An Explorative Study for Laundry Mobile Application

Bamasoud, Doaa M.; Alqahtani, Asma M.; Aljdea, Eman A.; Alshomrani, Reem A.; Alshahrani, Maha S.; Alghamdi, Zohoor A.; Alghamdi, Ameerah M.; Almaawi, Shahd F.; Alshahrani, Asrar D.

doi:10.14569/ijacsa.2018.090417

Cited by 1 publication

(1 citation statement)

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“…In education, DL has revolutionized pedagogical strategies through personalized learning platforms, adaptive assessment systems, and intelligent tutoring systems, tailoring education to individual needs [9]. In healthcare, DL's image and speech recognition capabilities have accelerated disease diagnosis, while predictive models have enhanced patient outcomes through early intervention [10][11][12][13][14][15][16]. Financial institutions benefit from DL's risk assessment, fraud detection, and algorithmic trading algorithms, enhancing decisionmaking processes [17].…”

Section: Deep Learningmentioning

confidence: 99%

Adaptability of deep learning: datasets and strategies in fruit classification

Gulzar,

Ünal,

Ayoub

et al. 2024

BIO Web Conf.

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This review aims to uncover the multifaceted landscape of methodologies employed by researchers for accurate fruit classification. The exploration encompasses an array of techniques and models, each tailored to address the nuanced challenges presented by fruit classification tasks. From convolutional neural networks (CNNs) to recurrent neural networks (RNNs), and transfer learning to ensemble methods, the spectrum of approaches underscores the innovative strategies harnessed to achieve precision in fruit categorization. A significant facet of this review lies in the analysis of the various datasets utilized by researchers for fruit classification. Different datasets present unique challenges and opportunities, thereby shaping the design and effectiveness of the models. From widely recognized datasets like Fruits-360 to specialized collections, the review navigates through a plethora of data sources, elucidating how these datasets contribute to the diversity of research endeavors. This insight not only highlights the variety in fruit types and attributes but also emphasizes the adaptability of deep learning techniques to accommodate these variations. By amalgamating findings from diverse articles, this study offers an enriched understanding of the evolving trends and advancements within the domain of fruit classification using deep learning. The synthesis of methodologies and dataset variations serves to inform future research pursuits, aiding in the refinement of accurate and robust fruit classification methods. As the field progresses, this review stands as a valuable compass, guiding researchers toward impactful contributions that enhance the accuracy and applicability of fruit classification models.

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