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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Poudyal, Khem Narayan
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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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.
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

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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.
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, Ramhari Shah, Krishna Bikram Shah, Krishna Bikram Bikram