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177 articles for “Early Intervention”
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Deep Learning-Based Pneumonia Diagnosis: A Comparative Review of Models and Metrics
Abstract: Pneumonia is a common viral infection that affects a large percentage of people worldwide. It is more common in developing and impoverished areas because of factors like poor sanitation, crowded living quarters, pollution in the environment, and restricted access to medical facilities. In order to improve survival chances and gain access to therapeutic therapies, pneumonia must be diagnosed as soon as possible. A type of artificial intelligence called deep learning …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 Read article
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Progression of Health and Wellness: Artificial Intelligence (AI) and Deep Learning (DL) for Precision Medicines
Abstract: Deep learning and artificial intelligence in the field of precision medicine is revolutionizing healthcare to make personalized therapeutic approaches desirable based on the unique characteristics of the patient. AI technologies improve diagnostic accuracy by analyzing medical data, spotting patterns and anomalies that human experts may miss. AI-driven models are instrumental in precision medicine, where they can predict patient response to therapies to tailor treatment plans, enhancing outcomes and reducing adverse …
Published in Emerging Trends in Personalized Medicines · Vol. 2, Issue 2, 2025 · pp. 1–5 Read article
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An Expected Cardiovascular Disease Detection Using Deep Learning Techniques
Abstract: Many avoidable deaths globally are caused by CVD, often due to individuals remaining unaware of their risk factors until severe symptoms, such as heart attacks or strokes, appear. This study utilizes retinal images as the dataset to explore the potential of retinal imaging as a non-invasive diagnostic tool for early detection of cardiovascular diseases (CVD). The delay in diagnosis and treatment highlights the need for sophisticated diagnostic instruments that can …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 Read article
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IoT and Smart Sensors for Structural Health Monitoring: Trends, Challenges, and Future Directions
Abstract: Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, resilience, and sustainability of civil infrastructure systems. In recent years, the convergence of Internet of Things (IoT) technologies and smart sensor systems has revolutionized the field of SHM. This integration enables continuous, real- time monitoring, facilitates predictive maintenance, and reduces the costs associated with structural inspections. IoT-based SHM frameworks leverage wireless sensor networks, cloud computing platforms, and intelligent …
Published in Recent Trends in Sensor Research & Technology · Vol. 12, Issue 3, 2025 · pp. 1–6 Read article
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Childhood Obesity: From Determinants to Clinical Management strategies
Abstract: Childhood obesity is one of the major global health problems in whole world, with a dramatic increase in prevalence over the recent few decades. Obesity associated with very serious long term health complications and sometimes very serious complications, such as metabolic disorders, cardiovascular, neurological, renal and psychological disorders. There are various factors such as genetic, environmental, behavioural, and socio-economic factors are some key player in the pathogenesis of obesity. In …
Published in Research and Reviews: A Journal of Health Professions · Vol. 16, Issue 1, 2026 · pp. 45–53 Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 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
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Deep Learning based Solution for Leaf disease Detection in Crops and Fertilizer Recommendation
Abstract: The field of agriculture faces significant threats, including diseases that attack plant leaves. To address this issue, our system assists farmers in promptly detecting plant diseases using advanced technology. The user, typically a farmer, only needs to capture an image of the affected leaf and input it into our system. Our system then analyzes the uploaded image to accurately identify the specific disease afflicting the leaf. This analytical process is …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 31–40 Read article
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Deep Learning-Based Alzheimer’s Disease Detection: A CNN Approach
Abstract: Alzheimer’s disease (AD) is a neurological condition that worsens with time and impairs a patient’s quality of life by causing cognitive loss. For prompt intervention and management of AD, early identification is essential. In this work, we propose a deep learning-based method for automatically classifying Alzheimer’s disease from medical imaging data using convolutional neural networks (CNNs). Our algorithm is intended to evaluate brain MRI images and detect anatomical variations suggestive …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 Read article
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Lemon Sign: The Diagnostic Indicator for Spina Bifida
Abstract: The “lemon sign” is a distinctive ultrasonographic finding that serves as a diagnostic indicator for spina bifida, a congenital neural tube defect characterized by incomplete closure of the spinal cord. This sign is observed in fetal imaging and is considered an early and reliable marker for detecting spina bifida, particularly when combined with other prenatal diagnostic tools such as the “banana sign.” The lemon sign is characterized by a flattened, …
Published in International Journal of Midwifery Nursing And Practices · Vol. 4, Issue 1, 2026 · pp. 1–6 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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Veterinary Immunology in Zoonotic Disease Control
Abstract: Zoonotic diseases, which are transmissible between animals and humans, pose a significant threat to global public health, food security, and economic stability. Veterinary immunology plays a pivotal role in understanding host–pathogen interactions, developing preventive strategies, and mitigating the impact of these diseases. Animals serve as reservoirs for a variety of pathogens, including viruses, bacteria, and fungi, which can persist asymptomatically or cause clinical disease. Understanding the immune mechanisms in both …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 14, Issue 3, 2025 · pp. 36–43 Read article
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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Study to Assess the Effectiveness of a Structured Teaching Program on Knowledge Regarding Management of Snake Bite at the Emergency Department Among Emergency Care Nurses at Selected Health Wellness Centers
Abstract: Animal bites are the leading cause of death; the delay in identification of bites, accessibility to medical aid, and transportation are contributing factors for morbidity. Among the animal bites snake bite is the most crucial and anxiety-filled situation for the casualties. Early rapid identification of the type of bite and prompt intervention will restore emergency. Emergency department working nurses are the first responders for the management of all casualties. The …
Published in International Journal of Emergency and Trauma Nursing and Practices · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
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Hormonal Shifts and Vision Changes: Addressing the Needs of Women During and After Pregnancy
Abstract: Pregnancy and the postpartum period are marked by significant hormonal fluctuations that can profoundly affect a woman’s vision health. This review explores the relationship between hormonal shifts during pregnancy and the onset of common vision changes, including but not limited to dry eyes, blurred vision, and retinal changes. These changes are often temporary but can indicate underlying complications such as gestational diabetes, preeclampsia, and other pregnancy-related conditions that may threaten …
Published in International Journal of Women's Health Nursing And Practices · Vol. 3, Issue 1, 2025 · pp. 19–24 Read article
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AI and ML-Driven Immersive Technologies: A New Era in Education
Abstract: The very fast adoption of Artificial Intelligence (AI) and Machine Learning (ML) in education has transformed contemporary teaching and learning ecosystems driven by advances in immersive technologies and the growing engagement of global technology leaders with virtual environments. AI-powered educational platforms enable adaptive and personalized learning pathways by dynamically adjusting content, pace and instructional strategies to learners’ preferences, abilities and learning styles by improving engagement, retention and academic outcomes. Deep …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 116–123 Read article
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Functional Recovery Among Stroke Survivors Following Targeted Nursing Interventions: Evidence from a Quasi-Experimental Study
Abstract: Background: Stroke rehabilitation is crucial for reducing disability and improving functional independence. However, in India, nursing-led rehabilitation interventions remain underexplored. This study evaluates the effectiveness of nursing-led rehabilitation programs in improving mobility, ADL independence, and psychological well-being among stroke survivors. Methods: A quasi-experimental study was conducted at Index Medical College, Hospital & Research Center, involving 200 stroke survivors. Participants were divided into two groups: (1) Intervention Group (n = 100): …
Published in International Journal of Evidence Based Nursing And Practices · Vol. 3, Issue 2, 2025 Read article
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Literature Review On Nanotechnology In Dentistry
Abstract: Nanotechnology is a relatively new field in dentistry that utilizes nanomaterials, nanorobots, and nanotechnology for diagnosing, treating, and preventing dental diseases. Nanotechnology has revolutionized various fields of dentistry, offering innovative solutions that enhance patient care, improve treatment outcomes, and contribute to the overall advancement of dental science. This cutting-edge technology has found applications in several areas, including restorative dentistry, dental implants, early cancer diagnosis, dental hypersensitivity, and pain management, among …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 15, Issue 2, 2025 · pp. 19–23 Read article
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A Review on Unmasking Merkel Cell Polyomavirus: From Discovery to Cancer Driver
Abstract: Merkel cell polyomavirus (MCPyV) is a small, non-enveloped, circular double-stranded DNA virus belonging to the Polyomaviridae family, first characterized in 2008. Epidemiological data suggest widespread exposure, with seroprevalence rates approaching 80% in adults. While primary infection is typically asymptomatic, rare integration events in Merkel cells can initiate oncogenesis, yielding Merkel cell carcinoma (MCC), a neuroendocrine skin cancer with a five-year survival rate under 60%. This review integrates findings from six …
Published in International Journal of Virus Studies · Vol. 2, Issue 2, 2025 · pp. 6–11 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article