Skin diseases represent a significant global health concern, requiring accurate and timely diagnosis for effective treatment. This paper presents a deep learning-based approach for classification of skin diseases using medical image analysis. The proposed system uses Convolutional Neural Networks (CNNs) and related deep learning techniques to automatically learn discriminative visual features from skin-lesion images and classify disease categories. Image preprocessing and data augmentation are used to improve consistency and robustness. The workflow covers image acquisition, preprocessing, augmentation, model training, validation, testing, and performance analysis. The system is intended to assist healthcare professionals by providing rapid image-based decision support while reducing dependence on purely manual screening. Standard measures such as accuracy, precision, recall, F1-score, and confusion-matrix analysis are considered for evaluation. The approach demonstrates the potential of artificial intelligence in dermatology while recognizing that dataset quality, external validation, interpretability, and clinical supervision remain essential for responsible deployment.
S. Behera, Sri Bapuji Bismaya Kumar Giri, Subham Singh Mundari et al.· International Research Journ...· 0 citations
Retrieval-Augmented Generation (RAG) has emerged as a transformative approach for enhancing the capabilities of conversational artificial intelligence by integrating large language models with external knowledge retrieval mechanisms. In the educational domain, RAG-powered chatbots address limitations of traditional AI systems, such as factual inaccuracies, outdated knowledge, and hallucinated responses, by retrieving relevant information from trusted academic resources before generating answers. This chapter examines the principles, architecture, and applications of RAG chatbots in teaching, learning, and academic support. It discusses how these systems facilitate personalized learning, intelligent tutoring, automated question answering, curriculum assistance, and institutional support while improving response accuracy, transparency, and contextual relevance. The chapter also explores the technological components of RAG systems, including embedding models, vector databases, retrieval strategies, and large language models, alongside practical implementation considerations in e Retrieval-Augmented Generation (RAG), educational chatbots, artificial intelligence in education, large language models, intelligent tutoring systems, personalized learning, vector databases, learning analytics, conversational AI, educational technology.
Kamalakant Pradhan, Swarnaprabha Pradhan, Shubhranshu Mallick et al.· International Research Journ...· 0 citations
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