Modern methods of extracting relevant features from images and texts are crucial to the recovery of fashionable garments. In this research, we compare and contrast a number of different approaches to extracting featur...
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As technology continues to advance at an unprecedented pace, the interaction between humans and computers has become an integral part of our daily lives. This study provides a comprehensive review of the evolving land...
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Non-contact based fingerprint recognition systems are emerging at rapid pace due the fast growing need in the era of contagious disease transmission i.e. COVID-19. This growth also necessitates an effective security s...
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Detecting malicious URLs by employing the Random Forest algorithm alongside URL parsing techniques. The dataset comprises URLs categorized into phishing, benign, and defacement classes. By parsing these URLs, various ...
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The increasing prevalence of skin cancer underscores the importance of accurate and efficient diagnostic tools. Deep Convolutional Neural Networks (DCNN) have demonstrated remarkable success in computer vision tasks, ...
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Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often s...
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Federated learning enables decentralized learning to run on multiple clients by training locally on each client's data and sharing new instances only on a central server. However, if privacy is not protected, sens...
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Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing ab...
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Diabetic foot ulcers (DFUs) present a significant health challenge, demanding innovative solutions for timely identification and evaluation. This study introduces "SoleScan,"a novel approach to revolutionize...
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When comparing anomaly detection methods in high-dimensional data environments, PCA and One Class SVM are often associated with high computational complexity and low computational efficiency. This article selects the ...
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