Pub Date : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193103
Pallavi Deshpande, Vivek Epili, Gauri Ghule, A. Ratnaparkhi, Shraddha K. Habbu
Semiconductor testing is an integral aspect of electronic device manufacturing, which verifies the functional operation, specifications compliance, and high-quality of semiconductor chips. Due to the ever-increasing complexity and size of integrated circuits (ICs), semiconductor testing has become even more significant. Minor defects or errors in the chips can result in expensive product recalls, adverse reputation impacts, and even hazardous situations. Various testing techniques are used in semiconductor testing, such as functional testing for the basic functions of ICs, structural testing for identifying physical defects, parametric testing for analyzing chip performance under varying conditions, and reliability testing for assessing chip durability and longevity. Effective semiconductor testing ensures that electronic devices integrate only high-quality and dependable ICs. This is essential to satisfy the rising demand for electronic devices in sectors like healthcare, automotive, aerospace, and communication. The usage of defective ICs in critical applications can lead to severe consequences such as medical equipment malfunctions, airplane accidents, and communication disruptions. In conclusion, semiconductor testing has a vital role in ensuring electronic device quality, reliability, and safety. By detecting and eliminating defects in the chips, semiconductor manufacturers can offer their customers superior quality and dependable electronic products. In conclusion, semiconductor testing has a vital role in ensuring electronic device quality, reliability, and safety. By detecting and eliminating defects in the chips, semiconductor manufacturers can offer their customers superior quality and dependable electronic products.
{"title":"Digital Semiconductor Testing Methodologies","authors":"Pallavi Deshpande, Vivek Epili, Gauri Ghule, A. Ratnaparkhi, Shraddha K. Habbu","doi":"10.1109/ICESC57686.2023.10193103","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193103","url":null,"abstract":"Semiconductor testing is an integral aspect of electronic device manufacturing, which verifies the functional operation, specifications compliance, and high-quality of semiconductor chips. Due to the ever-increasing complexity and size of integrated circuits (ICs), semiconductor testing has become even more significant. Minor defects or errors in the chips can result in expensive product recalls, adverse reputation impacts, and even hazardous situations. Various testing techniques are used in semiconductor testing, such as functional testing for the basic functions of ICs, structural testing for identifying physical defects, parametric testing for analyzing chip performance under varying conditions, and reliability testing for assessing chip durability and longevity. Effective semiconductor testing ensures that electronic devices integrate only high-quality and dependable ICs. This is essential to satisfy the rising demand for electronic devices in sectors like healthcare, automotive, aerospace, and communication. The usage of defective ICs in critical applications can lead to severe consequences such as medical equipment malfunctions, airplane accidents, and communication disruptions. In conclusion, semiconductor testing has a vital role in ensuring electronic device quality, reliability, and safety. By detecting and eliminating defects in the chips, semiconductor manufacturers can offer their customers superior quality and dependable electronic products. In conclusion, semiconductor testing has a vital role in ensuring electronic device quality, reliability, and safety. By detecting and eliminating defects in the chips, semiconductor manufacturers can offer their customers superior quality and dependable electronic products.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274245","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193110
J. Jesy, J.Santoshi Kumari, Aniket Singh Dr, A. R.Ch., Naidu, R. Sri, M. A. Prof
With the advancement of modern-day techniques in the field of Information Technology, the way of shopping through E-Commerce site is becoming outdated. There are two ways through which an individual can do shopping first is the online method and second is the offline one in today’s world online shopping by having more variety of products available on individual platform with easy way of shopping because of this day by day the retailers with offline method are facing challenges to increase their sales and obtaining data of demanding products that are available in the market, now with the growth of artificial intelligence, they can use lot of beneficiary tools to boost their business. If a giant next generation E-Commerce site is made with which we can connect all the wholesalers, retailers and customers with their own point of profits, then it can bring a new revolution in the market where there will be different layers will be available with separate user friendly graphic user interface for all wholesalers, retailers and customers, where they will be allowed to access their own layers accordingly with several unique features and benefits to save time and making shopping more amazing for customers and selling their products and boosting daily sales for the retailers with the influence of top wholesalers available to help them with the unique kind of trading system and daily analytics and progress report using data science.
{"title":"An Empirical Study on E-Commerce Site using Unique AI based Features and Data Science Tools","authors":"J. Jesy, J.Santoshi Kumari, Aniket Singh Dr, A. R.Ch., Naidu, R. Sri, M. A. Prof","doi":"10.1109/ICESC57686.2023.10193110","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193110","url":null,"abstract":"With the advancement of modern-day techniques in the field of Information Technology, the way of shopping through E-Commerce site is becoming outdated. There are two ways through which an individual can do shopping first is the online method and second is the offline one in today’s world online shopping by having more variety of products available on individual platform with easy way of shopping because of this day by day the retailers with offline method are facing challenges to increase their sales and obtaining data of demanding products that are available in the market, now with the growth of artificial intelligence, they can use lot of beneficiary tools to boost their business. If a giant next generation E-Commerce site is made with which we can connect all the wholesalers, retailers and customers with their own point of profits, then it can bring a new revolution in the market where there will be different layers will be available with separate user friendly graphic user interface for all wholesalers, retailers and customers, where they will be allowed to access their own layers accordingly with several unique features and benefits to save time and making shopping more amazing for customers and selling their products and boosting daily sales for the retailers with the influence of top wholesalers available to help them with the unique kind of trading system and daily analytics and progress report using data science.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134117729","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}
A major contributing factor in car accidents is driver distraction. This research suggests a distraction detecting system for drivers that detects various forms of distractions by watching the driver with a camera in an effort to decrease traffic accidents and enhance transportation safety. To develop practical driving situations and to test the algorithms for distracted detection, an assisted driving testbed is being constructed. Pictures of the drivers in both their regular and distracted driving postures were taken for the authors’ dataset. The VGG-16, AlexNet, GoogleNet, and residual network are four deep convolutional neural networks that are developed and assessed on a platform with integrated graphics processing units. A voice warning system is developed to notify the driver when they are not paying attention to the road. As VGG-16 is a huge network, it takes more time to train its parameters. On the other hand, ‘texting left’ was misclassified with ‘safe driving’ in some scenarios when the steering wheel blocked the left hand. According to experimental findings, the proposed strategy works better than the baseline approach, which only uses 256 neurons in the fully linked layers. GoogleNet uses inception module, used for running multiple operations (pooling, convolution) with multiple filter sizes in parallel so that it is not necessary to face any trade-off. It takes less time to train its parameters.
{"title":"Distracted Driver Detection using Inception V1","authors":"Ms. Prathipati, Silpa Chaitanya, Bhagya, Rafiya Kowsar Sk, Joshna Rani","doi":"10.1109/ICESC57686.2023.10193551","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193551","url":null,"abstract":"A major contributing factor in car accidents is driver distraction. This research suggests a distraction detecting system for drivers that detects various forms of distractions by watching the driver with a camera in an effort to decrease traffic accidents and enhance transportation safety. To develop practical driving situations and to test the algorithms for distracted detection, an assisted driving testbed is being constructed. Pictures of the drivers in both their regular and distracted driving postures were taken for the authors’ dataset. The VGG-16, AlexNet, GoogleNet, and residual network are four deep convolutional neural networks that are developed and assessed on a platform with integrated graphics processing units. A voice warning system is developed to notify the driver when they are not paying attention to the road. As VGG-16 is a huge network, it takes more time to train its parameters. On the other hand, ‘texting left’ was misclassified with ‘safe driving’ in some scenarios when the steering wheel blocked the left hand. According to experimental findings, the proposed strategy works better than the baseline approach, which only uses 256 neurons in the fully linked layers. GoogleNet uses inception module, used for running multiple operations (pooling, convolution) with multiple filter sizes in parallel so that it is not necessary to face any trade-off. It takes less time to train its parameters.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375833","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193521
K. Rajeshwaran, S. S. Keertanaa, S. Lidharshana, S. Madhumitha
Every year, traffic accidents involving vehicles result in hundreds of fatalities, serious injuries, and significant material losses. The primary causes of vehicular traffic accidents are infractions of traffic laws. Hence, having a reliable method of identifying violations will result in a decrease in traffic accidents and a reliable traffic control system. The vehicle environment has become one of the hottest study topics for the communications sector as a result of recent developments in telecommunications, computing, and sensor technologies. Computer networking researchers have proposed a new wireless networking concept called Vehicular Ad hoc Network (VANET), which can increase passenger safety and provide “efficient” road and policy monitoring. This concept aims to reduce the high number of vehicular traffic accidents, improve safety, and manage traffic control systems with high and reliable efficiency. Future VANET-based vehicle applications will include everything from transport automation systems to entertainment and comfort-based ones, making roads safer and better structured.
{"title":"Vehicle-to-Vehicle Communication using VANET","authors":"K. Rajeshwaran, S. S. Keertanaa, S. Lidharshana, S. Madhumitha","doi":"10.1109/ICESC57686.2023.10193521","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193521","url":null,"abstract":"Every year, traffic accidents involving vehicles result in hundreds of fatalities, serious injuries, and significant material losses. The primary causes of vehicular traffic accidents are infractions of traffic laws. Hence, having a reliable method of identifying violations will result in a decrease in traffic accidents and a reliable traffic control system. The vehicle environment has become one of the hottest study topics for the communications sector as a result of recent developments in telecommunications, computing, and sensor technologies. Computer networking researchers have proposed a new wireless networking concept called Vehicular Ad hoc Network (VANET), which can increase passenger safety and provide “efficient” road and policy monitoring. This concept aims to reduce the high number of vehicular traffic accidents, improve safety, and manage traffic control systems with high and reliable efficiency. Future VANET-based vehicle applications will include everything from transport automation systems to entertainment and comfort-based ones, making roads safer and better structured.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129697758","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10192978
A. Joshi, V. Subedha
Distributed computing through topology control involves making changes to the underlying network (modeled as a graph) to decrease the cost of distributed algorithms when executed across the modified networks. Topology construction builds reduced topology and topology maintenance adopts the reduced topology when the current topology is no longer optimal. This study makes major contributions to topology control by constructing new topologies named sparkle topologies using a ring topology, structured web topology, sun topology, and star topology. The efficiency of the sparkle topologies is checked by calculating the reliability using Wiener Index and data transfer using Cisco packet simulation. Data transmission times, the number of hops required, and the number of potential failure points are all reduced in sparkle topologies compared to ring topologies.
{"title":"Reliability and Packet Transfer Efficiency of Sparkle Topologies","authors":"A. Joshi, V. Subedha","doi":"10.1109/ICESC57686.2023.10192978","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10192978","url":null,"abstract":"Distributed computing through topology control involves making changes to the underlying network (modeled as a graph) to decrease the cost of distributed algorithms when executed across the modified networks. Topology construction builds reduced topology and topology maintenance adopts the reduced topology when the current topology is no longer optimal. This study makes major contributions to topology control by constructing new topologies named sparkle topologies using a ring topology, structured web topology, sun topology, and star topology. The efficiency of the sparkle topologies is checked by calculating the reliability using Wiener Index and data transfer using Cisco packet simulation. Data transmission times, the number of hops required, and the number of potential failure points are all reduced in sparkle topologies compared to ring topologies.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125078737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this work is to propose the use of FarmO’Cart, a cutting-edge online marketing platform, as an effective solution to modernize conventional agricultural trading practices by facilitating an electronic exchange that links farmers, retailers, and consumers. The platform provides an option to directly sell or buy agricultural products without the involvement of any middlemen, thus allowing farmers to benefit from their crop production by generating 15-20% returns and reducing the debt ratio among farmers and their suicide rate. This proposed solution, FarmO'Cart, integrates a range of innovative features designed like a multilingual voice assistant, powered by advanced ALAN AI (Actionable Artificial Intelligence) technology, enabling farmers to interact with the platform in their native language and revolutionize traditional agricultural trading practices. Farmers can put their queries or ask for assistance by simply speaking out in their native languages. The platform is also accessible in 130+ languages through the integration of the Google Translate API (Application Programming Interface), ensuring a truly global reach. These features make the proposed solution more usable for the farmer community who may not be able to understand international languages or English in general. The Bcrypt’s hashing algorithm was leveraged to provide enhanced security for user data and passwords and the incorporation of salted hashing and a variable cost factor adds robustness to thwart brute-force attacks and password-cracking attempts. By employing these cryptographic techniques, the platform ensures effective protection of sensitive information. The FarmO’Cart also offers a community platform for farmers to connect and collaborate with Agri-experts & fellow farmers to improve productivity and profitability.
{"title":"FarmO’Cart: Multilingual Voice-Assisted Machine Learning Based real-time price Prediction to Enhance Agricultural Income","authors":"Aastha Patel, Lina Khedikar, Manasi Lokakshi, Sarika Khandelwal","doi":"10.1109/ICESC57686.2023.10193010","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193010","url":null,"abstract":"The objective of this work is to propose the use of FarmO’Cart, a cutting-edge online marketing platform, as an effective solution to modernize conventional agricultural trading practices by facilitating an electronic exchange that links farmers, retailers, and consumers. The platform provides an option to directly sell or buy agricultural products without the involvement of any middlemen, thus allowing farmers to benefit from their crop production by generating 15-20% returns and reducing the debt ratio among farmers and their suicide rate. This proposed solution, FarmO'Cart, integrates a range of innovative features designed like a multilingual voice assistant, powered by advanced ALAN AI (Actionable Artificial Intelligence) technology, enabling farmers to interact with the platform in their native language and revolutionize traditional agricultural trading practices. Farmers can put their queries or ask for assistance by simply speaking out in their native languages. The platform is also accessible in 130+ languages through the integration of the Google Translate API (Application Programming Interface), ensuring a truly global reach. These features make the proposed solution more usable for the farmer community who may not be able to understand international languages or English in general. The Bcrypt’s hashing algorithm was leveraged to provide enhanced security for user data and passwords and the incorporation of salted hashing and a variable cost factor adds robustness to thwart brute-force attacks and password-cracking attempts. By employing these cryptographic techniques, the platform ensures effective protection of sensitive information. The FarmO’Cart also offers a community platform for farmers to connect and collaborate with Agri-experts & fellow farmers to improve productivity and profitability.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125172077","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193060
P. Nagaraj, V. Muneeswaran, B. Karthik Goud, K. Arjun, G. Vigneshwar Reddy, P. Girish Kumar Reddy
The main causes of death in India and around the world are chronic illnesses like heart disease, diabetes, and Parkinson’s disease. There is a need for potential treatments for chronic diseases because of its higher mortality rate than other diseases. The increase of medical data in healthcare domain and its accurate analysis are beneficial for early disease identification, patient treatment, and community services. Incorrect diagnosis increases the fatality. Thus, precise diagnosis tools for chronic diseases are required due to the high risk of diagnosis. Hence, to provide a promising solution with high accuracy, this study offers a unique diagnosis method based on machine learning. Several machine learning methods are being used in this study, and the algorithm for the prediction is chosen based on the model’s accuracy. The proposed model performs disease prediction with an accuracy of 87.66%.
{"title":"Identifying Multiple Diseases in the Human Body using Machine Learning","authors":"P. Nagaraj, V. Muneeswaran, B. Karthik Goud, K. Arjun, G. Vigneshwar Reddy, P. Girish Kumar Reddy","doi":"10.1109/ICESC57686.2023.10193060","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193060","url":null,"abstract":"The main causes of death in India and around the world are chronic illnesses like heart disease, diabetes, and Parkinson’s disease. There is a need for potential treatments for chronic diseases because of its higher mortality rate than other diseases. The increase of medical data in healthcare domain and its accurate analysis are beneficial for early disease identification, patient treatment, and community services. Incorrect diagnosis increases the fatality. Thus, precise diagnosis tools for chronic diseases are required due to the high risk of diagnosis. Hence, to provide a promising solution with high accuracy, this study offers a unique diagnosis method based on machine learning. Several machine learning methods are being used in this study, and the algorithm for the prediction is chosen based on the model’s accuracy. The proposed model performs disease prediction with an accuracy of 87.66%.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134579875","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193144
Shikher Thakur, S. K. Jha
Cloud computing and big data analytics are two rapidly growing fields in technology industry. The way that organizations store, handle, and analyze their data has been changed by cloud computing. Big Data Analytics has become an essential part of utilizing the potential of cloud computing as a result of the growing volume and complexity of data. This research study explores the emerging trends in big data analytics within the context of cloud computing and examines the fundamentals of cloud computing and how it has altered the field of big data analytics. This study analyzes the effects of different developing cloud computing technologies on big data analytics, including serverless computing, multi -cloud computing, and edge computing. This study also discusses about the opportunities and problems associated with using cloud computing for big data analytics, such as security, scalability, and cost effectiveness. At the end of the seminar, participants will have a thorough understanding of how cloud computing and Big Data Analytics relate to one another as well as knowledge of the most recent developments in this quickly developing sector.
{"title":"Cloud Computing and its Emerging Trends on Big Data Analytics","authors":"Shikher Thakur, S. K. Jha","doi":"10.1109/ICESC57686.2023.10193144","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193144","url":null,"abstract":"Cloud computing and big data analytics are two rapidly growing fields in technology industry. The way that organizations store, handle, and analyze their data has been changed by cloud computing. Big Data Analytics has become an essential part of utilizing the potential of cloud computing as a result of the growing volume and complexity of data. This research study explores the emerging trends in big data analytics within the context of cloud computing and examines the fundamentals of cloud computing and how it has altered the field of big data analytics. This study analyzes the effects of different developing cloud computing technologies on big data analytics, including serverless computing, multi -cloud computing, and edge computing. This study also discusses about the opportunities and problems associated with using cloud computing for big data analytics, such as security, scalability, and cost effectiveness. At the end of the seminar, participants will have a thorough understanding of how cloud computing and Big Data Analytics relate to one another as well as knowledge of the most recent developments in this quickly developing sector.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129074480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research presents a novel system to control a mouse using hand gestures. Traditional mouse controls require the user to use a physical device, such as a trackpad or a mouse. By using hand gestures, the user can interact with the virtual mouse in a more natural manner. The proposed system uses hand tracking techniques to capture and track hand gestures, and uses a set of customizable rules to interpret them into actions. Without using a hardware mouse, the computer can be operated remotely based on hand gestures and can perform left-click and right-click operations. It is based on artificial intelligence for detecting the hands. So, the usage of this virtual mouse will reduce the rapid spread of corona virus by reducing the human-computer interaction.
{"title":"Virtual Mouse with hand gestures using AI","authors":"Sk. Jilani Basha, L.S.L. Sowmya, Sk. Rumiya, Sk. Afroz","doi":"10.1109/ICESC57686.2023.10193709","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193709","url":null,"abstract":"This research presents a novel system to control a mouse using hand gestures. Traditional mouse controls require the user to use a physical device, such as a trackpad or a mouse. By using hand gestures, the user can interact with the virtual mouse in a more natural manner. The proposed system uses hand tracking techniques to capture and track hand gestures, and uses a set of customizable rules to interpret them into actions. Without using a hardware mouse, the computer can be operated remotely based on hand gestures and can perform left-click and right-click operations. It is based on artificial intelligence for detecting the hands. So, the usage of this virtual mouse will reduce the rapid spread of corona virus by reducing the human-computer interaction.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132269698","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193559
Mahesh, Ananth, Dheepthi
It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. The training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. This study has implemented a parallel data processing pipeline so that handle the large dataset. This pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users.
{"title":"Using Machine Learning to Detect and Classify URLs: A Phishing Detection Approach","authors":"Mahesh, Ananth, Dheepthi","doi":"10.1109/ICESC57686.2023.10193559","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193559","url":null,"abstract":"It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. The training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. This study has implemented a parallel data processing pipeline so that handle the large dataset. This pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132367386","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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