International Journal of Advances in Computer Science and Technology最新文献

A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare 物联网智能医疗入侵检测与缓解的混合机器学习方法

International Journal of Advances in Computer Science and Technology Pub Date : 2024-07-08 DOI: 10.30534/ijacst/2024/021372024

{"title":"A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare","authors":"","doi":"10.30534/ijacst/2024/021372024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/021372024","url":null,"abstract":"Strong cybersecurity solutions are becoming more and more important as Internet of Things (IoT) technology integration in healthcare settings develops. This study offers a method for feature extraction, selection, and attack classification by fusing the discriminative capacity of feedforward neural networks (FNNs) with the adaptability of fuzzy logic systems. In delicate healthcare database of IoT wearable devices, to reduce false alarm and guaranteeing intrusion detection dependability are the main priorities. The suggested method uses a feature extraction, selection technique, training and testing based on FNN, which allows the model to adjust to the dynamic and varied character of medical data. During the assessment stage, a dataset including a range of healthcare IoT scenarios, including different kinds of attacks, is used to train and evaluate the model, the ToN_IoT dataset was used. Fuzzy logic improves the system's resilience in identifying pertinent features by managing uncertainties and imprecise input. Fuzzy logic is one of the best technique for handling uncertainty, its linguistic representation and rule reasoning helps in better identification and classification. The findings indicate a noteworthy decrease in the frequency of false alarms when juxtaposed with conventional intrusion detection systems. Results obtained from the model are 99.2, 98.8, 99.5, 99.1 & 0.008 for accuracy, precision, recall, F1-Score and False alarm respectively. Promising outcomes in protecting IoT healthcare environments are demonstrated by the suggested system, opening the door to better patient data privacy and system resilience against cyberattacks.","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":" 376","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669331","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

Parkinson’s Disease Prediction Using Machine Learning Models 利用机器学习模型预测帕金森病

International Journal of Advances in Computer Science and Technology Pub Date : 2024-07-08 DOI: 10.30534/ijacst/2024/011372024

{"title":"Parkinson’s Disease Prediction Using Machine Learning Models","authors":"","doi":"10.30534/ijacst/2024/011372024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/011372024","url":null,"abstract":"Parkinson's disease is a neurodegenerative condition that affects billions of persons worldwide. This abstract aims to shed light on the causes and consequences of this debilitating condition. The primary cause of Parkinson's disease is the progressive degeneration of dopaminergic neurons in the substantia nigra region of brain. This neuronal loss results in a depletion of dopamine, a crucial neurotransmitter responsible for regulating movement and coordination. Therefore, individuals with Parkinson's disease have symptoms like tremors, rigidity, bradykinesia, and postural instability. These signs profoundly impact the quality of life, causing difficulties with daily activities and reducing independence. In addition to motor symptoms, non-motor symptoms such as depression, cognitive impairment, and autonomic dysfunction often accompany the disease, further complicating the clinical picture. Research into the causes and consequences of Parkinson's disease is ongoing, with a focus on using efficient medications and refining the quality of life for those affected by this condition. Now by Using machine learning algorithms, we can predict whether a person has a specific disease based on input values like gender and age. These algorithms analyze patterns and relationships in data to get predictions about an individual's health status. This technology can assist in early disease detection and improve healthcare outcomes..","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"119 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666642","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

Machine Learning used in the field of Pharmacy 机器学习在药学领域的应用

International Journal of Advances in Computer Science and Technology Pub Date : 2024-03-10 DOI: 10.30534/ijacst/2024/011332024

引用次数: 0

Applications of Augmented Reality in different domains 增强现实技术在不同领域的应用

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/041312024

{"title":"Applications of Augmented Reality in different domains","authors":"","doi":"10.30534/ijacst/2024/041312024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/041312024","url":null,"abstract":"This paper provides a comprehensive review of augmented reality's (AR) applications in three key sectors currently witnessing a surge in AR adoption— entertainment, medicine, and retail. The study aims to underscore how AR enhances user experiences in these domains. The authors introduce AR, differentiating it from virtual reality, and discuss the requisite software and hardware technologies for implementing AR systems, along with various display types crucial for an enriched user experience. The paper also briefly touches upon the growth of AR in markets. In the entertainment sector, AR is showcased for its impactful applications in multiplayer gaming, PC games, broadcasting, and media recordings, contributing significantly to an enhanced gaming and entertainment experience. Transitioning to the medical field, AR proves invaluable in clinical healing, medical training, clinical teaching, surgery, and post-clinical therapy, demonstrating its diverse applications in healthcare for professionals and improved patient outcomes. The retail sector is explored, revealing how AR is reshaping advertising, marketing, fashion retail, and online shopping. AR's transformative role in providing engaging and interactive retail experiences, both in physical stores and online platforms, is discussed in detail. The paper concludes by outlining potential future AR applications and conducting a thorough analysis of its merits and drawbacks in the current landscape. This in-depth review serves as a valuable resource for understanding the evolving role of augmented reality across diverse industries, shedding light on its transformative impact on user experiences.","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"32 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445775","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

Review: Tiny Face Detection and Recognition Techniques 回顾：微型人脸检测和识别技术

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/071312024

{"title":"Review: Tiny Face Detection and Recognition Techniques","authors":"","doi":"10.30534/ijacst/2024/071312024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/071312024","url":null,"abstract":"The exponential growth of video and image databases has created a need for intelligent systems to automatically analyze information, as manual efforts are no longer feasible. Faces plays a crucial role in social interaction, conveying identity and emotions, thus requiring efficient and accurate analysis. Deep learning techniques has brought about a significant revolution in face detection, despite their increased computational requirements. This paper presents a comprehensive analysis of representative deep learning-based methods for face detection, focusing on their accuracy and efficiency. It also compares and discusses popular and challenging datasets, including their evaluation metrics. Additionally, a thorough comparison of successful deep learning-based face detectors is conducted, evaluating their efficiency using Floating Point Operations (FLOPs) and latency as metrics. The results and findings of this study can serve as a valuable guide for selecting suitable face detectors for various applications. Moreover, they can contribute to the development of more efficient and accurate detectors. The paper aims to address the pressing needs for intelligent systems that can automatically understand and analyze visual information in an increasingly data-driven world","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"57 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447816","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

An Overview of Cardiac Disease Diagnosis using Machine Learning Algorithms 使用机器学习算法诊断心脏病概述

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/011312024

{"title":"An Overview of Cardiac Disease Diagnosis using Machine Learning Algorithms","authors":"","doi":"10.30534/ijacst/2024/011312024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/011312024","url":null,"abstract":"This abstract investigates the use of machine learning algorithms in the detection of cardiac illness, namely Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), and Decision Trees. Preprocessing and gathering patient data, including demographic information, medical history, and other health markers, is part of the study. Features are selected based on their relevance to heart disease diagnosis, and labeled datasets are employed for training and validation. SVM, with its capacity to find optimal hyperplanes, is employed to discern patterns in the data. Logistic Regression, known for its simplicity and interpretability, aids in probability estimation. KNN is a flexible instance-based algorithm that makes predictions by utilizing nearby data points. Decision trees are used because they may represent intricate linkages and provide clarity in decision-making. The abstract explores how Comprehensible these algorithms are and how that affects the precision with which heart disease is diagnosed. Robust generalization is ensured by model validation approaches like cross-validation. The study also explores continuous monitoring applications, providing ongoing risk assessments and contributing to personalized treatment plans. The choice of algorithm depends on dataset characteristics and the interpretability requirements of healthcare professionals.","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"32 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444858","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 Impact of 5G Networks on the Development of Connected and Autonomous Cars 5G 网络对互联汽车和自动驾驶汽车发展的影响

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/031312024

{"title":"The Impact of 5G Networks on the Development of Connected and Autonomous Cars","authors":"","doi":"10.30534/ijacst/2024/031312024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/031312024","url":null,"abstract":"Smart The development of connected and autonomous cars (CACs) is set to revolutionize transportation, offering increased safety, efficiency, and convenience. However, the widespread adoption of CACs relies heavily on the availability of reliable and high-speed wireless networks. This paper explores the impact of 5G networks on CACs, focusing on their ability to provide higher speeds, lower latency, and greater capacity. Additionally, it examines the benefits of 5G for CACs, including improved safety, increased efficiency, and the emergence of new transportation services. The paper concludes that 5G networks play an important role in advancing CAC technology and driving its adoption. 5G networks pave the way for the emergence of new transportation services that can revolutionize the mobility landscape. With the high-speed and low-latency capabilities of 5G, CACs can seamlessly connect to other smart devices and infrastructure, enabling innovative services such as ride-sharing, on-demand transportation, and mobility-as-a-service (MaaS) platforms. These services can transform the way people access transportation, offering flexible and convenient options that cater to individual needs","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"28 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445124","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

Machine Learning Technique for Practical Engineering Use 用于实际工程的机器学习技术

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/051312024

{"title":"Machine Learning Technique for Practical Engineering Use","authors":"","doi":"10.30534/ijacst/2024/051312024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/051312024","url":null,"abstract":"In the age of Industry 5.0, where the digital world generates massive amounts of data, AIML has emerged as a powerful tool for analyzing and interpreting this data. It has proven successful in various fields such as intelligent control, decision making, computer graphics, and computer vision and many more. The performance in AIML and deep learning methods has led to their widespread adoption in real-time engineering applications. These tools are necessarily required for creating intelligent, automated tools that can recognize the data in areas like healthcare, cybersecurity, and intelligent transportation systems. Machine learning encompasses different strategies, including reinforcement learning, semi- supervised, unsupervised and supervised learning algorithms. This study aims to comprehensively explore the utilization of ML in managing real world engineering applications, enhancing their functionality and intelligence. By investigating the applicability of various machine learning approaches in domains such as cybersecurity, healthcare, and intelligent transportation systems, this research contributes to our understanding of their effectiveness. Additionally, it addresses the research goals and difficulties associated with ML in practical life. This study serves as reference for industry professionals, academics, and decision-makers, providing insights and benchmarks for different use cases and real-world applications.","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445903","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

Advancements and Applications of Blockchain Technology: A Comprehensive Analysis 区块链技术的进步与应用：全面分析

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/061312024

{"title":"Advancements and Applications of Blockchain Technology: A Comprehensive Analysis","authors":"","doi":"10.30534/ijacst/2024/061312024","DOIUrl":"https://doi.org/10.30534/ijacst/2024/061312024","url":null,"abstract":"The digital code of blockchain technology has revolutionized every aspect of business, commerce and industry. This new system eliminates the need to store and manage codes by providing timely and immutable data. Unlike the traditional systems, these blocks are not specific to a particular organization but are monitored by a network of nodes or computers. Strong encryption protection and connect all blocks together. Blockchain’s immutability and security have revolutionized fundamental concepts such as trust, ownership, identity, and financial transactions[6]. This technology enables secure, fast, transparent and pseudonymous transactions. A source of in- formation about blockchain, this article provides an in-depth study of blockchain’s history, principles and popularity. Additionally, various consensus algorithms used in the blockchain technology are also carefully examined. Originally conceived as a system for cryptocurrencies, blockchain has evolved into a transformative force across industries. The article discusses the concepts, methods and applications of blockchain technology. Blockchain’s decentralized structure, driven by decentralized ledgers and encrypted confirmation, ensures reliability, and security and transparency of information transactions. This research contributes to the growing body of knowledge about blockchain and is useful for researchers, practitioners, and policymakers who want to better understand the technology’s impact and future directions. Additionally, this article will examine various applications and real-life examples of blockchain technology and addresses related issues and problems [2-8]. The presentation of non-current events expands the range of the potential applications. This study provides a better understanding of all aspects of blockchain by providing an overview of the products.","PeriodicalId":294118,"journal":{"name":"International Journal of Advances in Computer Science and Technology","volume":"52 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447383","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

Smart Synergy: Harnessing Machine Learning for Advanced Nanotechnology in Healthcare 智能协同：利用机器学习在医疗保健领域应用先进纳米技术

International Journal of Advances in Computer Science and Technology Pub Date : 2024-01-08 DOI: 10.30534/ijacst/2024/021312024

引用次数: 0