2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)最新文献

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Digital Semiconductor Testing Methodologies 数字半导体测试方法
Pallavi Deshpande, Vivek Epili, Gauri Ghule, A. Ratnaparkhi, Shraddha K. Habbu
{"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}
引用次数: 0
An Empirical Study on E-Commerce Site using Unique AI based Features and Data Science Tools 基于人工智能特征和数据科学工具的电子商务网站实证研究
J. Jesy, J.Santoshi Kumari, Aniket Singh Dr, A. R.Ch., Naidu, R. Sri, M. A. Prof
{"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}
引用次数: 0
Distracted Driver Detection using Inception V1 使用Inception V1的分心驾驶员检测
Ms. Prathipati, Silpa Chaitanya, Bhagya, Rafiya Kowsar Sk, Joshna Rani
{"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}
引用次数: 0
Deep Neural Network Algorithm to Improve Link Reliability in Wireless Sensor Networks 提高无线传感器网络链路可靠性的深度神经网络算法
K. Bhaskar, T. Kumanan, S. Sree Southry., Vetrimani Elangovan
{"title":"Deep Neural Network Algorithm to Improve Link Reliability in Wireless Sensor Networks","authors":"K. Bhaskar, T. Kumanan, S. Sree Southry., Vetrimani Elangovan","doi":"10.1109/ICESC57686.2023.10193180","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193180","url":null,"abstract":"Wireless Sensor Network (WSN) is distinguished by size, dynamism, and decentralization. These complicated properties give rise to various problems, one of which is the impact of wireless communications on the efficiency of networks and the protocols used for routing. The prediction methods of link reliability can boost the efficiency of the routing algorithms used in WSNs while preventing weak connections. This approach introduces a Deep neural network algorithm to improve link reliability (DILR) in WSN. A Deep neural network (DNN) algorithm is used to evaluate the input parameters like node Received Signal Strength, available bandwidth, delay, and packet received rate parameters for calculating the link reliability output. The available bandwidth parameter recognizes the efficient data transmitting path. The experimental outcomes illustrate that the DILR mechanism improves the link reliability among nodes and reduces routing overhead compared to the conventional mechanism.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"55 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":"115544183","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}
引用次数: 0
Shore Line Change Detection using ANN and Ground Water Variability Along Kerala Coast Using Random Forest Regression 基于随机森林回归的喀拉拉邦海岸岸线变化人工神经网络检测和地下水变化
Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana
{"title":"Shore Line Change Detection using ANN and Ground Water Variability Along Kerala Coast Using Random Forest Regression","authors":"Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana","doi":"10.1109/ICESC57686.2023.10193557","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193557","url":null,"abstract":"Shoreline change is a constantly evolving phenomenon that threatens people and their livelihoods around the globe. India observes this phenomenon strongly at different locations being a tropical peninsular country with 6635kms of coastline. This study analyzes the effect of shoreline along the entire coast of Kerala state in India. Net changes in coastline positions are statistically calculated and observed using Linear Regression Rate (LRR) and validated using Artificial Neural Network. The study also employes a random forest regression to predict the ground water level changes with respect to shoreline change rate in the region. The shoreline change rate shows most of the region are undergoing erosion, only few accretions or land formation are observed which is formed artificially due to harbor building. The highest erosion rate in terms of LRR is 7m/year and highest accretion is 28m/year.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"9 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":"114120477","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}
引用次数: 0
A Secure File Sharing and Audit Trail Tracking Platform with Advanced Encryption Standard for Cloud-Based Environments 基于云环境的安全文件共享和审计跟踪平台,具有先进的加密标准
Dr.M.Jagadeeswari, P.Naveen Karthi, S.Lokith, S. Ram, V.A.Nitish Kumar
{"title":"A Secure File Sharing and Audit Trail Tracking Platform with Advanced Encryption Standard for Cloud-Based Environments","authors":"Dr.M.Jagadeeswari, P.Naveen Karthi, S.Lokith, S. Ram, V.A.Nitish Kumar","doi":"10.1109/ICESC57686.2023.10193389","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193389","url":null,"abstract":"In the digital era, organizations, especially financial institutions, place an increasing emphasis on data security and privacy. To maintain data confidentiality, availability, and integrity, financial auditing organizations need secure file sharing and audit trail tracking technologies. Financial auditing firms demand a cloud-based audit trail monitoring platform as well as a secure file exchange platform with high encryption standards. Users may submit and download data using a secure online interface. An administrative dashboard simplifies user registration and deactivation. The audit trail function allows the administrator to know who requested a file, when they requested it, and when the file was downloaded. This audit trail monitoring technology raises compliance responsibilities. The platform uses Advanced Encryption Standard (AES) encryption to secure data. The platform encrypts submitted files using a random key. The file owner gets a download request, which he or she may accept or deny. If the request is granted, the owner sends the user the AES key required to decode and download the file. On the platform, Amazon Web Services and Relational Database Service (RDS) hold massive files (RDS). The Amazon database is protected by login and DoS alarms. Login notifications for Amazon root and IAM users notify the administrator of the browser, IP address, date, and number of attempted logins. The administrator receives DoS attack notifications and database traffic statistics from a variety of sources. Administrators may use alerts to prevent security breaches. The solution facilitates secure and timely communication between financial auditing firms. Data is protected by AES encryption and Amazon S3 storage, while audit trail monitoring and alerts prevent data breaches.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"29 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":"116178999","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}
引用次数: 0
Disabled Smart Parking Management using RFID Technology 使用RFID技术的残疾人智能停车管理
G. Jenulin Makros, J. Ancy Jenifer., B. V. Adithya, R. Rohan Samuel, M. Giribalan
{"title":"Disabled Smart Parking Management using RFID Technology","authors":"G. Jenulin Makros, J. Ancy Jenifer., B. V. Adithya, R. Rohan Samuel, M. Giribalan","doi":"10.1109/ICESC57686.2023.10193064","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193064","url":null,"abstract":"Parking spots for people with disabilities help to create an environment that is accessible to everyone. Abusing these parking spots by parking when you don’t have a disability or when you don’t have a valid accessible parking permit prevents persons with disabilities from accessing resources, which is both unlawful and immoral. Through the use of a mobile application, this project allows authorized users to secure a parking place. To determine if the reserved vehicle has parked or not, this system employs RFID readers that can help recognizing the disabled vehicle. Each handicapped parking area has an IR sensor to detect the presence of a car. To warn non-disabled drivers who try to park in places reserved for the disabled, this system also uses an alarm system. The goal of this research is to make clear how various of the smart parking approaches under investigation may be utilized to administer parking for handicapped individuals and improved by validating disability parking authorization.","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":"116272752","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}
引用次数: 0
AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines 人工智能驱动的向日葵病害多分类:融合卷积神经网络和支持向量机
D. Banerjee, V. Kukreja, Satvik Vats, Vishal Jain, Bhawna Goyal
{"title":"AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines","authors":"D. Banerjee, V. Kukreja, Satvik Vats, Vishal Jain, Bhawna Goyal","doi":"10.1109/ICESC57686.2023.10193473","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193473","url":null,"abstract":"This research utilizes a novel Convolutional Neural Network (CNN) and Support Vector Machine (SVM) based model to predict the sunflower diseases. For training the proposed model, three convolutional layers, three max-pooling layers, and two fully connected layers were used, with the second fully connected layer includes SVM. The proposed model is trained with a dataset of different diseases that affect sunflowers. The results of the proposed research study have resulted in a F1 score of 83.45 and a total accuracy of 83.59%. For classifying each disease, accuracy value has been obtained in the range of 80.65% to 85.37%. According to the meta-analysis of the layer parameters, the second fully connected layer highly influences the model’s accuracy. The results indicate that combining CNN and SVM could be an efficient strategy for predicting diseases in sunflowers and would also assist the process of disease management and crop yield.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"71 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":"123476966","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}
引用次数: 0
An Innovative Method for Housing Price Prediction using Least Square - SVM 一种基于最小二乘支持向量机的房价预测新方法
Yasha Goel, A. N. Swaminathen, Rishika Yadav, B. Kanthamma, Ravi Kant, Amit Chauhan
{"title":"An Innovative Method for Housing Price Prediction using Least Square - SVM","authors":"Yasha Goel, A. N. Swaminathen, Rishika Yadav, B. Kanthamma, Ravi Kant, Amit Chauhan","doi":"10.1109/ICESC57686.2023.10193369","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193369","url":null,"abstract":"The House Price Prediction is often employed to forecast housing market shifts. Individual house prices cannot be predicted using HPI alone due to the substantial correlation between housing price and other characteristics like location, area, and population. While several articles have used conventional machine learning methods to predict housing prices, these methods tend to focus on the market as a whole rather than on the performance of individual models. In addition, good data pretreatment methods are intended to be established to boost the precision of machine learning algorithms. The data is normalized and put to use. Features are selected using the correlation coefficient, and LSSVM is employed for model training. The proposed approach outperforms other models such as CNN and SVM.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"134 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":"124509642","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}
引用次数: 0
Face Recognition System using Artificial Intelligence: Comparison of Classifiers 基于人工智能的人脸识别系统:分类器的比较
Dipanshu Kumar Mishra, Deepak Kumar
{"title":"Face Recognition System using Artificial Intelligence: Comparison of Classifiers","authors":"Dipanshu Kumar Mishra, Deepak Kumar","doi":"10.1109/ICESC57686.2023.10193514","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193514","url":null,"abstract":"Facial recognition is the technique used to identify the face of a person which is already detected and shows the results whether it is known or an unknown face. Face recognition is followed by the process of face detection. Both the processes are difficult tasks at their level. There are several methods or techniques to develop the system of face recognition, viz., Eigenface and Fisherface. The challenge for this system is that face images are with different backgrounds, different lighting, different facial expressions and occlusions. This system starts when an image is processed to train it. It is continued on the test image, the face is being identified, then the trained faces are compared and ultimately categorized it using classifiers of OpenCV. This study discusses the comparative study of different algorithms and come up with the most effective and convenient technique for the mentioned system.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"102 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":"122968950","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}
引用次数: 0
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