International Journal of Innovative Research in Engineering and Management
Year: 2025, Volume: 13, Issue: 1
First page : ( 26) Last page : ( 33)
Online ISSN : 2350-0557.
DOI: 10.55524/ijircst.2025.13.1.4 | DOI URL: https://doi.org/10.55524/ijircst.2025.13.1.4
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Sai Ratna Prasad Dandamudi , Jaideep Sajja, Amit Khanna
The increasing reliance on interconnected systems has elevated the importance of robust cybersecurity and efficient data networking. As digital transformation accelerates, emerging cyber threats exploit vulnerabilities in critical infrastructure, emphasizing the need for innovative solutions. This paper investigates the application of machine learning in enhancing cybersecurity and data networking through predictive models. By analyzing empirical data from major network providers, cybersecurity firms, and detailed case studies, this research demonstrates the effectiveness of machine learning in improving threat detection, optimizing network performance, and mitigating risks.
Findings reveal that machine learning-driven prediction models enhance security measures by 85%, optimize network efficiency by 30%, and significantly reduce financial losses stemming from cyberattacks. These predictive systems provide early warnings and automate responses, enabling organizations to transition from reactive to proactive security strategies. Furthermore, machine learning algorithms dynamically allocate network resources, reducing latency and increasing bandwidth utilization.
The results showcase the transformative potential of machine learning in safeguarding digital ecosystems against evolving threats. As industries become increasingly reliant on data networking, the adoption of machine learning not only fortifies cybersecurity frameworks but also streamlines operational efficiency. Addressing challenges such as integration with legacy systems, high implementation costs, and the need for skilled personnel will be critical to unlocking the full potential of this technology. This research underscores the indispensable role of machine learning in shaping a secure and resilient digital future.
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M. Tariq, Y. Hayat, A. Hussain, A. Tariq, and S. Rasool, “Principles and perspectives in medical diagnostic systems employing artificial intelligence (AI) algorithms,” International Research Journal of Economics and Management Studies, vol. 3, no. 1, 2024.
M. Tariq, Y. Hayat, A. Hussain, A. Tariq, and S. Rasool, “Principles and perspectives in medical diagnostic systems employing artificial intelligence (AI) algorithms,” International Research Journal of Economics and Management Studies, vol. 3, no. 1, 2024.
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M. Farhan, H. Rafiq, H. Rafi, S. Rehman, and M. Arshad, “Quercetin impact against psychological disturbances induced by fat rich diet,” Pakistan Journal of Pharmaceutical Sciences, vol. 35, no. 5, 2022.
M. Tariq, A. Hayat, A. Hussain, A. Tariq, and S. Rasool, “Maximizing efficiency in telemedicine: An IoT-based artificial intelligence optimization framework for health analysis,” European Journal of Science, Innovation and Technology, vol. 3, no. 6, pp. 48–61, 2023.
A. Chowdhury, A. A. Sultana, A. Rafi, and M. Tariq, “AI-driven predictive analytics in orthopedic surgery outcomes,” Revista Espanola de Documentacion Cientifica, vol. 19, no. 2, pp. 104–124, 2024.
A. Chowdhury, A. A. Sultana, and A. H. Rafi, “AI in neurology: Predictive models for early detection of cognitive decline,” Revista Espanola de Documentacion Cientifica, vol. 17, no. 2, pp. 335–349, 2023.
A. A. Chowdhury, A. Sultana, and M. Tariq, “Artificial intelligence for early diagnosis and personalized treatment in gynecology,” International Journal of Advanced Engineering Technologies and Innovations, vol. 2, no. 1, pp. 286–306, 2024.
A. A. Sultana, A. H. Rafi, and A. Chowdhury, “Leveraging artificial intelligence in neuroimaging for enhanced brain health diagnosis,” Revista de Inteligencia Artificial en Medicina, vol. 14, no. 1, pp. 1217–1235, 2023.
A. Tariq, A. Gill, H. K. Hussain, and A. Y. Gill, “Revolutionizing healthcare: How machine learning is poised to transform patient diagnosis—a comprehensive review of AI’s impact on medical diagnosis,” Journal of World Science, vol. 2, no. 10, pp. 1638–1652, 2023.
S. Lodhi, A. Hussain, and A. Gill, “Artificial intelligence: Pioneering the future of sustainable cutting tools in smart manufacturing,” BIN: Bulletin of Informatics, vol. 2, no. 1, pp. 147–162, 2024.
S. Rasool, M. Ali, H. M. Shahroz, H. K. Hussain, and A. Y. Gill, “Innovations in AI-powered healthcare: Transforming cancer treatment with innovative methods,” BULLET: Jurnal Multidisiplin Ilmu, vol. 3, no. 1, pp. 118–128, 2024.
A. Y. Gill, A. Saeed, S. Rasool, A. Husnain, and H. K. Hussain, “Revolutionizing healthcare: How machine learning is transforming patient diagnoses—a comprehensive review of AI’s impact on medical diagnosis,” Journal of World Science, vol. 2, no. 10, pp. 1638–1652, 2023.
S. Lodhi, A. Hussain, and A. Gill, “3D printing techniques: Transforming manufacturing with precision and sustainability,” International Journal of Multidisciplinary Sciences and Arts, vol. 3, no. 3, pp. 129–138, 2024.
M. Tariq, A. Hayat, A. Hussain, A. Tariq, and S. Rasool, “Principles and perspectives in medical diagnostic systems employing artificial intelligence (AI) algorithms,” International Research Journal of Economics and Management Studies, vol. 3, no. 1, 2024. Available From: https://irjems.org/irjems v3i1p144.html
S. Lodhi, A. Hussain, and A. Gill, “Renewable energy technologies: Present patterns and upcoming paths in ecological power production,” Global Journal of Universal Studies, vol. 1, no. 1, pp. 108–131, 2024. Available From: https://shorturl.at/FL17h
S. Lodhi, A. Hussain, and A. Gill, “Green innovations: Artificial intelligence and sustainable materials in production,” BULLET: Jurnal Multidisiplin Ilmu, vol. 3, no. 4, pp. 492–507, 2024. Available From: https://journal.mediapublikasi.id/index.php/bullet/article/view/4 474
S. Lodhi, A. Hussain, and A. Gill, “Artificial intelligence: Pioneering the future of sustainable cutting tools in smart manufacturing,” BIN: Bulletin of Informatics, vol. 2, no. 1, pp. 147–162, 2024. Available From: https://ojs.jurnalmahasiswa.com/ojs/index.php/bin/article/view/3 55
A. Tariq, M. Hayat, A. Hussain, A. Tariq, and S. Rasool, “Maximizing efficiency in telemedicine: An IoT-based artificial intelligence optimization framework for health analysis,” European Journal of Science, Innovation and Technology, vol. 3, no. 6, pp. 48–61, 2023. Available From: https://shorturl.at/l4iom
M. Asif, Z. H. Raza, and T. Mahmood, “Bioengineering applications in forestry: Enhancing growth, disease resistance, and climate resilience,” Revista Espanola de Documentacion Cientifica, vol. 17, no. 1, pp. 62–88, 2023.
MS Scholar, Department of Computer Science, American National University, Virginia, USA
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January 2025 - Vol 13, Issue 1
Sai Ratna Prasad Dandamudi, Jaideep Sajja, Amit Khanna.
January 2025 - Vol 13, Issue 1
Sai Ratna Prasad Dandamudi, Jaideep Sajja, Amit Khanna.
January 2025 - Vol 13, Issue 1
