Yazar "Juyal, A." seçeneğine göre listele

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    Automated and Optimised Machine Learning Algorithms for Healthcare Informatics
    (Springer Science and Business Media Deutschland GmbH, 2024) Juyal, A.; Bhushan, B.; Hameed, A.A.; Jamil, A.; Pandey, S.
    Healthcare is a rapidly expanding field with a substantial amount of heterogeneous data driving innumerable health-related tasks. Various healthcare service providers still rely on manual procedures, which can be time-consuming and require significant effort. To automate such manual operations, recent technological advances have emerged in the domain of Machine Learning (ML). ML falls under the subject of Artificial Intelligence (AI), and it gets combined with ‘big data’ to draw meaningful insights. The integration of ML in the healthcare sector has optimized decision-making and predictive analysis. This paper discusses the various application areas of ML in healthcare. Additionally, several ML algorithms used by other researchers in healthcare-related experiments are summarized. A brief review is provided regarding the experiments. This paper delineates the challenges associated with using ML in healthcare. Finally, the paper offers insights into future research directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    Deep Learning Approaches for Cyber Threat Detection and Mitigation
    (Association for Computing Machinery, 2023) Juyal, A.; Bhushan, B.; Hameed, A.A.; Jamil, A.
    Cyberspace has inflated over the past decade, primarily driven by pervasive development and widespread usage of the internet. Prolonged cyber-attacks and security vulnerabilities have become more common as a consequence. A recent study conducted by Ponemon Institute revealed that 64% of businesses have suffered web-based attacks in 2023. Thus, cybersecurity measures have become mandatory in every sector, that leverages technology, to protect confidential information from malignant threats. The majority of the sectors use conventional security measures like anti-virus products to defend their systems against outside attacks, however, these products fail to work on unseen and polymorphic threats. With cutting-edge deep learning (DL) processes getting adopted in nearly every field, it has been widely exploited in cybersecurity to detect and classify cyber-attacks. This study discusses the application of common deep learning models in cybersecurity to detect malware, intrusion, phishes, and spam. This study provides a description of deep learning models and their mathematical backgrounds that are common in cybersecurity. This study also reviews and analyses the work of other researchers who experimented with deep learning models in cybersecurity. This study delineates the challenges faced by deep learning models in cybersecurity. Finally, concluding remarks and future research directions are summarized to foresee the potential improvement of deep learning in cybersecurity. © 2023 ACM.
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    Implementation of Homomorphic Encryption Schemes in Fog Computing
    (Springer Science and Business Media Deutschland GmbH, 2024) Pandey, S.; Bhushan, B.; Hameed, A.A.; Jamil, A.; Juyal, A.
    In today's date, fog computing is on the rise due to its characteristics. Its ability to provide smart devices with close-by computing capabilities offers a drastic reduction in cloud traffic and more efficient data transfer. However, data security in fog computing devices is a major concern. Homomorphic Encryption (HME) Scheme is a technique that protects private data from various threats, and to improve efficiency and reduce time, it offers modification of original data after encryption without decrypting it. In this paper, we explore the various homomorphic encryption schemes that can be implemented in fog computing to protect the data and provide data security. We present a detailed architecture and various characteristics of fog computing to better visualize the use of homomorphic encryption in different areas. Further, we present the applications of homomorphic encryption in different sectors and discuss future research directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.