rani","doi":"10.1109/ICAST55766.2022.10039656","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039656","url":null,"abstract":"Breast cancer is one cancer that is becoming more prevalent every day. It's becoming worse due to a lack of detection. Lowering the death rate may be possible with quick detection. Based on the Wisconsin Breast Cancer dataset, this study suggests a machine learning-based strategy for identifying breast cancer. There were five distinct machine learning algorithms tested. Logistic Regression has given 94.73% accuracy, Decision Tree has 92.98% accuracy, Random Forest has 98.24% accuracy, and Support Vector Machine (SVM) has 96.49% accuracy. Random Forest has given the highest accuracy which is 98.24 %.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122047198","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 paper proposes and emphasizes the requirement of an Blockchain based smart contract for NGO's and startup crowdfunding in the present circumstances. It also highlights the need of an online financial system for indigenous NGO's and seed fund utilization of startups. Conventionally, most charity organizations make use of hard cash for settling its transactions making the process less transparent. However, due to the COVID-19 pandemic, financial system has been largely affected. In this case an online financial transaction cum procurement portal would be crucial for the candidates applying relief in remote locations. The system analyses their eligibility based on their Curriculum Vitae (CV). Proposed system uses Ethereum based smart contract and Truffle Box to build a complete Dapp (decentralized application). Authors have used MetaMask Extension as a cryptocurrency wallet and Ganache blockchain to develop, deploy and test the decentralized application.
{"title":"Smart Contracts For NGOs and Startups using Blockchain","authors":"Mohil Sarvankar, Viraj Wasnik, Aditya Tarade, Payal Shah, Narendra Bhagat, S. Rathod","doi":"10.1109/ICAST55766.2022.10039621","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039621","url":null,"abstract":"This paper proposes and emphasizes the requirement of an Blockchain based smart contract for NGO's and startup crowdfunding in the present circumstances. It also highlights the need of an online financial system for indigenous NGO's and seed fund utilization of startups. Conventionally, most charity organizations make use of hard cash for settling its transactions making the process less transparent. However, due to the COVID-19 pandemic, financial system has been largely affected. In this case an online financial transaction cum procurement portal would be crucial for the candidates applying relief in remote locations. The system analyses their eligibility based on their Curriculum Vitae (CV). Proposed system uses Ethereum based smart contract and Truffle Box to build a complete Dapp (decentralized application). Authors have used MetaMask Extension as a cryptocurrency wallet and Ganache blockchain to develop, deploy and test the decentralized application.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781457","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039597
Kulaye Shreyal Ashok, Kulaye Aishwarya Ashok, Shaikh Mohammad Bilal Naseem
The comments sections of online forums and social media platforms have become the new playing field for cyber harassment. Correspondingly, various organizations and companies have decided to abolish toxic and nasty comments altogether to avoid this kind of issue. To protect authorized and genuine users from being exposed to comments which contain offensive language on online mediums or social media platforms, organizations have started flagging such comments and they are blocking those users who are using unpleasant forms of language. Most of the organizations use computerized algorithms for instinctive discovery of comment toxicity using machine learning and artificial intelligence based systems. In the present research study, we have tried to build multi headed comment toxicity detection models. We have built three toxicity detection models using deep learning techniques and compared the accuracy and results. We have also developed a menu driven interface which will help to link machine learning models which is uncomplicated for non programmers and this connection of model to interface will be convenient for making interactive programming interfaces with great accuracy and operationality.
{"title":"A Neuro-NLP Induced Deep Learning Model Developed Towards Comment Based Toxicity Prediction","authors":"Kulaye Shreyal Ashok, Kulaye Aishwarya Ashok, Shaikh Mohammad Bilal Naseem","doi":"10.1109/ICAST55766.2022.10039597","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039597","url":null,"abstract":"The comments sections of online forums and social media platforms have become the new playing field for cyber harassment. Correspondingly, various organizations and companies have decided to abolish toxic and nasty comments altogether to avoid this kind of issue. To protect authorized and genuine users from being exposed to comments which contain offensive language on online mediums or social media platforms, organizations have started flagging such comments and they are blocking those users who are using unpleasant forms of language. Most of the organizations use computerized algorithms for instinctive discovery of comment toxicity using machine learning and artificial intelligence based systems. In the present research study, we have tried to build multi headed comment toxicity detection models. We have built three toxicity detection models using deep learning techniques and compared the accuracy and results. We have also developed a menu driven interface which will help to link machine learning models which is uncomplicated for non programmers and this connection of model to interface will be convenient for making interactive programming interfaces with great accuracy and operationality.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126876595","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039516
Swati Sharma, Arjun Arora
Electroencephalogram (EEG) contains vital physiological information that provides important information about the human brain activity, which makes it of primary importance for the diagnosis and detection of epileptic seizures. According to experts, before a seizure, there is some abnormal activity in the brain called the preictal state and the challenging part is to distinguish preictal and interictal state of the brain. For such challenges, there is a need of automated models for detecting massive raw data and accurately classifying the data with low false positives. These models will help the patients as well as assist the medical team for accurate and time efficient detection. The right combination of data preprocessing methodology, feature extraction and classification will yield a higher accuracy, sensitivity and specificity resulting in accurate detection of epileptic seizures. In this research, the aim is to review different AI approaches and techniques that were used in previous research, for the detection of epilepticseizures. After review and analysis, the study aims at performing a comparative analysis on the machine learning algorithms and the bestperforming algorithms will be filtered out using Principal Component Analysis (PCA) method. Thefiltered algorithms will then finally be enhanced foraccurate detection of epileptic seizures.
{"title":"Detection of Epileptic Seizures using Machine Learning","authors":"Swati Sharma, Arjun Arora","doi":"10.1109/ICAST55766.2022.10039516","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039516","url":null,"abstract":"Electroencephalogram (EEG) contains vital physiological information that provides important information about the human brain activity, which makes it of primary importance for the diagnosis and detection of epileptic seizures. According to experts, before a seizure, there is some abnormal activity in the brain called the preictal state and the challenging part is to distinguish preictal and interictal state of the brain. For such challenges, there is a need of automated models for detecting massive raw data and accurately classifying the data with low false positives. These models will help the patients as well as assist the medical team for accurate and time efficient detection. The right combination of data preprocessing methodology, feature extraction and classification will yield a higher accuracy, sensitivity and specificity resulting in accurate detection of epileptic seizures. In this research, the aim is to review different AI approaches and techniques that were used in previous research, for the detection of epilepticseizures. After review and analysis, the study aims at performing a comparative analysis on the machine learning algorithms and the bestperforming algorithms will be filtered out using Principal Component Analysis (PCA) method. Thefiltered algorithms will then finally be enhanced foraccurate detection of epileptic seizures.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129286907","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039519
Soham Shreedhar Pandit, Shaikh Mohammad Bilal Naseem
The employment impact of advances in artificial intelligence has become a major concern in today's world in recent years. It is often discussed at conferences that rapid advances in machine learning, robotics, and other AI-related technologies could lead to massive unemployment in the country. This study focuses on comparing various research papers published by different authors based on the impact of Artificial Intelligence on human jobs. Various authors investigate the extent to which computerization and automation have the potential to replace available jobs. My research paper is based on thorough examination of research papers published by different authors and also online survey done by me via google forms. We studied the current and foreseeable effects of trends in job availability and human survival due to increase in automation in almost every sector. We examine the jobs most likely to be eliminated, the financial impact of automation, and how people may still be productive in a world when robots perform the majority of the labour.
{"title":"A composite Literature review on Impact of Artificial Intelligence on Jobs Profiling","authors":"Soham Shreedhar Pandit, Shaikh Mohammad Bilal Naseem","doi":"10.1109/ICAST55766.2022.10039519","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039519","url":null,"abstract":"The employment impact of advances in artificial intelligence has become a major concern in today's world in recent years. It is often discussed at conferences that rapid advances in machine learning, robotics, and other AI-related technologies could lead to massive unemployment in the country. This study focuses on comparing various research papers published by different authors based on the impact of Artificial Intelligence on human jobs. Various authors investigate the extent to which computerization and automation have the potential to replace available jobs. My research paper is based on thorough examination of research papers published by different authors and also online survey done by me via google forms. We studied the current and foreseeable effects of trends in job availability and human survival due to increase in automation in almost every sector. We examine the jobs most likely to be eliminated, the financial impact of automation, and how people may still be productive in a world when robots perform the majority of the labour.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196686","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 study presents a detailed survey of different works related to sentiment analysis. The COVID-19 pandemic and its impact on people's mental health act as the driving force behind this survey. The survey can help study sentiment analysis and approaches taken in many studies to detect human emotions via advanced technology. It can also help in improving present systems by finding loopholes and increasing their accuracy. Various lexicon and ML-based systems and models like Word2Vec and LSTM were studied in the surveyed papers. Some of the current and future directions highlighted were Twitter sentiment analysis, review-based market analysis, determining changing behavior and emotions in a given time period, and detecting the mental health of employees, and students. This survey provides details related to trends and topics in sentiment analysis and an in-depth understanding of various technologies used in different studies. It also gives an insight into the wide variety of applications related to sentiment analysis.
{"title":"A Comparative Survey of Multimodal Multilabel Sentiment Analysis and Its Applications Initiated Due to the Impact of COVID-19","authors":"Nisha Gharpure, Minakshee Narayankar, Ishita Tambat","doi":"10.1109/ICAST55766.2022.10039512","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039512","url":null,"abstract":"This study presents a detailed survey of different works related to sentiment analysis. The COVID-19 pandemic and its impact on people's mental health act as the driving force behind this survey. The survey can help study sentiment analysis and approaches taken in many studies to detect human emotions via advanced technology. It can also help in improving present systems by finding loopholes and increasing their accuracy. Various lexicon and ML-based systems and models like Word2Vec and LSTM were studied in the surveyed papers. Some of the current and future directions highlighted were Twitter sentiment analysis, review-based market analysis, determining changing behavior and emotions in a given time period, and detecting the mental health of employees, and students. This survey provides details related to trends and topics in sentiment analysis and an in-depth understanding of various technologies used in different studies. It also gives an insight into the wide variety of applications related to sentiment analysis.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126281631","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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