Asian Journal of Research in Computer Science最新文献

The Comprehensive Review: Internet Protocol (IP) Address a Primer for Digital Connectivity 全面回顾：互联网协议 (IP) 地址数字连接入门

Asian Journal of Research in Computer Science Pub Date : 2024-07-16 DOI: 10.9734/ajrcos/2024/v17i7488

Mayank Kumar Tiwari, Rahul Pal, Riya Singh, Ankur Kumar Singh, Vijay Kumar, Swati Sharma, Naheeda Zaib

{"title":"The Comprehensive Review: Internet Protocol (IP) Address a Primer for Digital Connectivity","authors":"Mayank Kumar Tiwari, Rahul Pal, Riya Singh, Ankur Kumar Singh, Vijay Kumar, Swati Sharma, Naheeda Zaib","doi":"10.9734/ajrcos/2024/v17i7488","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7488","url":null,"abstract":"The Internet Protocol (IP) is the rule book that governs how data is addressed and routed across networks on the internet. Think of it as the postal system for the digital world. This addressing system allows routers, the digital mail carriers, to efficiently transfer packets between devices and networks until they reach their destination. It ensures data reaches the right place by assigning addresses and enabling efficient routing. IP also encompasses various protocols and services, such as ICMP (Internet Control Message Protocol) for error reporting and diagnostics, and DHCP (Dynamic Host Configuration Protocol) for automatic IP address assignment. Additionally, IP can be configured to support different transmission modes, including uni-cast, multicast, and broadcast, catering to diverse communication requirements. Adopting technology that has been researched and developed commercially offers the military a cost-effective method of implementation. IP systems enable the forces to share a common network that supports voice, video, and data sharing. This systematic review article initially highlighted the basics on IP and lastly the brief discussion regards data gram format, NAT, IPv4, IPV6 IP fragmentation, CIDR, TCP and UDP individually.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":"58 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643551","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

Determining the Efficacy of Machine Learning Strategies in Quelling Cyber Security Threats: Evidence from Selected Literatures 确定机器学习策略在消除网络安全威胁方面的功效：来自部分文献的证据

Asian Journal of Research in Computer Science Pub Date : 2024-07-13 DOI: 10.9734/ajrcos/2024/v17i7487

Chandra Shikhi Kodete, Bharadwaj Thuraka, Vikram Pasupuleti, Saiteja Malisetty

{"title":"Determining the Efficacy of Machine Learning Strategies in Quelling Cyber Security Threats: Evidence from Selected Literatures","authors":"Chandra Shikhi Kodete, Bharadwaj Thuraka, Vikram Pasupuleti, Saiteja Malisetty","doi":"10.9734/ajrcos/2024/v17i7487","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7487","url":null,"abstract":"The alarming security threats in the internet world continually raise critical concerns among individuals, organizations and governments alike. The sophistication of cyber-attacks makes it imperative for a paradigm shift from traditional approaches and measures for quelling the attacks to modern sophisticated, digital and strategic ones, such as those involving machine learning and other technologies of artificial intelligence (AI). This study is aimed at examining machine learning (ML) strategies for effective cyber security. ML involves using algorithms and statistical models to enable computers learn from and make decisions or predictions based on data. The study relied on secondary data, which were subjected to a systematic review. The results of its thematic and qualitative analyses prove that majority of the literatures allude to the fact that the maximal performance abilities and tactics of the ML constitute its strategies for quelling cyber security. These include its: early detection of threats that are tackled before they cause damages; ability to analyze huge quantity of data quickly and accurately; and processing of datasets in real-time. The study argues that the noted abilities and tactics constitute ML strategies for quelling cyber security, regardless of its challenges like data quality, security vulnerabilities and possible incidences of bias. The study concludes that ML can indeed be used to detect and respond to threats in real-time, ascertain patterns of malicious behavior, and improve on internet security, which thereby prove it to be a viable tool for quelling cyber security.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":"36 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650828","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 Technology Use on Young People: A Case Study of Social Media and Internet Usage 技术使用对年轻人的影响：社交媒体和互联网使用案例研究

Asian Journal of Research in Computer Science Pub Date : 2024-07-12 DOI: 10.9734/ajrcos/2024/v17i7486

Refik Ramadani

{"title":"The Impact of Technology Use on Young People: A Case Study of Social Media and Internet Usage","authors":"Refik Ramadani","doi":"10.9734/ajrcos/2024/v17i7486","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7486","url":null,"abstract":"This study delves into the impact of the increased use of electronic appliances on the younger generation, particularly focusing on the risks of addiction and mental health issues. The main objectives are to explore the correlation between technology usage and psychological problems among individuals born after 2000, and to assess the influence of educational interventions and parental controls in moderating these effects. Using a mixed-methods approach, data were collected in line with the findings of the report by GSMA, DOCOMO, and Net Children Go Mobile (2014), which includes data from European countries such as Belgium, Denmark, Ireland, Italy, Portugal, Romania, and the UK. The results indicate a strong association between excessive screen time and elevated rates of depression, detachment from reality, and trauma among youth. \u0000Furthermore, the study highlights the necessity for educational institutions to incorporate digital literacy modules into their curricula, which would help students navigate the digital world more responsibly. \u0000Encouraging physical activities and face-to-face social interactions is also recommended to counterbalance the extensive use of screens. Policymakers are urged to develop frameworks that protect young individuals from the adverse effects of prolonged technology exposure, including regulations on screen time, content appropriateness, and online privacy protections. \u0000These recommendations aim to optimize the benefits of technology while minimizing its negative impacts on the younger generation, thus fulfilling the study's objectives and providing a basis for future policy and educational strategies.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653791","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 Media or Biased Media: The Impacts and Challenges of AI and Big Data on the Media Industry 智能媒体还是偏见媒体？人工智能和大数据对传媒业的影响和挑战

Asian Journal of Research in Computer Science Pub Date : 2024-07-06 DOI: 10.9734/ajrcos/2024/v17i7484

Amaka Debie Samuel-Okon

{"title":"Smart Media or Biased Media: The Impacts and Challenges of AI and Big Data on the Media Industry","authors":"Amaka Debie Samuel-Okon","doi":"10.9734/ajrcos/2024/v17i7484","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7484","url":null,"abstract":"This study critically analyzes the impact of artificial intelligence (AI) and big data on the media industry, focusing on the ethical challenges and biases introduced by these technologies. The research aims to uncover the extent to which AI and big data influence content personalization, creation, and marketing, and the ramifications of these influences on cultural diversity and societal norms. A mixed-methods approach was employed, combining quantitative analysis through a survey of 532 respondents and qualitative thematic analysis of 10 academic literatures. The findings reveal significant associations between automated content creation tools and societal biases, personalized recommendation systems and echo chambers, and algorithmic recommendations and cultural homogenization. Conversely, no significant association was found between big data analytics and privacy concerns. The study highlights the need for ethical guidelines, enhanced content diversity, strengthened data privacy measures, and increased algorithmic transparency to mitigate the ethical challenges and biases in AI-driven media platforms. These insights contribute to the broader understanding of AI and big data's role in shaping the media industry, offering valuable implications for future research, policy-making, and industry practices.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671833","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

Evaluating the Top Application Security Tools: From Static Analysis to Runtime Protection 评估顶级应用程序安全工具：从静态分析到运行时保护

Asian Journal of Research in Computer Science Pub Date : 2024-07-05 DOI: 10.9734/ajrcos/2024/v17i7483

Alexandre Anacleto Libanio Xavier Fernandes

引用次数: 0

Robustness and Reliability Testing in Healthcare Using Artificial Intelligence 利用人工智能进行医疗保健领域的稳健性和可靠性测试

Asian Journal of Research in Computer Science Pub Date : 2024-07-04 DOI: 10.9734/ajrcos/2024/v17i7482

Tushar Khinvasara, Abhishek Shankar, Connor Wong

{"title":"Robustness and Reliability Testing in Healthcare Using Artificial Intelligence","authors":"Tushar Khinvasara, Abhishek Shankar, Connor Wong","doi":"10.9734/ajrcos/2024/v17i7482","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7482","url":null,"abstract":"Testing the security, efficiency, and dependability of AI-driven healthcare systems is crucial. It is essential to perform thorough and rigorous testing to make sure the AI algorithms are capable. Our goal is to ensure that these algorithms can handle a wide range of scenarios that may occur in healthcare settings. We must observe, for instance, how well they function in the presence of changes in patient characteristics, data accuracy, and even environmental factors. Developers are able to go deeply and find any potential flaws, biases, or restrictions by thoroughly testing AI models. This enables them to enhance and maximize the algorithms' performance. Our goal is for these AI systems to be adaptable and strong, ready to overcome any challenges. Our goal is for these AI systems to be adaptable and robust, ready to overcome whatever challenges they encounter. Reliability testing is another crucial step in this process. Our goal is to guarantee that, over time, the AI predictions in actual medical contexts continue to be accurate and dependable. In the end, we rely on these systems to produce trustworthy outcomes that actually enhance patient care. Developers and healthcare institutions are not the only parties involved in this. Policymakers and regulatory bodies are also quite important. They put a lot of effort into developing standards and protocols for carrying out trustworthy and demanding AI testing in the medical field. Strict safety and efficacy standards are met by AI-driven healthcare solutions thanks to the requirements they set for testing procedures, data quality, and performance indicators. This article focuses on all the current robustness and reliability testing using AI in Healthcare.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677757","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-Based Human Movements Mimicking System for Animation and Virtual Reality 基于机器学习的动画和虚拟现实人体动作模仿系统

Asian Journal of Research in Computer Science Pub Date : 2024-07-03 DOI: 10.9734/ajrcos/2024/v17i7480

Asha Latha G, Enugu Vishwanth Reddy, Kothapalli Manideep, Om Agarwal

引用次数: 0

Optimizing Software Development Processes in Cloud Computing Environments Using Agile Methodologies and DevOps Practices 使用敏捷方法和 DevOps 实践优化云计算环境中的软件开发流程

Asian Journal of Research in Computer Science Pub Date : 2024-07-03 DOI: 10.9734/ajrcos/2024/v17i7479

Naimil Navnit Gadani

引用次数: 0

Corpus-based Approaches for Sentiment Analysis: A Review 基于语料库的情感分析方法：综述

Asian Journal of Research in Computer Science Pub Date : 2024-07-03 DOI: 10.9734/ajrcos/2024/v17i7481

Mahmood Umar, Hauwa Ibrahim Binji, Anas Tukur Balarabe

{"title":"Corpus-based Approaches for Sentiment Analysis: A Review","authors":"Mahmood Umar, Hauwa Ibrahim Binji, Anas Tukur Balarabe","doi":"10.9734/ajrcos/2024/v17i7481","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7481","url":null,"abstract":"The investigation studied the state of art on corpus-based approaches for sentiment analysis. Thus, detailing its methodologies, evaluation metrics, limitations, and future directions. The importance of sentiment analysis in fields such as marketing, customer feedback analysis, social media monitoring, financial analysis, and political science is emphasized. The methodology for corpus-based approaches in sentiment analysis includes the following key steps: data collection, preprocessing, feature extraction, and sentiment classification. The lexicon-based approaches include the corpus-based or bag of words (BOW) and dictionary (also called opinion lexicon). Evaluation of the corpus-based sentiment analysis approach is addressed through performance metrics such as accuracy, precision, recall, F1-score, and comparative analysis with other approaches including hybrid and rule-based systems. Limitations of corpus-based sentiment analysis, such as data sparsity and domain adaptation, are acknowledged, alongside potential enhancements and research directions including ensemble learning, deep learning architectures, and multimodal data integration. The conclusion emphasizes the versatility and scalability of corpus-based sentiment analysis, while ongoing research efforts aim to address its limitations and further enhance its applicability in diverse domains.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680616","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 Enhanced Model for Intrusion Detection in a Cloud Computing Environment 云计算环境中的入侵检测增强模型

Asian Journal of Research in Computer Science Pub Date : 2024-07-02 DOI: 10.9734/ajrcos/2024/v17i7478

Odikayor-Ogbomo. I.F., Anigbogu Sylvanus Okwudili, Anigbogu. Gloria Nkiru, Anigbogu Kenechukwu Sylvanus

{"title":"An Enhanced Model for Intrusion Detection in a Cloud Computing Environment","authors":"Odikayor-Ogbomo. I.F., Anigbogu Sylvanus Okwudili, Anigbogu. Gloria Nkiru, Anigbogu Kenechukwu Sylvanus","doi":"10.9734/ajrcos/2024/v17i7478","DOIUrl":"https://doi.org/10.9734/ajrcos/2024/v17i7478","url":null,"abstract":"Intrusion is an important issue in computer networks especially in cloud computing where all the services are served using the internet. The fully distributed and open structure of cloud computing and services has made it an even more attractive target for potential intruders. The more sophisticated hackers and attackers get, the more there is work for the defense to prevent such attacks. A cloud computing system can be exposed to threats which include the integrity, confidentiality, and availability of its resources, its data, and the virtualized infrastructure can be vulnerable. The problem becomes bigger when an internal intruder misuses a cloud with massive computing power and storage capacity as a malicious party. This research developed an enhanced model for intrusion detection that monitors and analyzes data in a cloud environment and detects intrusion in the system or network. The model can detect intrusions from external and malicious internal (authorized and unauthorized) users by normalizing and classifying all data packets using machine learning techniques. The developed system is an enhanced model of Zhang by combining it with two machine learning techniques: Support vector machine and Bayesian network to aid in the classification of normal data and intrusion data to detect intrusions. The developed model is evaluated and found to be able to make strong predictions, detect attacks, and still maintain the efficiency of the network. The system, when implemented, can detect intruders by classification of data packets and also improve the existing system in terms of providing more accurate and more efficient intrusion detection. It also provides worthwhile information about malicious network traffic, helping to identify the source of the incoming probes or attacks, collecting forensic evidence that can be used to identify intruders, and alerting security personnel that a network invasion may be in progress.","PeriodicalId":505559,"journal":{"name":"Asian Journal of Research in Computer Science","volume":"333 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141686916","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