International Journal of Innovative Research in Engineering最新文献_第3页

Data-Driven Inline Leak Detection for Pipelines Using Flow-Induced Acoustics Analysis 利用流动诱导声学分析进行数据驱动的管道在线泄漏检测

International Journal of Innovative Research in Engineering Pub Date : 2024-04-12 DOI: 10.59256/ijire.20240502027

Saravanabalaji M, Shakthi Raagavi S, Yogesh K, S. S, Hariharasudhan P

引用次数: 0

Performance analysis of routing protocols for an efficient data transmission in 5G WSN communication 5G WSN 通信中高效数据传输的路由协议性能分析

International Journal of Innovative Research in Engineering Pub Date : 2024-04-12 DOI: 10.59256/ijire.20240502026

Rohini D. Pochhi, Pravin Tajane, Sushmita V. Kamble

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Energy Optimization in Water Distribution System and Pump Scheduling 配水系统的能源优化与水泵调度

International Journal of Innovative Research in Engineering Pub Date : 2024-04-12 DOI: 10.59256/ijire.20240502028

Muthuramalingam E, Dharshini S, Jeevan Kumar S

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Wanderlust Chronicles- The Road Explorer Tourism Platform 漫游纪事--探路者旅游平台

International Journal of Innovative Research in Engineering Pub Date : 2024-04-11 DOI: 10.59256/ijire.20240502024

Tapas Chatterjee, Dipanjan Dey, Bijay Kumar Sethy, Rajib Kuri, Somnath Banerjee, Kaustuv Bhattacharjee, Anirban Das

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SWASTHYA: A Comprehensive System to Manage Hospitals SWASTHYA：管理医院的综合系统

International Journal of Innovative Research in Engineering Pub Date : 2024-04-11 DOI: 10.59256/ijire.20240502021

Akash Chowdhury, Pritam Mandal, Priyabrata Dutta, Sujoy Mondal, Poulami Ghosh, Kaustuv Bhattacharjee, Anirban Das

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Block chain Enabled Online Electoral System to Address Security Challenges and Transparency 区块链支持的在线选举系统应对安全挑战和提高透明度

International Journal of Innovative Research in Engineering Pub Date : 2024-04-11 DOI: 10.59256/ijire.20240502020

Yousuf Sk, Manirujjaman Sarkar, Sushmita Sarkar, Priyankan Guha, Ankur Biswas, Kaustuv Bhattacharjee, Anirban Das

{"title":"Block chain Enabled Online Electoral System to Address Security Challenges and Transparency","authors":"Yousuf Sk, Manirujjaman Sarkar, Sushmita Sarkar, Priyankan Guha, Ankur Biswas, Kaustuv Bhattacharjee, Anirban Das","doi":"10.59256/ijire.20240502020","DOIUrl":"https://doi.org/10.59256/ijire.20240502020","url":null,"abstract":"The right to vote is a foundational privilege for citizens in any democratic nation, empowering them to select future leaders and express their views on community matters. Voting fosters an understanding of the significance of citizenship and individual participation. Modern online voting systems, software platforms facilitating secure voting, have emerged as a digital alternative to traditional paper-based methods. These systems eliminate the need for physical presence, offering the convenience of voting from anywhere with an internet connection. Importantly, online voting platforms enhance the security and integrity of the voting process, employing measures such as encryption to prevent issues like voter fraud. Additionally, they address concerns by ensuring voters cannot cast multiple ballots, thus upholding the fairness of elections. While online voting presents advantages, it also poses challenges related to cybersecurity and privacy, necessitating a careful balance between accessibility and security considerations. It's crucial to stay updated on the latest developments in the field of online voting. Keyword: Block chain, Online Electoral System, Security, Transparency, Immutability.","PeriodicalId":516932,"journal":{"name":"International Journal of Innovative Research in Engineering","volume":"24 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714283","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

Supply Chain Resilience: Adapting To Global Disruptions and Uncertainty 供应链复原力：适应全球干扰和不确定性

International Journal of Innovative Research in Engineering Pub Date : 2024-04-11 DOI: 10.59256/ijire.20240502025

Dr. Arjita Mishra, Nidhi Gupta, Gautam Kumar Jha

{"title":"Supply Chain Resilience: Adapting To Global Disruptions and Uncertainty","authors":"Dr. Arjita Mishra, Nidhi Gupta, Gautam Kumar Jha","doi":"10.59256/ijire.20240502025","DOIUrl":"https://doi.org/10.59256/ijire.20240502025","url":null,"abstract":"In an increasingly interconnected world, supply chain resilience has emerged as a critical factor for businesses to navigate disruptions and uncertainty. This paper delves into the dynamic landscape of supply chain management, emphasizing the need for organizations to adapt and fortify their operations against a myriad of challenges, ranging from natural disasters to geopolitical tensions and pandemics. Drawing on a comprehensive review of existing literature and real-world case studies, this research explores the key components of supply chain resilience and identifies best practices for building robust systems. It examines the role of technology, collaboration, and risk management strategies in enhancing resilience across various industry sectors. Furthermore, this paper sheds light on the impact of recent global disruptions, such as the COVID-19 pandemic, on supply chains worldwide, highlighting both the vulnerabilities exposed and innovative responses adopted by organizations. A nuanced analysis elucidates the lessons learned and opportunities for improvement in supply chain resilience frameworks. By synthesizing insights from academia and industry, this study offers practical recommendations for executives and policymakers to bolster supply chain resilience in an era characterized by volatility and complexity. It underscores the imperative for proactive measures, agile strategies, and continuous monitoring to mitigate risks and ensure operational continuity in the face of uncertainty. Ultimately, this research contributes to a deeper understanding of supply chain resilience as a strategic imperative, empowering organizations to thrive amidst global disruptions and safeguard the flow of goods and services in an interconnected global economy.","PeriodicalId":516932,"journal":{"name":"International Journal of Innovative Research in Engineering","volume":"84 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712665","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 Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention 保障信用卡交易和预防欺诈的综合方法

International Journal of Innovative Research in Engineering Pub Date : 2024-04-11 DOI: 10.59256/ijire.20240502022

Manish Kumar, Sushma Kumari, Rinku Kumar, Ajit Kumar, Somnath Banerjee, Kaustuv Bhattacharjee, Anirban Das

{"title":"A Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention","authors":"Manish Kumar, Sushma Kumari, Rinku Kumar, Ajit Kumar, Somnath Banerjee, Kaustuv Bhattacharjee, Anirban Das","doi":"10.59256/ijire.20240502022","DOIUrl":"https://doi.org/10.59256/ijire.20240502022","url":null,"abstract":"The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.","PeriodicalId":516932,"journal":{"name":"International Journal of Innovative Research in Engineering","volume":"20 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715748","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

Image Processing in MATLAB Enhancement, Encryption and Decryption MATLAB 中的图像处理增强、加密和解密

International Journal of Innovative Research in Engineering Pub Date : 2024-04-08 DOI: 10.59256/ijire.20240502019

Arpit Goyal, Gayatri Chauhan, Divya Meena, Dr. Deepak Jhanwar

{"title":"Image Processing in MATLAB Enhancement, Encryption and Decryption","authors":"Arpit Goyal, Gayatri Chauhan, Divya Meena, Dr. Deepak Jhanwar","doi":"10.59256/ijire.20240502019","DOIUrl":"https://doi.org/10.59256/ijire.20240502019","url":null,"abstract":"Photography was invented in 1826 by French inventor Joseph Nicéphore Niépce. Since then, it has revolutionized the modern world. From medical evidence in court & drawing images of industrial machines to personal photos for memory, not a single field is left untouched by photography. Nowadays when there is so many photo editing software, not a single one ensures true privacy and provides open-source code to ensure that no data is being collected. In six months of project making, we have developed an Image processing app that not only provides enhancement tools but also displays the power of steganography, a user-friendly environment to encrypt your enhanced images before sending them forward, and a magnificent display of Fourier series epicycles which creates an image using Fourier series, an intuitive way to learn and understand the beauty of mathematics. Mathematics could be called the language of science. Our tool proves why it is said so. And what could be a better platform to make all this other than MATLAB which not only provides numerous libraries but also is a powerful debugging tool that can display all the things happening inside the code. This app is built using the Guide function in MATLAB and has the potential to influence this generation toward data security as well as mathematics. Keyword: Enhancement, Cryptography, Epicycles, and Steganography","PeriodicalId":516932,"journal":{"name":"International Journal of Innovative Research in Engineering","volume":"63 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729557","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

Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application 基于深度学习的农作物叶片病害检测图像在农业中的应用

International Journal of Innovative Research in Engineering Pub Date : 2024-04-08 DOI: 10.59256/ijire.20240502018

Sameer Rajendra Nakhale, Dr. Sanjay Asutkar

{"title":"Deep Learning-Based Leaf Disease Detection in Crop Using Images for Agricultural Application","authors":"Sameer Rajendra Nakhale, Dr. Sanjay Asutkar","doi":"10.59256/ijire.20240502018","DOIUrl":"https://doi.org/10.59256/ijire.20240502018","url":null,"abstract":"The \"Leaf Disease Detection\" system addresses the critical challenge of plant diseases in agriculture through the implementation of an automated solution leveraging deep learning techniques. In this comprehensive endeavor, convolutional neural networks (CNNs), specifically DenseNet-121, ResNet-50, VGG-16, and Inception V4, are fine-tuned for efficient and accurate identification of plant diseases. The project utilizes the Plant Village dataset, encompassing 54,305 images across 38 plant disease classes, to conduct a comparative analysis of model performance. DenseNet-121 emerged as the top-performing model, achieving an exceptional 99.81% classification accuracy, surpassing other state-of-the-art models. The system's methodology strategically employs transfer learning to overcome computational challenges associated with training deep CNN layers. This approach, coupled with the multi-class classification strategy, proves robust in handling diverse plant species and diseases within each class. The results highlight the superior efficiency of transfer learning in comparison to building models from scratch, showcasing the potential for real-world applications in agriculture. The system's success is attributed to the careful optimization of hyper parameters and the adoption of advanced deep learning techniques, offering a promising avenue for automated and accurate plant disease detection, with implications for improving agricultural practices, minimizing economic losses, and ensuring global food security.","PeriodicalId":516932,"journal":{"name":"International Journal of Innovative Research in Engineering","volume":"211 S661","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731058","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