2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)最新文献_第4页

Machine Learning Models for Life Expectancy 预期寿命的机器学习模型

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169737

Deepanshi Jalan, Anandita Tuli, Vanshika Chaudhary, N. Sharma, Manik Rakhra

{"title":"Machine Learning Models for Life Expectancy","authors":"Deepanshi Jalan, Anandita Tuli, Vanshika Chaudhary, N. Sharma, Manik Rakhra","doi":"10.1109/ICAIA57370.2023.10169737","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169737","url":null,"abstract":"Life expectancy (LE) models provide a lot of ways to improve healthcare and other social welfares related to society. Life expectancy models provide solutions to problems like how to decide on retirement age or manage financial issues related to public matters. These models are becoming prominent in many regions as they are being widely used by government bodies and private sector for their policy making and developing health integrated systems. Thus, this paper aims to analyze the Trends in Life Expectancy in about 72 countries of the world over a span of 16 years, i.e., from 2000-2015. The study gives plots of attributes such as life expectancy, GDP, infant deaths, adult mortality, etc. across year which would help the countries understand the life expectancy trends over the course of time and suggest areas which should be focused upon to efficiently increase the life expectancy of its population. The simulations are done in Google Collab by using various Python libraries like pandas, numpy, matplotlib (used for plotting graphs), seaborn (used for plotting 3-D graphs and advanced visualization features of python), sklearn (used for handling missing data), and plotly express (used for plotting choropleth).","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133891083","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

An Improved Approach To Classify Plant Disease Using CNN And Random Forest 一种基于CNN和随机森林的植物病害分类改进方法

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169830

Shivdutt Dixit, Navneet Kaur

引用次数: 0

A Study on Tuberculosis With Deep Learning and Machine Learning Approaches 基于深度学习和机器学习方法的肺结核研究

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169724

Madhvan Bajaj, Priyanshu Rawat, A. Bhatt., Satvik Vats, Vikrant Sharma

引用次数: 0

Compact & Efficient Monopole Antenna Designs Based on AI-Driven EM Optimization Techniques 基于ai驱动的电磁优化技术的紧凑高效单极天线设计

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169413

Kadiyam Sreelekha, C. S. Sudeep, S. Sreekar, B. R. Behera

{"title":"Compact & Efficient Monopole Antenna Designs Based on AI-Driven EM Optimization Techniques","authors":"Kadiyam Sreelekha, C. S. Sudeep, S. Sreekar, B. R. Behera","doi":"10.1109/ICAIA57370.2023.10169413","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169413","url":null,"abstract":"In this present paper, two compact and efficient printed monopole antennas of different shapes such as circular shaped & rectangular shaped are investigated based on the EM optimization techniques. The proposed antennas are capable of covering out GSM 1800 (1.8 GHz), UMTS (2.1 GHz), LTE (2.3-2.4 GHz), Wi-Fi(2.4 GHz), ISM (2.4-2.5 GHz), LTE-Advanced (2.5 GHz), WiMAX (3.6 GHz), WLAN (5.2/5.S GHz), and IEEE 802.11 b/g/n (2.4/5.2 GHz) frequency bands. They are designed using the EM solver CST-microwave studio 2022, in which FR-4 substrate is used, a low cost commercially available substrate. The sizes of antennas are $80times 60times 1.6mathrm{mm}^{3}$. To study the use of optimization in the printed monopole antenna, both of designed antennas are interpreted with the EM optimization techniques, available in the CST-MWS’22 platform. The optimized printed circular shaped monopole antenna (PCSMA) exhibits a −10dB impedance bandwidth of 1.3-7.4 GHz (6.1 GHz) with average realized gain of 3.98 dB & antenna efficiency of 90%, whereas printed rectangular shaped monopole antenna (PRSMA) offers −10dB impedance bandwidth of 1.3-7.45 GHz (6.15 GHz) with average realized gain of 4.21 dB & antenna efficiency of 93%. With outlined outcomes, the proposed antennas can be utilized in the case of RF energy harvesting, RFID Tags, & even can be extended to onboard telemetry applications.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323049","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

State of Health of Lithium-ion Batteries by Data-Driven Technique with Optimized Gaussian Process Regression 基于优化高斯过程回归的数据驱动技术研究锂离子电池的健康状态

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169188

S. Vamsi, K. M. Nagabushanam, K. V. Kumar, Somesh Vinayak Tewari, Tarkeshwar Mahto

{"title":"State of Health of Lithium-ion Batteries by Data-Driven Technique with Optimized Gaussian Process Regression","authors":"S. Vamsi, K. M. Nagabushanam, K. V. Kumar, Somesh Vinayak Tewari, Tarkeshwar Mahto","doi":"10.1109/ICAIA57370.2023.10169188","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169188","url":null,"abstract":"Lithium ion batteries are a promising energy source for electric vehicles due to their high specific energy and power output. Overall system reliability and stability can be improved by effectively planning battery replacement intervals and monitoring their condition. To guarantee the battery system operates safely, steadily, and effectively, it is necessary to accurately assess the state of health (SOH) of the lithium-ion battery. Capacity might be used to anticipate it directly. To improve the accuracy of the SOH estimate, hyperparameter-optimized Gaussian process regression (GPR) is used. Gaussian process models have the advantage of being flexible, stochastic, nonparametric models with uncertainty forecasts, and may have variance around the mean forecast to account for the associated uncertainties in evaluation and forecasting. The lithium-ion battery data set made available by NASA is examined in this article. The outcomes demonstrate its efficacy and demonstrate that the algorithm may be successfully used for battery monitoring and prognostics. Additionally, the prediction for battery health has been improved through the comparison of predictions with various quantities of training data.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121254258","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

Design of a Low Power Complementary Current Controlled Skewed Delay Voltage Controlled Oscillator using CNTFET 基于CNTFET的低功率互补电流控制偏斜延迟压控振荡器的设计

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169252

Smriti Kantroo, Vikram Singh, Ritika Mattoo, Neeraj Tripathi, A. Bhardwaj

引用次数: 0

Breast Cancer Prediction Using Different Machine Learning Algorithms: A Comparative Study 使用不同机器学习算法预测乳腺癌:一项比较研究

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169729

Chitra Saini, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar

{"title":"Breast Cancer Prediction Using Different Machine Learning Algorithms: A Comparative Study","authors":"Chitra Saini, Kapil Dev Mahato, Chandrashekhar Azad, U. Kumar","doi":"10.1109/ICAIA57370.2023.10169729","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169729","url":null,"abstract":"According to the World Health organization’s (WHO) 2020 report, 2.3 million new cases of breast cancer were recorded, and 685,000 women died due to breast cancer. To treat breast cancer early, a lot of research has been proposed using different types of techniques in the past few years. In recent years, machine learning algorithms (MLAs) have become popular for detection due to their improved accuracy and performance. This paper used 13 supervised machine learning (SML) techniques, namely: Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Adaptive Boosting (AB), Categorical Boosting (CB), Light Gradient Boosting Machine (LGBM), Multi-Layer Perceptron (MLP), and Extra trees (ET) to predict the outcomes of the Wisconsin Breast Cancer Original (WBCO) dataset from the UCI repository. When all thirteen algorithms were evaluated and compared, MLP outperforms them all with the highest accuracy (98.76%). This accuracy value is 0.56% greater than the recently reported accuracy value of 98.2% for the MLP classifier for the same dataset.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132305534","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 Survey Paper on Precision Agriculture based Intelligent system for Plant Leaf Disease Identification 基于精准农业的植物叶片病害智能识别系统研究综述

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169852

Supriya, Ashutosh Shukla, Mahesh Manchanda

引用次数: 0

Fabric fault and Extra thread Detection Using Convolutional Neural Network 基于卷积神经网络的织物故障和多线检测

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169472

Sowmiya A, Karunamoorthy B

引用次数: 0

Pehchaan: A Touchless Attendance System Pehchaan:一个非接触式考勤系统

2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Pub Date : 2023-04-21 DOI: 10.1109/ICAIA57370.2023.10169372

Prerak Moolchandani, Shreya Hegde, Muskan Hassanandani, Garv Jhangiani, G. Bhatia, A. Tewari, S. Dugad

引用次数: 0