2022 International Conference on Business Analytics for Technology and Security (ICBATS)最新文献

Novel Framework for the Improvement of Object Detection Accuracy of Smart Surveillance Camera Visuals using Modified Convolutional Neural Network Technique compared with Support Vector Machine 与支持向量机相比，改进卷积神经网络技术提高智能监控摄像机视觉目标检测精度的新框架

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759020

C. Pooja, K. Jaisharma

引用次数: 2

A Framework System for Plant Leaf Disease Detection using K-Nearest Neighbours and comparison of its features with Naive Bayes Classification 基于k近邻的植物叶片病害检测框架系统及其与朴素贝叶斯分类的特征比较

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9758924

Y. A. Reddy, A. M

引用次数: 2

Analysis of Patient Health Monitoring System Using Global System for Mobile communication (GSM) and Global Positioning System (GPS) and Comparing with Conventional System by Monitoring Pulse Rate and Body Temperature 基于全球移动通信系统(GSM)和全球定位系统(GPS)的患者健康监测系统分析，并通过监测脉搏和体温与传统系统进行比较

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759015

Srikireddy Sai Kiran Reddy, S. K. Kumari

引用次数: 0

Residential Power Load Prediction in Smart Cities using Machine Learning Approaches 基于机器学习方法的智慧城市居民用电负荷预测

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759024

Waleed Alomoush, T. A. Khan, Mehwish Nadeem, J. Janjua, Anwaar Saeed, Atifa Athar

引用次数: 0

Improving the Accuracy for Prediction of Heart Disease by Novel Feature Selection Scheme using Decision tree comparing with Naive-Bayes Classifier Algorithms 基于决策树的特征选择方案与朴素贝叶斯分类器算法的比较提高了心脏病预测的准确性

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9758926

S.K.L. Sameer, P. Sriramya

{"title":"Improving the Accuracy for Prediction of Heart Disease by Novel Feature Selection Scheme using Decision tree comparing with Naive-Bayes Classifier Algorithms","authors":"S.K.L. Sameer, P. Sriramya","doi":"10.1109/ICBATS54253.2022.9758926","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9758926","url":null,"abstract":"Aim: Two machine learning methods are employed in this study: DT and Naive Bayes. Heart disease detection and prediction can be improved by combining these two methods. Here are the components and steps: Heart disease can be predicted using the Decision Tree algorithm and the Naive Bayes approach. Both the Decision Tree and the Naive Bayes algorithms employ machine learning to make predictions about heart disease. I repeated this process 20 times to get the best results from heart disease images with a G power of 80 percent and a 0.05 percent threshold, the mean and standard deviation of which were in the 95 percent confidence interval (CI) 95 percent. This was necessary to get the best results. I have come to this conclusion after a lot of thought. It appears that when the Decision tree algorithm is compared to the Naive Bayes classifier algorithm, the Decision tree method outperforms the Naive Bayes classifier algorithm by a factor of 90.16 percent, according to the testing data. The Decision Tree classification algorithm outperforms the other classification algorithms, according on the data collected. the Naive Bayes classifier method in predicting heart disease.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114179392","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

Comparative Analysis of Accuracy and Prediction of Customer Loyalty in the Telecom Industry using Novel Diverse Algorithm 基于新多元算法的电信行业客户忠诚度预测精度与预测精度的对比分析

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759079

P. Surya, K. Anitha

引用次数: 1

Ovary Cancer Diagnosing Empowered with Machine Learning 卵巢癌诊断与机器学习授权

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759010

N. Taleb, Shahid Mehmood, Muhammad Zubair, Iftikhar Naseer, Beenu Mago, Muhammad Umar Nasir

{"title":"Ovary Cancer Diagnosing Empowered with Machine Learning","authors":"N. Taleb, Shahid Mehmood, Muhammad Zubair, Iftikhar Naseer, Beenu Mago, Muhammad Umar Nasir","doi":"10.1109/ICBATS54253.2022.9759010","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759010","url":null,"abstract":"A high mortality rate is associated with ovarian cancer, one of the most common types of cancers in women. Ovarian cancer refers to a group of disorders that develop in the ovaries and spread to the fallopian tubes and peritoneum. Treatment is most effective when ovarian cancer is discovered in its early stages. Machine learning has recently demonstrated that it is capable of better identifying ovarian cancer and its stages. Most modern research studies on ovarian cancer use a single classification model, leading to poor performance in diagnosis. For the detection of ovarian cancer, the highly sophisticated and efficient machine learning algorithms Support vector machine (SVM) and K-Nearest Neighbor (KNN) are employed in this study. Before diagnosing illness, the suggested approach can optimize and standardize data. Experimental results show that SVM has outperformed KNN in both training and validation performance and achieved an accuracy of 98.1% & 97.16% for training and validation respectively. If used in medical diagnosis systems, the proposed model can significantly improve the accuracy of ovarian cancer detection leading to effective treatment and an increase in patient survival rates.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129696924","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: 7

Impact of Cloud-Based Customer Relationship Management (CRM) in Healthcare Sector 基于云的客户关系管理(CRM)对医疗保健行业的影响

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9758931

Mahdi Sahlabadi, R. C. Muniyandi, Nazanin Doroudian, O. L. Usman

引用次数: 1

Improvement Of Performance In Grid Connected Solar Photovoltaic For Water Pumping System Using Induction Motor 利用感应电机提高并网太阳能光伏水泵系统的性能

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759053

C. Manideep, M. V. Priya

{"title":"Improvement Of Performance In Grid Connected Solar Photovoltaic For Water Pumping System Using Induction Motor","authors":"C. Manideep, M. V. Priya","doi":"10.1109/ICBATS54253.2022.9759053","DOIUrl":"https://doi.org/10.1109/ICBATS54253.2022.9759053","url":null,"abstract":"The aim of the study is to achieve a simple and decreasing cost configuration on photovoltaic systems composed of stages converters of maximum power and high efficiency on Induction motors. This system consists of two stages of controllers, one is maximum power point controller and fuzzy logic controller in which novel fuzzy membership is created in an induction motor. A total of 20 samples were collected from the two sets of irradiance in the solar photovoltaic system. These samples are analysed with incremental conductance, duty ratio, solar PV power and induction motor power performance motor power with a maximum power point tracking. The performance result improves the efficiency and forces a maximum power point on the induction motor at irradiance values and step size with respective control strategies. The G power used in an induction motor is 80.00% and the statistical test-difference between two independent means is kept at 0.05. In this study, it’s found that cost configuration and efficiency of improving induction motors with respective irradiance values.","PeriodicalId":289224,"journal":{"name":"2022 International Conference on Business Analytics for Technology and Security (ICBATS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289427","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 Enhancing Success Rate in Social Media Profile Matching using Decision Table over Random committee 基于随机委员会决策表的社交媒体个人资料匹配成功率提高方法

2022 International Conference on Business Analytics for Technology and Security (ICBATS) Pub Date : 2022-02-16 DOI: 10.1109/ICBATS54253.2022.9759086

O. Sudheer, K. Anitha

引用次数: 1