Inspecting and Finding Faults in Railway Tracks Using Wireless Sensor Networks

Ahamad, Firoj; Lobiyal, D. K.; Degadwala, Sheshang; Vyas, Dhairya

doi:10.1109/icict57646.2023.10134164

Cited by 14 publications

(2 citation statements)

References 4 publications

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“…Weather satellites are spacecraft that monitor weather and environmental conditions. Equipped with radiometers, these satellites scan the Earth to create Ahamad et al [12] inspected railway tracks using wireless sensor networks. Their approach aims to improve railway safety by detecting faults more efficiently, leveraging the capabilities of IoT and sensor technology.…”

Section: Introductionmentioning

confidence: 99%

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

Amit Solanki,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Weather is influenced by several factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth's rotation. Forecasting weather with high geographic precision is both challenging and computationally demanding. This study explores a nowcasting method for meteorological radar images. Building on the concept of an unsupervised representation problem in deep learning, next-frame prediction is an emerging area in computer vision where future images are predicted based on previous ones. This has numerous applications, including in robotics and autonomous driving. The paper reviews the latest next-frame prediction networks, categorizing them into two main approaches: machine learning and deep learning. It compares these strategies by discussing their respective advantages and disadvantages. Lastly, the paper outlines the most promising directions for future research.

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Section: Introductionmentioning

confidence: 99%

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

Amit Solanki,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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“…One of the reasons for failure, after which the object goes into an inoperable state, may be the presence of errors due to different software. To solve such problems, various fault detection methods are used [39]- [42].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Mardenov,

Adamova,

Zhukabayeva

et al. 2023

Journal of Robotics and Control

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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.

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A Review on Identifying Lung Disease Sounds using different ML and DL Models

Jigisha Trivedi,

Dr. Sheshang Degadwala

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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This comprehensive review explores the efficacy of various machine learning (ML) and deep learning (DL) models in identifying lung disease sounds, addressing the complex diagnostic challenges posed by the diverse acoustic patterns associated with lung diseases. ML algorithms like Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN) offer robust classification frameworks, while DL architectures such as Convolutional Neural Networks (CNN) excel in extracting intricate audio patterns. By analyzing the performance metrics of these models, including accuracy, sensitivity, specificity, and area under the curve (AUC), the review aims to assess their comparative strengths and limitations in accurately identifying lung disease sounds. The insights gained from this review can significantly contribute to the development of more precise and effective diagnostic tools and interventions tailored to lung diseases, thus improving patient outcomes and healthcare efficiency in the realm of respiratory disorders.

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Inspecting and Finding Faults in Railway Tracks Using Wireless Sensor Networks

Cited by 14 publications

References 4 publications

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

A Review on Identifying Lung Disease Sounds using different ML and DL Models

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