Comparative Analysis of High-Performance Computing Systems and Machine Learning in Enhancing Cyber Infrastructure: A Multiple Regression Analysis Approach

Torres-Cruz, Fred; Sahu, Ajay; Huayhua, Ruben Ticona; Julio, Martin; Bellido, Merma; Limachi, Isaac Ortega; Huanca, Julio Cesar Laura

doi:10.1109/iciptm54933.2022.9753839

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…By using this model, it may be possible to improve road safety and reduce the number of injuries and fatalities caused by distracted driving. We identified and made a machine learning model that if we input an image into it we will get the prediction as the correct prediction and what driver is actually doing and we can set an alarm or create a product in which the driver's distracted activity can be identified [18].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Convolutional Neural Network Approach for Detecting the Distracted Drivers

Sahu,

Gupta,

Singh

et al. 2023

IJSRCSE

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In recent years, there has been an increase in traffic accidents caused by distracted driving. According to a 2022 report by the Federal Ministry of Road Transport and Highways, in India have 18 deaths in one hour in two road accidents. Therefore, measures to reduce traffic fatalities are essential. Tragically, the number of lives lost on the roads this year has reached a peak not seen since 2014, with 155,622 people losing their lives in preventable accidents. This is a stark reminder of the importance of safe driving practices and the need to prioritize road safety in our communities. The main cause of these accidents is driver error. This document proposes solutions for detecting driver distraction and avoiding potential accidents. In this document, we present the use of various convolution neural network (CNN) models for classifying distracted drivers. These models include small-scale CNN, VGG16, VGG19, and the Kaggle State Farm Distracted Driver Detection Challenge model. We are using the Keras library with Tensor Flow as our deep learning platform. Our highest performing model achieved a categorical cross-entropy loss of 0.899 on the validation set. These models may serve as a starting point for further research on distracted driver detection.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Convolutional Neural Network Approach for Detecting the Distracted Drivers

Sahu,

Gupta,

Singh

et al. 2023

IJSRCSE

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show abstract

“…Devices can reside on one device, so long term space immediately. A common security promise size is data Encoding, tag frequency change, tag abort Rules, use of blocker tags, algorithms and data flow control policy, uniqueness verification There is a mechanism, etc., and there is a security problem Causes serious problems and should be addressed at various stages [13].…”

Section: Security and Privacy Many Physical Devicesmentioning

confidence: 99%

Agriculture Solutions Using Artificial Intelligence

Sahu¹

2023

IJRASET

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Agriculture plays a major role in the economic sector. Automation in agriculture is a major concern and a growing topic around the world. The population is growing significantly, and with it the need for food and jobs. The standard methods used by farmers were not sufficient to meet these needs. Therefore, a new method of machine control was introduced. These new roads have created a need for food and provided employment opportunities for billions of people. Informatics in agriculture has revolutionized agriculture

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Chlorophyll Prediction System with Machine Learning Algorithms in Lake Titicaca (Peruvian Sector)

Paz

Atencio²

2023

Communications in Computer and Information Science

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Comparative Analysis of High-Performance Computing Systems and Machine Learning in Enhancing Cyber Infrastructure: A Multiple Regression Analysis Approach

Cited by 7 publications

References 12 publications

A Convolutional Neural Network Approach for Detecting the Distracted Drivers

A Convolutional Neural Network Approach for Detecting the Distracted Drivers

Agriculture Solutions Using Artificial Intelligence

Chlorophyll Prediction System with Machine Learning Algorithms in Lake Titicaca (Peruvian Sector)

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