2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)最新文献

{"title":"Fuzzy Logic Control for Solar based PV-Battery Storage System with MPPT Technique","authors":"Rayalla Anjani Kumar, S. H. Valli, V. Rafi, R. Kiranmayi","doi":"10.1109/ICOEI56765.2023.10125706","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125706","url":null,"abstract":"Renewable energy sources like solar and wind are getting more research attention as a result of technological breakthroughs in these fields and a corresponding decrease in price. Due to its widespread availability, solar energy promises to be a sustainable answer to rising demand. However, the sun irradiance's unpredictable nature creates practical difficulties. This paper describes the control of a solar and battery storage based micro-grid in grid linked mode to meet the requirement for optimal coordination. The maximum power point tracking (MPPT) control is a feature of the PV array. Battery storage and charge controllers are dotted across the micro-grid to sustain erratic solar generator output. The FUZZY control mode is used to run the grid-side inverter. The Simulink Environment in MATLAB was used to create the model. The two instances of constant load and variable irradiance and variable load and variable irradiance have both been studied. The outcomes clearly demonstrate the effectiveness of the control strategy in preserving the balance of voltage, frequency, and power at PCC.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121852313","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":"A Study on ML Algorithms for Big Data Analytics in the field of Medical Reasoning","authors":"B. Ramyanjali, R. Agarwal","doi":"10.1109/ICOEI56765.2023.10126002","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10126002","url":null,"abstract":"Machine learning for healthcare is the future technology. Big Data Analytics is one of the recent technological developments as it assures to provide better information from the big data resources. It incorporates selecting the suitable Big Data stockpiling and determines the structure extended by MLstrategies. In this digital era, a lot of information is available on public domain, which is further gathered by machine learning to help treat and analyse patients' medical condition. There are several interesting developments whereby medical experts are good at interpreting the data that they see and the information that they get from models, and on the other side, machine learning algorithms are used. These algorithms do not require any medical expertise guidance but can very effectively extract patterns. As a result, the focus of this study is on how the combination of human experience and trained machine learning algorithm models may be used to yield various research insights in the field of healthcare. This research study focuses on and represents unique ML computations in BDAthat are useful in the field of Health Care Analytics.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"934 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125260274","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":"Product Rental Web Application using HTML, CSS, BOOTSTRAP, PHP, and SQL","authors":"Ravindhar Nv, Raga Ranjini R, S. Ch, Kiruthiga M","doi":"10.1109/ICOEI56765.2023.10125895","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125895","url":null,"abstract":"New web technologies, languages, and approaches have aided in creating dynamic apps that represent a new form of cooperation and collaboration among many users. The objective of this study is to develop a website that would help people in reducing their expenditure for products that are temporarily required. In order to do so, an application has been developed in this research, in which people can request their needs (products for temporary use) and offer products to others as well. In this way, people can get products that are required for them temporarily. At the same time, cost can also be saved since the required product is brought only for rent rather than buying. Another advantage is that the other person offering products for rent as well gains some money. This system can solve the problem of people owning non-essential products readily. Hence this application could be very helpful for people in their day-to-day life.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125275186","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":"Compact Firefly Algorithm with Deep Learning Based Chromatic Condition Predictive Model for Organic Synthesis Purification","authors":"S. Kumaraswamy, Md. Abul Ala Walid, Neetesh K. Sharma, M. Jaimini, Deepak Sharma, Arnab Chakraborty","doi":"10.1109/ICOEI56765.2023.10125798","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125798","url":null,"abstract":"Chromatography is an effective method utilized in organic synthesis to purify and separate chemical compounds. There are many features which affect the efficacy and efficiency of chromatography, comprising the kind of chromatography utilized, the nature of instances, the type and size of columns, type of mobile phase, and flow rate. In recent times, Deep Learning (DL) has the potential to significantly increase the effectiveness and efficiency of chromatography for purification in organic synthesis allowing the analysis and optimizer of difficult procedures at a much quicker rate than is possible with classical approaches. With this motivation, this study develops a novel Compact Firefly Algorithm with Deep Learning based Chromatic Condition Predictive (CFADL-CCP) Model for Organic Synthesis Purification. The presented CFADL-CCP technique mainly predicts the chromatic conditions accurately and timely for organic synthesis purification. In the presented CFADL-CCP technique, two stage pipeline is involved. At the initial stage, the CFADL-CCP technique uses Deep Neural Network (DNN) model for prediction process. Next, in the second stage, the CFA is used for the optimal hyperparameter tuning of the DNN model which helps to accomplish enhanced predictive outcomes. To illustrate the enhanced predictive results of the CFADL-CCP method, an extensive range of simulations were performed. Extensive result analysis shows the betterment of the CFADL-CCP method over other compared methods.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132370138","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":"Reducing Hydrogen Consumption in the Automotive Systems Using Fuel cells and Whale Optimization","authors":"J. Fernandes, K. Manideep, M. V. Sainadh, M. L. Sowmica","doi":"10.1109/ICOEI56765.2023.10125843","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125843","url":null,"abstract":"As it can provide electricity for loads in dangerous areas without a power infrastructure, a back-up power delivery system is essential for military application and disaster relief. The advantages of the proton-exchange membrane electric cell over conventional energy sources including superior thermal efficiency, noise cancelling, key effect, and density, and zero greenhouse gas emissions gradually replace them as the significant supplier of power for the secondary energy provision system. A “dual electric cell and metallic element battery” backup power infrastructure is developed in this work, and fuzzy control-based energy management strategies are also investigated.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122523451","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":"Smart Traffic Monitoring System using YOLO and Deep Learning Techniques","authors":"Akhil Reddy Kalva, Jyothi Swarup Chelluboina, B. Bharathi","doi":"10.1109/ICOEI56765.2023.10126048","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10126048","url":null,"abstract":"As the world's population grows, there are more vehicles on the road every day, which leads to an increase in heavy traffic. Traffic monitoring is essential for preventing accidents. To detect reckless drivers and other traffic infractions, a model that can track, identify, and categorize vehicles is needed. The task of counting the number of vehicles is crucial in traffic situations because it allows the authorities to prevent accidents and traffic jams caused by heavy traffic. The approach outlined in the study uses the image processing methods YOLO and OpenCV to count the number of vehicles, classify them, and identify them. By processing the images from the input video given to OpenCV, a software library, the objects are detected and identified. In comparison to other object detection algorithms, the real-time object detection algorithm YOLO is both quicker and more accurate. The accuracy and efficiency of vehicle detection and classification have been greatly enhanced by convolutional neural networks and other machine learning algorithms, enabling real-time analysis of enormous amounts of data. With the help of this technology, driving safety will be increased, traffic flow will be optimized, and autonomous driving will be made possible.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"466 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116511370","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":"A Secure Platform for Storing, Generating and Verifying Degree Certificates using Blockchain","authors":"Tanzila Nargis, Preethi Salian K, Prathyakshini, V. J, Manasa G R, S. Salian","doi":"10.1109/ICOEI56765.2023.10125598","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125598","url":null,"abstract":"Humans deal with many document generation and verification processes in day–to–day life, such as academic certificates, land registries, vehicle registration, medical records, etc. Academic certificates play an essential role in graduates' lives, as it is the proof of completing a required educational qualification for applying jobs or higher education. The current era of technology is rapidly evolving every day, and as a result, the generation of fake certificates becomes easier. So the utmost priority is given to preserving these certificates and making them tamper-proof. There are various methods to secure these certificates. One such method involves a decentralized storage system which uses blockchain technology to generate and store the certificates. The Universities will add the student details on to the blockchain which generates the unique certification ID and transaction hash which cannot be easily tampered. Later the organization can verify the candidate who is seeking the job using these details. Hence blockchain technology can be used to to secure and standardize a digital certificate format, in which institutions and organizations can benefit by making the verification process faster and easier by eliminating the fake certificates.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132789614","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":"Analysis of Diabetic Prediction Using Machine Learning Algorithms on BRFSS Dataset","authors":"Lakshmi H.N., A. Reddy, Kritika Naidu","doi":"10.1109/ICOEI56765.2023.10125804","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125804","url":null,"abstract":"Due to the detrimental effects it has on everyone's health, diabetes is a chronic condition that still poses a serious threat to the global population. It is a metabolic disorder that increases blood sugar levels and increasing the risk of heart disease, kidney failure, stroke, issues with the nerves and heart, among other issues. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. The paper investigates healthcare prediction analytics and addresses the issues using four different machine learning methods. This study has utilized the Early detection and Binary 012 databases. Based on these datasets, the precision, recall, and accuracy of KNNs and Random Forest methods are calculated. The study's findings may be valuable to health professionals, stakeholders, students, and researchers engaged in diabetes prediction research and development because SVM performs better than KNN and Logistic Regression.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133640290","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":"Abnormal Activity Detection Techniques in Intelligent Video Surveillance: A Survey","authors":"S.Sony Priya, R. Minu","doi":"10.1109/ICOEI56765.2023.10125671","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125671","url":null,"abstract":"Currently, CCTV (Closed Circuit Television) cameras are used for surveillance by alerting the security officer if any malfunction or abnormal activity happens. Abnormal activities may be theft, violence, or explosion. CCTV cameras are used in public places like city streets, parks, communities, and neighborhoods to help detect crime and enhance public safety. Manual surveillance for this is tedious and time-consuming. Detecting abnormal crowd behavior in real-time is an exciting research area. Presently, most researchers are interested in developing Dynamic abnormal detection mechanisms to ensure security. However, this is challenging due to climate change, human movement, occlusions, and low video quality. Due to the high dimensionality of video data, Space and time complexity are also increased. This paper explains the various methods of abnormal activity detection under deep learning and the handcrafted approach.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131808771","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":"Study of Image Data Security with Cloud","authors":"Pronika Chawla, Harshit Parihar, Ankit Mathur","doi":"10.1109/ICOEI56765.2023.10125614","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125614","url":null,"abstract":"The on-demand availableness of computer systems resources, especially storage of data (cloud storage) and rectifying power, with no direct ongoing administration by the user is what they call cloud computing. Large cloud functions are usually spread among several locations, each of which constitutes a data center. Customers may save money on capital expenses by using cloud computing, which frequently takes a “pay-as-you-go” approach. Coherence in cloud computing is achieved by sharing resources. In today's world of the Internet, demand for cloud services is increasing drastically leading to the production of new services day by day. As the services increase, the data gets primarily targeted by spiteful users who attempt to steal the data for their own atrocious and unethical activities. Users and trustworthy applications are considering more security and privacy and services get more in demand. Moreover, this study has reviewed several algorithms such as CPE-ABE (Ciphertext policy attribute-based encryption), ABE, KP-ABE (Key policy attribute-based encryption) CSP (Constraint satisfaction problem), PKG, AES (Advanced Encryption Standards), SHA-1 (Secure Hash Algorithm), Photo encryption, Photo decryption, PRE, IDEA (International Data Encryption algorithm) and LSBG (Least Significant Bit Grouping) for image data security. As already discussed, the increasing threats and frauds around the world, safe and secure applications and services should be created to resolve this problem so that people can store data on a platform that can be relied on. This research study has discussed about the concept of what are the paradigms required for securing and protecting the data and securing the image data at an encrypted level. This study has reviewed several existing research works, studied different algorithms which have been used in different research articles, and compared their strengths and drawbacks accordingly. Different research works have been summarized in a form of table, which includes multiple algorithms that are used to secure data, which are then distinguished them based on the strengths and drawbacks.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134345701","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}