Some Effective Techniques for Recognizing a Person Across Aging

Nimbarte, Mrudula; Pal, Madhuri; Sonekar, Shrikant V.; Ulhe, Pranjali

doi:10.1007/978-981-15-5258-8_8

Cited by 1 publication

(1 citation statement)

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“…Thanks to advances in deep learning, there is no longer a requirement to separately apply feature extraction for categorization. It is possible to complete both procedures in a single unit [21]. A subset of machine learning (ML) and artificial intelligence (AI) called "deep learning" seeks to mimic the functions of the human brain in areas including speech recognition, object detection, and decision-making.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

Haider,

Khan,

Nazir

et al. 2024

CSSE

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Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for the validation process, such as apple, grape, and Citrus. In the first step, a noisy dataset is prepared by employing the original images to learn the designed framework better. The EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy data. After that, features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm (PFA). This algorithm aims to select the best features based on the fitness score and ignore redundant information. The selected features are finally classified using machine learning classifiers such as Medium Neural Network, Wide Neural Network, and Support Vector Machine. The experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%, 99.7%, 99.7%, and 93.4% for apple, grape, Citrus fruit, and citrus plant leaves, respectively. A detailed analysis is conducted and also compared with the recent techniques, and the proposed framework shows improved accuracy.

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Section: Introductionmentioning

confidence: 99%