Machine Learning and Deep Learning: Recent Overview in Medical Care

Chalabi, Nour Elhouda; Attia, Abdelouahab; Akrouf, Samir

doi:10.1007/978-3-030-49932-7_22

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Increased yield, reduced costs, and higher fruit quality can all be achieved by early disease detection [12]. Machine learning and deep learning techniques are important not only in agriculture but also in a variety of other fields, including text processing [13], deep fake diagnosis [14], medical diagnosis [15] [16], and many more. Regulatory organizations, including the French government and the European Commission, have devised incentive schemes aimed at lowering chemical use as a crucial option to prevent environmental damage [17].…”

Section: Introductionmentioning

confidence: 99%

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

Bonkra

Bhatt

Kaur

et al. 2023

Preprint

View full text Add to dashboard Cite

The study’s goals were to examine the effects of unforeseen weather circumstances on agricultural productivity and to test artificial intelligence-based strategies for feature extraction and segmentation for plant leaf disease identification. A bibliometric analysis was used in the study to look into current trends, publications, citation structures, patterns of ownership and collaboration, production patterns, and other aspects connected to the early detection of apple diseases. While there have been many studies on recognising apple illnesses, there have been few attempts to develop a thorough multidisciplinary science map because disease detection is not exclusive to any one field of study. The study’s objectives were to combine knowledge frameworks and identify the current state of the research field. A scientometric examination of 200 papers from the Scopus database between 2017 and 2022 was done by the researchers in order to accomplish this goal. VOSviewer was utilised for the research, and its automated workflow was used to pick significant journals, authors, nations, articles, and themes. To find patterns and trends, social network studies, citation and co-citation checks, and citation counts were all carried out. The study revealed changes in the cognitive and social structure of the area and concluded that the interdisciplinary field of sickness detection employing segmentation has expanded over time. The study also revealed areas that need additional investigation and clarified the intellectual underpinnings of the topic.

show abstract

Section: Introductionmentioning

confidence: 99%

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

Bonkra

Bhatt

Kaur

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

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

Bonkra,

Pathak,

Kaur

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

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

show abstract

Recent Challenges in Medical Science using Machine Learning Techniques: A Review

Shah,

Padhiyar,

Parmar

et al. 2023

2023 7th International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

View full text Add to dashboard Cite

Machine Learning and Deep Learning: Recent Overview in Medical Care

Cited by 3 publications

References 20 publications

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

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

Recent Challenges in Medical Science using Machine Learning Techniques: A Review

Contact Info

Product

Resources

About