Exploring the trend of recognizing apple leaf disease detection through machine learning: a comprehensive analysis using bibliometric techniques

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(2)

Published: Jan. 30, 2024

Abstract This study’s foremost objectives were to scrutinize how unexpected weather affects agricultural output and assess well AI-based machine learning deep leaning algorithms work for spotting apple leaf diseases. The researchers carried out a bibliometric study obtain understanding of the current research trends, citation patterns, ownership partnership arrangements, publishing other parameters related early identification illnesses. Comprehensive interdisciplinary scientific maps are limited because syndrome recognition is not restricted any solitary arena research, despite fact that there have been many studies on By employing scientometric technique 109 publications from Scopus database published between 2011 2022, this attempted condition area combine knowledge frameworks. To find important journals, authors, nations, articles, topics, used automated processes VOSviewer Biblioshiny software. Patterns trends discovered using counts, social network analysis, co-citation studies.

Language: Английский

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Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(19), P. 57317 - 57345

Published: Dec. 19, 2023

Language: Английский

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Fake News Detection Datasets: A Review and Research Opportunities

International Journal of Computing and Digital Systems, Journal Year: 2024, Volume and Issue: 15(1), P. 39 - 55

Published: April 23, 2024

The impact of fake news is far-reaching, affecting journalism, the economy, and democracy.In response, there has been a surge in research focused on detecting combating news, resulting development datasets, techniques, fact-verification methods.One crucial aspect this effort creation diverse representative datasets for training evaluating machine learning models detection.This review paper examines available relevant to with particular emphasis those Indian context, where few resources exist.By identifying opportunities highlighting existing corpora, aims assist researchers improving their detection studies contributing more comprehensive topic.To best our knowledge, no survey specifically accessible corpora making valuable resource field.

Language: Английский

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Deep Learning Techniques for Sentiment Analysis: A Comparative Study

Published: Sept. 14, 2023

In today's digital era, exposure to the internet has changed economic space; now people give their views regarding products and services provided them online so that others can decide based on these reviews, service providers also take benefits from improve product or service. This ultimately helpful in economy growth. this direction, sentiment analysis provides a means gain insight into people's opinions. study examines use of three distinct deep learning models: LSTM, GRU, ELECTRA apply Spotify music app. Comparing accuracy each model, it is discovered LSTM GRU perform better than ELECTRA, with values 77.21 % 77.32%, respectively, as opposed ELECTRA's value 72.03 %. These findings imply particular dataset attributes utilized for training models may affect efficacy. conclusion, work sheds light effectiveness emphasizes significance careful model feature selection obtaining correct findings.

Language: Английский

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Emotion Infused Rumour Detection Model using LSTM

International Journal of Computing and Digital Systems, Journal Year: 2024, Volume and Issue: 15(1), P. 1175 - 1190

Published: May 26, 2024

Twitter now 'X', is a highly favored platform for sharing brief messages, known as tweets, read and shared among users at rapid pace.Hence, the dissemination of information occurs quickly within community in network.Twitter's unregulated environment provides suitable individuals to share circulate unverified information; this propagation rumours can greatly affect society.The detection rumour accurately on from tweets crucial task.In study, we suggested an Emotion Infused Rumour Detection model based LSTM that employs tweet text twenty-one distinct linguistic, user, post, network features classify between non-rumour tweets.comparison proposed i.e.Emotion using was done with two different deep learning models check achieved outcomes.The findings evaluations exhibit supremacy learning-based identifying rumours.The model, which uses earned F1-score 0.91 outperforming state-of-the-art findings.The approach lessen influence society, prevent loss life money, increase users' confidence social media platforms.The has potential promptly recognize containing rumours, aiding prevention spread misinformation.

Language: Английский

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Analyzing Breast Cancer Detection: A Scopus-driven Study on the Role of Machine Learning and Deep Learning

Published: Sept. 14, 2023

The common and occasionally fatal condition known as breast cancer affects millions of people worldwide. Early detection precise diagnosis are essential for enhancing patient health, their importance cannot be overstated. Machine learning (ML) deep (DL) techniques obligate recently made substantial progress in the identification cancer. This paper gives a thorough review how ML DL used to detect evaluation yearly publication trends, citation analysis, prominent sources, keyword distributions is done using data gathered from Scopus 2008 through 2023 order give future academics insight into this field's research tendencies. technique emphasizes dynamic character by showing that more recent papers have shorter epochs influence.

Language: Английский

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Unveiling Fake News: A Machine and Deep Learning Approach

Published: Dec. 15, 2023

Language: Английский

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