Pub Date : 2024-07-15DOI: 10.47392/irjaeh.2024.0272
Senthil Kumari P, Aishwarya S, Nageshwari B, Saravana Kumar G J, Joshika S, Nihetha M, Arul Chandru A N, Kalaivanan K
The study presents an innovative approach aimed at amplifying student engagement with the Selfmade Ninja labs, utilizing a reward-centric framework that prioritizes user efficiency. This approach involves the meticulous calculation of various CPU metrics, encompassing elements such as CPU usage, memory usage, download and upload statistics, process identifiers, as well as read and write statistics. These metrics collectively offer a comprehensive view of user interactions within the platform. The gathered data is thoughtfully curated and stored in a JSON file, facilitating efficient data management and analysis. To facilitate the realization of this approach, a sophisticated machine-learning model is deployed. This model serves the pivotal purpose of predicting user efficiency, a crucial factor in determining the efficacy of their engagement with the Selfmade Ninja labs. Building upon this predictive prowess, a system of credits is established, intricately tied to a leaderboard that reflects individual user performances. Through this dynamic reward distribution mechanism, users are incentivized to actively participate and continually enhance their proficiency, thereby fostering a vibrant learning ecosystem. The culmination of this endeavour is a finely tuned predictive model that seamlessly allocates rewards to users based on their demonstrated engagement and proficiency. This tailored approach not only magnifies user motivation but also significantly augments the overall educational impact of the Selfmade Ninja platform. The integration of insights derived from both exploratory data analysis (EDA) and the predictive model ensures a holistic understanding of user behaviors and preferences. Consequently, the proposed reward-based system is elevated to a new level of efficacy, nurturing a learning environment where students are empowered to engage more meaningfully with the Selfmade Ninja labs, fostering enhanced learning outcomes.
本研究提出了一种创新方法，旨在利用以奖励为中心、优先考虑用户效率的框架，提高学生对 "自制忍者 "实验室的参与度。这种方法涉及对各种 CPU 指标的细致计算，包括 CPU 使用率、内存使用率、下载和上传统计、进程标识符以及读写统计等要素。这些指标共同提供了平台内用户交互的全面视图。收集到的数据经过精心整理后存储在 JSON 文件中，便于进行高效的数据管理和分析。为了促进这种方法的实现，我们部署了一个复杂的机器学习模型。该模型的关键目的是预测用户效率，这是决定用户参与 "自制忍者 "实验室效率的关键因素。在这一预测能力的基础上，建立了一套积分系统，与反映用户个人表现的排行榜紧密相连。通过这种动态奖励分配机制，激励用户积极参与并不断提高自己的能力，从而形成一个充满活力的学习生态系统。这一努力的最终成果是建立了一个经过精心调整的预测模型，可根据用户的参与度和熟练程度无缝分配奖励。这种量身定制的方法不仅提高了用户的积极性，还大大增强了 "自制忍者 "平台的整体教育效果。将探索性数据分析（EDA）和预测模型得出的见解整合在一起，确保了对用户行为和偏好的全面了解。因此，所建议的基于奖励的系统被提升到了一个新的效能水平，营造了一种学习环境，使学生能够更有意义地参与到 "自制忍者 "实验室中，从而促进学习成果的提高。
{"title":"Performance tracker: Real-time CPU Metrics and Gamified Ranking System","authors":"Senthil Kumari P, Aishwarya S, Nageshwari B, Saravana Kumar G J, Joshika S, Nihetha M, Arul Chandru A N, Kalaivanan K","doi":"10.47392/irjaeh.2024.0272","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0272","url":null,"abstract":"The study presents an innovative approach aimed at amplifying student engagement with the Selfmade Ninja labs, utilizing a reward-centric framework that prioritizes user efficiency. This approach involves the meticulous calculation of various CPU metrics, encompassing elements such as CPU usage, memory usage, download and upload statistics, process identifiers, as well as read and write statistics. These metrics collectively offer a comprehensive view of user interactions within the platform. The gathered data is thoughtfully curated and stored in a JSON file, facilitating efficient data management and analysis. To facilitate the realization of this approach, a sophisticated machine-learning model is deployed. This model serves the pivotal purpose of predicting user efficiency, a crucial factor in determining the efficacy of their engagement with the Selfmade Ninja labs. Building upon this predictive prowess, a system of credits is established, intricately tied to a leaderboard that reflects individual user performances. Through this dynamic reward distribution mechanism, users are incentivized to actively participate and continually enhance their proficiency, thereby fostering a vibrant learning ecosystem. The culmination of this endeavour is a finely tuned predictive model that seamlessly allocates rewards to users based on their demonstrated engagement and proficiency. This tailored approach not only magnifies user motivation but also significantly augments the overall educational impact of the Selfmade Ninja platform. The integration of insights derived from both exploratory data analysis (EDA) and the predictive model ensures a holistic understanding of user behaviors and preferences. Consequently, the proposed reward-based system is elevated to a new level of efficacy, nurturing a learning environment where students are empowered to engage more meaningfully with the Selfmade Ninja labs, fostering enhanced learning outcomes.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141833154","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 : 2024-07-15DOI: 10.47392/irjaeh.2024.0273
Rahi Gaikwad, Maitreya Ganeshpure, Dr. S. D. Bharkad, S. B. Gundre
Today, in order to meet the specifications of net zero carbon emissions we are trying to find out a way to gain sustainable development. While the solution of Electric Vehicles (EV) to reduce carbon emissions has been largely proposed, a review regarding various other aspects concerning the components and sections used in an EV is being presented in this paper. For sustainable development we need to clearly analyze and know the lifecycle of all components and sections of an EV. This review aims to highlight the lifecycle of an EV and its components.
{"title":"Electric Vehicle Lifecycle: A Review","authors":"Rahi Gaikwad, Maitreya Ganeshpure, Dr. S. D. Bharkad, S. B. Gundre","doi":"10.47392/irjaeh.2024.0273","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0273","url":null,"abstract":"Today, in order to meet the specifications of net zero carbon emissions we are trying to find out a way to gain sustainable development. While the solution of Electric Vehicles (EV) to reduce carbon emissions has been largely proposed, a review regarding various other aspects concerning the components and sections used in an EV is being presented in this paper. For sustainable development we need to clearly analyze and know the lifecycle of all components and sections of an EV. This review aims to highlight the lifecycle of an EV and its components.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832662","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}
The incorporation of blockchain technology in supply chain management has emerged as a revolutionary solution, offering enhanced transparency, efficiency, and security across the entire supply chain network. This study presents a thorough examination of the intersection between supply chain management and blockchain, emphasizing the key advantages, obstacles, and recent advancements. The integration of blockchain in supply chain management tackles crucial issues such as traceability, origin, and trust. Through the establishment of a decentralized and unchangeable ledger, stakeholders throughout the supply chain obtain immediate insight into the flow and condition of products. This level of transparency not only minimizes the risk of fraud and counterfeiting but also strengthens the overall resilience of the supply chain. The proposed framework will leverage Blockchain technology to establish a transparent and tamper-resistant record of every transaction and event across the supply chain lifecycle. Every participant in the supply chain ecosystem, including suppliers, manufacturers, distributors, logistics providers, and consumers, will be able to access a shared ledger, facilitating real-time monitoring of the movement and status of products.
{"title":"Supply Chain Management Using Block Chain Technology","authors":"Apurv Jha¹, Aditya Raut, Hemant Taneja, Vishwajeet Dalvi, Ms. Uttara Varade","doi":"10.47392/irjaeh.2024.0264","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0264","url":null,"abstract":"The incorporation of blockchain technology in supply chain management has emerged as a revolutionary solution, offering enhanced transparency, efficiency, and security across the entire supply chain network. This study presents a thorough examination of the intersection between supply chain management and blockchain, emphasizing the key advantages, obstacles, and recent advancements. The integration of blockchain in supply chain management tackles crucial issues such as traceability, origin, and trust. Through the establishment of a decentralized and unchangeable ledger, stakeholders throughout the supply chain obtain immediate insight into the flow and condition of products. This level of transparency not only minimizes the risk of fraud and counterfeiting but also strengthens the overall resilience of the supply chain. The proposed framework will leverage Blockchain technology to establish a transparent and tamper-resistant record of every transaction and event across the supply chain lifecycle. Every participant in the supply chain ecosystem, including suppliers, manufacturers, distributors, logistics providers, and consumers, will be able to access a shared ledger, facilitating real-time monitoring of the movement and status of products.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"49 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660312","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 : 2024-07-10DOI: 10.47392/irjaeh.2024.0270
Isha Mishra, Vedika Kashyap, Nancy Yadav, Dr. Ritu Pahwa
Artificial intelligence (AI) is transforming the way we interact with data, leading to a growing concern about bias. This study aims to address this issue by developing intelligent algorithms that can identify and prevent new biases in AI systems. The strategy involves combining innovative machine-learning techniques, ethical considerations, and interdisciplinary perspectives to address bias at various stages, including data collection, model training, and decision-making processes. The proposed strategy uses robust model evaluation techniques, adaptive learning strategies, and fairness-aware machine learning algorithms to ensure AI systems function fairly across diverse demographic groups. The paper also highlights the importance of diverse and representative datasets and the inclusion of underrepresented groups in training. The goal is to develop AI models that reduce prejudice while maintaining moral norms, promoting user acceptance and trust. Empirical evaluations and case studies demonstrate the effectiveness of this approach, contributing to the ongoing conversation about bias reduction in AI.
{"title":"Harmonizing Intelligence: A Holistic Approach to Bias Mitigation in Artificial Intelligence (AI)","authors":"Isha Mishra, Vedika Kashyap, Nancy Yadav, Dr. Ritu Pahwa","doi":"10.47392/irjaeh.2024.0270","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0270","url":null,"abstract":"Artificial intelligence (AI) is transforming the way we interact with data, leading to a growing concern about bias. This study aims to address this issue by developing intelligent algorithms that can identify and prevent new biases in AI systems. The strategy involves combining innovative machine-learning techniques, ethical considerations, and interdisciplinary perspectives to address bias at various stages, including data collection, model training, and decision-making processes. The proposed strategy uses robust model evaluation techniques, adaptive learning strategies, and fairness-aware machine learning algorithms to ensure AI systems function fairly across diverse demographic groups. The paper also highlights the importance of diverse and representative datasets and the inclusion of underrepresented groups in training. The goal is to develop AI models that reduce prejudice while maintaining moral norms, promoting user acceptance and trust. Empirical evaluations and case studies demonstrate the effectiveness of this approach, contributing to the ongoing conversation about bias reduction in AI.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"8 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835476","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}
Email remains a crucial means of communication in personal and professional spheres; however, its efficiency is often compromised by the widespread presence of unwanted messages. The increase in unsolicited emails not only inundates email inboxes but also poses significant threats such as phishing, malware distribution, and financial fraud. To tackle these issues and enhance the effectiveness of email exchanges, there has been a notable emphasis on utilizing machine learning techniques for identifying spam. This paper will explore various machine learning algorithms and apply them to our datasets. The most optimal algorithm will be selected for email spam detection based on its exceptional precision and accuracy.
{"title":"Email Spam Detection Based on Exceptional Precision","authors":"Madhav Aggarwal, Manik Thakur, Sahil Nagpal, Anup Singh Kushwaha","doi":"10.47392/irjaeh.2024.0258","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0258","url":null,"abstract":"Email remains a crucial means of communication in personal and professional spheres; however, its efficiency is often compromised by the widespread presence of unwanted messages. The increase in unsolicited emails not only inundates email inboxes but also poses significant threats such as phishing, malware distribution, and financial fraud. To tackle these issues and enhance the effectiveness of email exchanges, there has been a notable emphasis on utilizing machine learning techniques for identifying spam. This paper will explore various machine learning algorithms and apply them to our datasets. The most optimal algorithm will be selected for email spam detection based on its exceptional precision and accuracy.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"5 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661345","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 : 2024-07-10DOI: 10.47392/irjaeh.2024.0266
Amritha Lakshmi, Meghna, Mukesh Raj, Mrs. U Vijayalakshmi
The global rise in eye diseases highlights the need for advanced diagnostic tools in ophthalmic care. This project introduces a deep learning model for classifying eye diseases, streamlining diagnosis, and improving accuracy. Using real-time images from reputable healthcare facilities like Bajwa Hospital in Punjab and Shang gong Medical Tech in China, the model is fine-tuned to clinical nuances. Segmentation of the optic disc and blood vessels is key for precise retinal structure delineation, enhancing disease identification. Various CNN models, including Mobile Net, Dense Net, Reset, and a custom CNN, were utilized for retinal image analysis. Additionally, the Vision Transformer (ViT) model was integrated to capture intricate patterns. The model is deployed as a web application using Django, HTML, SQLite, and Bootstrap, featuring a secure, user-friendly interface. Users can input images to receive prompt disease predictions, along with verified information on prevention, treatment options, and medications. This system not only automates and improves diagnostic processes but also provides reliable medical guidance.
{"title":"Advancements in Ophthalmic Healthcare with Deep Learning-Driven Segmentation for Multi-Stage Eye Fundus Disease Diagnosis","authors":"Amritha Lakshmi, Meghna, Mukesh Raj, Mrs. U Vijayalakshmi","doi":"10.47392/irjaeh.2024.0266","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0266","url":null,"abstract":"The global rise in eye diseases highlights the need for advanced diagnostic tools in ophthalmic care. This project introduces a deep learning model for classifying eye diseases, streamlining diagnosis, and improving accuracy. Using real-time images from reputable healthcare facilities like Bajwa Hospital in Punjab and Shang gong Medical Tech in China, the model is fine-tuned to clinical nuances. Segmentation of the optic disc and blood vessels is key for precise retinal structure delineation, enhancing disease identification. Various CNN models, including Mobile Net, Dense Net, Reset, and a custom CNN, were utilized for retinal image analysis. Additionally, the Vision Transformer (ViT) model was integrated to capture intricate patterns. The model is deployed as a web application using Django, HTML, SQLite, and Bootstrap, featuring a secure, user-friendly interface. Users can input images to receive prompt disease predictions, along with verified information on prevention, treatment options, and medications. This system not only automates and improves diagnostic processes but also provides reliable medical guidance.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"42 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660275","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}
The increasing amount of medical data emphasizes the urgent need for efficient methods in classifying electrocardiogram (ECG) signals. While current approaches are valuable, they struggle to achieve both high sensitivity and specificity, limiting their effectiveness in timely cardiac diagnosis. These challenges underscore the importance of more robust methodologies to improve the accuracy of ECG signal classification. To tackle these issues, this research suggests a comprehensive approach using machine learning techniques. Our framework incorporates various algorithms such as Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Logistic Regression, and an ensemble classifier. This ensemble method aims to leverage the strengths of individual models, enhancing the overall classification performance. The application of this approach shows promising results, with increased sensitivity and specificity in categorizing ECG signals. The versatility of our proposed framework has significant potential for various applications, contributing to advancements in cardiovascular health monitoring and diagnosis.
{"title":"Enhanced ECG Signal Classification","authors":"Vinaya Kulkarni, Sanah Naik, Suruchi Bibikar, Ankita Ochani, Sakshi Pratap","doi":"10.47392/irjaeh.2024.0262","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0262","url":null,"abstract":"The increasing amount of medical data emphasizes the urgent need for efficient methods in classifying electrocardiogram (ECG) signals. While current approaches are valuable, they struggle to achieve both high sensitivity and specificity, limiting their effectiveness in timely cardiac diagnosis. These challenges underscore the importance of more robust methodologies to improve the accuracy of ECG signal classification. To tackle these issues, this research suggests a comprehensive approach using machine learning techniques. Our framework incorporates various algorithms such as Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Logistic Regression, and an ensemble classifier. This ensemble method aims to leverage the strengths of individual models, enhancing the overall classification performance. The application of this approach shows promising results, with increased sensitivity and specificity in categorizing ECG signals. The versatility of our proposed framework has significant potential for various applications, contributing to advancements in cardiovascular health monitoring and diagnosis.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"19 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659543","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 : 2024-07-10DOI: 10.47392/irjaeh.2024.0269
Aswathy M C, Rajkumar T
In the constantly changing field of cybersecurity, real-time intrusion detection using machine learning algorithms has become crucial for protecting network infrastructures. This paper presents a comprehensive literature survey focusing on the comparative study of diverse machine learning algorithms employed for anomaly detection in network traffic. The objective is to critically evaluate the effectiveness of various algorithms in identifying and mitigating threats in real-time scenarios. The study delves into the nuances of prominent machine learning models, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and ensemble methods, as they apply to the domain of anomaly detection. Each algorithm is scrutinized based on its ability to adapt to dynamic network behaviors, handle imbalanced datasets, and provide accurate real-time threat assessments. Throughout the survey, key research contributions are analyzed, encompassing methodologies, datasets, and performance metrics. Comparative insights are provided to emphasize the strengths and weaknesses of each algorithm, elucidating their appropriateness for real-time intrusion detection in network traffic. Notably, the examination extends beyond traditional approaches, exploring recent advancements such as deep learning and ensemble techniques. The findings from this comparative study aim to provide practitioners and researchers with valuable insights into selecting the most suitable machine learning algorithm for real-time anomaly detection in the context of network security. By understanding the comparative performance of these algorithms, organizations can make informed decisions to enhance their cybersecurity posture and fortify their defenses against emerging threats.
{"title":"Real Time Anomaly Detection in Network Traffic: A Comparative Analysis of Machine Learning Algorithms","authors":"Aswathy M C, Rajkumar T","doi":"10.47392/irjaeh.2024.0269","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0269","url":null,"abstract":"In the constantly changing field of cybersecurity, real-time intrusion detection using machine learning algorithms has become crucial for protecting network infrastructures. This paper presents a comprehensive literature survey focusing on the comparative study of diverse machine learning algorithms employed for anomaly detection in network traffic. The objective is to critically evaluate the effectiveness of various algorithms in identifying and mitigating threats in real-time scenarios. The study delves into the nuances of prominent machine learning models, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and ensemble methods, as they apply to the domain of anomaly detection. Each algorithm is scrutinized based on its ability to adapt to dynamic network behaviors, handle imbalanced datasets, and provide accurate real-time threat assessments. Throughout the survey, key research contributions are analyzed, encompassing methodologies, datasets, and performance metrics. Comparative insights are provided to emphasize the strengths and weaknesses of each algorithm, elucidating their appropriateness for real-time intrusion detection in network traffic. Notably, the examination extends beyond traditional approaches, exploring recent advancements such as deep learning and ensemble techniques. The findings from this comparative study aim to provide practitioners and researchers with valuable insights into selecting the most suitable machine learning algorithm for real-time anomaly detection in the context of network security. By understanding the comparative performance of these algorithms, organizations can make informed decisions to enhance their cybersecurity posture and fortify their defenses against emerging threats. ","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"11 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659182","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 : 2024-07-10DOI: 10.47392/irjaeh.2024.0265
Chirag Ka Patel, Dr. R. K. Sheth
Transverse openings in reinforced concrete beams use to house the utility services like electricity cable, Internet cable, air-conditioning pipe, fire safety pipe line and water-drainage system. These beams opening pipe line system are usually placed underneath the soffit of the beam in term of dead space. This height of dead space that increase the overall building height. Beam opening in the reinforced concrete beams significantly decreases the ultimate load carrying capacity of beam. Transverse beam opening in the web portion of beam produces discontinuities in the usual flow of stresses and that leading stress concentration around the opening region. The importance of this study is to evaluate the performance of reinforced concrete beam with varying size rectangular opening at flexure and shear location ware investigated. A nonlinear finite element analysis was conducted to investigate the effects of different size openings, in terms of ultimate load carrying capacity, Elemental Stresses, load-deflection plot, crack pattern. The work involves investigating performance of different size of small and large rectangular opening. This paper gives new challenges for engineering practice which is in the field of strengthening of concrete structures especially in transverse RC beams with rectangular openings.
{"title":"Performance of Reinforced Concrete Beams with Rectangular Opening in Flexural and Shear Zone","authors":"Chirag Ka Patel, Dr. R. K. Sheth","doi":"10.47392/irjaeh.2024.0265","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0265","url":null,"abstract":"Transverse openings in reinforced concrete beams use to house the utility services like electricity cable, Internet cable, air-conditioning pipe, fire safety pipe line and water-drainage system. These beams opening pipe line system are usually placed underneath the soffit of the beam in term of dead space. This height of dead space that increase the overall building height. Beam opening in the reinforced concrete beams significantly decreases the ultimate load carrying capacity of beam. Transverse beam opening in the web portion of beam produces discontinuities in the usual flow of stresses and that leading stress concentration around the opening region. The importance of this study is to evaluate the performance of reinforced concrete beam with varying size rectangular opening at flexure and shear location ware investigated. A nonlinear finite element analysis was conducted to investigate the effects of different size openings, in terms of ultimate load carrying capacity, Elemental Stresses, load-deflection plot, crack pattern. The work involves investigating performance of different size of small and large rectangular opening. This paper gives new challenges for engineering practice which is in the field of strengthening of concrete structures especially in transverse RC beams with rectangular openings.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"38 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141661746","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 : 2024-07-10DOI: 10.47392/irjaeh.2024.0257
Isha Mishra, Vedika Kashyap, Dr. Ritu Pahwa, Dr. R. Dheivanai
Artificial Intelligence (AI) has revolutionized the healthcare sector by improving patient care and treatment through diagnostic revolutionization. AI is used for diagnosing and detecting diseases, analyzing large-scale patient data sets to find trends and abnormalities. This has led to increased precision and speed of disease identification, enabling early intervention and individualized treatment programs. AI-driven diagnostic systems have shown effectiveness in reducing incorrect diagnoses and enhancing patient outcomes for diseases like diabetes, cancer, and heart issues.AI algorithms also aid in treatment planning and drug discovery, predicting patient responses to treatments and optimizing therapeutic strategies. In clinical settings, AI-powered systems automate administrative tasks, manage patient records, and improve workflow efficiency. Chatbots and virtual health assistants can offer patient guidance and support, reducing healthcare staff burden and enhancing patient experiences. However, AI integration in healthcare faces challenges such as data privacy, security, financial resources, and ethical considerations. Bias in AI algorithms can perpetuate healthcare disparities, and efforts are being made to reduce bias through diverse datasets and transparent AI systems. Legal and ethical frameworks are needed to address these issues.In conclusion, AI in healthcare has the potential to improve patient outcomes, but challenges such as funding, security, data privacy, and ethical considerations need to be addressed.
{"title":"Revolutionizing Healthcare: The Impact and Growth of Artificial Intelligence(AI)","authors":"Isha Mishra, Vedika Kashyap, Dr. Ritu Pahwa, Dr. R. Dheivanai","doi":"10.47392/irjaeh.2024.0257","DOIUrl":"https://doi.org/10.47392/irjaeh.2024.0257","url":null,"abstract":"Artificial Intelligence (AI) has revolutionized the healthcare sector by improving patient care and treatment through diagnostic revolutionization. AI is used for diagnosing and detecting diseases, analyzing large-scale patient data sets to find trends and abnormalities. This has led to increased precision and speed of disease identification, enabling early intervention and individualized treatment programs. AI-driven diagnostic systems have shown effectiveness in reducing incorrect diagnoses and enhancing patient outcomes for diseases like diabetes, cancer, and heart issues.AI algorithms also aid in treatment planning and drug discovery, predicting patient responses to treatments and optimizing therapeutic strategies. In clinical settings, AI-powered systems automate administrative tasks, manage patient records, and improve workflow efficiency. Chatbots and virtual health assistants can offer patient guidance and support, reducing healthcare staff burden and enhancing patient experiences. However, AI integration in healthcare faces challenges such as data privacy, security, financial resources, and ethical considerations. Bias in AI algorithms can perpetuate healthcare disparities, and efforts are being made to reduce bias through diverse datasets and transparent AI systems. Legal and ethical frameworks are needed to address these issues.In conclusion, AI in healthcare has the potential to improve patient outcomes, but challenges such as funding, security, data privacy, and ethical considerations need to be addressed.","PeriodicalId":517766,"journal":{"name":"International Research Journal on Advanced Engineering Hub (IRJAEH)","volume":"22 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659518","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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