2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)最新文献_第2页

Diabetes Prognostication – An Aptness of Machine Learning 糖尿病预测——机器学习的一种能力

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117465

Vinod Maan, Jayati Vijaywargiya, M. Srivastava

{"title":"Diabetes Prognostication – An Aptness of Machine Learning","authors":"Vinod Maan, Jayati Vijaywargiya, M. Srivastava","doi":"10.1109/ICONC345789.2020.9117465","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117465","url":null,"abstract":"The expanse of machine learning has reached physical and medical sciences. The testing that were done before by physically examining a person can now be efficiently predicted by using machine learning algorithm. In today's generation diabetes is a growing health problem that has heterogeneous effects. In a report by World Health Organization (WHO), it revealed that in 2015 close to 1.6 million people died due to diabetes. The report also predicts that by 2030 diabetes will be seventh leading cause of death. In the assessment by International Diabetes Federation, more than 150 million cases of diabetes are undiagnosed. Busy lifestyle, improper food consumption and lack of physical activity on daily basis for long time has given birth to many diseases. One such disease is diabetes. It is already labelled as a Global disease. Treatment of diabetes is available but millions of people live with diabetes unknowing the fact they are suffering from it. The aim of this work is to make an user friendly, accurate and efficient low cost diabetes diagnose software, a Graphical User Interface which can predict diabetes and can be used by NGO's to diagnose people belonging to economically weaker section. This paper refers to a project which aims to classify a person's data into two classes, 'Yes' and ‘No’, based on ten factors., namely age, family history, alcoholic, smoker, etc.. In this work the accumulated result is obtained from mode of the four outputs, from four machine learning algorithms namely, SVM, KNN, ANN and Naive Bayes.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114578870","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

LQR Control of Multiple Product Inventory Systems for Profit and Warehouse Capacity Maximization 多产品库存系统的LQR控制，以实现利润和仓库容量最大化

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117511

Rajashree Taparia, S. Janardhanan, Rajeev Gupta

引用次数: 2

Vibration Suppression of Non-Deformable Metal Strip for Robot Assisted Assembly Operation 机器人辅助装配中不可变形金属带材的振动抑制

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117551

Chetan Jalendra, B. K. Rout

引用次数: 4

Diagnosis of Breast Cancer using Machine Learning Algorithms-A Review 使用机器学习算法诊断乳腺癌综述

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117320

Aditi Kajala, V. Jain

{"title":"Diagnosis of Breast Cancer using Machine Learning Algorithms-A Review","authors":"Aditi Kajala, V. Jain","doi":"10.1109/ICONC345789.2020.9117320","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117320","url":null,"abstract":"Breast cancer is one of the common diseases specifically in women now days. It has become the second main reason of cancer death in females. Every year 4.5-5% new cancer cases are recorded and increasing the morbidity at worldwide. It has proved that early detection of any cancer when followed up with appropriate diagnosis and treatment can increase the survival rate of the patients. Breast cancer is diagnosed by mammography. Mammograms are films generated by radiologist with a device. These mammograms are observed and diagnosed by the oncologist for further treatment. Since all general hospitals do not have the specialist and patients used to wait for their report. So waiting for diagnosing a breast cancer may take time. This delay may be responsible for cancer spreading and reducing the survival rate of the patient. Therefore machine learning can be used to diagnose breast cancer by a computer to make the diagnosing efficient and effective. This does not mean to replace expert or physician by computer but it means that computer can assist the expert for better understanding the particular case and the results can be produced early. This paper presents a brief summary on breast cancer diagnosis using machine learning algorithms used to increase the efficiency and effectiveness of predicting cancer. The correct diagnosis and accurate classification are the main objective of the reviewed papers","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843335","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: 5

Emerging Artificial Intelligence Devices and The Underlying Technology 新兴的人工智能设备和底层技术

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117293

Avireni Srinivasulu, C. Ravariu

引用次数: 1

Malware Detection & Classification using Machine Learning 使用机器学习的恶意软件检测和分类

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117547

S. Choudhary, Anand Sharma

引用次数: 19

Development of a Shape Aware Path Planning Algorithm for a Mobile Robot 移动机器人形状感知路径规划算法的研究

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117475

Sangram Keshari Das, Sabyasachi Dash, B. K. Rout

引用次数: 1

Vertical Handoff in Heterogeneous Mechanism for Wireless LTE Network - An Optimal Approach 无线LTE网络异构机制中的垂直切换——一种最优方法

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117281

Pushparaj Pal, Taranpreet Kaur, Dinesh Sethi, Anil Kumar, Sanjay Kumar, A. Lamba, Umang Rastogi

引用次数: 2

Commercial Sustainability of Vehicle-to-Grid Concept: An Overview 车辆到电网概念的商业可持续性:概述

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117523

K. Pramod Kumar, H. Bansal

引用次数: 2

Absorption in Al0.20Ga0.80As-GaAs MQWs Heterostructure Al0.20Ga0.80As-GaAs MQWs异质结构中的吸收

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) Pub Date : 2020-02-01 DOI: 10.1109/ICONC345789.2020.9117499

B. Kumari, Aavishkar Katti, P. A. Alvi

引用次数: 1