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All Journal Sainstech Nusantara International Journal of Technology and Modeling
Mehta, Rahul
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Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Web-Based Level Crossing Control System Monitoring Device (Software) Mehta, Rahul; Wasudarae Khond, Viveko
SAINSTECH NUSANTARA Vol. 1 No. 2 (2024): May 2024
Publisher : Nusantara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71225/jstn.v1i2.1

Abstract

Failure function of level crossing control starts from not fulfillied the technical specifications in the level crossing operation. So it is necessary to have a monitoring system for the control of a level crossing door. This research was conducted at the level crossing of the Indonesian Railway Academy. The device circuit is arranged by attaching the sensor to the battery input, and inputing the flashing unit. Installation of sensors on the battery input is intended so that when the supply of voltage and current entering the battery does not meet specifications, it will be known that the charger battery is in trouble. Installation of sensors at the flashing input unit is intended so that when the supply of voltage and current entering the flashing unit is not according to specifications, it will be known that the supplier has a problem. The sensor will be connected to Arduino Mega which will then be sent to the database. From the data database the voltage and current will be processed in the web and displayed. If the voltage and current data from the database do not match specifications, a notification will appear on the web. Afterthat, it can be repaired by a technician.
Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction Patel, Priya; Sharma, Arjun; Mehta, Rahul; Iyer, Ananya
International Journal of Technology and Modeling Vol. 2 No. 2 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i2.109

Abstract

Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
Co-Authors Iyer, Ananya Patel, Priya Sharma, Arjun Wasudarae Khond, Viveko