Sensors (Basel) January 2022 PMID 35161815

LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the -Barriers for Intrusion Detection Using Wireless Sensor Network.

Abhilash Singh, J Amutha, Jaiprakash Nagar, Sandeep Sharma, Cheng-Chi Lee

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Mentions

Peak: 3 in Jan 2022

across 2 sources since Jan 2022

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Jan 2022 – Aug 2023

Where it's discussed

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YouTube2 videos

AI synopsis

What people are saying

The discussions primarily highlight the availability of the datasets and publication acceptance, with posts like 'LT-FS-ID datasets is a synthetically generated data' and 'Paper accepted! Enjoy reading!' demonstrating mild enthusiasm. The tone is neutral to mildly positive, reflecting moderate engagement and interest without delving into the technical or practical impacts in detail.

AgreementNeither agree nor disagree

There is a neutral stance with some support for the research, but no strong consensus or disagreement is evident.

InterestModerate interest

Discussions show moderate interest, especially around the datasets and acceptance of the publication, but lack deep engagement with technical details.

EngagementModerate engagement

Participants mention the acceptance of the paper and datasets, indicating some level of engagement, but there is little critical analysis or in-depth discussion.

ImpactModerate impact

The focus on datasets and acknowledgment of the publication suggests some recognition of its relevance, but limited discussion of broader implications.

Abstract in brief

This study developed a machine learning model, LTFSSVR, that use features like sensor range and number of sensors, extracted via simulations, to accurately predict the number of barriers needed for quick intrusion detection in wireless sensor networks.
The researchers applied data processing techniques such as log transformation and feature scaling, and evaluated the importance and sensitivity of each feature to ensure the model's effectiveness.
The proposed LTFSSVR model outperformed other benchmark algorithms like GPR, GRNN, ANN, and RF, achieving high accuracy with a correlation coefficient of 0.98 and low error metrics, making it a promising tool for intrusion detection.]

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All mentions

Machine Learning for Predicting Barriers in Wireless Sensor Networks

This video discusses a machine learning model, LTFSSVR, that uses sensor data to accurately predict the number of barriers for quick intrusion detection in wireless sensor networks. It highlights the data processing…

Abhilash Singh · Apr 25, 2022 680 views

Introduction to Artificial Neural Networks and Convolutional Neural Networks in Machine Learning

This lecture covers the fundamentals of ANN, its differences from biological neurons, how it operates, and the mathematics behind it. It also provides a brief introduction to CNNs, suitable for beginners interested in…

Abhilash Singh · Apr 15, 2022 485 views

Sensors MDPI

@Sensors_MDPI · X

Aug 25, 2023 · 6,983 followers View post →

Dr. Jaiprakash Nagar

@jpnagar91 · X

RT @MrIfAndOnlyIf: LT-FS-ID datasets is a synthetically generated data available in the public domain (3/3). Data link:https://t.co/5Cgo6MO…

Aug 12, 2022 · 1 share · 3 followers View post →

Abhilash Singh

@abhilash_iiserb · X

LT-FS-ID datasets is a synthetically generated data available in the public domain (3/3). Data link:https://t.co/5Cgo6MOD53 Publication link: https://t.co/HZY2GSmcQf

Jul 23, 2022 · 2 likes · 1 share · 379 followers View post →

scite Reference Check

@sciterefcheck · X

We've detected a paper from Sensors that cites a retracted paper post-retraction. Citing paper: https://t.co/tAom8ad24g Retracted paper: https://t.co/oaVchuDD02

Mar 5, 2022 · 533 followers View post →

Priyanjan Datta

@priyanjan_datta · X

RT @MrIfAndOnlyIf: Paper accepted! Enjoy reading! Data: https://t.co/XeLrbvruRX Paper: https://t.co/VSAijtfaPP #ArtificialInteligence #A…

Jan 30, 2022 · 2 shares · 202 followers View post →

Amit Kumar

@amitkum80458614 · X