2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)最新文献_第3页

Accounting Auditing Risk Factors and its Information System Design based on the Analysis of Multi-Dimensional Data Characteristics 基于多维数据特征分析的会计审计风险因素及其信息系统设计

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760947

Changfu Yuan

引用次数: 0

Machine Learning based Network Security in Healthcare System 医疗系统中基于机器学习的网络安全

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760977

Desalegn Aweke, Assefa Senbato Genale, B. Sundaram, Amit Pandey, Vijaykumar Janga, P. Karthika

{"title":"Machine Learning based Network Security in Healthcare System","authors":"Desalegn Aweke, Assefa Senbato Genale, B. Sundaram, Amit Pandey, Vijaykumar Janga, P. Karthika","doi":"10.1109/ICSCDS53736.2022.9760977","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760977","url":null,"abstract":"The world is filled with exciting technologies and ideas; scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the Machine Learning (ML) Technology intervention. With the technological development, certain processes or consultations are performed with the aid of doctors available around the world. In this scenario, it could be noticed that health care is one of the world's expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker's issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. Finally, the discussion would cross over human-looping systems, which act as one of the common problems that are affected mentally for a person. According to the results, the suggested model achieved the accuracy of 98.89%, that is 4.76% greater than the previous model.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325851","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

Alzheimer's Disease Analysis using Explainable Artificial Intelligence (XAI) 使用可解释人工智能(XAI)分析阿尔茨海默病

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760858

K. Sudar, P. Nagaraj, S. Nithisaa, R. Aishwarya, M. Aakash, S. I. Lakshmi

{"title":"Alzheimer's Disease Analysis using Explainable Artificial Intelligence (XAI)","authors":"K. Sudar, P. Nagaraj, S. Nithisaa, R. Aishwarya, M. Aakash, S. I. Lakshmi","doi":"10.1109/ICSCDS53736.2022.9760858","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760858","url":null,"abstract":"Alzheimer's disease is a progressive neurologic disorder that results in causing the brain to undergo atrophy that is it results in the brain shrinking and the brain cells to die. It occurs to a person who is in their 30's to middle 60's. Around 5.8 million people in the United States of America of age 65 and more have Alzheimer's disease. It is the common cause of dementia. No treatment has been found for Alzheimer's till date. Alzheimer's not cured can result in severe loss of brain function and finally results in death. So, it is important for one to identify it in its early stage and cure it. In this project, we have attempted to identify the stages of Alzheimer using Layer wise relevance propagation method in Explainable artificial intelligence by taking image as the input. Other than LRP, some other algorithms such as vgg-16 and CNN has been used for achieving better results and a good batch accuracy. From this article, one can understand the analysis of Alzheimer's by using XAI with the corresponding feature explanation. The result is achieved with an explanation for the analysis, so that the result is much more trustable and reliable.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122259395","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: 19

Deep Learning with Statistical Analysis for Stress Prediction of Remote Working IT Employees in COVID-19 Pandemic 基于深度学习统计分析的远程办公IT员工压力预测

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9761009

VG Jayasutha, Thiruchelvi Arunachalam

{"title":"Deep Learning with Statistical Analysis for Stress Prediction of Remote Working IT Employees in COVID-19 Pandemic","authors":"VG Jayasutha, Thiruchelvi Arunachalam","doi":"10.1109/ICSCDS53736.2022.9761009","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761009","url":null,"abstract":"The COVID-19 pandemic has transformed the working environment of employees in information technology (IT) sector from traditional office environment into remote working environment. The changes in working environment, lack of physical activities, and food intake result in direct impact on physical and mental well-being. The stress among IT employees in remote working gets increased owing to the absence of proper physical workstation, and extended inactive behaviour results in high discomfort and pain. So, the advent of deep learning (DL) models assists the stress predictive procedure in understanding the pattern proficiently and delivers efficient perceptions about probable forthcoming interventions. In this view, this study develops a novel deep learning based knowledge management for stress prediction (DLKM-SP) technique among IT employees working from remote places in COVID-19 pandemic. The proposed DLKM-SP model aims to predict the stress level of the IT employees by the selection of features and optimal classification process. In addition, the DLKM-SP technique involves correlation based feature selection and principal component analysis (PCA) based feature reduction technique to choose an optimal subset of features. Moreover, attention based bidirectional long short term memory (ABiLS TM) technique was employed for the classification process for determining the proper class labels. Furthermore, arithmetic optimization algorithm is applied to improve the training process of the ABiLS TM approach. The effectiveness of the proposed model is examined using its own stress prediction dataset with numerous samples collected from IT employees. A detailed comparison study is implemented to highlight the enhanced predictive performance of the DLKM-SP approach in terms of different evaluation measures.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"227 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228401","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

Grid Integration of Renewable Energy Sources With IoT System 可再生能源与物联网系统的并网

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9761039

R. L. Kumar, M. Sanjai, R. Sivashanmugam, S. Saranya, S. Sinega, T. Logeswaran

引用次数: 1

Prediction of Diabetic Retinopathy using Novel Decision Tree Method in Comparison with Support Vector Machine Model to Improve Accuracy 基于决策树的糖尿病视网膜病变预测方法与支持向量机模型的比较研究

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760842

S. Jyotheeswar, K. Kanimozhi

引用次数: 5

An Intelligent Energy Management System with an Efficient IoT based Deep Learning Framework 基于高效物联网深度学习框架的智能能源管理系统

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760757

C. Bazil Wilfred, Santhi M. George, S. Sivaranjani, S. Selvan, J. M. Feros Khan, D. Beulah David

{"title":"An Intelligent Energy Management System with an Efficient IoT based Deep Learning Framework","authors":"C. Bazil Wilfred, Santhi M. George, S. Sivaranjani, S. Selvan, J. M. Feros Khan, D. Beulah David","doi":"10.1109/ICSCDS53736.2022.9760757","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760757","url":null,"abstract":"Efficient and economical energy utilization is ensured using green energy management systems that currently exist. However, integration of this technology with the Internet of Things (IoT) and edge intelligence is not completely explored. A smart energy management system with a deep learning framework is presented in this paper to address the requirements of energy management in smart industries, homes and grids. An efficient communication is established between the consumers and energy distributors while predicting the future energy consumptions over short time intervals. With least error rate and reduced time complexity, a smart energy management system with optimal normalization model selection and cloud-based data supervising server for energy forecasting in IoT and edge devices is introduced. Communication between the smart grids and the edge devices in the IoT networks connected to a common cloud server regarding efficient energy demand and response features occur in a secure and continuous manner. Short-term energy requirement forecasting is performed with an efficient decision making algorithm while using various preprocessing techniques to manage the electricity data which is of diverse nature. This model is implemented in resource constrained devices and shows promising outcomes. For commercial and residential datasets, the proposed system offers reduced mean-square error (MSE) and root MSE (RMSE) values.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134404770","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

Detection of Spinal Cord Injury using Deep Learning Algorithm 基于深度学习算法的脊髓损伤检测

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760935

P. S, A. S., D. A, Gokul N

{"title":"Detection of Spinal Cord Injury using Deep Learning Algorithm","authors":"P. S, A. S., D. A, Gokul N","doi":"10.1109/ICSCDS53736.2022.9760935","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760935","url":null,"abstract":"Using feature sets to detect the afflicted portion of the spinal cord areas from MRI images is one of the most difficult process. Because of the changes in structure, size, and white matter, detecting spinal cord atrophy is challenging. The ability to distinguish grey and white matter is crucial in detecting and assessing spinal cord degeneration. Automatic division and sorting are both effective approaches to determine the seriousness of SCI. Hierarchies of classification, division classification, graphing, and SCI sections in static xed places are identified using watershed segmentation algorithms. Furthermore, due to excess the segmented regions, there are characteristics and distortion, these segmentation algorithms have a significant rate of false positives. Furthermore, due to over segmentation, these classification algorithms are unable to segregate and assess the severity of the problem in the affected area. A novel section classification method is needed to identify the degree of the damage and predict illnesses across the over segmented images and aspects to overcome these difficulties. In this model, the spinal cord areas are segmented using a hybrid image threshold technique for a non-linear SVM classification strategy. The suggested technique offers superior correctness for SCI identification than typical feature segmentation-based classification models. When it comes to the genuine positive rate (0.9783) and accuracy, the results show that the current design is extra effective than previous methods (0.9683).","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131473516","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

System for Evaluating Professional Development of Physical Education Guider Considering Different Layer SVM Data Mining 基于分层支持向量机数据挖掘的体育指导员专业发展评价体系

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760888

Linxia Wang

{"title":"System for Evaluating Professional Development of Physical Education Guider Considering Different Layer SVM Data Mining","authors":"Linxia Wang","doi":"10.1109/ICSCDS53736.2022.9760888","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760888","url":null,"abstract":"System for evaluating professional development of the physical education guider considering different layer SVM data mining is studied in the paper. In solving the multi classification problem, the support vector machine is equivalent to using multiple hyperplanes to segment the data samples in the region surrounded by hyperplanes. Spherical structure support vector machine defines the sample set represented by each label with a sphere, and the sample space is equivalent to several hyperspheres. With this combination, the sensor monitoring system is improved from 2 aspects. (1) The WIFI module installed on the on-site machine establishes reliable and stable communication with the core main control center control computer through the 5G network to complete the data monitoring. (2) IP address of the WIFI module is not fixed, so two core dynamic IP address devices cannot be directly connected to the Internet communication. Then, the system is applied to the task of evaluating professional development of the physical education guider. The simulation has been considered to validate the robustness and efficient of the designed model.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131069951","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

Building Energy Management and Conservation using Internet of Things 基于物联网的建筑能源管理与节约

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) Pub Date : 2022-04-07 DOI: 10.1109/ICSCDS53736.2022.9760907

L. Raju, P. K, S. S, V. V, Bharathraj V

{"title":"Building Energy Management and Conservation using Internet of Things","authors":"L. Raju, P. K, S. S, V. V, Bharathraj V","doi":"10.1109/ICSCDS53736.2022.9760907","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760907","url":null,"abstract":"Energy conservation is not much focused than finding new energy resources without harming the environment. There is more talk on new energy generation and renewable energy than Energy conservation. This paper implements the building energy management and conservation using advanced technologies. Internet of Things (IOT) and machine learning are used to automate the building appliances and monitor the energy usage. This paper deals with conservation of energy through building automation and energy management through sensors and actuators using advanced IOT technologies. The building appliances are sensed through various types of sensors and monitored through Arduino for effective use of electricity in the building. Bluetooth controlled android app called Smart Home is built using MIT App Inventor. Local automation is done through Bluetooth and MIT app inventor so as to monitor and control all the electrical appliances of the building through the hand held devices. The careless use of electricity by the customers is noticed and necessary actions are taken. As the energy consumption is monitored continuously all the time which eventually brings awareness about use of energy. The Artificial intelligence Techniques are used are used to predict the load which eventually leads to effective building energy management. This paper aims to conserve energy in building at least by ten percent using advanced technologies","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957829","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