American Journal of Artificial Intelligence最新文献

Feature Selection AI Technique for Predicting Chronic Kidney Disease 预测慢性肾病的特征选择人工智能技术

American Journal of Artificial Intelligence Pub Date : 2024-07-08 DOI: 10.11648/j.ajai.20240802.11

Preethi Ramanaiah

{"title":"Feature Selection AI Technique for Predicting Chronic Kidney Disease","authors":"Preethi Ramanaiah","doi":"10.11648/j.ajai.20240802.11","DOIUrl":"https://doi.org/10.11648/j.ajai.20240802.11","url":null,"abstract":"The kidney is a vital organ that plays a crucial role in eliminating waste and excess water from the bloodstream. When renal function is impaired, the filtration process also ceases. This leads to an elevation of harmful molecules in the body, a condition referred to as chronic kidney disease (CKD). Early-stage chronic kidney disease often lacks noticeable symptoms, making it challenging to detect in its early stages. Diagnosing chronic kidney disease (CKD) typically involves advanced blood and urine tests, but unfortunately, by the time these tests are conducted, the disease may already be life-threatening. Our research focuses on the early prediction of chronic kidney disease (CKD) using machine learning (ML) and deep learning (DL) techniques. Utilized a dataset from the machine learning repository at the University of California, Irvine (UCI) to train various machine learning algorithms in conjunction with a Convolutional Neural Network (CNN) model. The algorithms encompassed in this set are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). Based on the experimental results, the CNN model achieves a prediction accuracy of precisely 97% after feature selection, the highest among all models tested. Hence, the objective of this project is to develop a deep learning-based prediction model to aid healthcare professionals in the timely identification of chronic kidney disease (CKD), potentially leading to life-saving interventions for patients.\u0000","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141836218","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

Proteomics Data Classification Using Advanced Machine Learning Algorithm 利用先进的机器学习算法进行蛋白质组学数据分类

American Journal of Artificial Intelligence Pub Date : 2024-05-17 DOI: 10.11648/j.ajai.20240801.13

Preethi Ramanaiah

{"title":"Proteomics Data Classification Using Advanced Machine Learning Algorithm","authors":"Preethi Ramanaiah","doi":"10.11648/j.ajai.20240801.13","DOIUrl":"https://doi.org/10.11648/j.ajai.20240801.13","url":null,"abstract":"Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this attempt aims to transform proteomics research. This work encompasses three key objectives. First, collect, clean, and integrate proteomics data from diverse sources, ensuring data quality and consistency. Second, employ ML algorithms to analyze this data, revealing crucial insights, identifying proteins, and predicting their functions. Third, implement blockchain technology to safeguard the authenticity and integrity of the proteomics data, providing an auditable and tamper-proof record. Implemented a user-friendly web interface, facilitating collaboration among researchers and scientists by granting access to shared data and results. This study included various classification methods for the investigation of protein classification, namely, random forests, logistic regression, neural networks, support vector machines, and decision trees. In conclusion, the proposed work is poised to revolutionize proteomics research by enhancing data analytics capabilities and securing data integrity, thereby enabling scientists to make more informed and confident discoveries in this critical field.\u0000","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"106 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126029","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

Boosting Workplace Well-Being: A Novel Approach with a Mental Health Chatbot for Employee Engagement and Satisfaction 提升工作场所的幸福感：利用心理健康聊天机器人提高员工参与度和满意度的新方法

American Journal of Artificial Intelligence Pub Date : 2024-01-11 DOI: 10.11648/j.ajai.20240801.12

Sourav Banerjee, Ayushi Agarwal, Promila Ghosh, Ayush Kumar Bar

引用次数: 0

Corporate Social Responsibility in the MedTech Industry, the Emergence of Artificial Intelligence in the ERA of COVID-19 医疗技术行业的企业社会责任，COVID-19 时代人工智能的兴起

American Journal of Artificial Intelligence Pub Date : 2024-01-08 DOI: 10.11648/j.ajai.20240801.11

James Monroe

引用次数: 0

Amazon Marketplace: An Analysis of External Factors and Machine Learning Models - Survey 亚马逊市场：外部因素和机器学习模型分析 - 调查

American Journal of Artificial Intelligence Pub Date : 2023-11-29 DOI: 10.11648/j.ajai.20230702.13

Muneer Hazaa Alsurori, Waheeb Abdo Almorhebi

引用次数: 0

Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach 利用深度学习方法将手势语翻译成阿法安奥罗莫语文本

American Journal of Artificial Intelligence Pub Date : 2023-11-17 DOI: 10.11648/j.ajai.20230702.12

Diriba Negash Tesso, Etana Fikadu Dinsa, Hawi Fikadu Kenani

引用次数: 0

Determinate Student Final Project Supervisor Based AHP and SAW 基于AHP和SAW确定学生期末项目主管

American Journal of Artificial Intelligence Pub Date : 2023-08-05 DOI: 10.11648/j.ajai.20230702.11

Teotino Gomes Soares, Marcelo Fernandes Xavier Cham, Abdullah Bin Zainol Abidin

引用次数: 0

A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics 基于大数据分析的商业智能与人工智能的比较研究

American Journal of Artificial Intelligence Pub Date : 2023-06-27 DOI: 10.11648/j.ajai.20230701.14

Jasmin Praful Bharadiya

引用次数: 4

Police Use of Facial Recognition Technology and Racial Bias – An Assessment of Criticisms of Its Current Use 警方使用面部识别技术与种族偏见--对目前使用面部识别技术的批评意见的评估

American Journal of Artificial Intelligence Pub Date : 2023-06-15 DOI: 10.11648/j.ajai.20230701.13

Seppy Pour

引用次数: 0

Evaluation of Machine Learning Techniques Towards Early Detection of Cardiovascular Diseases 对心血管疾病早期检测的机器学习技术的评估

American Journal of Artificial Intelligence Pub Date : 2023-04-15 DOI: 10.11648/j.ajai.20230701.12

A. Ekong

引用次数: 0