Pub Date : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633919
Mohammed Ehsan Ur Rahman, Aishwarya Yelishetty
The current trends in viewers' behavior and attitudes while viewing an advertisement are suggestive of the fact that eventually, every viewer who can become annoyed due to an advertisement will be using advertisement blocking software and/or anti-annoyance software as proposed in this paper. This paper presents an abstract, theoretically framed proposed work to avoid annoyance in viewers caused due to distracting digital advertisements and content, by customizing the response a viewer's device, such as mobile phones/desktops, can give while an advertisement is being displayed. The paper deals with the solutions to the emotional trigger due to advertisements, proposing a special hardware device that extracts information required for the ad-blocking or anti-annoyance applications running on the device, and avoiding annoyance, displeasure, and lack of concentration in individuals by considering various human characteristics, like one's aesthetic sense, physical characteristics, entertainment taste, etc. Our research work also provides theoretical and quantitative analysis, proof of an intelligent and customizable system to prevent disturbance, distraction, anxiety, and other unnecessary emotional imbalances due to repeated online advertisements. The results show the real-world marketing effects on targeted people. The work also discusses to what degree the vexation and impatience levels vary with the ads containing different levels of product class and socioeconomic class. As blocking advertisements has a lot of psychological and financial implications on one's life, our work leaves an outlet for substantive investigation into innovative, high-quality marketing content, marketing strategies, and significant unseen effects on users and leaves a pathway for relaxing effects on the user through the proposed hardware and software, spanning a wide range of subject areas.
{"title":"Midway Advertisement: A Mechanism to Curb Annoyance due to Unwanted Advertisements","authors":"Mohammed Ehsan Ur Rahman, Aishwarya Yelishetty","doi":"10.1109/ICSES52305.2021.9633919","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633919","url":null,"abstract":"The current trends in viewers' behavior and attitudes while viewing an advertisement are suggestive of the fact that eventually, every viewer who can become annoyed due to an advertisement will be using advertisement blocking software and/or anti-annoyance software as proposed in this paper. This paper presents an abstract, theoretically framed proposed work to avoid annoyance in viewers caused due to distracting digital advertisements and content, by customizing the response a viewer's device, such as mobile phones/desktops, can give while an advertisement is being displayed. The paper deals with the solutions to the emotional trigger due to advertisements, proposing a special hardware device that extracts information required for the ad-blocking or anti-annoyance applications running on the device, and avoiding annoyance, displeasure, and lack of concentration in individuals by considering various human characteristics, like one's aesthetic sense, physical characteristics, entertainment taste, etc. Our research work also provides theoretical and quantitative analysis, proof of an intelligent and customizable system to prevent disturbance, distraction, anxiety, and other unnecessary emotional imbalances due to repeated online advertisements. The results show the real-world marketing effects on targeted people. The work also discusses to what degree the vexation and impatience levels vary with the ads containing different levels of product class and socioeconomic class. As blocking advertisements has a lot of psychological and financial implications on one's life, our work leaves an outlet for substantive investigation into innovative, high-quality marketing content, marketing strategies, and significant unseen effects on users and leaves a pathway for relaxing effects on the user through the proposed hardware and software, spanning a wide range of subject areas.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"41 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76282512","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633857
Meenu Gupta, Rakesh Kumar, Geet Pradhan, Dheeraj Kumawat
The phrase post-reality coined with the aid of using the dictionary of Oxford word in the Year 2016. The adjective name, referring to the describing conditions of which goal information have little impact on reframing public opinion instead of being attractive to non-public emotions and beliefs. This ends in incorrect information and social problems. Therefore, it's far essential to take the time to locate this information and save you them from spreading. In this paper, astrategyis used for device mastering, particularly surveyed reading, to reap fake information. Specifically, this work used a database of non-fiction tales to educate the device mastering version, the use of the Scikit-study which is a library in Python. Records were extracted by us from the database the use of textual content illustration fashions together with a bag of words, the term frequency Inverse document frequency, and the bi diagram frequency. After which we tested strategies of type, particularly the feasible type and the linear department of the name and content material, searching at whether it changed into a typical/no-click on feed, in a fake / real sequence. The end result of our take a look at is that line segregation works high-quality with the TF-IDF version withinside the content material segmentation process. The Bi-gram frequency version furnished an awful lot of decrease accuracy of theme separation as compared to the term bag of words and TF-IDF.
{"title":"Content Based Offline Fake News Detection using Classification Technique","authors":"Meenu Gupta, Rakesh Kumar, Geet Pradhan, Dheeraj Kumawat","doi":"10.1109/ICSES52305.2021.9633857","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633857","url":null,"abstract":"The phrase post-reality coined with the aid of using the dictionary of Oxford word in the Year 2016. The adjective name, referring to the describing conditions of which goal information have little impact on reframing public opinion instead of being attractive to non-public emotions and beliefs. This ends in incorrect information and social problems. Therefore, it's far essential to take the time to locate this information and save you them from spreading. In this paper, astrategyis used for device mastering, particularly surveyed reading, to reap fake information. Specifically, this work used a database of non-fiction tales to educate the device mastering version, the use of the Scikit-study which is a library in Python. Records were extracted by us from the database the use of textual content illustration fashions together with a bag of words, the term frequency Inverse document frequency, and the bi diagram frequency. After which we tested strategies of type, particularly the feasible type and the linear department of the name and content material, searching at whether it changed into a typical/no-click on feed, in a fake / real sequence. The end result of our take a look at is that line segregation works high-quality with the TF-IDF version withinside the content material segmentation process. The Bi-gram frequency version furnished an awful lot of decrease accuracy of theme separation as compared to the term bag of words and TF-IDF.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"7 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84383747","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633906
Sahreen Sajad, S. Dharshika, Merin Meleet
Listening to music is a pastime most people enjoy. We're all fascinated with music and resort to listening to it in times when we're in a good mood and also while in distress. While a variety of applications and softwares exist that let musicians make music, there is not much development in the field for novices who do not understand music. This paper aims to change that. Not everyone should need to be an expert in the field to be able to create melodious pieces of music. This paper gives an approach to be able to do the same using Recurrent Neural Networks. The idea is to build a model that trains using existing melodies or instrumentals and generate new music based on the training. The approach will not only be helpful to people who do not know the field well but also to musicians to be able to generate fine quality music that can be developed further to make decent length songs. We aim to create music without having a need to play musical instruments physically.
{"title":"Music Generation for Novices Using Recurrent Neural Network (RNN)","authors":"Sahreen Sajad, S. Dharshika, Merin Meleet","doi":"10.1109/ICSES52305.2021.9633906","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633906","url":null,"abstract":"Listening to music is a pastime most people enjoy. We're all fascinated with music and resort to listening to it in times when we're in a good mood and also while in distress. While a variety of applications and softwares exist that let musicians make music, there is not much development in the field for novices who do not understand music. This paper aims to change that. Not everyone should need to be an expert in the field to be able to create melodious pieces of music. This paper gives an approach to be able to do the same using Recurrent Neural Networks. The idea is to build a model that trains using existing melodies or instrumentals and generate new music based on the training. The approach will not only be helpful to people who do not know the field well but also to musicians to be able to generate fine quality music that can be developed further to make decent length songs. We aim to create music without having a need to play musical instruments physically.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"72 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85923591","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633976
A. Menon, M. Prabhakar
Agriculture has been one of the ultimate factors contributing to the survival and development of human civilizations for generations across the globe. Due to extreme climatic changes, destruction of forest cover and industrial advancements the production rates and quality of crops grown in agricultural farms have been drastically affected. This has affected the very livelihood of human beings. Thus, there is a need for a real-time monitoring device to continuously monitor the crops and ensure that it remains healthy until harvest. The system proposed in this paper is based on Internet of Things technology with the Arduino Mega Development board. The system performs monitoring of Weather, Soil Parameters and detects fire, insects or pests surrounding the area. It provides a sprinkler system for spraying water, organic pesticides, and insecticides according to the monitored data analysed by the microcontroller. The system is automated as it is powered by solar energy and all functions and geographic coordinates of each crop are pre-programmed into the microcontroller. This system will also aid in water conservation through controlled irrigation and increases production rate.
{"title":"Smart Agriculture Monitoring Rover for Small-Scale Farms in Rural Areas using IoT","authors":"A. Menon, M. Prabhakar","doi":"10.1109/ICSES52305.2021.9633976","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633976","url":null,"abstract":"Agriculture has been one of the ultimate factors contributing to the survival and development of human civilizations for generations across the globe. Due to extreme climatic changes, destruction of forest cover and industrial advancements the production rates and quality of crops grown in agricultural farms have been drastically affected. This has affected the very livelihood of human beings. Thus, there is a need for a real-time monitoring device to continuously monitor the crops and ensure that it remains healthy until harvest. The system proposed in this paper is based on Internet of Things technology with the Arduino Mega Development board. The system performs monitoring of Weather, Soil Parameters and detects fire, insects or pests surrounding the area. It provides a sprinkler system for spraying water, organic pesticides, and insecticides according to the monitored data analysed by the microcontroller. The system is automated as it is powered by solar energy and all functions and geographic coordinates of each crop are pre-programmed into the microcontroller. This system will also aid in water conservation through controlled irrigation and increases production rate.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"62 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90254431","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633788
K. Maheswari, G. Shobana, S. Bushra, N. Subramanian
Even if there is a rapid proliferation with the advantages of low cost, the emerging on-demand cloud services have led to an increase in cybercrime activities. Cyber criminals are utilizing cloud services through its distributed nature of infrastructure and create a lot of challenges to detect and investigate the incidents by the security personnel. The tracing of command flow forms a clue for the detection of malicious activity occurring in the system through System Calls Analysis (SCA). As machine learning based approaches are known to automate the work in detecting malwares, simple Support Vector Machine (SVM) based approaches are often reporting low value of accuracy. In this work, a malware classification system proposed with the supervised machine learning of unknown malware instances through Support Vector Machine - Stochastic Gradient Descent (SVM-SGD) algorithm. The performance of the system evaluated on CIC-IDS2017 dataset with labelled attacks. The system is compared with traditional signature based detection model and observed to report less number of false alerts with improved accuracy. The signature based detection gets an accuracy of 86.12%, while the SVM-SGD gets the best accuracy of 99.13%. The model is found to be lightweight but efficient in detecting malware with high degree of accuracy.
{"title":"Supervised malware learning in cloud through System calls analysis","authors":"K. Maheswari, G. Shobana, S. Bushra, N. Subramanian","doi":"10.1109/ICSES52305.2021.9633788","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633788","url":null,"abstract":"Even if there is a rapid proliferation with the advantages of low cost, the emerging on-demand cloud services have led to an increase in cybercrime activities. Cyber criminals are utilizing cloud services through its distributed nature of infrastructure and create a lot of challenges to detect and investigate the incidents by the security personnel. The tracing of command flow forms a clue for the detection of malicious activity occurring in the system through System Calls Analysis (SCA). As machine learning based approaches are known to automate the work in detecting malwares, simple Support Vector Machine (SVM) based approaches are often reporting low value of accuracy. In this work, a malware classification system proposed with the supervised machine learning of unknown malware instances through Support Vector Machine - Stochastic Gradient Descent (SVM-SGD) algorithm. The performance of the system evaluated on CIC-IDS2017 dataset with labelled attacks. The system is compared with traditional signature based detection model and observed to report less number of false alerts with improved accuracy. The signature based detection gets an accuracy of 86.12%, while the SVM-SGD gets the best accuracy of 99.13%. The model is found to be lightweight but efficient in detecting malware with high degree of accuracy.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90291962","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633928
Prashant Ghimire, Sweekar Piya, Anish Man Gurung
With around 194 million cases and around 4 million reported deaths, affecting 220 countries [1], Coronavirus (COVID-19) is still prevalent. Wearing facemasks in crowded areas is one of the undemanding and effective measures among the multitude of preventive guidelines provided by the World Health Organization (WHO). However, unruly humans are present; monitoring if people are wearing facemasks in dense areas is taxing and cumbersome. In this paper, we have experimented two ways of tackling facemask detection for comparison purposes: (1) by using transfer learning on four pretrained State-Of- The-Art (SOTA) models - Inception-V3, Resnet-50, VGG-16, and Densenet-121, (2) using these SOTA models as feature extractors and training ML classifiers (Support Vector Machine (SVM), Decision Tree, and Gaussian Naive Bayes) on them. Simulated Face Mask Dataset (SMFD) is used to train and validate all of the models, including data augmentation to enhance data samples. The SOTA models displayed exceptional validation accuracy (greater than 90%), with VGG-16 and ResNet-50 performing the best. Similarly, all combinations of SOTA-ML models have remarkable performance with the Densenet-121-SVM model obtaining highest accuracy with lesser training time.
{"title":"Comparative study of Face Mask Recognition using Deep Learning and Machine learning classifiers","authors":"Prashant Ghimire, Sweekar Piya, Anish Man Gurung","doi":"10.1109/ICSES52305.2021.9633928","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633928","url":null,"abstract":"With around 194 million cases and around 4 million reported deaths, affecting 220 countries [1], Coronavirus (COVID-19) is still prevalent. Wearing facemasks in crowded areas is one of the undemanding and effective measures among the multitude of preventive guidelines provided by the World Health Organization (WHO). However, unruly humans are present; monitoring if people are wearing facemasks in dense areas is taxing and cumbersome. In this paper, we have experimented two ways of tackling facemask detection for comparison purposes: (1) by using transfer learning on four pretrained State-Of- The-Art (SOTA) models - Inception-V3, Resnet-50, VGG-16, and Densenet-121, (2) using these SOTA models as feature extractors and training ML classifiers (Support Vector Machine (SVM), Decision Tree, and Gaussian Naive Bayes) on them. Simulated Face Mask Dataset (SMFD) is used to train and validate all of the models, including data augmentation to enhance data samples. The SOTA models displayed exceptional validation accuracy (greater than 90%), with VGG-16 and ResNet-50 performing the best. Similarly, all combinations of SOTA-ML models have remarkable performance with the Densenet-121-SVM model obtaining highest accuracy with lesser training time.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"30 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91488044","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}
Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.
{"title":"AI-Assisted Risk Based Two Factor Authentication Method (AIA-RB-2FA)","authors":"Shiburaj Pappu, Dhanashree Kangane, Varsha Shah, Junaid Mandwiwala","doi":"10.1109/ICSES52305.2021.9633937","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633937","url":null,"abstract":"Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"10 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78500118","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633841
G. Samhitha, D. S. Rao, C. Rupa, Y. Ekshitha, R. Jaswanthi
As a famous saying goes “Exercise not only changes our body it changes our mind, attitude, and mood”. Fitness is being a trend today. Everyone wants to be fit, beautiful, and healthy. But during this pandemic, everyone can't hire a trainer or go to a gym. Another option is wearable devices in which everyone can't afford it. This paper proposed an AI Trainer model. The proposed model used by anyone irrespective of their age and health condition. The AI Model uses Human Pose Estimation. It is a popular approach and it determines the position and orientation of the human body. This approach generates key points on the human body and based on that it creates a virtual skeleton in 2D dimension. The input is the live video which is taken from a person's webcam and the output is capturing landmarks or key points on the human body. The AI Trainer specifies the count and time of the settings the person needs to perform. It also specifies mistakes and feedback if any. This paper provides a methodology to use the pose estimation running on the CPU to find the correct points. Based on the points the gestures and other curls (biceps) are calculated. This paper proposes an approach using OpenCV to implement human pose estimation.
{"title":"Vyayam: Artificial Intelligence based Bicep Curl Workout Tacking System","authors":"G. Samhitha, D. S. Rao, C. Rupa, Y. Ekshitha, R. Jaswanthi","doi":"10.1109/ICSES52305.2021.9633841","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633841","url":null,"abstract":"As a famous saying goes “Exercise not only changes our body it changes our mind, attitude, and mood”. Fitness is being a trend today. Everyone wants to be fit, beautiful, and healthy. But during this pandemic, everyone can't hire a trainer or go to a gym. Another option is wearable devices in which everyone can't afford it. This paper proposed an AI Trainer model. The proposed model used by anyone irrespective of their age and health condition. The AI Model uses Human Pose Estimation. It is a popular approach and it determines the position and orientation of the human body. This approach generates key points on the human body and based on that it creates a virtual skeleton in 2D dimension. The input is the live video which is taken from a person's webcam and the output is capturing landmarks or key points on the human body. The AI Trainer specifies the count and time of the settings the person needs to perform. It also specifies mistakes and feedback if any. This paper provides a methodology to use the pose estimation running on the CPU to find the correct points. Based on the points the gestures and other curls (biceps) are calculated. This paper proposes an approach using OpenCV to implement human pose estimation.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83570832","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633853
S. Padmakala, S. Gomathi, A. Akilandeswari, M. Banu, S. Padmapriya, M. Gnanaprakash
In recent year requirement of Renewable Energy plays a vital role. These energy sources became familiar due to its characteristics like no emission of greenhouse gases and it makes an environment to be healthier. Hybrid system mostly uses the input sources like solar energy, wind energy, fuel cell or etc. The Hybrid system uses two or more sources instantly for power generation. Modified Multiport Bidirectional Boost Converter (MMBC) acting as an interfacing device between source and load. Normally boost converters are enhanced in the system to achieve high voltage gain and high efficiency. MMBC providing better dynamic characteristics with high gain, high efficiency with low ripple factor. MMBC found to be a good conversion device for the hybrid system. MMBC implemented by Hybrid system gives good stability. MMBC consists of three input ports from that two inputs are considered as Renewable Energy sources and remaining one source as a battery.
{"title":"Enhancement of Modified Multiport Boost Converter for Hybrid System","authors":"S. Padmakala, S. Gomathi, A. Akilandeswari, M. Banu, S. Padmapriya, M. Gnanaprakash","doi":"10.1109/ICSES52305.2021.9633853","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633853","url":null,"abstract":"In recent year requirement of Renewable Energy plays a vital role. These energy sources became familiar due to its characteristics like no emission of greenhouse gases and it makes an environment to be healthier. Hybrid system mostly uses the input sources like solar energy, wind energy, fuel cell or etc. The Hybrid system uses two or more sources instantly for power generation. Modified Multiport Bidirectional Boost Converter (MMBC) acting as an interfacing device between source and load. Normally boost converters are enhanced in the system to achieve high voltage gain and high efficiency. MMBC providing better dynamic characteristics with high gain, high efficiency with low ripple factor. MMBC found to be a good conversion device for the hybrid system. MMBC implemented by Hybrid system gives good stability. MMBC consists of three input ports from that two inputs are considered as Renewable Energy sources and remaining one source as a battery.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82460770","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633923
D. Shubhangi, A.K Pratibha
The framework provides a historical, current state, and forward-looking view of the production as well as intelligent analysis of audio data from the view point of machine learning, as well as a look at some future advancements in artificial intelligence. It discusses several aspects of the voice recognition domain in medical diagnosis that appear to be crucial for using machine learning. This paper contains identification of three respiratory diseases based on changes in the voice using MLP algorithm.
{"title":"Asthma, Alzheimer's and Dementia Disease Detection based on Voice Recognition using Multi-Layer Perceptron Algorithm","authors":"D. Shubhangi, A.K Pratibha","doi":"10.1109/ICSES52305.2021.9633923","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633923","url":null,"abstract":"The framework provides a historical, current state, and forward-looking view of the production as well as intelligent analysis of audio data from the view point of machine learning, as well as a look at some future advancements in artificial intelligence. It discusses several aspects of the voice recognition domain in medical diagnosis that appear to be crucial for using machine learning. This paper contains identification of three respiratory diseases based on changes in the voice using MLP algorithm.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"16 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73207033","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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