2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)最新文献

{"title":"The Smart Development with Dynamic Recollection Adaptive Loom for Frequent Pattern Monitoring in Large Scale Databases","authors":"st Sindhu, G. Madhuri, T. Mahesh","doi":"10.1109/ICDCECE57866.2023.10150481","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150481","url":null,"abstract":"The Dynamic Recollection Adaptive Loom (DRAL) is a groundbreaking technology that provides real-time monitoring and analysis of frequent patterns in data streams. This technology is based on the concept of dynamic memory, which allows the system to quickly adapt to changing patterns and data flows and automatically adjust to new patterns and trends. DRAL is designed to provide a comprehensive and efficient way of detecting, analyzing and responding to frequent patterns in data streams. It uses a combination of machine learning algorithms and data mining techniques to accurately detect and analyze patterns in data streams. This technology is able to rapidly detect outliers and anomalies in the data stream and quickly identify frequent patterns. Additionally, it can quickly respond to changes in the data stream and provide datadriven recommendations for optimization and future predictions. DRAL also provides a robust and secure data management platform that enables users to securely store and manage their data streams in a secure and efficient manner. This technology also provides a comprehensive security framework that ensures the confidentiality and integrity of the data streams. It enables users to easily monitor and manage their data streams and quickly respond to any changes.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129850184","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":"Absolute Sinewave Based Modulation Technique for Reduced Switch Multilevel Inverter for better Total Harmonic Distortion","authors":"Durga Prasad Garapati, V. J. Tayaru, D. H. Sree, P. Yasasvini, V. Sireesha, B. Keerthana","doi":"10.1109/ICDCECE57866.2023.10151320","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151320","url":null,"abstract":"This research presents a unique three-phase design for balanced input sources multilevel inverters. To obtain the voltage output with the least amount of THD, the upgraded H-bridge multilevel inverter uses the absolute sinusoidal method. Leveraging MATLAB/SIMULINK, an investigation for induction motor fed water pump is conducted using this approach for fifteen level inverters. Less switches and uncomplicated control are both advantages of this technology over traditional systems. However, as the amount of level rises, so do the switching components and dc sources. Utilizing hybrid technology to create the inverter solves this problem. The purpose of the present work is to create a hybrid cascaded Hbridge inverter that employs pulse width modulation to require lesser switching devices and dc sources.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"95 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128732674","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":"Design and Application of College Psychological Education System Platform based on Deep Learning","authors":"Chen Shanshan","doi":"10.1109/ICDCECE57866.2023.10150664","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150664","url":null,"abstract":"With the continuous progress of society, people's life pressure is increasing, and college students' mental health problems are becoming more and more serious, which brings great challenges to college students' education and management. Universities should not only cultivate students' professional theoretical knowledge and practical operation skills, but also pay attention to their mental health. It is necessary to develop and design a simple and efficient student psychological evaluation system. The digitalized student psychological education and comprehensive counseling management information system can not only reduce management costs, save resources and manpower, but also improve the efficiency of mental health education. The simple and easy online test replaces the traditional paper-and-pencil test. The student psychological education management system designed in this paper can be generally divided into the following modules according to their functions:Management evaluation, file management, consulting management, personal information management, public information management, and system management. Guided by the design idea of MVC, this system explores a mental health management system suitable for colleges and universities through B/S structure, using Java language, SSH architecture, MySQL database, and other technologies. It constructs a mental health management system for colleges and universities, improving the level of psychological education management in colleges and universities, and promoting the healthy and orderly development of student management.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126674945","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":"Adaptive Fuzzy Optimized Routing based on Maximum Energy Support Routing Protocol using Synchronized SleepAwake Model Routing Algorithm for WSN","authors":"Aman Mittal, F. Correa","doi":"10.1109/ICDCECE57866.2023.10151019","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151019","url":null,"abstract":"In Mobile Wireless Sensor Network (MWSN), it is important that every node is to be energy efficient to manage the power in the network due to its mobility nature. The major demand limitations are often to microcontrollers for accessing the service in network. Mainly Mobile network system voice, video, message and other information shared in network may vary with topology system. Due to routing failure network performance is very low. To improve the performance, we propose an Adaptive fuzzy optimized routing based on Maximum Energy support routing protocol using Synchronized sleepawake model routing (SSAMR) algorithm for improving network performance. This system provides better network performance while comparing with the existing system of miniature sensor nodes. All the proposed systems utilize the minimum delivery time for data transmission. Cluster based fuzzy model for route optimization have shown that the proposed technique improves the Throughput ratio 56% effectively.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130597530","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":"Financial and Economic Risk Security Early Warning System Based on BP Neural Network Algorithm","authors":"Linlin Yu","doi":"10.1109/ICDCECE57866.2023.10150834","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150834","url":null,"abstract":"Finance is the blood of modern economy, today's world is an era of economic globalization, there is a profound connection and influence between the world's economies. Financial risks that arise between countries can spread across the world's economies, creating indirect risk effects. The subjective factor of traditional evaluation method is very strong. Artificial neural network model overcomes the deficiency of traditional project evaluation which relies on expert experience, and opens up a new way for financial and economic risk safety evaluation. The purpose of this paper is to study the construction of financial and economic risk security early warning system based on BP algorithm. Based on the latest achievements of BP, combined with the early warning index constructed in this paper, the BP neural network financial and economic risk early warning model is given. The empirical experiment shows that the BP model is satisfactory.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124110078","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":"Proposed CNN Model for Classification of Brain Tumor Disease","authors":"Rahul Singh, N. Sharma, Rupesh Gupta","doi":"10.1109/ICDCECE57866.2023.10151070","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151070","url":null,"abstract":"A brain tumor is a group of abnormal cells within the brain or surrounding tissues. Several variables, including family history, radiation exposure, and some genetic disorders, might increase the likelihood of developing a brain tumor. The typical method for detecting brain tumors is to perform MRI scans, which a medical specialist then examines for diagnosis. While time-consuming, this process is fraught with the possibility of human error, especially when the tumor is in its early stages. As a result, brain tumor diagnosis must be made properly and as soon as possible. With quick and accurate brain tumor identification, this work aims to prevent premature death, provide health in resource-constrained conditions, and promote patients' healthy lifestyles. A CNN model is created in this study to detect brain cancers, and the dataset contains 251 scans. Because datasets are limited in availability, data augmentation is employed to expand the dataset's coverage. The suggested CNN model's outputs were evaluated using the metrics Accuracy, F1-Score, Precision, and Recall. In aggregate, the model has an accuracy of 85%. As a result, deep-learning CNN models have been demonstrated to detect brain tumors while spending no time or resources effectively.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124220031","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":"Random Logistic Vector Analysis Based Opinion Mining For Identifying Best Product Using User Reviews in Ecommerce Applications","authors":"Mohan Garg","doi":"10.1109/ICDCECE57866.2023.10150493","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150493","url":null,"abstract":"Social Media (SM) has emerged as a new communication channel between consumers and enterprises to generate a large volume of unstructured text data about products. Many web users post their opinions on several products through the blog, review sites and social networking sites-based text of the attitude. Customer feedback plays a very important role in the daily movements of products. Opinions of others are also taken into account when making decisions to select the best products. Event though, it reads reviews of all the customers, it has difficulty in making decisions based on the information about whether or not to purchase the product. Keeping track of the customer's opinion, manufacturers are also finding it difficult to manage the products which lead to economic collapse. To address this problem, the proposed Random Logistic Vector (RLV) algorithm is used to analyze the product quality and life of the products based on reviews. The first process is data collection based on customer content-based reviews about products from Ecommerce applications. Then, collected data are trained into preprocessing to remove unwanted data and noise. Secondly, preprocessed data are trained into feature extraction to select the best features of the lexicon-based sentiment words, adverbs, adjectives word based on consumer reviews about products from the dataset. Finally, feature extraction data are trained into the proposed Random Logistic Vector (RLV) algorithm is done to identify the polarity or subjectivity orientation that indicates the customer opinion text expressed by the user or client in terms of value. Random Logistic Vector (RLV) algorithm which is used to classify the data to help select the best products and analyze the product quality. It will also lead to the economic growth of productive enterprises.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121484918","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":"Novel Approach to Record the Attendance of Students Using Facial Recognition","authors":"R. N. Kulkarni, Anjali T S S L, Rohith B, Raghuveer Nadagouda, Veeresh S","doi":"10.1109/ICDCECE57866.2023.10150539","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150539","url":null,"abstract":"During ancient Indian times, the Gurukul system of education was the style of learning in the country. In this system the students were learning with their mentors (Gurus) and receiving education, knowledge, moral values and life skills under the guidance of their gurus. This system of education was practiced in ancient times, where all students who resided at the place of the guru in the Gurukula were considered equal. As the days passed the things took a drastic change and government schools were introduced. A few decades ago, the number of students in government schools decreased due to the privatization of the education system. Later, more schools were introduced by corporations, which led to a significant increase in the number of schools. To perform the tasks like monitoring and taking the attendance of each class was tedious job at the same time it was time consuming, where the total number of students in each class has increased and number of subjects for each class also increased and every teacher has to document the number of students attending the classes for each subject and they need to submit it to the higher authorities. To overcome these difficulties, In this paper a novel approach is schemed to record attendance of all the students of a class. In the proposed system to take the attendance of the students all at once, a live video is processed for each frame and to recognize the faces of all the students it uses a deep convolutional neural network face recognition algorithm which extract 128-dimensional face encodings from images and then compares these encodings with the faces stored in the dataset to determine the best match and further the attendance of the students present in the class is recorded in the form of excel sheet so that the teacher can carry out the further analysis. The findings of the experiment overcomes the difficulties faced in the existing systems and eyewitnesses the furturistic transition in marking attendance.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121706920","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":"Optimum Sizing and Pricing for Multigrids using Deep Learning Techniques","authors":"S. Minocha, Neeti Taneja","doi":"10.1109/ICDCECE57866.2023.10150440","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150440","url":null,"abstract":"Due to the clean, effective, but reliable power they supply, substations have growing in popularity. Substations required tankers power in order to use the saved fuel during emergencies or peak loads. Given that dc microgrid will be dominant energy resource with in coming, the battery should be geared toward producing power. Batteries are utilized throughout the day, especially during rush hour and emergency situations. There are various battery types, including batteries, lion capacitors, etc. New systems like hybrid cars and other devices are constrained by the difficult challenge of considered as the ability capacity for microgrids. To acquire the best battery design for micro - grid, it is critical to understand several various properties such as standby time, energy efficiency, and total independence. A proven method for integrating and optimizing various energy sources and characteristics for the long battery sizing is blended time varying (MILP). Inside this effort, a brand-new Style. For instance, datasets are presented. To determine the ideal battery, computational approach called Support Vector Machine (SVM) based CNN is employed. The suggested machines learning-based Typical's response to feature selection techniques is assessed. The effectiveness of the top six feature selection algorithms is examined. The test data show that the approach performs better when types of filters are used.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114847310","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":"Advanced Privacy-Aware Protocol Placement in Cloud Security","authors":"Dhiraj Singh, Mansi Chitkara","doi":"10.1109/ICDCECE57866.2023.10150504","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150504","url":null,"abstract":"The use of the service or set of available resources through the Internet has been referred to as cloud computing. Distributed data centers around the world offer a cloud service. Because of the rapid growth of the art, recently, security is always a significant issue. Recent demand for cloud computing has raised the security issue for service providers and consumers. Data individuals or organizations that can be stored in the information cloud face a potential threat from hackers, and safer can be powerful, able to identify the potential security vulnerabilities. Our work in this area allows a there are a few strategies and methods that are utilized to keep up the security of information. It will reinforce the security at all levels. The proposed work is finished using the cloud device. We point a strategy for giving staggered security of text information appropriated in the cloud. The proposed Advanced Privacy-Aware Protocol (APAP) development ensures data extraction computations to ensure data protection (APAP) development and technique used in the proposed framework.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124505648","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}