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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Zakir Hossain
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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ENHANCING DATA PRIVACY AND SECURITY IN MULTI CLOUD ENVIRONMENTS Md Emran Hossain; Md Farhad Kabir; Abdullah Al Noman; Nipa Akter; Zakir Hossain
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 05 (2022): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

In this study, we present and realize a solution for contributing to the provision of data security and data privacy in a hybrid configuration based Multi Cloud environment. This method combines prevention of independent cloud security attacks and server failures through a Byzantine fault tolerance protocol, a data encoding and decoding mechanism using the Dusky architecture to improve reliability and confidentiality; and Shamir's secret sharing scheme to guarantee data trustworthiness and privacy during storage at the cost of a minor performance implication. They compared the security and privacy of their hybrid approach with well-known protocols such as SAML with proxy encryption and Kerberos, showing the benefits in terms of memory footprint, encryption/decryption time and totaltimetoauthenticate. The experimental results show that our hybrid scheme provides considerable improvements with regard to encryption\\/decryption time, memory consumption and average precision.
The Role of AI and Machine Learning in Optimizing Cloud Resource Allocation Abdullah Al Noman; Zakir Hossain; Md Abu Shihab; Nipa Akter; Nur Nahar Rimi; Md Farhad Kabir
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 1 (2023): International Journal of Multidisciplinary Sciences and Arts, Article January 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v1i2.2190

Abstract

Resource allocation inside the cloud infrastructure is an essential requirement for better performance and cost-effectiveness. With workloads growing so much more complex and dynamic, we now need automated solutions that allow us to move past tedious, manual resource management. AI provides a unique paradigm for optimizing resource allocation in such complex and dynamic environments. In this paper, we have compared various AI techniques such as machine learning, evolutionary algorithms and deep reinforcement learning for the problem of resource allocation in cloud infrastructure. We analyze their effectiveness for dynamic resource provisioning to satisfy performance objectives while reducing operational expenditure. We offer an in-depth performance comparison using real-world datasets and simulations to showcase the merits and drawbacks of each. Implications: We believe that our findings can offer guidelines to cloud providers, researchers, and practitioners who are interested in making proactive improvements on managing cloud infrastructures using intelligent resource allocation techniques.
Integrating AI with Edge Computing and Cloud Services for Real-Time Data Processing and Decision Making Md Emran Hossain; Md Tanvir Rahman Tarafder; Nisher Ahmed; Abdullah Al Noman; Md Imran Sarkar; Zakir Hossain
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 4 (2023): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i1.2559

Abstract

Connecting Multimodal AI and Edge computing for better real-time decision making: a paper on their synergy Edge computing is a solution that allows to overcome latency issue and processing data closer to the source, Multimodal AI on the other hand integrates and analyzes different types of data (images, audio, sensor data, etc.) to provide richer insights. Such a combination has a strong significance in autonomous vehicles and healthcare monitoring applications which require timely decision making with informed decisions. However, there are some inherent limitations to edge devices in computational power, energy expense, and data confidentiality. The paper examines several optimization methods such as model pruning that reduces model size, quantization that decreases the limit of precision, and domain specific AI accelerators to increase the processing speed to counteract these difficulties. The purpose of these strategies is to get a complex AI model to deploy on an edge device with limited computing resources at the cost of minimum performance. Combining Multimodal AI with edge computing can potentially transform data driven real-time decision-making applications across various fields. As Development of hardware and software never stops, formulated boundaries continue to expand, enabling more intelligent and responsive systems.
Co-Authors Abdullah Al Noman Md Abu Shihab Md Emran Hossain Md Farhad Kabir Md Imran Sarkar Md Tanvir Rahman Tarafder Nipa Akter Nisher Ahmed Nur Nahar Rimi