Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170332
Narayana Darapaneni, B. Sudha, A. Reddy, Ab Abdul Karim, Dhanalakshmi Marothu, S. Kulkarni, Deepak Das Menon
The field of computer vision is constantly expanding and evolving, and it has seen tremendous growth in recent years. Computer vision includes image classification as a fundamental component. The critical components for making the best decisions are image categorization and interpretation. This study intends to examine several etiology clots labels, such as Cardiac Embolic and Large Artery Atherosclerosis (CE & LAA), for researchers and practitioners of medical image analysis (particularly of blood clot origin). An analysis of the accuracy and processing speed of various image classification methods using neural network topologies. This report also describes the available medical data set and explains the performance measures of the techniques that are currently accessible. Some of the Deep Learning architectures, including CNN, VGG-16, Efficient-Net, and Res-Net, are studied in the article and discuss the trends with challenges in the application of medical image analysis.
{"title":"Image Classification of Stroke Blood Clot Origin","authors":"Narayana Darapaneni, B. Sudha, A. Reddy, Ab Abdul Karim, Dhanalakshmi Marothu, S. Kulkarni, Deepak Das Menon","doi":"10.1109/IConSCEPT57958.2023.10170332","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170332","url":null,"abstract":"The field of computer vision is constantly expanding and evolving, and it has seen tremendous growth in recent years. Computer vision includes image classification as a fundamental component. The critical components for making the best decisions are image categorization and interpretation. This study intends to examine several etiology clots labels, such as Cardiac Embolic and Large Artery Atherosclerosis (CE & LAA), for researchers and practitioners of medical image analysis (particularly of blood clot origin). An analysis of the accuracy and processing speed of various image classification methods using neural network topologies. This report also describes the available medical data set and explains the performance measures of the techniques that are currently accessible. Some of the Deep Learning architectures, including CNN, VGG-16, Efficient-Net, and Res-Net, are studied in the article and discuss the trends with challenges in the application of medical image analysis.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132257575","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170396
Sagar Deep Deb, R. Kashyap, A. Abhishek, R. Lavanya, Pushp Paritosh, R. K. Jha
Numerous studies have focused on enhancing the effectiveness of identifying leaf diseases through image classification. However, it is essential to develop a classification system with fewer parameters to enable it to operate efficiently on mobile devices. As a result, A lot of research works are going on to make the neural network computationally light so that we can utilise these networks on a mobile device as it cannot afford a GPU to run in background because of the space and memory limitations of a portable device. In this study, we propose a deep learningbased approach for tomato leaf disease detection using a series of convolutional and depthwise convolutional layers. The proposed model contains only 17,209 trainable parameters. The model was able to achieve high accuracy of 92.10 % on tomato crop from a publicly available PlantVillage dataset while utilizing a smaller number of parameters.
{"title":"Tomato leaf disease detection using series of Convolutional and Depthwise Convolutional Layers","authors":"Sagar Deep Deb, R. Kashyap, A. Abhishek, R. Lavanya, Pushp Paritosh, R. K. Jha","doi":"10.1109/IConSCEPT57958.2023.10170396","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170396","url":null,"abstract":"Numerous studies have focused on enhancing the effectiveness of identifying leaf diseases through image classification. However, it is essential to develop a classification system with fewer parameters to enable it to operate efficiently on mobile devices. As a result, A lot of research works are going on to make the neural network computationally light so that we can utilise these networks on a mobile device as it cannot afford a GPU to run in background because of the space and memory limitations of a portable device. In this study, we propose a deep learningbased approach for tomato leaf disease detection using a series of convolutional and depthwise convolutional layers. The proposed model contains only 17,209 trainable parameters. The model was able to achieve high accuracy of 92.10 % on tomato crop from a publicly available PlantVillage dataset while utilizing a smaller number of parameters.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128555876","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170123
Koteswaramma Dodda, G. Muneeswari
Early detection of cancer improves survival chances. Some cancers, such as pancreatic cancer, are hard to identify or detect earlier, and the stages progress aggressively. This review discusses the recent advancements of biomarkers for the early detection of pancreatic cancer. Genomic, protein, blood, and urine biomarkers of pancreatic cancer, as well as corresponding biosensors for diagnosis of pancreatic cancer, have been evaluated, each of these instances show that new biosensors are emerging as an incredibly prominent substitute to defined processes. In order to predict the overall survival of patients with pancreatic ductal adenocarcinoma cancer (PDAC) this review discusses the state-of-the-art machine learning (ML) techniques utilized and a panel of biomarkers for early cancer diagnosis. Recent studies emphasize the significance of machine learning algorithms like support vector machines (SVM), decision tree (DT), naive bayes like algorithms confusing and enormous volumes of data. The phases of the disease and the chance of survival do not significantly correlate. In clinical practice, ML techniques need to undergo the proper level of validation. Pathologists can better manage patients when they have knowledge of the patient’s condition, the surgical procedure to be performed, individualized therapy, the best use of available resources and medications to prescribe due to accurate predictions.
{"title":"Biomarkers for Early Detection of Pancreatic Cancer: A Review","authors":"Koteswaramma Dodda, G. Muneeswari","doi":"10.1109/IConSCEPT57958.2023.10170123","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170123","url":null,"abstract":"Early detection of cancer improves survival chances. Some cancers, such as pancreatic cancer, are hard to identify or detect earlier, and the stages progress aggressively. This review discusses the recent advancements of biomarkers for the early detection of pancreatic cancer. Genomic, protein, blood, and urine biomarkers of pancreatic cancer, as well as corresponding biosensors for diagnosis of pancreatic cancer, have been evaluated, each of these instances show that new biosensors are emerging as an incredibly prominent substitute to defined processes. In order to predict the overall survival of patients with pancreatic ductal adenocarcinoma cancer (PDAC) this review discusses the state-of-the-art machine learning (ML) techniques utilized and a panel of biomarkers for early cancer diagnosis. Recent studies emphasize the significance of machine learning algorithms like support vector machines (SVM), decision tree (DT), naive bayes like algorithms confusing and enormous volumes of data. The phases of the disease and the chance of survival do not significantly correlate. In clinical practice, ML techniques need to undergo the proper level of validation. Pathologists can better manage patients when they have knowledge of the patient’s condition, the surgical procedure to be performed, individualized therapy, the best use of available resources and medications to prescribe due to accurate predictions.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196378","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170654
Varunika Arya, Neha Makattil, Vasudha Sasikumar, V. Anuparvathi, S. Khandare
Posture is a way in which a human holds his body so that there is less strain on muscles during any movement. Poor body posture may lead to many health issues which range from back pain to fatigue, this may rise up and affect our daily activities. The human ability to stay upright has been compromised over the past few years and health has been overshadowed by improper routine. Majority of the population spend most of their time working seated in one position. Monitoring sitting posture can give a better understanding of the underlying cause of lower back pain. Lower spinal back pain problem treatments cost billions of dollars every year. As a solution to this cause, a wearable posture detection system has been developed in the form of a belt which is connected to a mobile application. The sensors (i.e Flex sensor and Accelerometer) on the belt detect the bending angle and decide the wrong posture. When a wrong posture is detected a buzzer beeps in real time and at the same time a notification is sent to the mobile application connected to the device. The mobile application displays the users daily report and gives personalized yoga and exercise recommendations based on their daily report. This system is designed to identify incorrect posture in real time and give solutions to rectify it with yoga and exercise recommendations on a daily basis.
{"title":"Know Your Posture : Real Time Posture Detection and Correction with Yoga and Exercise Recommendations.","authors":"Varunika Arya, Neha Makattil, Vasudha Sasikumar, V. Anuparvathi, S. Khandare","doi":"10.1109/IConSCEPT57958.2023.10170654","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170654","url":null,"abstract":"Posture is a way in which a human holds his body so that there is less strain on muscles during any movement. Poor body posture may lead to many health issues which range from back pain to fatigue, this may rise up and affect our daily activities. The human ability to stay upright has been compromised over the past few years and health has been overshadowed by improper routine. Majority of the population spend most of their time working seated in one position. Monitoring sitting posture can give a better understanding of the underlying cause of lower back pain. Lower spinal back pain problem treatments cost billions of dollars every year. As a solution to this cause, a wearable posture detection system has been developed in the form of a belt which is connected to a mobile application. The sensors (i.e Flex sensor and Accelerometer) on the belt detect the bending angle and decide the wrong posture. When a wrong posture is detected a buzzer beeps in real time and at the same time a notification is sent to the mobile application connected to the device. The mobile application displays the users daily report and gives personalized yoga and exercise recommendations based on their daily report. This system is designed to identify incorrect posture in real time and give solutions to rectify it with yoga and exercise recommendations on a daily basis.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122989037","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170200
V. Ramya, R. Marimuthu
This paper proposes a single-phase AC-DC-DC converter circuit for charging and discharging batteries and powering loads. The battery is added to the proposed system to reduce the energy consumption caused by the primary AC input voltage. The article implies an AC-DC-DC system with a single-stage, three-port, full-bridge converter. Like a conventional single-phase inverter with H-bridge topology, the ac input is single-phase and operates on two legs. Consequently, each leg serves as both an inverter and a buck-boost converter. Furthermore, the converter only employs four switches and diodes to regulate the flow of electricity between the three ports. A thorough topological analysis and simulation results validate the proposed converter system’s benefits
{"title":"Three Port Full Bridge PFC Converter for Hybrid AC/DC/DC System with Fuzzy Logic Control","authors":"V. Ramya, R. Marimuthu","doi":"10.1109/IConSCEPT57958.2023.10170200","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170200","url":null,"abstract":"This paper proposes a single-phase AC-DC-DC converter circuit for charging and discharging batteries and powering loads. The battery is added to the proposed system to reduce the energy consumption caused by the primary AC input voltage. The article implies an AC-DC-DC system with a single-stage, three-port, full-bridge converter. Like a conventional single-phase inverter with H-bridge topology, the ac input is single-phase and operates on two legs. Consequently, each leg serves as both an inverter and a buck-boost converter. Furthermore, the converter only employs four switches and diodes to regulate the flow of electricity between the three ports. A thorough topological analysis and simulation results validate the proposed converter system’s benefits","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019790","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10169917
Ann Mary Pradeep, Irene Cyriac Merly, Sneha Saju George, Sruthy J Kurian, P. Swapna
The communication era is evolving exponentially with new technologies emerging progressively, to satisfy ubiquitous high data rate transfer. In this context, antenna design has become critical, since efficient communication system requires appropriately designed antenna serving its purpose. An antenna design strategy based on machine learning that accomplishes directional communication using patch antenna is presented here. Genetic Algorithm (GA) is popularly employed for solving limited and unbounded optimization issues that is based on natural selection, which is the primary driver of biological evolution, where the population of individual solutions are repeatedly transformed into newer versions, in search for optimal solutions. NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) is an optimization technique that enables to optimize multiple objectives without being dominated by any one solution. The algorithm is configured to maximize gain & directivity and minimize aperture. The simulation results confirm that suggested antenna design is suitable for high gain applications where miniaturization is of priority.
{"title":"Machine Learning Based Antenna Design","authors":"Ann Mary Pradeep, Irene Cyriac Merly, Sneha Saju George, Sruthy J Kurian, P. Swapna","doi":"10.1109/IConSCEPT57958.2023.10169917","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10169917","url":null,"abstract":"The communication era is evolving exponentially with new technologies emerging progressively, to satisfy ubiquitous high data rate transfer. In this context, antenna design has become critical, since efficient communication system requires appropriately designed antenna serving its purpose. An antenna design strategy based on machine learning that accomplishes directional communication using patch antenna is presented here. Genetic Algorithm (GA) is popularly employed for solving limited and unbounded optimization issues that is based on natural selection, which is the primary driver of biological evolution, where the population of individual solutions are repeatedly transformed into newer versions, in search for optimal solutions. NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) is an optimization technique that enables to optimize multiple objectives without being dominated by any one solution. The algorithm is configured to maximize gain & directivity and minimize aperture. The simulation results confirm that suggested antenna design is suitable for high gain applications where miniaturization is of priority.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122126205","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170191
Subha Danushika Fernando, S. Yasakethu, P.W.M.G.N. Wanasinghe, H. M. K. K. M. B. Herath
Due to their cutting-edge features and capacity to improve the whole camping experience, smart tents sometimes referred to as intelligent or high-tech tents, are becoming more and more significant in the camping and outdoor business. Modern technology included in these tents, such as built-in sensors, Wi-Fi connectivity, and automation systems, allow users to control numerous aspects of the tent from their smartphones. Due to their high-tech nature, smart tents are pricey. It is evident that tropical countries like Sri Lanka cannot effectively utilize the available smart tents for camping. Additionally, there is a need to inexpensively transform a conventional tent into a smart camping tent. In order to address these issues, this research aimed at developing a smart camping tent that can adapt to its dynamic environment. The system was developed by aiding fuzzy-P controlling mechanisms and IoT (Internet of Things) technologies. The experiment results suggested that the smart tent worked at 82.5% accuracy. The fuzzy system showed 81.5% accuracy while the P controller showed 85.0% accuracy.
{"title":"IoT-Enabled Smart Camping Tent for Dynamic Environment","authors":"Subha Danushika Fernando, S. Yasakethu, P.W.M.G.N. Wanasinghe, H. M. K. K. M. B. Herath","doi":"10.1109/IConSCEPT57958.2023.10170191","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170191","url":null,"abstract":"Due to their cutting-edge features and capacity to improve the whole camping experience, smart tents sometimes referred to as intelligent or high-tech tents, are becoming more and more significant in the camping and outdoor business. Modern technology included in these tents, such as built-in sensors, Wi-Fi connectivity, and automation systems, allow users to control numerous aspects of the tent from their smartphones. Due to their high-tech nature, smart tents are pricey. It is evident that tropical countries like Sri Lanka cannot effectively utilize the available smart tents for camping. Additionally, there is a need to inexpensively transform a conventional tent into a smart camping tent. In order to address these issues, this research aimed at developing a smart camping tent that can adapt to its dynamic environment. The system was developed by aiding fuzzy-P controlling mechanisms and IoT (Internet of Things) technologies. The experiment results suggested that the smart tent worked at 82.5% accuracy. The fuzzy system showed 81.5% accuracy while the P controller showed 85.0% accuracy.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127886794","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10169951
N. Sankar, V. Vaideeswaran, J. S. Kumar, M. Rajan Singaravel
This paper proposes an off-board charger for electric vehicles (EV) that can charge multiple EVs with grid power in “grid-to-vehicle” (G2V) mode and in “vehicle-to-vehicle” (V2V) mode. In addition, the proposed charger can feed power to the grid in “vehicle-to-grid” (V2G) mode. In the G2V and V2V combined modes, both grid power and another EV’s power are used simultaneously to charge another EV. By using this mode, the power fed from the grid can be reduced. A three-phase pulse width modulation (PWM) rectifier is used as the front-end converter that maintains a constant DC link voltage and unity power factor (UPF) at the grid side. In accordance with the IEEE 519 standard, the total harmonic distortion (THD) of grid current in V2G, G2V, and combined G2V and V2V modes is maintained at less than 5%. To maintain a constant charging and discharging current for EVs, a half-bridge bidirectional DC/DC converter is employed. The simulation of all four modes is validated using PSIM Professional.
{"title":"Grid Connected Off-Board EV Charger with V2G / G2V and V2V Capability","authors":"N. Sankar, V. Vaideeswaran, J. S. Kumar, M. Rajan Singaravel","doi":"10.1109/IConSCEPT57958.2023.10169951","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10169951","url":null,"abstract":"This paper proposes an off-board charger for electric vehicles (EV) that can charge multiple EVs with grid power in “grid-to-vehicle” (G2V) mode and in “vehicle-to-vehicle” (V2V) mode. In addition, the proposed charger can feed power to the grid in “vehicle-to-grid” (V2G) mode. In the G2V and V2V combined modes, both grid power and another EV’s power are used simultaneously to charge another EV. By using this mode, the power fed from the grid can be reduced. A three-phase pulse width modulation (PWM) rectifier is used as the front-end converter that maintains a constant DC link voltage and unity power factor (UPF) at the grid side. In accordance with the IEEE 519 standard, the total harmonic distortion (THD) of grid current in V2G, G2V, and combined G2V and V2V modes is maintained at less than 5%. To maintain a constant charging and discharging current for EVs, a half-bridge bidirectional DC/DC converter is employed. The simulation of all four modes is validated using PSIM Professional.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126957104","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10169904
N. Poornima, D. Abilash, M. Theodaniel
In the Stock market, the volatility of leading MNCs’(Multi-National Corporations) shares is a major matter of concern and comes under the limelight nowadays. Unlike the 1920s sudden surge and dot-com crash, the contemporary world has never seen such a biggest bull or bear particularly in the past few decades. The stock market is majorly influenced by the credibility opinion of the general public on the firm. In the 21st century, the emergence of research LLC (Limited Liability Company) which gains profit from short selling of the shares by manipulating the share of a certain firm by exposing the legality of trespassing norms has made the researchers include a current public sentiment on the firm since short selling is a matter of one day. The first and foremost impact of such exposure would be instantly taken to Twitter, a credible social media. In order to infer the associativity of sentiment analysis on the stock market analysis we have taken time-series data of a recently exposed firm which faces the biggest bear in the market from Yahoo Finance for the timeline of 07-02-22 to 03-02-2023 and the Twitter data for the same timeline had been accessed by is a scraper for Social Networking Services (SNS). The extracted tweet data with almost 1000 tweets each day has been analyzed by Meta’s roBERTa, an NLP(Natural Language Processing)-based framework for sentiment analysis. It is used to predict whether the market will be bearish or bullish on the day. Then the sentiment flag attribute and the market data attribute have been used to build a 3-layered Long Short Term Memory (LSTM), an ANN(Artificial Neural Network) where the data will be predicted for the same day’s stock movement. The results show that the sentiment reflects on the stock’s movement and the accuracy of the proposed work is about 96.14%.
{"title":"Improvising the Stock Prediction by Integrating with roBERTa and LSTM","authors":"N. Poornima, D. Abilash, M. Theodaniel","doi":"10.1109/IConSCEPT57958.2023.10169904","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10169904","url":null,"abstract":"In the Stock market, the volatility of leading MNCs’(Multi-National Corporations) shares is a major matter of concern and comes under the limelight nowadays. Unlike the 1920s sudden surge and dot-com crash, the contemporary world has never seen such a biggest bull or bear particularly in the past few decades. The stock market is majorly influenced by the credibility opinion of the general public on the firm. In the 21st century, the emergence of research LLC (Limited Liability Company) which gains profit from short selling of the shares by manipulating the share of a certain firm by exposing the legality of trespassing norms has made the researchers include a current public sentiment on the firm since short selling is a matter of one day. The first and foremost impact of such exposure would be instantly taken to Twitter, a credible social media. In order to infer the associativity of sentiment analysis on the stock market analysis we have taken time-series data of a recently exposed firm which faces the biggest bear in the market from Yahoo Finance for the timeline of 07-02-22 to 03-02-2023 and the Twitter data for the same timeline had been accessed by is a scraper for Social Networking Services (SNS). The extracted tweet data with almost 1000 tweets each day has been analyzed by Meta’s roBERTa, an NLP(Natural Language Processing)-based framework for sentiment analysis. It is used to predict whether the market will be bearish or bullish on the day. Then the sentiment flag attribute and the market data attribute have been used to build a 3-layered Long Short Term Memory (LSTM), an ANN(Artificial Neural Network) where the data will be predicted for the same day’s stock movement. The results show that the sentiment reflects on the stock’s movement and the accuracy of the proposed work is about 96.14%.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127039024","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170469
Anto Manuel, Gancis Franco Sathyaraj, Rose Chirackal Joseph, Sachin Anu Philip, Sheethal Maria Thomas
Air pollution is the contamination of air due to human and natural activities. It is estimated that air pollution leads to 7 million deaths, a number which is projected to rise over the coming years. Illnesses like asthma, bronchitis, chronic obstructive pulmonary disease (COPD), etc., are worsened by exposure to air pollutants, which also exacerbate any underlying cardiac and respiratory disorders. Thus it is essential to constantly monitor air quality and to provide a detailed analysis of air pollutants in the user’s environment. Additionally, this may be used to predict diseases that can be brought on by both short- and long-term exposure to air pollution. Furthermore, wearable technology and health monitoring have seen an increase in popularity in recent years. A wearable device that analyses air quality, and respiratory parameters, protects the wearer from breathing in high concentrations of pollutants, sends SOS alert in case of emergency, and also makes generalised disease predictions based on the dataset provided will be beneficial. In addition, the wearable device must: (i) be able to wirelessly communicate with other devices, (ii) consume very little energy, (iii) have a long battery life, and (iv) be able to share patient data with family, friends, and healthcare professionals. The project aims to design and develop a smart mask that can measure air quality, and monitor the respiratory rate, temperature, and humidity of the user. The device is AI-integrated and IoT-enabled thereby the collected data is analysed and uploaded to the cloud. The user’s analysed data is available to be viewed on an application. A provision to alert emergency contacts and medical professionals shall be added as well.
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