2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)最新文献

{"title":"NFT Marketplace for Blockchain based Digital Assets using ERC-721 Token Standard","authors":"Ashritha Konagari, Hari Priya Kusuma, Sangeetha Chetharasi, Ramu Kuchipudi, P. R. Babu, T. Murthy","doi":"10.1109/ICSCSS57650.2023.10169350","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169350","url":null,"abstract":"Blockchain technology has drawn increasing interest in recent years as a technology that will dominate a wide range of industries in the future The non-fungible token is a brand-new kind of Ethereum - based token that is unique and indivisible (NFT). Recently, investors have shown a lot of interest in a specific category of blockchain-based virtual assets called NFTs. Since the beginning of 2021, the phenomenon and its marketplaces have expanded considerably. The Ethereum network is used in this project to demonstrate and test the concept of digital ownership transfers and control. This project offers the ability to create, purchase, sell, and trade NFTs. This experiment shows how the reader can generate an NFT token on their own using the Ethereum network’s ERC721 token standard. Moreover, it addresses distribution and setup. Yet it is the another case for the blockchain which is the possibility of making money with it. This application’s users will be able to buy and sell NFTs using the virtual currency, Ether.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123820069","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}

{"title":"Link Connectivity based Reliability with Security in Autonomous Mobile Network","authors":"C. Sivasankar, T. Kumanan, C. Sekhar, R. Srinivasan T","doi":"10.1109/ICSCSS57650.2023.10169377","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169377","url":null,"abstract":"Autonomous Mobile Network (AMN) contains mobile nodes which can predict the surrounding and forward the data to the receiver in undesirable conditions. AMN can observe, activate, calculate, and interconnect with others by applying point-to-point multi-hop transactions. Achieving such a fundamental agreement in a resource-constrained environment is a challenging problem since security methods always need additional energy and computational storage burdens. This paper proposes Link Connectivity based Reliability with Security (LCRS) in AMN to overcome this problem. This paper aims to detect abnormal nodes and improve link connectivity. The Link Connectivity Threshold (LCT) parameter estimates the link availability among the present sender and the subsequent forwarder node on the route to the destination. It selects the forwarder node by energy, link connectivity, and distance parameters. Furthermore, it isolates abnormal nodes using the lightweight Extended Kalman Filter (EKF) algorithm. This technique isolates the abnormal nodes and provides an effective route. The experimental outcomes demonstrate that the LCRS scheme diminishes the packet loss rate and delay. Furthermore, it raises the abnormal node detection ratio.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123853714","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}

{"title":"Student Attendance Recognition using the Haar Cascade Algorithm","authors":"Lagisetty Keerthi, Poleboina Divya, U. Sairam","doi":"10.1109/ICSCSS57650.2023.10169646","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169646","url":null,"abstract":"Attendance systems are very important in schools, colleges, and many organizations. It tells about whether a student is regular to the school/college or not, an employee is regular to the work or not. The traditional method of attendance is to take manually by using roll calls and notes in the attendance register given by the organization. In some organizations, students need to sign in the register for attendance. But it is a difficult process, there may be a chance for the occurrence of proxy. It creates a lot of disturbances in class and a waste of time. Proxy leads to the irregularity of the students and decreases performance in academics. It may affect their goals and deviates into other activities. Alternating methods need to be implemented that should rectify the issues that exist with the traditional method, and save both time and manual work.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121390933","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}

{"title":"Real-time Driver Drowsiness Detection System using Cascaded ConvNet Framework","authors":"Nongmeikapam Thoiba Singh, Saurav, Nishant Pathak, Abhishek Raizada, Sahil Shukla","doi":"10.1109/ICSCSS57650.2023.10169434","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169434","url":null,"abstract":"Driver fatigue and drowsiness are major causes of traffic accidents. Globally, the number of casualties and fatalities increases each year. These accidents cause a lot of serious and fatal injuries each year, which is very expensive for both individuals and governments. Many drivers suffer from drowsiness as a result of their long workdays, insufficient sleep, and fatigue. Due to their reduced concentration, awareness, and control over their vehicles, tired drivers are just as dangerous as drunk ones. The goal of this research study is to provide an overview of an ADAS (Advanced Driving (Assistance System) that is specifically designed to detect driver fatigue and avert traffic accidents. This study has created a machine-learning algorithm to solve this problem. Upon detecting pupil and eye movement, the devices (after model installation) can make an audible noise to keep the drivers awake. The proposed model was tested on the Inception v3 model. The proposed model is trained using a convolutional neural network (ConvNet) and transfer learning, and it achieved 98.1% accuracy.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131166477","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}

{"title":"Naive Bayes Classifier based Movie Recommendation System","authors":"Yeresime Suresh, Br Rohit Kumar, C. Jahnavi Reddy, P. Rohini, K. Sharath","doi":"10.1109/ICSCSS57650.2023.10169718","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169718","url":null,"abstract":"In 21st Century almost every single piece of data is available over the internet. All we need is to surf through the internet. User sometimes spend more time in surfing the internet to get information about a movie. So the emphasis is to make every piece of data available in a single page which will help user to surf faster. Our approach is to build a website with customized search engine for Hollywood movies. Initially, the movie database gathers data from IMDB and Kaggle movie dataset. When the user enters the movie title in the webpage. TMDB API gathers the data from the TMDB website. This provides the user with the simplistic and user-friendly environment. It also provides the basic information about the movie, classification of the movie review based on classifier algorithm and generates a list of top 10 similar movies based on cosine-similarity measure and content-based filtering approach.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"256 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133288564","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}

{"title":"Detecting Malicious URLs using Data Analytics","authors":"K. Saranya, K. Varunvikash, K. Karthikeyan, S. Rosanakthar","doi":"10.1109/ICSCSS57650.2023.10169447","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169447","url":null,"abstract":"Phishing is a type of social engineering hack in which attackers trick victims into providing their login information on a form that sends the information to a malicious server. The fast developing subfield of data science is dependent on the application of machine learning as a central component. One vs All Classification is a strong machine learning technique that may be used to detect dangerous URLs by classifying them as belonging to one of several categories, such as malware, phishing, or spam. This algorithm can be used to detect malicious URLs by classifying them as belonging to one of these categories. Training numerous binary classifiers is required for this method. Each of these classifiers is responsible for differentiating one category from the others. The suggested approach makes use of One Vs All Classification in order to obtain a high level of accuracy in the detection of dangerous URLs. It is possible to train the system using a tiny dataset that just includes the pertinent aspects of the URLs, such as the domain and the path. In conclusion, one vs All Classification is a promising strategy for detecting malicious URLs. It can deliver high accuracy even with tiny datasets and low computational resources, which is a significant advantage over existing approaches.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133465264","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}

{"title":"Epileptic Seizure Analysis using Scalp EEG Signals with Deep Learning","authors":"B.Madhavi Devi, Rayedi Srilekha, G. Harshitha, Burri Sushma","doi":"10.1109/ICSCSS57650.2023.10169286","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169286","url":null,"abstract":"There has been an increase in interest in using Electroencephalogram (EEG) data for emotion detection in recent years, with possible applications in industries including affective computing and medicine. It has been discovered that persons with depression, a common mental health disorder, have differential spatial responses to neurophysiological signals when exposed to positive and negative stimuli that differ from those in healthy people. Our investigation contrasts previous research in the field and focuses on important elements essential to emotion recognition, such as themes, extracted features, classifiers, and more. This research study develops an emotion activation curve to more clearly depict the activation process of emotions. The proposed study employs machine learning to classify emotions by extracting features from EEG signals. It leverages data from several experiments to train a suggested model, which is then tested for its effectiveness in detecting emotions. The main goal of our project is to identify emotions from brain signals by applying a novel adaptive channel selection method that can distinguish between how brain activity operates in various people and emotional states.The results of our investigation show a significant improvement in the proposed method’s ability to correctly categorize positive or negative emotions in depressed patients.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132239014","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}

{"title":"CNN based Image Processing for Crack Detection on HT Insulator’s Surface","authors":"G. Krishnan, Kalikiri Sukanya, J. S. S. Reddy, K. S. P. Reddy, Nookala Niharika, M. S. S. Kiran","doi":"10.1109/ICSCSS57650.2023.10169853","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169853","url":null,"abstract":"The proposed work provides an innovative method for employing real-time image processing to find flaws or cracks in electrical equipment using real-time image processing with Raspberry Pi in Proteus software. There are two distinct phases to this strategy. To find any cracks in the electrical equipment, an image of the apparatus is first taken, and then the image is analyzed using a convolutional neural network (CNN). Manual inspection of the equipment is a traditional approach to finding cracks in electrical equipment. Manual inspection, however, takes a lot of time and effort. A number of challenges need to be addressed to develop an effective insulator fault detection system, acquire a large and diverse dataset of insulator images, develop a robust and accurate CNN model, and deploy the insulator fault detection system in a real-world setting. This study proposed a new method for segmentation of the insulator image by using the idea of the U-Net segmentation technique with VGG classification. While the part of segmentation result is still significantly influenced by the threshold of segmentation; this strategy increases the effect of segmentation of insulator pictures with many ups and downs. As a result, insulator fault identification using morphological characteristics and classifiers was accomplished. Compared with other unsupervised learning methods, this approach had strong observations of self-learning, for maintaining multiple classifiers which are weak into classifiers that are strong.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"490 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134479516","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}

{"title":"Wireless Control System for Custom-Designed Application using IoT-Enabled Stepper Motor","authors":"J. Swarup Kumar, B. Babu, Pera Manoj, M. Vignesh, T. Vamsi, Reminisetti Alekhya","doi":"10.1109/ICSCSS57650.2023.10169841","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169841","url":null,"abstract":"Stepper motors have gained widespread popularity for their precision and ease of control in various industrial applications. With the integration of the Internet of Things (IoT), it has become possible to remotely control stepper motors through a network connection. In this study, the use of Wi-Fi modules is examined for controlling stepper motors. The motor is connected to a Wi-Fi module that provides a wireless connection to a remote device, such as a smart phone or computer. The system utilizes a custom-designed application that communicates with the Wi-Fi module and sends commands to the motor, enabling precise control of the motor’s position and speed. The use of WI-FI modules in stepper motor control represents a significant advancement in the field, offering greater flexibility and ease of use for the user, and paving the way for new and innovative applications in both industrial and consumer settings.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133883080","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}

{"title":"Controlling and Analysis of Battery Energy System in Electric Vehicle","authors":"R. Nagalakshmi, Dr. K. Rahimunnisa","doi":"10.1109/ICSCSS57650.2023.10169296","DOIUrl":"https://doi.org/10.1109/ICSCSS57650.2023.10169296","url":null,"abstract":"The largest concern in the situation of today, when the majority of people use automobiles is whether or not the car will be safe. Due to the alarming increase in the number of car accidents and life-threatening injuries, it is now essential that vehicles have suitable safety features. It is believed that the concept of smart-hybrid vehicle is appropriate enough to achieve fuel efficiency and improved safely for the user which makes the city and transportation smart at an initial level environmental pollution is worsening as per capita energy consumption increases. With the new alternative energy source, it is essential to reduce the use of traditional fuel-powered engines. Pure electric vehicles cannot be mass-produced due to many problems such as short running time and in-sufficient charging equipment. Therefore, the development of the hybrid electric vehicle over a period of time is of particular importance. The price of battery technology, battery longevity, the quantity of charging stations, and charging time are some of the issues with electrification. Electric vehicle’s energy management and propulsion optimization are achieved in an efficient way using machine learning and deep learning algorithms. The primary factors that will determine the improvement of the propulsion system in EVs include vehicle range, cost of the vehicle, battery pack replacement cost, battery pack life, rapid and simple recharging. The majority of propulsion system development will focus on battery solutions, with the exception of vehicle cost. The charging system of electrical should be robust and reliable as the usage is increasing day by day. Significant efforts were made to address the fuel shortages and pollution issues. The lower emissions and operational costs are due to the variation in primary global consumer demand. The price of battery technology, battery longevity, the quantity of charging stations, and charging time are some of the issues with electrification. Electric vehicle’s energy management and propulsion optimization are achieved in an efficient way using machine learning and deep learning algorithms.","PeriodicalId":217957,"journal":{"name":"2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)","volume":"56 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123015554","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}