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All Journal Buana Information Technology and Computer Sciences (BIT and CS) Journal of Soft Computing Exploration International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Aerospace Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Geosciences and Environmental Studies Jurnal Teknokes
Paneru, Biplov
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Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Future Trends in Pharmaceuticals: Investigation of the Role of AI in Drug Discovery, 3D Printing of Medications, and Nanomedicine: Artificial Intelligence in Nanomedicine Paneru, Biplov; Paneru, Bishwash
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i2.11259

Abstract

The pharmaceutical sector has to deal with issues like high costs, difficult diseases, and the demand for tailored therapy. The transformational potential of AI, 3D printing, and nanomedicine is examined in this paper. Drug development is revolutionized by AI, which also predicts effectiveness and personalizes therapies. Tailors, prescriptions, and complex documents can all be 3D printed to help with compliance. Nanoparticles are used in nanomedicine to deliver drugs more precisely and enhance solubility. Future themes include AI-driven target identification and individualized treatment; the effectiveness and role of 3D printing in personalized medicine; and improved medication delivery through nanomedicine. These developments promise to alter healthcare, which will help a lot of people. The study results offers a thorough examination of upcoming trends in the pharmaceutical industry and similarly discusses developments in 3D printing and nanomedicine.
Exploring the Nexus of User Interface (UI) and User Experience (UX) in the Context of Emerging Trends and Customer Experience, Human Computer Interaction, Applications of Artificial Intelligence Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Bikram Shah, Krishna
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 1 (2024): INJIISCOM: VOLUME 5, ISSUE 1, JUNE 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i1.12488

Abstract

The complexities of User Interface (UI) and User Experience (UX) design are explored in this research paper, along with their respective functions, areas of overlap, and the changing field of customer experience. In the digital age, where technology is developing at a rapid pace, designing innovative and user-focused digital products requires an understanding of the dynamic interplay between UI and UX. This research also examines how emerging trends in the UI/UX field will affect overall customer satisfaction. Additionally, this paper delves into applications of artificial intelligence (AI) in the domains of human-computer interaction (HCI), user experience (UX), and emerging trends in these fields
A Low-Cost Prototype for Edge-Computing Powered Smart Display Board: Edge Computing based notice board system Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Shah, Krishna Bikram Bikram; Poudyal, Khem Narayan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12508

Abstract

This study examines how Edge Computing technology, through the creation and use of smart notice boards, has changed the way that organizations communicate. Notice boards have historically relied on manually operated or wired electronic devices, which provide drawbacks like slowness, security flaws, and a lack of adaptability. But a new way of looking at notice board systems has developed with the advent of Edge Computing, which is driven by hardware like the ESP8266 server and communication protocols like MQTT (Message Queuing Telemetry Transport). We explore the advantages of Edge Computing in the context of smart notice boards in this study, emphasizing its capacity to support real-time data processing, improve security via local data management, login credentials, and provide users with user-friendly interfaces for content management. Smart notice boards can outperform traditional systems in terms of efficiency, security, and adaptability by utilizing the concepts of Edge Computing.
Emergence in Space Technologies with Nanosatellites, Exploring the Applications of AI in Space Development, and Future Trends Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari
Aerospace Engineering Vol. 1 No. 1 (2024): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i1.2434

Abstract

Since, the first satellite was launched years ago, more than 6000 have been flown into orbit. Space science is the most fascinating field of study and research in the twenty-first century. The satellites are objects that travel in an elliptical orbit around the planet. Artificial satellites are being produced in large numbers. One of the most important aspects of modernizing science and communication technology, including space technology, mobile and radio communication, is satellite communication. The focus of the research work is on nanosatellites like cube sats, which are becoming increasingly well-known because to their wide-ranging applications in the field of radio transmission. This review paper explains some recent developments and the importance of nanosatellites in advancing space technology, highlight the recent development of space technologies in developing Asian nations and presents the future prospects of satellites development. Also, study is done in the role of AI in space technologies enhancement and space industries development its applications and future trends are discussed in this paper.
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.
Deep Learning-Based Classification of Remote Sensing Images: Challenges, Techniques, and Future Directions in Global Sustainability Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri
Aerospace Engineering Vol. 1 No. 3 (2024): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i3.2772

Abstract

With its high accuracy and efficiency, deep learning has greatly improved the classification of remote sensing (RS) photos. In order to categorize RS photos, this research analyzes the effectiveness of three cutting-edge deep learning models: ResNet-50, EfficientNetB2, and MobileNetV2. The models' accuracy on training and validation data were noted after they were trained and assessed on a dataset containing a variety of situations. Our findings illustrate each model's advantages and disadvantages and shed light on how well suited each is for various RS image categorization applications. The ResNet-50 model performed well in our study, achieving 74.41% training accuracy and 75.00% validation accuracy. With a training accuracy of 74.66% and a higher validation accuracy of 80.33%, the EfficientNetB2 model performed marginally better, demonstrating its strong generalization capabilities. On the other hand, the MobileNetV2 model had severe overfitting, as evidenced by its validation accuracy of 22.79%, which was much lower than its extraordinary high training accuracy of 99.21%. In order to achieve balanced performance between training and validation datasets in remote sensing image classification tasks, these results emphasize the significance of model architecture and regularization strategies. The proposed model can be utilized for sustainable remote sensing based applications in global water, environment and air health.
Water Sustainability Enhancement with UAV and AIoT: An Integrated Technology for Water Quality and Flood Hazard Monitoring using the Internet of Drones Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri; Shah, Krishna Bikram; Poudel, Yam Krishna
Aerospace Engineering Vol. 1 No. 4 (2024): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i4.2773

Abstract

Globally, there are challenges in minimizing the effects of water pollution and global warming everywhere in the world. In order to map the flood conditions, we want to apply a sensor network connected to a Esp32 and Tensorflow lite integrated system for drone-based water surface waste collection. Finally, a GSM sim 800L Module incorporated is used to send notifications to the user about the monitored conditions, such as trash level and other data. An ultrasonic sensor is utilized to detect the water level. The outcome shows that there is a high chance of tracking water levels and monitoring floods. By using this innovative technology, users can receive warnings and be warned remotely. The Inception-v3 model on clean and unclean water images obtained 97% accuracy on testing USING Inception-v3 and using the proposed circuit diagram a prototype is developed for possible deployment in such water resource region for possible operation and application is presented in the paper.
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.
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers Paneru, Biplov; Paneru, Bishwash; Shah, Krishna Bikram
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 3 (2024): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i3.4

Abstract

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. A thorough investigation of deep learning methods is carried out using the UCI dataset in order to create a reliable and effective model that can correctly identify a range of rice diseases. The suggested transfer learning models performs better at identifying subtle features and complex patterns in the dataset, which results in extremely accurate disease classification. Moreover, the study goes beyond the creation of models by incorporating an intuitive Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer Learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy.  On Renset101 only an accuracy of 54% could be achieved along with only 33% on efficientNetB0. MobileNetV2 trained model was successfully deployed on a tkinter GUI application to make predictions using image or real time video capturing.
Co-Authors Bikram Shah, Krishna Paneru, Bishwash Poudel, Yam Krishna Poudyal, Khem Poudyal, Khem Narayan Poudyal, Ramhari Sapkota, Sanjog Chhetri Shah, Krishna Bikram Shah, Krishna Bikram Bikram