Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
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This Author published in this journals
All Journal International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR)
Pragati Mahale
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Social Sciences

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An Exploring IOT Solution for Enhanced Smart Traffic Management System Akash Maji; Pragati Mahale
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 2 (2023): October 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i2.481

Abstract

This paper delves into the use of the Internet of Things (IoT) to enhance smart traffic management systems. It acts as a middle layer built upon IoT technology, expanding the concept of a smart city by improving traffic light control, parking management, emergency assistance, anti-theft security, and more. IoT facilitates seamless communication between web-connected devices and various components like traffic sensors, services, and actuators, creating a robust network. Consequently, IoT's application in smart traffic management extends beyond just reducing traffic congestion and optimizing traffic flow; it also encompasses continuous monitoring and ensuring the safety of elderly individuals. By collecting data from multiple traffic sources and utilizing IoT, we can analyze traffic patterns, regulate traffic operations, and store valuable insights for future reference. While there are certain limitations to implementing this technology, such as challenges related to advanced machine learning and data-driven techniques, this survey provides a valuable overview of how IoT can be applied to enhance smart traffic management systems, drawing from existing research in the field.
Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms Pragati Mahale; Sejal Khopade
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.664

Abstract

This study discusses fully distributed fault detection via a wireless sensor network. Initially, we suggested using the Convex hull approach to determine a range of extreme points including nearby nodes. As the number of nodes rises, the message's duration is constrained. Secondly, in order to enhance convergence performance and identify node errors, we suggested using a convolution neural network (CNN) and a Naïve Bayes classifier. Lastly, we use real-world datasets to examine CNN, convex hull, and Naïve bayes algorithms to find and classify the defects. Based on performance measures, the results of simulations and experiments demonstrate that the CNN algorithm has better-identified defects than the convex hull technique while maintaining feasibility and economy.
Co-Authors Akash Maji Sejal Khopade