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All Journal Tsaqofah: Jurnal Penelitian Guru Indonesia ANWARUL: Jurnal Pendidikan dan Dakwah Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat Journal of Multidisciplinary Science: MIKAILALSYS Asian Journal of Science, Technology, Engineering, and Art Asian Journal of Early Childhood and Elementary Education ALSYSTECH Journal of Education Technology International Journal of Education, Culture, and Society Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics International Journal of Education, Management, and Technology International Journal of Humanities, Education, and Social Sciences African Multidisciplinary Journal of Sciences and Artificial Intelligence Open Access DRIVERset
Suresh Kumar Sahani
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Data-Driven Identification of Stochastic Dynamical Systems Rishav Jha; Kameshwar Sahani; Suresh Kumar Sahani; Ravi Kumar Raj; Dilip Kumar Sah
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 2 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i2.10238

Abstract

Identifying stochastic dynamical systems from observational data remains a major challenge in applied mathematics and engineering, particularly when complex systems are influenced by random perturbations and incomplete empirical information. This comprehensive review aims to examine state-of-the-art data-driven methods for discovering governing equations, estimating parameters, and predicting the behavior of stochastic dynamical systems. The review systematically analyzes key methodological approaches, including Sparse Identification of Nonlinear Dynamics (SINDy), Dynamic Mode Decomposition (DMD) and its extensions, Koopman operator theory, neural ordinary differential equations, and Bayesian inference. Each approach is evaluated in terms of its theoretical foundations, computational requirements, robustness to noise, and applicability to different classes of stochastic systems. Drawing on numerical experiments and real-world case studies, the findings show that no single method consistently outperforms others across all scenarios. Instead, hybrid approaches that integrate physics-informed constraints with machine learning demonstrate the strongest potential for advancing data-driven system identification. The review concludes that future research should address real-time identification, uncertainty quantification, and the integration of multi-fidelity data sources to improve the reliability and scalability of stochastic system modeling. This work contributes a comprehensive framework for guiding researchers and practitioners in selecting and implementing appropriate identification methods for stochastic dynamical systems.
Model-Free Reinforcement Learning for Parabolic Trajectory Optimization in Robotic Arms Aadarsh Karn; Neha Shah; Dilip Kumar Sah; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 1 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i1.9338

Abstract

Robotic arms are widely employed in applications that require smooth motion and energy-efficient operation, particularly in tasks such as object throwing and liquid dispensing, where movement often follows a curved path toward a target point. However, conventional trajectory planning methods that rely on predefined mathematical equations may not accurately represent real-world robotic systems due to uncertainties and payload variations. This study aims to optimize the trajectory of a robotic arm moving along a parabolic path using reinforcement learning and to evaluate whether this approach can successfully learn improved trajectory patterns during motion. The research integrates initial classical physics principles for curved motion with a reinforcement learning framework to enhance trajectory following toward a desired point. The findings indicate that reinforcement learning can effectively learn optimized trajectory paths and improve the motion performance of the robotic arm. The study concludes that reinforcement learning offers a promising approach for achieving smoother robotic motion with satisfactory energy efficiency under dynamic conditions. This work contributes to the advancement of intelligent motion planning by demonstrating the potential of reinforcement learning to improve trajectory optimization in robotic systems operating under practical uncertainties.
Mathematical Modeling and Deep Learning for Early Prediction of Fluid Hazards in Mountain Communities Aman Gupta; Suresh Kumar Sahani
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 3 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i3.12121

Abstract

Mountain communities are increasingly exposed to rapidly evolving fluid hazards, including flash floods, rainfall-triggered debris flows, glacial lake outburst floods (GLOFs), and flood–landslide cascades. Their short response times, complex terrain, sparse observational networks, and changing hydro-climatic conditions limit the generalizability of conventional threshold-based warning systems. This study develops a hybrid research framework that integrates physically interpretable mathematical modeling with deep learning for the early prediction of fluid hazards in mountain environments, with particular relevance to Nepal. The proposed framework integrates rainfall, snow and glacier indicators, soil moisture, terrain attributes, river stage and discharge, surface deformation, and historical hazard observations. A rainfall–runoff and threshold-based component provides physical constraints, while LSTM and CNN-LSTM models capture nonlinear temporal and spatial relationships. A probabilistic decision layer then translates model outputs into actionable warning levels while explicitly representing predictive uncertainty. Model performance is evaluated using precision, recall, F1-score, false-alarm ratio, probability of detection, critical success index, lead time, and calibration. Evidence considered in the study indicates that LSTM models can outperform conceptual rainfall–runoff models in some mountainous catchments in Nepal, while community-based warning systems can substantially extend warning lead time when forecasts are effectively integrated with local response mechanisms. The study further identifies sensor limitations, domain shift, rare-event imbalance, communication failures, model interpretability, and institutional coordination as major implementation challenges. The proposed framework contributes an explainable and community-oriented architecture for data-scarce mountain regions by combining physical process representation, machine-learning-based hazard probability estimation, uncertainty communication, and locally meaningful warning dissemination. It provides a basis for developing next-generation early-warning systems in which mathematical models constrain prediction, machine learning updates hazard probabilities, and communities receive timely and actionable warnings.
Co-Authors Aadarsh Karn Aayush Thakur Aman Gupta Ambika Kumari Thakur Aniket Sahani Asmita Thakur Avishek Sah Bardan Sah Bharat Kumar Sah Binod Kumar Sah Deep Raj Sharma Dilip Kumar Sah Kameshwar Sahani Kripa Mandal Kritika Kumari Thakur MD Faijan Raja Nand Kishor Kumar Nayan Kumar Prasad Neha Shah Omkar Poudel Partiva Kumari Sahani Pradeep Acharya Praveen Sah Praveen Shah Prem Kumar Nayak Rajnandani Das Rakesh Das Ravi Kumar Raj Rishav Jha Ritika Jayswal Roshni Purbay Satyam Thakur Shivam Sah Shyam Narayan Labh Sujeena Kumari Shrestha