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All Journal AMPLITUDO: Journal of Science & Technology Innovation Gameology and Multimedia Expert
Hamayoon Ghafory
Kabul Education University
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Green Artificial intelligence Foundations, Applications, and Pathways to Sustainable Development Musawer Hakimi; Omid Tarashtwal; Hamayoon Ghafory
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.524

Abstract

The fast evolution of artificial intelligence (AI) systems has worried people about their environmental impact thus prompting the rise of Green AI. In the present systematic review, we are going through the 32 articles published in peer-reviewed journals that were analyzed based on PRISMA standards regarding the conceptual bases, applications, and the future of Green AI. The review identified three paradigms: Green AI (computational efficiency), Sustainable AI (holistic socio-technical responsibility), and AI for Green (AI applied to sustainability challenges). A large part of the resources that would be used for the environments, monitoring, agriculture, and smart city applications can be saved by 15-30% through Green AI. The main difficulties are performance and efficiency balancing, limiting budget, and a research mentality that values precision more than sustainability. The research points out the dual function of AI in environmental matters as that of polluter and of a device for making the planet greener through humane practices and technologies. To sustainable AI, efficient algorithm design, regulatory support, the establishment of carbon-aware metrics, and collaboration among different disciplines to create the adoption of AI that is both economical and ethical are needed
Blockchain-Enabled Artificial Intelligence Framework for Intrusion Detection in Cloud-Based Information Systems Mohammad Nawab Turan; Hamayoon Ghafory; Sadiq Aminzai
Gameology and Multimedia Expert Vol. 3 No. 2 (2026): Gameology and Multimedia Expert - April 2026
Publisher : Department of Informatics Faculty of Engineering Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/game.v3i2.26900

Abstract

The rapid proliferation of cloud-based information systems has introduced unprecedented cybersecurity challenges, necessitating robust and adaptive intrusion detection mechanisms. This paper proposes a novel Blockchain-Enabled Artificial Intelligence Framework for Intrusion Detection (BAIFD) in cloud environments. The proposed framework integrates a federated deep learning architecture with immutable blockchain ledger technology to achieve decentralized, tamper-resistant, and highly accurate threat identification. Two formal models are presented: (i) a Federated Threat Detection Model (FTDM) that coordinates distributed AI agents across heterogeneous cloud nodes without sharing raw data, and (ii) a Blockchain Consensus Validation Model (BCVM) that ensures the integrity and provenance of threat intelligence records. Extensive experiments conducted on three benchmark datasets: NSL-KDD, CICIDS2017, and UNSW-NB15 demonstrate that BAIFD achieves a detection accuracy of 99.1%, a false-positive rate of 0.43%, and an average latency of 18.7 ms, outperforming seven state-of-the-art baselines. Six architectural and analytical figures and five comparative performance tables are provided to illustrate the framework design, model workflows, and evaluation results. The findings confirm that the convergence of blockchain and federated deep learning delivers a scalable, privacy-preserving, and computationally efficient solution for next-generation cloud intrusion detection systems.
Co-Authors Mohammad Nawab Turan Musawer Hakimi Omid Tarashtwal Sadiq Aminzai