Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Hamam, Habib
Unknown Affiliation
Computer Science & IT Engineering Mechanical Engineering Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Artificial intelligence-based weather prediction framework using neural networks Kaushik, Keshav; Chhabra, Gunjan; Bharany, Salil; Rehman, Ateeq Ur; Hamam, Habib
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1836-1848

Abstract

For humans, weather prediction is vital in making rational everyday choices and avoiding risk. Accurate weather forecasting is regarded as one of the world’s most difficult issues. New weather forecasting, unlike conventional techniques, relies on a mixture of computer simulations, observation (via balloons and satellites), and information of patterns and trends (via local weather analysts and weather stations). Predictions are rendered with fair precision using such techniques. Prediction algorithms based on complicated formulas run the majority of computational models used for prediction. This paper highlights the prediction of weather with the artificial neural networks (ANN) using the latest available smart computing devices. To assess the effectiveness of the model, comparison research is conducted with the other existing models in the same area. The result demonstrates that our approach is better in comparison to other similar research and products. The comparative analysis has been undergone which confirms the superiority of our proposed techniques with an accuracy of 90.4%.
Rethinking Intelligence: From Human Cognition to Artificial Futures Hamam, Habib
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.44232

Abstract

The rapid advancement of AI technologies raises pressing questions about the nature and future direction of intelligence. A key challenge is to understand how human and artificial intelligences differ, not just in form but in function, and how they should be evaluated in a shared context. This paper proposes a structured framework based on 15 measurable conditions of intelligence, such as memory, adaptability, specialization, and ethical alignment. Our main contribution lies in connecting these conditions to nine key directions of AI development—such as responsible AI, human–machine collaboration, and quantum AI—to outline how intelligence can be evaluated and guided across both natural and synthetic domains. Methodologically, we cross-analyze these dimensions using a 15×9 matrix, providing both a diagnostic tool and a conceptual roadmap for future AI development. This approach blends insights from cognitive science, applied AI, ethics, and philosophy. Our findings show that intelligence must be judged not just by computational capability but by interpretability, ethical grounding, and social utility. Contextual and hybrid systems—those that adapt to environments and align with human values—emerge as the most promising. We conclude by calling for an interdisciplinary approach to build intelligence systems that are not only powerful but also trustworthy and socially meaningful.
Co-Authors Bharany, Salil Chhabra, Gunjan Kaushik, Keshav Rehman, Ateeq Ur