Pub Date : 2024-07-08DOI: 10.11648/j.ajai.20240802.11
Preethi Ramanaiah
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.
{"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}
Pub Date : 2024-05-17DOI: 10.11648/j.ajai.20240801.13
Preethi Ramanaiah
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.
蛋白质组学是对蛋白质及其在生物系统中的功能进行研究的学科，其数据密集程度与日俱增，既带来了机遇，也带来了挑战。本项目旨在满足蛋白质组学研究对高级数据分析和数据完整性的需求。利用机器学习（ML）和区块链技术的力量，这一尝试旨在改变蛋白质组学研究。这项工作包括三个关键目标。首先，收集、清理和整合不同来源的蛋白质组学数据，确保数据质量和一致性。其次，采用 ML 算法分析这些数据，揭示关键见解，识别蛋白质并预测其功能。第三，采用区块链技术保护蛋白质组学数据的真实性和完整性，提供可审计和防篡改的记录。实施用户友好型网络界面，通过允许访问共享数据和结果，促进研究人员和科学家之间的合作。这项研究包括研究蛋白质分类的各种分类方法，即随机森林、逻辑回归、神经网络、支持向量机和决策树。总之，通过增强数据分析能力和确保数据完整性，拟议的工作有望彻底改变蛋白质组学研究，从而使科学家在这一关键领域做出更明智、更自信的发现。
{"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}
Pub Date : 2024-01-11DOI: 10.11648/j.ajai.20240801.12
Sourav Banerjee, Ayushi Agarwal, Promila Ghosh, Ayush Kumar Bar
{"title":"Boosting Workplace Well-Being: A Novel Approach with a Mental Health Chatbot for Employee Engagement and Satisfaction","authors":"Sourav Banerjee, Ayushi Agarwal, Promila Ghosh, Ayush Kumar Bar","doi":"10.11648/j.ajai.20240801.12","DOIUrl":"https://doi.org/10.11648/j.ajai.20240801.12","url":null,"abstract":"","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139626966","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}
Pub Date : 2024-01-08DOI: 10.11648/j.ajai.20240801.11
James Monroe
{"title":"Corporate Social Responsibility in the MedTech Industry, the Emergence of Artificial Intelligence in the ERA of COVID-19","authors":"James Monroe","doi":"10.11648/j.ajai.20240801.11","DOIUrl":"https://doi.org/10.11648/j.ajai.20240801.11","url":null,"abstract":"","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"78 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139535462","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}
{"title":"Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach","authors":"Diriba Negash Tesso, Etana Fikadu Dinsa, Hawi Fikadu Kenani","doi":"10.11648/j.ajai.20230702.12","DOIUrl":"https://doi.org/10.11648/j.ajai.20230702.12","url":null,"abstract":"","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263099","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}
Pub Date : 2023-08-05DOI: 10.11648/j.ajai.20230702.11
Teotino Gomes Soares, Marcelo Fernandes Xavier Cham, Abdullah Bin Zainol Abidin
: Determining competent supervisors for student research projects is one of the factors that play the most important role because it can affect the success of student education, so it deserves attention. However, the process of determining a supervisor is not an easy thing because it involves various complex criteria and sub-criteria for making decisions consistently and objectively. Therefore
{"title":"Determinate Student Final Project Supervisor Based AHP and SAW","authors":"Teotino Gomes Soares, Marcelo Fernandes Xavier Cham, Abdullah Bin Zainol Abidin","doi":"10.11648/j.ajai.20230702.11","DOIUrl":"https://doi.org/10.11648/j.ajai.20230702.11","url":null,"abstract":": Determining competent supervisors for student research projects is one of the factors that play the most important role because it can affect the success of student education, so it deserves attention. However, the process of determining a supervisor is not an easy thing because it involves various complex criteria and sub-criteria for making decisions consistently and objectively. Therefore","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129115919","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}
Pub Date : 2023-06-27DOI: 10.11648/j.ajai.20230701.14
Jasmin Praful Bharadiya
{"title":"A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics","authors":"Jasmin Praful Bharadiya","doi":"10.11648/j.ajai.20230701.14","DOIUrl":"https://doi.org/10.11648/j.ajai.20230701.14","url":null,"abstract":"","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125126021","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}
Pub Date : 2023-06-15DOI: 10.11648/j.ajai.20230701.13
Seppy Pour
{"title":"Police Use of Facial Recognition Technology and Racial Bias – An Assessment of Criticisms of Its Current Use","authors":"Seppy Pour","doi":"10.11648/j.ajai.20230701.13","DOIUrl":"https://doi.org/10.11648/j.ajai.20230701.13","url":null,"abstract":"","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139369741","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}
Pub Date : 2023-04-15DOI: 10.11648/j.ajai.20230701.12
A. Ekong
{"title":"Evaluation of Machine Learning Techniques Towards Early Detection of Cardiovascular Diseases","authors":"A. Ekong","doi":"10.11648/j.ajai.20230701.12","DOIUrl":"https://doi.org/10.11648/j.ajai.20230701.12","url":null,"abstract":"","PeriodicalId":404597,"journal":{"name":"American Journal of Artificial Intelligence","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124181751","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}
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