This dataset contains 2,100 curated skin images collected from multiple publicly available sources for research on automated skin disease classification. The dataset consists of three classes: Ringworm (1,000 images), Scabies (700 images), and Normal Human Skin (400 images). Duplicate images were removed during curation, and the final dataset was organized into training (1,470 images) and testing (630 images) subsets.<br>This dataset was used in the study:"Bridging the Gap in Rural Dermatology: Robust and Lightweight Deep Learning for Ringworm and Scabies Detection."<br>The dataset is intended for non-commercial research and educational purposes related to medical image analysis, computer vision, and deep learning.
This dataset includes an exploratory collection of Bangladeshi paddy variety images withvariety-based labels. The dataset was developed for research and experimentation purposesin the fields of computer vision, machine learning, and agricultural artificial intelligence. Thedatase…
<b>Abstract:</b>Modern real estate platforms manage heterogeneous buyer populations ranging from first-time residential home buyers to institutional corporate entities and international high-net-worth investors. Traditional marketing strategies relying on broad demographic genera…
This dataset contains 8,888 real-world images of cooking oils collected to support Artificial Intelligence (AI) and computer vision research in cooking oil quality assessment. The dataset consists of two cooking oil categories: Soyabean Oil and Mustard Oil. Images were collected…
Providing a comprehensive synthetic genomic reference panel for clinical-grade diagnostic assay validation and research applications across 55 important cancer-associated genes, this project offers a full complement. The dataset consists of 100,000 pairs of normal and mutated seq…
Optical neural networks (ONNs) promise ultra-fast and energy-efficient computing but are hampered by the critical challenge of on-chip training. Here, we propose an on-chip training distillation-guided optical neural network (DGONN) and introduce a forward distilled algorithm to…
Precise and interpretable classification of autism-related behaviors is importantfor initial diagnosis, personalized intervention, and support arrangements. This studyproposes an interpretable machine learning (ML) model using Light GradientBoosting Machine (LightGBM) and Categor…