International Journal of Scientific Research in Computer Science, Engineering and Information Technology最新文献

Impact and Challenges of Data Mining : A Comprehensive Analysis 数据挖掘的影响与挑战：全面分析

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-07-25 DOI: 10.32628/cseit241049

Chandrakant D. Prajapati, Asha K. Patel, Dr. Krupa J. Bhavsar

{"title":"Impact and Challenges of Data Mining : A Comprehensive Analysis","authors":"Chandrakant D. Prajapati, Asha K. Patel, Dr. Krupa J. Bhavsar","doi":"10.32628/cseit241049","DOIUrl":"https://doi.org/10.32628/cseit241049","url":null,"abstract":"This review paper provides a concise overview of Data Mining, a multidisciplinary field focused on extracting valuable insights and patterns from extensive datasets. It highlights the use of statistical analysis, machine learning, and pattern recognition techniques to discover hidden relationships and trends within data. The paper emphasizes data mining's significance as a powerful technology that extracts predictive information from large databases, enabling businesses to prioritize crucial data. It showcases how data mining tools predict future trends, empowering proactive, knowledge-driven decision-making. Furthermore, it discusses the superiority of data mining over retrospective tools, offering automated, prospective analyses to resolve complex business questions efficiently. It uncovers hidden patterns and predictive information beyond human expectations. The core concepts of data mining encountered challenges, data analysis techniques, and their profound impact on various domains are also addressed in this paper. The proposed paper offers a comprehensive overview of data mining's importance, applications, and transformative potential in modern data-driven decision-making processes.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"58 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804972","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

Design and Implementation of Hamming Code with Error Correction Using Xilinx 使用 Xilinx 设计和实现带纠错功能的汉明码

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-07-25 DOI: 10.32628/cseit24104117

Ms. Delphine Mary. P, Simran. A

引用次数: 0

Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution 利用基于多群体差分进化优化的曲线小波变换增强平移锐化功能

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-07-23 DOI: 10.32628/cseit24104116

Mustafa Hüsrevoğlu, Ahmet Emin Karkınlı

引用次数: 0

Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines 利用随机森林和支持向量机进行多模态数据整合以早期检测阿尔茨海默氏症

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-07-22 DOI: 10.32628/cseit241047

Muhammad Nadeem, Wei Zhang, Sarwat Aslam, Liaqat Ali, Abdul Majid

{"title":"Multimodal Data Integration for Early Alzheimer’s Detection Using Random Forest and Support Vector Machines","authors":"Muhammad Nadeem, Wei Zhang, Sarwat Aslam, Liaqat Ali, Abdul Majid","doi":"10.32628/cseit241047","DOIUrl":"https://doi.org/10.32628/cseit241047","url":null,"abstract":"Alzheimer's is a very challenging brain disease to recognize, diagnose, and treat correctly when it appears in its earliest forms. The primary contribution of this research study is about machine learning models, techniques, and approaches. In contrast, Random Forest and Support Vector Machine (SVM) are particularly suitable for identifying and staging Alzheimer's disease stages using multimodal data sources. In this paper, the aim was to develop well-performing predictive models to help diagnose Alzheimer's disease at an early stage by combining neuroimaging data (MRI/PET images), imaging-based biomarkers, both structural and functional measures from MRI(P) /PET image analysis along with subject-specific demographics like age using clinical features in a probabilistic fashion obtained from the Alzheimer's Disease Neuro-Imaging Initiative (ADNI) database. The methodology focuses on data pre-processing, feature selection, and model building using supervised learning algorithms. The accuracy of the Random Forest model is 78%, having a high performance in classifying some classes while showing different marks of performances across other courses. SVM reached an accuracy of 61%, or the model's performance is good in some classes and not reliable to identify samples from the others. The findings of this study underscore the capabilities and limits of these machine learning models in identifying Alzheimer’s disease and highlight the importance of feature engineering, data pre-processing, and model tuning to increase performance and correct class unevenness and misclassification.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"77 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817836","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

The Future of Enterprise resource planning (ERP): Harnessing Artificial Intelligence 企业资源规划（ERP）的未来：利用人工智能

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-07-11 DOI: 10.32628/cseit24104112

Gaurav Kumar

{"title":"The Future of Enterprise resource planning (ERP): Harnessing Artificial Intelligence","authors":"Gaurav Kumar","doi":"10.32628/cseit24104112","DOIUrl":"https://doi.org/10.32628/cseit24104112","url":null,"abstract":"A large pharmaceuticals corporation utilizing a complex IT infrastructure such as SAP ERP typically faces a substantial volume GMP and Serialization data annually, numbering in the hundreds of thousands. These inquiries, whether initiated over the phone or online via platforms like integration, seek assistance with various issues. Enterprise resource planning (ERP) software streamlines business processes by integrating technology, services, and human resources across interconnected applications. This research proposes implementing an intelligent system to streamline volume of the data and analyzation for the SAP ERP. This system aims to automate responses to user queries, reducing the time required for issue investigation and resolution, and enhancing user responsiveness. Employing machine learning algorithms, the system efficiently interprets and classifies text across multiple categories, facilitating accurate question comprehension. Additionally, it utilizes a specialized framework to retrieve relevant evidence, ensuring the delivery of optimal responses. Furthermore, its conversational AI capabilities enable the creation of chatbots, fostering collaborative problem-solving among user groups in real-time.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141834987","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

Starlink Satellite Project in A Developing Country 发展中国家的星链卫星项目

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-06-15 DOI: 10.32628/cseit2410330

Vivek Reddy Gadipally

{"title":"Starlink Satellite Project in A Developing Country","authors":"Vivek Reddy Gadipally","doi":"10.32628/cseit2410330","DOIUrl":"https://doi.org/10.32628/cseit2410330","url":null,"abstract":"SpaceX and Elon Musk are leading a long-term initiative called Starlink to solve inequities in rural broadband Internet access.In order to provide constant, high-speed Internet access worldwide, the project aims to launch thousands of smallsat-class satellites into low Earth orbit (LEO) as a component of a mega constellation. SpaceX thinks that by utilizing shallow orbits, their technology may surpass the competition. Starlink assures its clients of reduced latency and improved connection quality in contrast to conventional geosynchronous satellite Internet infrastructure (Walker & Elliott, 2021). The purpose of this study is to look at how the Thai public views the Starlink satellite project. An online survey was employed to gather data from a convenience sample of 1,258 people in Thailand, using a quantitative methodology. To analyze the data, binary regression analysis was done. The results showed that factors such as gender, age, computer, laptop, tablet, wearable device, length of Internet use, mobile Internet, Instagram, TikTok, and YouTube could all be used to characterize how the public in Thailand felt about the Starlink Satellite project.The major reason is Starlink needs to come up with a clever plan to encourage customers to adopt satellite Internet in nations where fiber Internet is more readily available and reasonably priced.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337013","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 Literature Review : Enhancing Sentiment Analysis of Deep Learning Techniques Using Generative AI Model 文献综述：利用生成式人工智能模型加强深度学习技术的情感分析

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-06-15 DOI: 10.32628/cseit24103204

Sharma Vishalkumar Sureshbhai, Dr. Tulsidas Nakrani

{"title":"A Literature Review : Enhancing Sentiment Analysis of Deep Learning Techniques Using Generative AI Model","authors":"Sharma Vishalkumar Sureshbhai, Dr. Tulsidas Nakrani","doi":"10.32628/cseit24103204","DOIUrl":"https://doi.org/10.32628/cseit24103204","url":null,"abstract":"Sentiment analysis is possibly one of the most desirable areas of study within Natural Language Processing (NLP). Generative AI can be used in sentiment analysis through the generation of text that reflects the sentiment or emotional tone of a given input. The process typically involves training a generative AI model on a large dataset of text examples labeled with sentiments (positive, negative, neutral, etc.). Once trained, the model can generate new text based on the learned patterns, providing an automated way to analyze sentiments in user reviews, comments, or any other form of textual data. The main goal of this research topic is to identify the emotions as well as opinions of users or customers using textual means. Though a lot of research has been done in this area using a variety of models, sentiment analysis is still regarded as a difficult topic with a lot of unresolved issues. Slang terms, novel languages, grammatical and spelling errors, etc. are some of the current issues. This work aims to conduct a review of the literature by utilizing multiple deep learning methods on a range of data sets. Nearly 21 contributions, covering a variety of sentimental analysis applications, are surveyed in the current literature study. Initially, the analysis looks at the kinds of deep learning algorithms that are being utilized and tries to show the contributions of each work. Additionally, the research focuses on identifying the kind of data that was used. Additionally, each work's performance metrics and setting are assessed, and the conclusion includes appropriate research gaps and challenges. This will help in identifying the non-saturated application for which sentimental analysis is most needed in future studies.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"3 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337031","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 Review on Machine Learning and Deep Learning Methods on Medical Image Classification 医学影像分类中的机器学习和深度学习方法综述

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-06-15 DOI: 10.32628/cseit24103205

Dr.Sheshang Degadwala, Dhairya Vyas Degadwala

{"title":"A Review on Machine Learning and Deep Learning Methods on Medical Image Classification","authors":"Dr.Sheshang Degadwala, Dhairya Vyas Degadwala","doi":"10.32628/cseit24103205","DOIUrl":"https://doi.org/10.32628/cseit24103205","url":null,"abstract":"Medical image classification, a critical component in medical diagnostics, has significantly advanced through the integration of machine learning (ML) and deep learning (DL) techniques. This review comprehensively explores the evolution, methodologies, and applications of ML and DL in medical image classification. Traditional ML methods, including support vector machines and decision trees, have provided a foundation for early advancements by utilizing handcrafted features. However, the advent of DL, particularly convolutional neural networks (CNNs), has revolutionized the field by enabling automatic feature extraction and achieving superior performance. This review examines various DL architectures, such as ResNet, VGG, and Inception, highlighting their contributions to tasks like tumor detection, organ segmentation, and disease classification. Furthermore, it addresses challenges like data scarcity, interpretability, and computational demands, discussing potential solutions like data augmentation, transfer learning, and model optimization. The review also considers the ethical implications and the need for robust validation to ensure clinical applicability. Through a comparative analysis of existing studies, this review underscores the transformative impact of ML and DL on medical imaging, emphasizing the continuous need for innovation and interdisciplinary collaboration to enhance diagnostic accuracy and patient outcomes.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141336700","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

Survey on Systematic Analysis of Deep Learning Models Compare to Machine Learning 深度学习模型与机器学习对比的系统分析调查

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-06-15 DOI: 10.32628/cseit24103206

Dr.Sheshang Degadwala, Dhairya Vyas Degadwala

{"title":"Survey on Systematic Analysis of Deep Learning Models Compare to Machine Learning","authors":"Dr.Sheshang Degadwala, Dhairya Vyas Degadwala","doi":"10.32628/cseit24103206","DOIUrl":"https://doi.org/10.32628/cseit24103206","url":null,"abstract":"This survey provides a comprehensive analysis of the systematic differences and advancements between deep learning (DL) and traditional machine learning (ML) models. By examining a wide array of research papers, the study highlights the unique strengths and applications of both methodologies. Deep learning, with its multi-layered neural networks, excels in handling large, unstructured datasets, making significant strides in image and speech recognition, natural language processing, and complex pattern recognition tasks. Conversely, traditional machine learning models, which rely on feature extraction and simpler algorithms, remain highly effective in structured data scenarios such as classification, regression, and clustering problems. The survey elucidates the criteria for choosing between DL and ML, focusing on factors like data size, computational resources, and specific application requirements. Furthermore, it discusses the evolving landscape of hybrid models that integrate DL and ML techniques to leverage the strengths of both approaches. This analysis provides valuable insights for researchers and practitioners aiming to deploy the most suitable AI models for their specific needs, emphasizing the importance of contextual understanding in the rapidly advancing field of artificial intelligence.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141336797","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

Research on Advance Machine Learning Based Decision Support System for Frauds Detection and Prevention in Online Banking System 基于机器学习的高级决策支持系统在网上银行系统中的欺诈检测和预防研究

International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2024-06-14 DOI: 10.32628/cseit24103131

Miss Nikita C. Nandeshwar, Prof. Dr. K.A. Waghmare, Prof. A.V. Deorankar

{"title":"Research on Advance Machine Learning Based Decision Support System for Frauds Detection and Prevention in Online Banking System","authors":"Miss Nikita C. Nandeshwar, Prof. Dr. K.A. Waghmare, Prof. A.V. Deorankar","doi":"10.32628/cseit24103131","DOIUrl":"https://doi.org/10.32628/cseit24103131","url":null,"abstract":"The rise in online banking fraud, driven by the underground malware economy, underscores the crucial need for robust fraud analysis systems. Regrettably, the majority of existing approaches rely on black box models that lack transparency and fail to provide justifications to analysts. Additionally, the scarcity of available Internet banking data for the scientific community hinders the development of effective methods. This paper presents a decision support system meticulously crafted to identify and thwart fraud in online banking transactions. The chosen approach involves the application of a Random Forest decision tree model—a supervised machine learning technique renowned for its effectiveness in enhancing fraud detection within online banking systems, yielding substantial real-world impact. Constant monitoring of both the system and data ensures optimal performance, enabling timely responses to deviations. The overarching objective of the system is to furnish analysts with a powerful decision support tool capable of preempting financial crimes before they occur.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"53 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339218","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