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2022 IEEE International Conference on Data Science and Information System (ICDSIS)最新文献

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Comparative approach for VM Scheduling using Modified Particle Swarm Optimization and Genetic Algorithm in Cloud Computing 基于改进粒子群算法和遗传算法的云计算虚拟机调度比较研究
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915907
S. Supreeth, Kirankumari Patil, S. Patil, S. Rohith
{"title":"Comparative approach for VM Scheduling using Modified Particle Swarm Optimization and Genetic Algorithm in Cloud Computing","authors":"S. Supreeth, Kirankumari Patil, S. Patil, S. Rohith","doi":"10.1109/ICDSIS55133.2022.9915907","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915907","url":null,"abstract":"The Users can access Cloud services anytime and from any location, depending on their needs. In a cloud platform, data of a vast amount is transferred from the user to the server and vice-versa. Whenever the VM Scheduling takes longer than expected, or the selected VM does not exist in the datacenter may utilize more Energy consumption and SLA (Service Level Agreement) violations with more VM Migrations. Because the VM is the primary element in the Cloud Environment, the VM’s assignment must be done correctly; resources must be utilized effectively, and no violations must occur with less VM Migrations. Two approaches are implemented for the comparison i.e., Modified Particle Swarm optimization (MPSO), and Genetic Algorithm (GA). The MPSO resulted better than GA by 6.0S%, LR-MMT by 32.2%, and GA at 27.81% compared to Local Regression-Minimum Migration Time (LR-MMT) in energy consumption. The MPSO resulted better than GA by 48.39%, LR-MMT by 91.6%, and GA by S3.73% compared to LR-MMT in VM migrations. The MPSO resulted better than GA by 5%, Local RegressionRandom Selection (LR-RS) by 71.21%, and GA resulted in 67.21% compared to Local Regression-Maximum Correlation (LR-MC) in SLA Violation. Therefore, the acquired results indicated that the suggested approach converges to optimal solutions with higher quality than existing algorithms compared to the QoS parameters.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"2003 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131201643","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: 8
A Risk Predictive Model for Primary Tumor using Machine Learning with Initial Missing Values 基于初始缺失值的机器学习的原发性肿瘤风险预测模型
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915957
R. Bajaj, Dr. Shandilya, Shivangi Gagneja, Khushi Gupta, Deepak Rawat
{"title":"A Risk Predictive Model for Primary Tumor using Machine Learning with Initial Missing Values","authors":"R. Bajaj, Dr. Shandilya, Shivangi Gagneja, Khushi Gupta, Deepak Rawat","doi":"10.1109/ICDSIS55133.2022.9915957","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915957","url":null,"abstract":"The biological term primary tumor, is growing at the anatomical place where tumor growth began and progressed to produce a malignant mass. The further stage of the primary tumor can lead to cancer. Machine learning assists researchers in identifying and classifying tumors based on growth features, size, speed of spread, and other factors, as well as grouping them based on a comparable set of predicting outcomes. But Missing values in medical data can lead to biased study conclusions and makes it difficult to predict and analyze data with high performance. Therefore, using python KNN imputation was implemented, which sorts multiple complete samples with the nearest measurements using Euclidean distance in the primary tumor missing dataset to find the optimal value of K. After imputing inconsistent data and performing several simulations, the overall performance increased. Hence, this approach may be used to diagnose diseases using more intricate clinical data.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131859291","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
A Novel Approach for Detecting the Malignant Features of Breast Cancer using Algorithms of ML 一种基于ML算法的乳腺癌恶性特征检测新方法
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915954
Ritu Aggarwal, Prateek Thakral
{"title":"A Novel Approach for Detecting the Malignant Features of Breast Cancer using Algorithms of ML","authors":"Ritu Aggarwal, Prateek Thakral","doi":"10.1109/ICDSIS55133.2022.9915954","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915954","url":null,"abstract":"Machine learning (ML) is trending knowledge tool for finding disease. It facilitates systems that can learn data automatically. Breast cancer (BC) is second largest disease among the women. In all over world 50 % women are dying due to the BC. ML is used in finding the results in BC as malignant or benign because predication of BC at early stage is going to be very challenging. This proposed work is to identify the BC disease before time stage with the help of machine learning algorithms viz. K-Nearest Neighbor, Random Forest, Naive Bayes, Support Vector Machine (SVM) & Decision Tree. Herein projected work, the dataset used has been collect from the UCI repository. In Breast cancer dataset Out of total 450 samples, 150 samples are found either benign or malignant. The results obtained to achieve higher performance measures show that RF gives best outcome by smallest amount inaccuracy rate.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134110864","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
Human Face Detection and Recognition in eHealth Implications for Blockchain Data Theory 电子健康中的人脸检测和识别对区块链数据理论的影响
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915845
U. Jannat, M. Mohankumar, Syed Islam
{"title":"Human Face Detection and Recognition in eHealth Implications for Blockchain Data Theory","authors":"U. Jannat, M. Mohankumar, Syed Islam","doi":"10.1109/ICDSIS55133.2022.9915845","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915845","url":null,"abstract":"This Facial recognition is a broad category of technologies used largely for surveillance. Now a days universities and multinational companies have already begun to use these incredibly important technologies to track attendance and identify individuals. The primary goal of a facial recognition system is to confirm a person’s identity by using a training database. A large number of training samples are required by the facial recognition system, which must be maintained in every storage site. As a result, storing facial photos on the blockchain creates a secure platform that is impervious to data breaches and cyber-attacks while also ensuring data availability. The facial recognition software system and the blockchain function are discussed, and a real-time facial recognition system has been built. Blockchain is also utilised to manage databases in an eHealth data management system. Patient facial data is saved on a distributed server in a secure manner.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128598128","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
Ranking of Sensor Nodes by Optimizing Sensor Data in Energy Harvesting Wireless Sensor Network 能量采集无线传感器网络中基于传感器数据优化的传感器节点排序
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915829
P. Mohan, N. R
{"title":"Ranking of Sensor Nodes by Optimizing Sensor Data in Energy Harvesting Wireless Sensor Network","authors":"P. Mohan, N. R","doi":"10.1109/ICDSIS55133.2022.9915829","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915829","url":null,"abstract":"Wireless Sensor Networks should be self-automated and there must be a continuous power supply for the proper functioning of sensor networks. The Energy Harvesting Wireless Sensor Network plays an important role when engaging in long-term ecological monitoring, when sensor nodes are established, and when data from the environment is meant to be collected and relayed to a base station. The Internet of Things (IoT) has sparked interest in the present era, so there is a huge demand for low-power energy-harvesting wireless sensor networks in a variety of industries, such as healthcare, the military, and transportation. These networks are assessed by conducting tasks such as data collection, process monitoring, and autonomous activity control. The use of batteries to power wireless sensors limits their life and functionality in these sensor networks. By harvesting energy from the sensor’s local environment to power the device, it is possible to increase the sensor’s lifespan while simultaneously making it more ecologically friendly. The use of energy harvesting in sensor nodes allows them to be powered by batteries, dramatically lowering the cost of battery replacement. The research proposes a method for collecting sensor data from a simulator utilising six different sensors like Temperature, Wind, Humidity, Vibration, Pressure, and Light in each node and optimising the sensor nodes using the Naive Bayes machine learning approach. The final data will be represented graphically.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117142898","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: 3
RED: An Intelligent Edge based Speaker System with Ambient Sensing Technology RED:基于智能边缘的扬声器系统与环境传感技术
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915823
Tanvi K Jois, M. V. Bharadwaj, A. Mukhopadhyay
{"title":"RED: An Intelligent Edge based Speaker System with Ambient Sensing Technology","authors":"Tanvi K Jois, M. V. Bharadwaj, A. Mukhopadhyay","doi":"10.1109/ICDSIS55133.2022.9915823","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915823","url":null,"abstract":"Smart speakers have become widely common. A smart speaker allows people to do a wide range of tasks, including presenting information such as the time, date, and several other things. It can also stream songs on its own. Smart speakers that can carry out tasks are known as virtual assistants. In this work, a smart speaker RED is demonstrated. RED is particularly designed for audio sensing activities. Mel’s spectrograms are obtained and subjected to rudimentary neural networks to achieve automatic speech recognition in RED. RED can adjust to ambient noise, communicate with the user, and function as a virtual assistant by cracking jokes, playing/pausing music, and altering volume on command. Users can dynamically assign wake words of their desire in RED. RED was able to reach an overall accuracy of 91%.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115399660","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
A Lightweight Deep Learning Model for Agricultural Pathology 农业病理学轻量级深度学习模型
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915842
K. S. S. Sai, M. Nandeesh, M. Pushpa
{"title":"A Lightweight Deep Learning Model for Agricultural Pathology","authors":"K. S. S. Sai, M. Nandeesh, M. Pushpa","doi":"10.1109/ICDSIS55133.2022.9915842","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915842","url":null,"abstract":"A technique to determine diseases in agricultural crops from leaf images using deep convolutional neural networks is proposed. 12 different diseases in potato, tomato and bell pepper crops are determined from leaf images. As many methods proposed in the existing literature are computationally expensive and are restricted to specific plant species, the proposed technique is capable of determining diseases in multiple crops using a single neural network based model while also minimizing computational complexity. The method obtains an F1 score, accuracy, precision, of 93.66%, 93.72%, 93.57% respectively. Enhancements in the performance are seen when compared to the already existing methods.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115426462","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
GSM and Wi-Fi Module Based Advanced Smart Irrigation Monitoring System using IoT 基于GSM和Wi-Fi模块的物联网先进智能灌溉监测系统
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915921
T. Narasimhulu, K. Deepthi, N. Patnaik, G. Rao
{"title":"GSM and Wi-Fi Module Based Advanced Smart Irrigation Monitoring System using IoT","authors":"T. Narasimhulu, K. Deepthi, N. Patnaik, G. Rao","doi":"10.1109/ICDSIS55133.2022.9915921","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915921","url":null,"abstract":"This paper proposes an Automated Irrigation System in Farming using Internet of Things (IoT) which aids farmers in acquiring information on mobile phone regarding temperature and soil moisture for efficient environment monitoring. This will promote the gross yield and quality of the production. An irrigation system built on automation technology saves the farmer’s expenses and electricity, and yield time. The technology of IoT has made the life of ordinary people easier by introducing revolutionary changes in every field, where the changes include the automation of tools. IoT defines the system of physical thing rooted with software, sensors as well as additional technologies. The design methodology of Automated Irrigation System in Farming using IoT-based devices is gradually enhancing the agriculture production, enhancing it, making it economical environmental-friendly, and decreasing waste production to conserve the yield. It is designed with Arduino Technology integrated with different sensors and poses a GSM module for SMS alerts to a specified mobile number and a Wi-Fi module producing a live data feed acquired online from Thingsspeak.com. The prototype developed has been tested on live agriculture fields, which gives the highest precision of 98% in data feeds.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115284673","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: 2
User Profiling for Web Personalization Web个性化的用户分析
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915969
M. Sowbhagya, H. Yogish, G. Raju
{"title":"User Profiling for Web Personalization","authors":"M. Sowbhagya, H. Yogish, G. Raju","doi":"10.1109/ICDSIS55133.2022.9915969","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915969","url":null,"abstract":"This study tackles the issue of delivering personalized information on the Web based on user profile. Personalized data distribution built on customer& document reporting is an important stage toward building computers more useful and reachable to a larger audience. The technique of adapting Web sites to a particular user’s profile in order to make Web browsing easier is known as web customization. The ability of a user to easily identify relevant things or contents is referred to as Web browsing. The main goal of user profiling is to make user actions more efficient by giving more personalized information.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125257131","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
Web Scraping based Product Comparison Model for E-Commerce Websites 基于Web抓取的电子商务网站产品比较模型
2022 IEEE International Conference on Data Science and Information System (ICDSIS) Pub Date : 2022-07-29 DOI: 10.1109/ICDSIS55133.2022.9915892
Harsh Khatter, Dravid, Akshat Sharma, A. Kushwaha
{"title":"Web Scraping based Product Comparison Model for E-Commerce Websites","authors":"Harsh Khatter, Dravid, Akshat Sharma, A. Kushwaha","doi":"10.1109/ICDSIS55133.2022.9915892","DOIUrl":"https://doi.org/10.1109/ICDSIS55133.2022.9915892","url":null,"abstract":"Existing e-commerce applications offer various functionalities to buy any products from their websites. However, a comparison for any product in terms of price offers, and quality among these applications is time-consuming and involves the user’s time to check reviews and surf other websites to check prices. The objective of this paper is to propose a web application that identifies those basic details for any product from different e-commerce websites. These details are compared, and the result is displayed to the user graphically for the final decision. The proposed web application uses the web-scraping methodology with selenium and is implemented on the python framework using various algorithms and techniques which are discussed in the paper. As an outcome, the system will give the simulation results, so that the user can get the recommendations on purchasing the relevant product with better user satisfaction and in minimum clicks and time.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126776042","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: 2
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