Pub Date : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752161
R. Bibave, P. Thokal, Ram Hajare, Anurag M. Deulkar, P. William, Ajaysingh Chandan
In this paper two topologies of single phase to three phase power converters (SPTP) using one single phase rectifier and two single phase rectifiers connected in parallel at front side respectively are discussed. The topology using two single phase rectifiers reduces the switch current, total harmonic distortion in the input supply current and makes the input current nearly in phase with supply voltage. Though the numbers of switches are more in two single phase rectifier topology but the power loss may be lower than the topology using one single phase rectifier. Both the topologies are simulated in MATLAB/SIMULINK and the outputs are compared in terms of THD of input supply current. In both the rectifier PWM controlled technique is used.
{"title":"A Comparative Analysis of Single Phase to Three Phase Power Converter for Input Current THD Reduction","authors":"R. Bibave, P. Thokal, Ram Hajare, Anurag M. Deulkar, P. William, Ajaysingh Chandan","doi":"10.1109/ICEARS53579.2022.9752161","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752161","url":null,"abstract":"In this paper two topologies of single phase to three phase power converters (SPTP) using one single phase rectifier and two single phase rectifiers connected in parallel at front side respectively are discussed. The topology using two single phase rectifiers reduces the switch current, total harmonic distortion in the input supply current and makes the input current nearly in phase with supply voltage. Though the numbers of switches are more in two single phase rectifier topology but the power loss may be lower than the topology using one single phase rectifier. Both the topologies are simulated in MATLAB/SIMULINK and the outputs are compared in terms of THD of input supply current. In both the rectifier PWM controlled technique is used.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"143 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125841525","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752162
K. Sumalatha, E. Muneender
In push-button control places featuring villages, isles along with irregular areas, there is a chance of repeating energy breakdowns, existing drops, or energy variations as a result of grid-side deficiencies. Grid-connected renewable resource systems or even micro-grid systems are a lot better for such remote internet sites to satisfy the nearby vital lots criteria during grid-side failings. In renewable energy systems, solar electric power systems come in addition, to the hybrid PV-battery systems or even possibly energy storage systems, which are a great deal added reliable in providing uninterruptible energy to the place essential bunches throughout grid-side weakness. This energy storage device additionally enhances the system parts during electrical energy renovations. In the present paper, a PV-battery mixture system along with DC-side integrating is checked out, and also an electric energy stabilizing control is highly recommended to move the electrical power to grid/load along with the electric battery. This short article targets to deliver a comprehensive top-down view of research study on crossbreed PV, Wind, electric battery unit for ON-network and also OFF-GRID applications.
{"title":"Study on Hybrid PV, Wind, Battery System for ON–GRID and OFF–GRID Applications : Vision, Requirements, Challenges, Insights, and Opportunities","authors":"K. Sumalatha, E. Muneender","doi":"10.1109/ICEARS53579.2022.9752162","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752162","url":null,"abstract":"In push-button control places featuring villages, isles along with irregular areas, there is a chance of repeating energy breakdowns, existing drops, or energy variations as a result of grid-side deficiencies. Grid-connected renewable resource systems or even micro-grid systems are a lot better for such remote internet sites to satisfy the nearby vital lots criteria during grid-side failings. In renewable energy systems, solar electric power systems come in addition, to the hybrid PV-battery systems or even possibly energy storage systems, which are a great deal added reliable in providing uninterruptible energy to the place essential bunches throughout grid-side weakness. This energy storage device additionally enhances the system parts during electrical energy renovations. In the present paper, a PV-battery mixture system along with DC-side integrating is checked out, and also an electric energy stabilizing control is highly recommended to move the electrical power to grid/load along with the electric battery. This short article targets to deliver a comprehensive top-down view of research study on crossbreed PV, Wind, electric battery unit for ON-network and also OFF-GRID applications.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125907654","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752072
Sankararao Majji, C. S. Dash, Asisa Kumar Panigrahy
The ion-sensitive field-effect transistor (ISFET) is one of the most sensitive and adaptable sensors available, and it may be employed in modern complementary metal-oxide semiconductor (CMOS) techniques. As a result of its tiny size, low power consumption, and compatibility with industry-standard complementary metal oxide semiconductor (CMOS) technologies, potentiometric sensors like ISFETs are gaining appeal among sensor scientists and industrialists. These past decades have been broken down into three distinct time periods, which is described in detail in this paper to give an overview of what has been accomplished in the field over this. This work, briefly reviews about history, characteristic of the ISFET, and further discussion is performed about vivid applications of the ISFET.
{"title":"Ion Sensitive Field Effect Transistor as a Bio-compatible Device: A Review","authors":"Sankararao Majji, C. S. Dash, Asisa Kumar Panigrahy","doi":"10.1109/ICEARS53579.2022.9752072","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752072","url":null,"abstract":"The ion-sensitive field-effect transistor (ISFET) is one of the most sensitive and adaptable sensors available, and it may be employed in modern complementary metal-oxide semiconductor (CMOS) techniques. As a result of its tiny size, low power consumption, and compatibility with industry-standard complementary metal oxide semiconductor (CMOS) technologies, potentiometric sensors like ISFETs are gaining appeal among sensor scientists and industrialists. These past decades have been broken down into three distinct time periods, which is described in detail in this paper to give an overview of what has been accomplished in the field over this. This work, briefly reviews about history, characteristic of the ISFET, and further discussion is performed about vivid applications of the ISFET.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129854129","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752158
A. Sweatha, Naluguru Udaya Kumar, S. Bachu
Real world applications like robotic wheelchairs need the automatic detection of roads, potholes, and anomalies. Conventional image processing methods perform the improper recognition of anomalies and result in poor performance. Thus, this article mainly focuses on the implementation of Self-Supervised Label Generator (SSLG) based road anomaly detection system using vertical disparity maps. Initially, the disparity maps are used to identify the borders of the road and then anomalies are identified using filtered disparity maps. Further, the depth anomaly map is calculated using probabilistic approaches. Further, the implementations are performed on real world Red-Green-Blue-Depth (RGB-D) dataset. The simulation results show that the performance of proposed method results in superior performance as compared to the state-of-the-art approaches.
{"title":"Drivable Area and Road Anomaly Segmentation using SSLG with V-Disparity Maps","authors":"A. Sweatha, Naluguru Udaya Kumar, S. Bachu","doi":"10.1109/ICEARS53579.2022.9752158","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752158","url":null,"abstract":"Real world applications like robotic wheelchairs need the automatic detection of roads, potholes, and anomalies. Conventional image processing methods perform the improper recognition of anomalies and result in poor performance. Thus, this article mainly focuses on the implementation of Self-Supervised Label Generator (SSLG) based road anomaly detection system using vertical disparity maps. Initially, the disparity maps are used to identify the borders of the road and then anomalies are identified using filtered disparity maps. Further, the depth anomaly map is calculated using probabilistic approaches. Further, the implementations are performed on real world Red-Green-Blue-Depth (RGB-D) dataset. The simulation results show that the performance of proposed method results in superior performance as compared to the state-of-the-art approaches.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127171183","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752290
Meghna Madhu, Anushka Xavier, N. Jayapandian
One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening.
{"title":"Covid-19 Classification and Detection Model using Deep Learning","authors":"Meghna Madhu, Anushka Xavier, N. Jayapandian","doi":"10.1109/ICEARS53579.2022.9752290","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752290","url":null,"abstract":"One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"17 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126967219","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}
Renewable energy is quickly gaining importance as an alternative energy resource since fossil fuels are limited and their prices are very costly, sun being the biggest source of free energy. The main aim is to utilize the energy getting from the sun in the most efficient way. Also, farmers and other non-technical people in our country are unable to calculate the power consumed and amount of back-up that will be getting according to the load connected to the battery. Thus, the proposed system gives the solution for both the problems by making proper and efficient use of it to solve the crisis of reduction in fossil fuels, since solar is available in abundance. This is a smart system which aims to develop a dual axis solar tracker with an IoT (Internet of ThingS) monitoring system using a microcontroller. Solar panels must be aligned with the sun using a system that tracks the sun in order to optimum power output. Using panels that can revolve along an axis in relation to the location of the sun can increase conversion efficiency by at least 30-40%. Proposed system can be remotely operated using IoT .This report represents the design of a smart solar tracking system which is based on the MSP430 Microcontroller which provides movement of the solar panel in dual axis mode in direction where maximum sunlight is incident. The data which is collected from the system is stored in a cloud. So as it is observed, a two-axis solar tracking system generates more power. It is easier to maintain, no electricity required, no fuel cost and easy to install with long operating life.
{"title":"Smart Solar Tracker With Energy Monitoring","authors":"Shaista Khanam, Rohit Chavan, Shubham Bari, Komal Gupta, Shruti Kuvekar, Trupti Shah, Jayshree Mhatre","doi":"10.1109/ICEARS53579.2022.9752255","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752255","url":null,"abstract":"Renewable energy is quickly gaining importance as an alternative energy resource since fossil fuels are limited and their prices are very costly, sun being the biggest source of free energy. The main aim is to utilize the energy getting from the sun in the most efficient way. Also, farmers and other non-technical people in our country are unable to calculate the power consumed and amount of back-up that will be getting according to the load connected to the battery. Thus, the proposed system gives the solution for both the problems by making proper and efficient use of it to solve the crisis of reduction in fossil fuels, since solar is available in abundance. This is a smart system which aims to develop a dual axis solar tracker with an IoT (Internet of ThingS) monitoring system using a microcontroller. Solar panels must be aligned with the sun using a system that tracks the sun in order to optimum power output. Using panels that can revolve along an axis in relation to the location of the sun can increase conversion efficiency by at least 30-40%. Proposed system can be remotely operated using IoT .This report represents the design of a smart solar tracking system which is based on the MSP430 Microcontroller which provides movement of the solar panel in dual axis mode in direction where maximum sunlight is incident. The data which is collected from the system is stored in a cloud. So as it is observed, a two-axis solar tracking system generates more power. It is easier to maintain, no electricity required, no fuel cost and easy to install with long operating life.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130672201","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752458
Chandini Nekkanti, Prabha K Venkata Ratna, Anupama Korabathina, Sathya Sai Guddanti, L. Vallabhaneni, P. Ramesh
In recent years, early identification of brain tumors has become a major topic of research. Early detection of a tumor for initial therapy enhances the likelihood of the victims life span. Computing Magnetic Resonance Imaging (MRI) for prior tumor identification has the dispute of high computing overhead due to the large volume of image input to the computing system. As a result, there was a significant delay and a drop in system efficiency. As a result, the demand for a more advanced detection system that can accurately segment and represent data for quicker and more precise computing has grown in the latest years. In recent literatures, new methodologies for brain tumor detection based on better learning and processing have been proposed. This study provides a brief overview of recent advances in the field of MRI computing for prompt identification and diagnosis of brain tumors, including representation, segmentation and the application of novel Image Processing and Artificial Intelligence (AI) approaches in analyzing. The present tendency in brain tumor detection computerization, as well as the benefits, limitations, and prospects of existing systems for computer aided diagnostics in detection of brain tumor, are discussed.
近年来，脑肿瘤的早期识别已成为一个重要的研究课题。早期发现肿瘤进行初始治疗可以提高患者寿命的可能性。计算磁共振成像(computational Magnetic Resonance Imaging, MRI)用于肿瘤的预先识别，由于需要向计算系统输入大量的图像，因此存在计算开销大的争议。结果，出现了明显的延迟和系统效率的下降。因此，近年来，对更先进的检测系统的需求不断增长，该系统可以准确地分割和表示数据，以实现更快、更精确的计算。在最近的文献中，提出了基于更好的学习和处理的脑肿瘤检测新方法。本研究简要概述了MRI计算在快速识别和诊断脑肿瘤方面的最新进展，包括表征、分割以及新型图像处理和人工智能(AI)方法在分析中的应用。本文讨论了目前脑肿瘤检测计算机化的发展趋势，以及现有计算机辅助诊断系统在脑肿瘤检测中的优势、局限性和前景。
{"title":"A Review of Technical Coherence between Brain Tumors and their Diagnostic Imaging Spectra","authors":"Chandini Nekkanti, Prabha K Venkata Ratna, Anupama Korabathina, Sathya Sai Guddanti, L. Vallabhaneni, P. Ramesh","doi":"10.1109/ICEARS53579.2022.9752458","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752458","url":null,"abstract":"In recent years, early identification of brain tumors has become a major topic of research. Early detection of a tumor for initial therapy enhances the likelihood of the victims life span. Computing Magnetic Resonance Imaging (MRI) for prior tumor identification has the dispute of high computing overhead due to the large volume of image input to the computing system. As a result, there was a significant delay and a drop in system efficiency. As a result, the demand for a more advanced detection system that can accurately segment and represent data for quicker and more precise computing has grown in the latest years. In recent literatures, new methodologies for brain tumor detection based on better learning and processing have been proposed. This study provides a brief overview of recent advances in the field of MRI computing for prompt identification and diagnosis of brain tumors, including representation, segmentation and the application of novel Image Processing and Artificial Intelligence (AI) approaches in analyzing. The present tendency in brain tumor detection computerization, as well as the benefits, limitations, and prospects of existing systems for computer aided diagnostics in detection of brain tumor, are discussed.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132397082","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9751731
Y. Vedavyas, S. Harsha, M. Subhash, S. Vasavi
Nowadays Drones are being widely used for surveillance and various other activities. The video stream produced by the drone can be disturbing or can contain noise data which might reduce the quality of the video stream. The video stream can be enhanced so that there is no disturbance in the video stream. The video enhancement can be done in real-time with the help of field programmable gate array (FPGA) which reduces the processing time with low energy consumption. Our project mainly focuses on enhancing the quality of the video stream using enhanced super-resolution generative adversarial networks (ESRGAN), contrast-limited Adaptive histogram equalization (CLAHE), Gamma Correction and Saturation Adjustment by integrating the image source in the drone with the FPGA.
{"title":"Quality Enhancement for Drone Based Video using FPGA","authors":"Y. Vedavyas, S. Harsha, M. Subhash, S. Vasavi","doi":"10.1109/ICEARS53579.2022.9751731","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9751731","url":null,"abstract":"Nowadays Drones are being widely used for surveillance and various other activities. The video stream produced by the drone can be disturbing or can contain noise data which might reduce the quality of the video stream. The video stream can be enhanced so that there is no disturbance in the video stream. The video enhancement can be done in real-time with the help of field programmable gate array (FPGA) which reduces the processing time with low energy consumption. Our project mainly focuses on enhancing the quality of the video stream using enhanced super-resolution generative adversarial networks (ESRGAN), contrast-limited Adaptive histogram equalization (CLAHE), Gamma Correction and Saturation Adjustment by integrating the image source in the drone with the FPGA.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130996280","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9752220
S. Biruntha, B. S. Sowmiya, R. Subashri, M. Vasanth
Rainfall forecasting is extremely important in a variety of situations and contexts. By implementing good security precautions in advance, it is possible to significantly limit the consequences of unexpected and excessive rains. Accurate rainfall forecasts have become more difficult than ever before due to climatic changes. Data mining algorithms can forecast rainfall by identifying hidden patterns in meteorological variables from previous data. This study contributes by investigating the application of two data mining approaches for rainfall prediction in the city of Austin. k Nearest Neighbour (kNN) and Decision Trees are some of the techniques used. The dataset comes from a weather forecasting service and includes numerous atmospheric parameters. The pre-processing approach, which includes cleaning and normalising operations, is utilised for successful prediction. The performance of data mining algorithms are evaluated in terms of accuracy, recall, and f-measure with varied training/test data ratios. The future year's rainfall is estimated using the Decision Tree and kNN machine learning algorithms and compare the results obtained by each approach.
{"title":"Rainfall Prediction using kNN and Decision Tree","authors":"S. Biruntha, B. S. Sowmiya, R. Subashri, M. Vasanth","doi":"10.1109/ICEARS53579.2022.9752220","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9752220","url":null,"abstract":"Rainfall forecasting is extremely important in a variety of situations and contexts. By implementing good security precautions in advance, it is possible to significantly limit the consequences of unexpected and excessive rains. Accurate rainfall forecasts have become more difficult than ever before due to climatic changes. Data mining algorithms can forecast rainfall by identifying hidden patterns in meteorological variables from previous data. This study contributes by investigating the application of two data mining approaches for rainfall prediction in the city of Austin. k Nearest Neighbour (kNN) and Decision Trees are some of the techniques used. The dataset comes from a weather forecasting service and includes numerous atmospheric parameters. The pre-processing approach, which includes cleaning and normalising operations, is utilised for successful prediction. The performance of data mining algorithms are evaluated in terms of accuracy, recall, and f-measure with varied training/test data ratios. The future year's rainfall is estimated using the Decision Tree and kNN machine learning algorithms and compare the results obtained by each approach.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127907394","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 : 2022-03-16DOI: 10.1109/ICEARS53579.2022.9751851
C. Sivakumar, D. Sathyanarayanan, P. Karthikeyan, S. Velliangiri
Recently, social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. In this paper, a deep learning-based approach is developed to detect the anomalies in social media using text mining. The emotional classification is considered as a part of the model that classifies emotional anomalies present in the text. Classification of such text is conducted via proper training and testing of the classifier.
{"title":"An Improvised Method for Anomaly Detection in social media using Deep Learning","authors":"C. Sivakumar, D. Sathyanarayanan, P. Karthikeyan, S. Velliangiri","doi":"10.1109/ICEARS53579.2022.9751851","DOIUrl":"https://doi.org/10.1109/ICEARS53579.2022.9751851","url":null,"abstract":"Recently, social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. In this paper, a deep learning-based approach is developed to detect the anomalies in social media using text mining. The emotional classification is considered as a part of the model that classifies emotional anomalies present in the text. Classification of such text is conducted via proper training and testing of the classifier.","PeriodicalId":252961,"journal":{"name":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760425","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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