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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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A Deep Learning Application Built with Tkinter for Waste Recycling and Recommending Solutions Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Shah, Krishna Bikram; Poudyal, Khem Narayan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 1 (2024): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/j3hrme70

Abstract

This paper presents a novel PyTorch model integrated with a Tkinter-based Recycling Recommendation Application to address the pressing issue of waste management. Our waste prediction and classification model achieve high precision by leveraging advanced machine learning techniques and a large dataset. We improve classification accuracy and speed using pre-trained models and transfer learning, which is critical for effective waste management. The accompanying Tkinter application improves recycling recommendations by allowing users to input information through an intuitive interface. Our PyTorch model has exceptional accuracy, scoring 99% on the training set and approximately 96% on validation, which is supported by robust stratified cross-validation. This fusion of cutting-edge machine learning and user-centered design represents a significant step toward more efficient waste management and environmentally friendly waste disposal practices. The system's potential for widespread adoption is highlighted by its accuracy in categorizing various waste items and providing tailored solutions, resulting in a positive environmental impact.
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.
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
Journal of Geosciences and Environmental Studies Vol. 2 No. 1 (2025): March
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/ijgaes.v2i1.3343

Abstract

Globally, there are challenges in minimizing the effects of water pollution and global warming everywhere. We want to apply a sensor network connected to an Esp32 and Tensorflow lite integrated system to map the flood conditions for drone-based water surface waste collection. Finally, a GSM sim 800L Module is incorporated to notify 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 a high chance of tracking water levels and monitoring floods. This innovative technology allows users to 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.
Automated Environmental Stewardship: A Ribbon-Cutting Robot with Machine Vision for Sustainable Operation Paneru, Biplov; Paneru, Bishwash; Poudyal, Ramhari; Shah, Krishna Bikram; Poudyal, Khem Narayan; Poudel, Yam Krishna
Jurnal Teknokes Vol. 17 No. 1 (2024): March
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This paper provides a novel way for automating ribbon-cutting rituals that use a specifically constructed robot with superior computer vision capabilities. The system achieves an outstanding 92% accuracy rate when assessing picture data by using a servo motor for ribbon identification, a motor driver for robot movement control, and nichrome wire for precision cutting. The robot's ability to recognize and interact with the ribbon is greatly improved when it uses a Keras and TensorFlowbased red ribbon identification model which obtained accuracy of about 93% on testing set before deployment in system. Implemented within a Raspberry Pi robot, the method exhibits amazing success in automating ceremonial activities, removing the need for human intervention. This multidisciplinary method assures the precision and speed of ribbon-cutting events, representing a significant step forward in the merging of tradition and technology via the seamless integration of robots and computer vision.
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