2023 7th International Conference on Computing Methodologies and Communication (ICCMC)最新文献

Analysis of Dental X-Ray Images for the Diagnosis and Classification of Oral Conditions 口腔x线影像的诊断与分类分析

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083914

Dhandapani Samiappan, Jayaraj R, Nijanth Shankar K, Nithish Kumar N

引用次数: 0

A Trust Prediction Mechanism in Edge Communications using Optimized Support Vector Regression 基于优化支持向量回归的边缘通信信任预测机制

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083686

N. Gowda, B. A

{"title":"A Trust Prediction Mechanism in Edge Communications using Optimized Support Vector Regression","authors":"N. Gowda, B. A","doi":"10.1109/ICCMC56507.2023.10083686","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083686","url":null,"abstract":"The number of edge devices is increasing every day in the fog computing environment. According to Gartner's prediction, around 42 billion edge devices will be involved in digital communications by 2025. Different kinds of edge devices will be involved in various applications such as healthcare, transportation, and education to provide services at anytime and anywhere to the user. At the same time, attackers are trying to intrude into the communication system by taking the advantage of heterogeneity of devices. Consequently, trust management among edge devices is one of the major security concerns in identifying untrustworthy activities in the communication system. This paper proposes a mechanism to predict the trust values of every edge device participating in the communication based on the attributes using support vector regression (SVR). Accuracy, loss rate, recall, precision, and F-measure are used to assess the performance of the suggested model on various data samples of various sizes. Performance comparisons with existing machine learning models demonstrate superior results with various iteration counts. The proposed model attained 99.98% accuracy, 0.0048 loss rate, 99.96% precision, 100% recall, 99.96% F-Measure and took almost 356 seconds for 100 iterations.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121273200","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: 1

Brain Tumor Detection using Mask RCNN 利用掩膜RCNN检测脑肿瘤

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083833

V. Asha, S. Sreeja, Binju Saju, Pavan Desai, K. M. Pavan, G. Kumari

引用次数: 0

Smart Health Monitoring System for Coma Patients using IoT 基于物联网的昏迷患者智能健康监测系统

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10084196

Sheikdavood K, Soundar K, Yaswanth M G, T. P

引用次数: 1

The Three-Tier Architecture of Federated Learning for Recommendation Systems 推荐系统联邦学习的三层体系结构

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10084109

Vaishnavi M, Srikanth Vemuru

{"title":"The Three-Tier Architecture of Federated Learning for Recommendation Systems","authors":"Vaishnavi M, Srikanth Vemuru","doi":"10.1109/ICCMC56507.2023.10084109","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10084109","url":null,"abstract":"Recommender systems are now vital in the Internet age to assist users in finding helpful stuff and reducing information overload. To assist users in finding personalized stuff, a large amount of user-sensitive data used for recommendations may lead to privacy violations. In recent research, federated learning-based recommender systems structures have made tremendous progress in boosting prediction accuracy while providing privacy. However, challenges still need to be concentrated on while employing federated learning 1) Ensuring user privacy and security of data and model privacy. 2) Heterogeneity of data in distributed entities to train a model with the best representation for better analysis, and 3) The communication between the user and server leads to increase overhead and latency. Developing a secured, privacy-protected recommender system that can accomplish high prediction accuracy is crucial and valuable. To address the above issues, a theoretical approach called a three-tier architectural solution is proposed to assure privacy guarantee without sacrificing accurate predictions on the recommendation with less overburden on a server. Further, discussed the future directions of recommendation systems by using federated learning.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123748771","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

Super Capacitor Integrated Battery System for Electric Vehicles 电动汽车用超级电容集成电池系统

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10084005

Balachander K, A. A, S. G

引用次数: 0

First Aid and Emergency Assistance Robot for Individuals at Home using IoT and Deep Learning 使用物联网和深度学习的家庭个人急救和紧急援助机器人

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083934

Mario Dias, Hansie Aloj, Nijo Ninan, Dipali Koshti, Supriya Kamoji

{"title":"First Aid and Emergency Assistance Robot for Individuals at Home using IoT and Deep Learning","authors":"Mario Dias, Hansie Aloj, Nijo Ninan, Dipali Koshti, Supriya Kamoji","doi":"10.1109/ICCMC56507.2023.10083934","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083934","url":null,"abstract":"With urbanization and societal changes, there has been an increase in the number of people living alone. This raises concern for elderly people as many mishaps or accidents can happen in a household environment when they are alone. This study proposes a smart IOT and Deep learning based robotic system to assist people, especially the elderly, in case they are alone at home. The objective is to detect anomalies and provide first aid to the victim or call emergency contacts if necessary in minimal time. The system has three stages: Distress detection, Navigation and Searching, and Assistance with feedback. The robot detects distress in form of audible screams and also monitors its surroundings frequently. Once it detects a tragic situation, it tries to detect the person in its camera frame. The robot then searches the person and attempts to get feedback from the person and tries to provide an appropriate remedy to the victim. If the victim is unconscious, it contacts emergency services. The prototype of the robot was designed and tested with three different test cases to draw conclusions and evaluate the system. To test the efficiency of the robot, three evaluation parameters are defined, they are, Robot Activation Time, Search Time, and Response Time. Since it is an emergency robot, the main objective is to minimize these parameters. Experimental results show that the robot is able to locate the victim in various scenarios in a reasonable amount of time when placed in a central location in a home environment.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116151439","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

Spam Email Filtering using Machine Learning Algorithm 利用机器学习算法过滤垃圾邮件

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083607

Dinesh Komarasamy, Oviya Duraisamy, M. S, Sandhiya Krishnamoorthy, SanjeevKumar Rajendran, Dharani M K

引用次数: 0

Pancreatic Cancer Classification using Deep Learning 使用深度学习的胰腺癌分类

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083716

Naga Vardhani, Gottam Gayathri, Kolusu Leela, Tummala Bhavya, Yalamandala Divya Sravani

{"title":"Pancreatic Cancer Classification using Deep Learning","authors":"Naga Vardhani, Gottam Gayathri, Kolusu Leela, Tummala Bhavya, Yalamandala Divya Sravani","doi":"10.1109/ICCMC56507.2023.10083716","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083716","url":null,"abstract":"The great majority of the computer systems that are now being utilized for research on medical health systems are based on the most recent technical breakthroughs. Because of the prevalence of pancreatic cancer, a significant number of novel approaches and techniques have emerged in the field of medicine. There are several various classifications that may be applied to the pancreatic cancer that can be found. Utilization of the deep learning technology is going to be the means by which the classification of pancreatic cancer is going to be completed. The classification of pancreatic cancer may be tackled from a variety of angles, each of which can be accomplished via using either technology for machine learning or technology for deep learning. In the past, a diagnosis of pancreatic cancer could be made by using methods such as the Support Vector Machine (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines. However, these methods are no longer effective (TWSVM). However, these strategies do not deliver an accurate performance. As a result, this study has implemented an Advanced Convolution Neural Networks (ACNN), which are examples of the type of technology known as deep learning. In the vast majority of the existing research works, the classification has been determined by analyzing the images of the patient, which are not always accurately classified; in contrast, the classification in this one is determined by looking at the genetic data of the patient. An accurate number can be obtained by using the blood and urine samples collected from patients since these samples were utilized to construct the genetic data. With the help of constant values and ACNN strategies, the performance rate was enhanced in contrast to the approaches that were currently being used.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121879227","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

Employing IoT and Machine Learning to Minimize Industrial Structure Resource Utilization 利用物联网和机器学习实现产业结构资源利用率最小化

2023 7th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2023-02-23 DOI: 10.1109/ICCMC56507.2023.10083959

A. Rajalingam, G. Charulatha, Kamalakannan Machap, R. Kumudham, M. Prabhu

{"title":"Employing IoT and Machine Learning to Minimize Industrial Structure Resource Utilization","authors":"A. Rajalingam, G. Charulatha, Kamalakannan Machap, R. Kumudham, M. Prabhu","doi":"10.1109/ICCMC56507.2023.10083959","DOIUrl":"https://doi.org/10.1109/ICCMC56507.2023.10083959","url":null,"abstract":"Systems associated with the Internet of Things (IoT) must have long battery life, a large coverage area, and low implementation costs. The architecture of Heating, Ventilation, and Air Conditioning (HVAC) solutions in commercial buildings was created using LoRa and evaluated to short-range wireless signals in an indoor setting. This study has compared things like battery life, coverage area, and storage capacity. The sensor node's battery usage was also tested with the LoRa transmission power. LoRa was shown to have a 60.4% greater indoor coverage range than short-range communication. Up to 198% of the energy usage may be saved by the intelligent controller's ability to determine while the area is vacant and the HVAC is turned off. Despite using 7.23% additional power, LoRa exhibited no container failures besides providing a global over58.98 percent more significant over the RFM 69HW detector, which is then compared with the RFM 69HW transceiver. LoRa is chosen for the implementation of smart controller in commercial buildings since it requires fewer base stations and hence has a lower cost because of the expanded exposure assortment inside structures.","PeriodicalId":197059,"journal":{"name":"2023 7th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"259 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838442","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