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89 articles for “Clinical features”
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson's Disease
Abstract: This paper presents a web-based disease prediction system that integrates machine learning and deep learning techniques to assist in the early detection of Parkinson’s Disease, Diabetes, and Brain Tumors. By utilizing clinical data and MRI images, the platform provides rapid and interpretable predictions to support proactive health management. Logistic Regression models are applied to classify structured datasets for predicting Parkinson’s disease and Diabetes, making use of their effectiveness in binary …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 44–54 Read article
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Breast Cancer Detection Using Machine Learning: A Comparative Analysis of Supervised Learning Algorithms
Abstract: Globally, breast cancer remains a predominant cause of mortality among women, highlighting the urgent need for timely and precise diagnostic approaches. This research explores the application of machine learning algorithms—including Logistic Regression, SVM, Naïve Bayes, KNN, and Random Forest—on the Wisconsin Breast Cancer Dataset for effective tumor classification. Key pre-processing steps such as missing value handling, feature scaling, and dimensionality reduction were employed to improve model performance. The study evaluated …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 46–52 Read article
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Using Artificial Intelligence for The Design of Polymeric Drug Delivery Systems
Abstract: AI is cost effective and time efficient process. Artificial intelligence is a method which uses high speed calculation, the improvement regarding algorithm along with collection about biological and chemical features in pharmaceutical exploration and development .Present day because continuous progress towards machine learning (ML),Artificial intelligence an effective statistics analysing technology might have been broadly utilized in drug design .In pharmaceutical exploration and research employ of AI in target identification, compound …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1018–1027 Read article
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Enhancing the User Experience of Asthma Inhalers: A Redesign Approach
Abstract: Asthma is a widespread chronic respiratory condition impacting millions globally, presenting significant challenges in its management and treatment. While conventional inhalers effectively administer medication, they often encounter usability issues, hindering patient adherence and treatment outcomes. This abstract delineates the development and potential impact of a redesigned asthma inhaler aimed at addressing these challenges. Incorporating human-centered design principles, the revamped inhaler prioritizes usability, portability, and effectiveness to enhance the overall management …
Published in International Journal of Solid State Innovations & Research · Vol. 1, Issue 2, 2023 · pp. 32–36 Read article
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Depression Detection Using Machine Learning: A Comprehensive Review
Abstract: Depression remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 27–32 Read article
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Integrative Perspectives on Lipoma: Traditional Therapeutics from Siddha, Ayurveda, and Unani with Biomedical Correlates
Abstract: Background: Lipoma is the most common benign soft tissue tumor, with an incidence of approximately 2 per 1,000 individuals annually. Modern biomedicine attributes its pathogenesis to genetic abnormalities like HMGA2 rearrangements and dysregulated adipogenesis via PPARγ pathways. Effective pharmacological therapies are lacking. Traditional Indian systems – Siddha, Ayurveda, and Unani – offer unique perspectives and non‑surgical approaches yet remain underexplored in integrative research. Objective: To critically evaluate the descriptions, pathophysiological …
Published in Research & Reviews : A Journal of Unani, Siddha and Homeopathy · Vol. 13, Issue 1, 2026 · pp. 19–33 Read article
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Necrotizing Enterocolitis (NEC): A Devastating Neonatal Gastrointestinal Disorder
Abstract: Necrotizing enterocolitis (NEC) represents a formidable challenge in neonatal medicine, particularly afflicting premature infants with its characteristic features of severe intestinal inflammation and necrosis. Despite advancements in neonatal care, NEC continues to exact a heavy toll, standing as a prominent cause of morbidity and mortality in this vulnerable population. Its multifaceted etiology, encompassing prematurity, enteral feeding practices, alterations in microbial colonization, and episodes of intestinal ischemia, underscores the complexity of …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 78–83 Read article
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Advances in Multiclass Oral Cancer Detection Using Spectroscopic and AI Techniques
Abstract: Oral cancer, primarily OSCC, is still a major health issue worldwide, especially in low-HDI countries. Early diagnosis is essential since survival rates for early detection are much higher than for late-stage detection. However, traditional methods like visual inspection and biopsy are time-consuming, invasive, and rely on the clinician's skill, which is a limitation in accessibility and efficiency. Oral cancer detection has just been revolutionized by recent advances in spectroscopic techniques, …
Published in Research and Reviews: A Journal of Dentistry · Vol. 16, Issue 3, 2025 · pp. 39–48 Read article
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Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics
Abstract: Bone fracture detection is a critical aspect of medical diagnostics, traditionally relying on manual interpretation of radiographic images by experienced radiologists. This discipline has undergone a revolution with the introduction of machine learning (ML), which can improve accuracy, shorten diagnosis times, and lessen human error. This study investigates the use of different machine learning methods to enhance and automate the identification of bone fractures in radiography pictures. We utilized a …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 2, 2024 · pp. 17–22 Read article
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Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants
Abstract: Polymer-based medical devices are widely used in clinical practice, where long-term material degradation can compromise performance and patient safety. Traditional polymer degradation studies predominantly rely on laboratory-based experiments, which often fail to capture real-world operational and usage conditions. In this study, a multimodal, data-driven framework is proposed for the quantitative assessment of degradation patterns in polymer-based medical devices using publicly available clinical failure data. Structured operational parameters, including cumulative usage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1510–1518 Read article
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A Comparative Study of Deep Learning Methods for Depression Detection in Social Media Data
Abstract: With the rise of social media platforms like Twitter, Reddit, and Facebook, individuals increasingly share personal information about their moods, behaviors, and mental states. This trend provides a unique opportunity to leverage large-scale textual data for understanding and monitoring mental health conditions, particularly depression, a prevalent and challenging mental health issue. Traditional depression assessments are often confined to clinical environments and lack the capacity for real-time monitoring. In contrast, social …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 55–65 Read article
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A Comparative Study of Transfer Learning-Based Deep Learning Models for Breast Cancer Detection
Abstract: Breast cancer is a major concern in the world today, and early and accurate diagnosis is most crucial in the case of breast cancer, as it is among the disorders where the total cost of loss of life is high. Traditional screening processes are subjective and vulnerable to inter-observer reliability issues and diagnostic errors, being primarily based on manual interpretation of medical images. To address these limitations, Deep Learning (DL) …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 · pp. 24–34 Read article
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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article
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Algorithm for the prediction of cardiovascular disease (CVD)
Abstract: cardiovascular diseases (CVD) still claim a significant number of deaths globally and remain the number one killer with an annual death toll of nearly 17.9 million. While several medical advancements have been made, an early diagnosis is still hard to obtain, which often leads to worsening conditions and intricate treatment options. With the advancement of modern technology, Machine learning has demonstrated to be a miraculous tool which can greatly impact …
Published in Research and Reviews : A Journal of Immunology · Vol. 15, Issue 2, 2025 Read article
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Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
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Targeting Papain-Like Protease of Re-Emerging Coronaviruses
Abstract: Re-emerging Corona viruses (CoVs), including SARS-CoV (S-CoVs), and SARS-CoV-2 (S-CoV-2), continue to pose a global health threat due to their high mutation rates, zoonotic spillover potential, and capacity for immune evasion. The global pandemic of year 2019, caused by S-CoV-2, has highlighted the importance for robust antiviral strategies beyond vaccines and RNA polymerase or main protease inhibitors, which are susceptible to resistance. One promising therapeutic target is the S-CoV-2 papain-like …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 15, Issue 3, 2025 · pp. 46–62 Read article
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Developing an Assessment Protocol in Ayurvedic Research for Hyperemesis Gravidarum with Special Reference to Garbhini Chardi
Abstract: Garbhini Chardi (nausea and vomiting in pregnancy) is described in Ayurvedic texts as one of the Vyakta Garbha Lakshanas – early physiological signs indicating pregnancy. However, when this symptom becomes excessive, persistent, or distressing, it may transition from a normal event to a pathological condition, requiring timely intervention to safeguard maternal and fetal health. In advanced stages, it leads to day-to-day incapacitation, weight loss, dehydration, and metabolic disturbances – clinically …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 44–61 Read article
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A Review on: Cotard’s Syndrome
Abstract: Cotard’s Syndrome is a rare and severe mental health condition characterized by nihilistic delusions, in which individuals firmly believe that they are dead, no longer exist, or that parts of their body are decaying or missing. These beliefs are not symbolic or metaphorical but are experienced as absolute truths, making the disorder particularly distressing and difficult to manage. The syndrome is most commonly observed in association with major depressive disorder, …
Published in Recent Trends in Infectious Diseases · Vol. 3, Issue 1, 2026 · pp. 5–9 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article