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8 articles for “personalized learning sequences”
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Recommending Personalized Learning Sequences for Special Needs Learner using Ant Colony Optimization
Abstract: Learning through technology is a knowledge management concept where the learning resources have to be presented in a clear and comprehensive manner to the learners. This study presents a new approach for recommending suitable learning paths for special needs learners by applying artificial intelligence technique, ‘Ant colony optimization algorithm’. The study is carried out for Attention Deficit and Hyperactive Disorder (ADHD) and children facing Learning Disability (LD). We propose a …
Published in Journal of Open Source Developments · Vol. 6, Issue 1, 2019 · pp. 32–39 Read article
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Bridging the Gap and Unlocking Health Literacy: A Guide to Medical Report Clarity
Abstract: This research highlights the significant challenges faced by the general population in interpreting laboratory reports, medical charts, and other health-related documents. Studies show that approximately 9 out of 10 individuals struggle to understand such medical information, primarily due to low health literacy levels. This lack of understanding has become a hidden epidemic, affecting the way people engage with and respond to their own healthcare. While medical tests are essential for …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 31–36 Read article
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AI-Driven Pharmacogenomics and Precision Medicine: Future of Personalized Therapy
Abstract: Pharmacogenomics and artificial intelligence (AI) are emerging as important drivers of precision medicine, enabling healthcare systems to adopt individualized therapeutic approaches. Pharmacogenomics examines how genetic variations influence drug response, efficacy, metabolism, and toxicity, while AI provides advanced computational tools for analyzing complex genomic and clinical data. This review highlights the integration of AI-driven pharmacogenomics in personalized therapy and its potential to improve treatment outcomes. Machine learning, deep learning, natural language …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 1–12 Read article
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A Study on Flipped Learning Models in Engineering Pedagogy
Abstract: The aim of this article is to provide an overview of the existing knowledge and application of the flipped learning method within the realm of engineering education. This paper discusses Pedagogical logistics of Flipped learning models in engineering while examining various teaching models used in collegesand universities all over India, especially NITS and IIITs. It has been the mission of the author to focus on the implementation of integration various …
Published in Journal of Materials & Metallurgical Engineering · Vol. 13, Issue 2, 2023 · pp. 31–35 Read article
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Comprehensive Review of Adenoid Cystic Carcinoma: Pathogenesis, Diagnosis, and Emerging Therapeutic Approaches
Abstract: Adenoid cystic carcinoma (ACC)is an infrequent neoplasm, highly malignant, that develops mainly in the salivary glands with the potential to exist in any secretory glandular sites, including the lacrimal glands, breast, and respiratory tract. ACC usually has a benign initial course, but conversely, it is notoriously aggressive in behavior with high incidence of perineural invasion, local recurrence, and distant metastasis, mostly to the lungs. The tumor’s molecular features are characterized …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–17 Read article
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Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis
Abstract: The proliferation of artificial intelligence (AI) and machine learning (ML) technologies has transformed the digital education landscape by enabling adaptive e-learning systems capable of personalizing content and optimizing learning paths. This study provides a systematic analysis of adaptive e-learning algorithms within the framework of heutagogy, an educational paradigm that emphasizes learner autonomy, self-direction, and capability development. The convergence of adaptive technologies with heutagogical principles offers new avenues for creating more …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 33–38 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Complications with Malware Identification in IoT and an Overview of Artificial Immune Approaches
Abstract: An immunity is facilitated by lymphocyte T&B-cells that possess a wide range of T&B-cell receptors, respectively. These cells can identify and react to pathogens and diseased cells by presenting peptide antigens by means of significant histocompatibility complexes (MHCs). The amount of data on the repertoire of adaptive immune receptors has increased dramatically in recent years because to advancements in deep sequencing. Furthermore, the presentation of peptides with MHC has been …
Published in Research and Reviews : A Journal of Immunology · Vol. 14, Issue 1, 2024 · pp. 54–62 Read article