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All Journal Aerospace Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Geosciences and Environmental Studies Jurnal Teknokes
Shah, Krishna Bikram
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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 Materials Science & Nanotechnology Social Sciences

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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.
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.
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.
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.
Co-Authors Paneru, Biplov Paneru, Bishwash Poudel, Yam Krishna Poudyal, Khem Narayan Poudyal, Ramhari Sapkota, Sanjog Chhetri