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All Journal Buana Information Technology and Computer Sciences (BIT and CS) Journal of Soft Computing Exploration Aerospace Engineering Journal of Geosciences and Environmental Studies
Poudyal, Khem
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Aerospace Engineering Astronomy Computer Science & IT Earth & Planetary Sciences Energy Engineering Environmental Science Social Sciences

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Application of UAVs and Remote Sensing Technologies for Atmospheric CO2 Capturing: A Study Application of UAVs and Remote Sensing in CO2 Reductions Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
Aerospace Engineering Vol. 1 No. 2 (2024): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i2.2508

Abstract

Human activities are a major contributor to climate change, with rising levels of CO₂ in the atmosphere. To address this essential issue, several carbon capture and sequestration (CCS) methods have been developed. Unmanned Aerial Vehicles (UAVs) and remote sensing technologies are emerging as major improvements to the efficiency and effectiveness of atmospheric carbon capture initiatives. This research examines the use of UAVs and remote sensing technologies to monitor, quantify, and manage atmospheric CO₂ levels. Furthermore, the study explores the broader implications of integrating robotic-drone technology, emphasizing their ability to contribute to a sustainable future. These technologies, which incorporate modern data collection and analysis methodologies, provide promising answers for both climate change mitigation and long-term environmental sustainability.
Enhancing soccer pass receiver prediction in broadcast images through advanced deep learning techniques: A comprehensive study on model optimization and performance evaluation Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.301

Abstract

In this study, we present a graph neural network (GNN) model specifically designed for football pass receiver prediction in Broadcast Images is presented in this study. Important node properties, including ball possession indicators, hot-encoded team values, and normalized ground placements, are incorporated into the model along with a careful weighting of edges to account for player distances. With weighted BCE loss used to overcome class imbalance, its architecture consists of a linear layer, numerous GNN Message Passing layers, a SoftMax activation, and binary cross-entropy (BCE) loss for training. Of 206 examples, 101 valid predictions were made, indicating a predictive accuracy of 0.50 according to the evaluation data. Comparative analyzes show that GAT-V2 (0.85) and GAT (0.63) perform better in terms of optimization and accuracy, respectively. The effectiveness in recognizing football pass receivers is demonstrated in this paper, highlighting developments in computer vision applications for sports analytics.
Reinforcement Learning-Based Autonomous Soccer Agents: A Study in Multi-Agent Coordination and Strategy Development Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
Buana Information Technology and Computer Sciences (BIT and CS) Vol 6 No 1 (2025): Buana Information Technology and Computer Sciences (BIT and CS) (InProcess)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v6i1.7270

Abstract

Reinforcement learning (RL) approaches, particularly Q-learning, have emerged as strong tools for autonomous agent training, allowing agents to acquire optimum decision-making rules through interaction with their surroundings. This research investigates the use of Q-learning in the context of training autonomous agents for robotic soccer, a complex and dynamic arena that necessitates strategic planning, coordination, and adaptation. We studied the learning progress and performance of agents taught using Q-learning in a series of experiments carried out in a simulated soccer setting. During training, agents interacted with the soccer environment, iteratively changing their Q-values in response to observable rewards and behaviors. Despite the high-dimensional and stochastic character of the soccer domain, Q-learning helped the agents develop excellent tactics and decision-making capabilities. Notably, our study found that, on average, the agents required 64 steps to reach a stable policy with an average reward of -1.
Application of UAVs and Remote Sensing Technologies for Atmospheric CO2 Capturing: A Study Application of UAVs and Remote Sensing in CO2 Reductions Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Poudyal, Khem
Journal of Geosciences and Environmental Studies Vol. 1 No. 3 (2024): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/ijgaes.v1i3.3348

Abstract

Human activities are a significant contributor to climate change, with rising levels of CO₂ in the atmosphere. Several carbon capture and sequestration (CCS) methods have been developed to address this issue. Uncrewed Aerial Vehicles (UAVs) and remote sensing technologies are emerging as significant improvements to the efficiency and effectiveness of atmospheric carbon capture initiatives. This research examines using UAVs and remote sensing technologies to monitor, quantify, and manage atmospheric CO₂ levels. Furthermore, the study explores the implications of integrating robotic-drone technology, emphasizing their ability to contribute to a sustainable future. These technologies, incorporating modern data collection and analysis methodologies, provide promising answers for climate change mitigation and long-term environmental sustainability.
Co-Authors Paneru, Biplov Paneru, Bishwash Poudyal, Ramhari