International Research Journal on Advanced Engineering Hub (IRJAEH)最新文献_第2页

Performance tracker: Real-time CPU Metrics and Gamified Ranking System 性能追踪器实时 CPU 指标和游戏化排名系统

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-15 DOI: 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

{"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}

引用次数: 0

Electric Vehicle Lifecycle: A Review 电动汽车的生命周期：回顾

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-15 DOI: 10.47392/irjaeh.2024.0273

Rahi Gaikwad, Maitreya Ganeshpure, Dr. S. D. Bharkad, S. B. Gundre

引用次数: 0

Supply Chain Management Using Block Chain Technology 利用区块链技术进行供应链管理

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0264

Apurv Jha¹, Aditya Raut, Hemant Taneja, Vishwajeet Dalvi, Ms. Uttara Varade

{"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}

引用次数: 0

Harmonizing Intelligence: A Holistic Approach to Bias Mitigation in Artificial Intelligence (AI) 协调智能：减少人工智能（AI）偏差的整体方法

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0270

Isha Mishra, Vedika Kashyap, Nancy Yadav, Dr. Ritu Pahwa

{"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}

引用次数: 0

Email Spam Detection Based on Exceptional Precision 基于超高精度的垃圾邮件检测

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0258

Madhav Aggarwal, Manik Thakur, Sahil Nagpal, Anup Singh Kushwaha

引用次数: 0

Advancements in Ophthalmic Healthcare with Deep Learning-Driven Segmentation for Multi-Stage Eye Fundus Disease Diagnosis 利用深度学习驱动的眼底疾病多阶段诊断分割技术，推动眼科医疗保健的发展

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0266

Amritha Lakshmi, Meghna, Mukesh Raj, Mrs. U Vijayalakshmi

{"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}

引用次数: 0

Enhanced ECG Signal Classification 增强型心电信号分类

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0262

Vinaya Kulkarni, Sanah Naik, Suruchi Bibikar, Ankita Ochani, Sakshi Pratap

引用次数: 0

Real Time Anomaly Detection in Network Traffic: A Comparative Analysis of Machine Learning Algorithms 网络流量中的实时异常检测：机器学习算法的比较分析

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0269

Aswathy M C, Rajkumar T

{"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}

引用次数: 0

Performance of Reinforced Concrete Beams with Rectangular Opening in Flexural and Shear Zone 带矩形开口的钢筋混凝土梁在抗弯和抗剪区的性能

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0265

Chirag Ka Patel, Dr. R. K. Sheth

{"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}

引用次数: 0

Revolutionizing Healthcare: The Impact and Growth of Artificial Intelligence(AI) 医疗保健的变革：人工智能的影响与发展

International Research Journal on Advanced Engineering Hub (IRJAEH) Pub Date : 2024-07-10 DOI: 10.47392/irjaeh.2024.0257

Isha Mishra, Vedika Kashyap, Dr. Ritu Pahwa, Dr. R. Dheivanai

{"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}

引用次数: 0