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224 articles for “Disease Modeling”
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Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
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Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
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Advancements in AI-Driven Diagnostics for Dental Health: A Comprehensive Review
Abstract: Dental diseases, also known as oral diseases or dental conditions, encompass a range of health problems affecting the teeth, gums, mouth, and associated structures. These conditions can lead to pain, discomfort, and severe complications if left untreated. Early detection and accurate diagnosis are crucial for effective treatment and prevention of further complications. This comprehensive literature review aims to identify common dental problems such as Tooth Decay (Cavities), Gingivitis, Periodontitis, and …
Published in Current Trends in Signal Processing · Vol. 14, Issue 2, 2024 · pp. 1–7 Read article
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Machine Learning Based Early Cataract Detection: A Predictive Modeling Approach
Abstract: Cataracts, characterized by dense cloudy areas in the eye’s lens, afflict more than 50% of elderly individuals, leading to impaired vision and potential blindness. Detecting cataracts at an early stage is crucial to facilitate simpler treatments, as neglecting the condition may necessitate complex eye surgery. To address this issue, we are creating a predictive system that identifies cataract disease by analyzing user-provided eye features. To achieve this, we leverage OpenCV, …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 2, 2023 · pp. 1–8 Read article
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Next-Gen Agriculture: Deep Learning Algorithms for Real-Time Plant Disease Detection via IoT
Abstract: In addition to providing high-quality food, the agriculture industry plays a critical role in supporting expanding people and economies. Plant diseases can have a detrimental effect on biodiversity and result in significant losses in food production. Automated methods for early and precise identification of plant diseases can reduce financial losses and enhance the quality of food produced. Deep learning has significantly improved object detection and picture classification accuracy in recent …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 1, 2024 · pp. 18–23 Read article
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Heart Disease Evaluation Through Echocardiography Using CNN, ResetNet50, VGG16, and Image Processing
Abstract: Heart conditions stand out as primary contributors to untimely mortality among adults aged 30 and above, notably among those grappling with elevated cholesterol levels and diabetes. Detecting such ailments often necessitates the use of an echocardiogram, providing an intricate portrayal of the heart. However, precise analysis hinges on both the proper functioning of the echocardiogram apparatus and the proficiency of a skilled radiologist, a condition not always met. Manual scrutiny …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 25–35 Read article
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Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
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Emerging Digital Trends in Virology Software: Optimizing Viral Discovery, Surveillance,and Patient Management.
Abstract: Virology and antiviral therapeutics are being reshaped by rapid advances in computational tools, automation platforms, and virus-focused digital health applications. Software systems now span the entire virology value chain, from in silico viral target identification and antigen design, to AI-supported clinical trial management for vaccines and antivirals, to post-marketing pharmacovigilance and patient-facing mobile tools. This review examines current and emerging software trends relevant to virus studies, emphasizing applications in viral …
Published in International Journal of Virus Studies · Vol. 3, Issue 1, 2026 · pp. 29–38 Read article
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Psychological Profile of Patients Undergoing Hemodialysis and Its Association with Physiological Parameters
Abstract: Background: End-stage renal disease (ESRD) is a chronic, progressive condition requiring maintenance hemodialysis, which imposes substantial physiological, psychological, and social burdens on patients. While physiological indicators are routinely monitored during treatment, the influence of psychological factors on these indicators and patients' quality of life remains inadequately explored, particularly in the Indian context. Objective: To investigate the relationship between psychological variables (depression, anxiety, illness intrusiveness, and quality of life) and physiological …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 16, Issue 2, 2026 Read article
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The Future of Camelids in South America: Recent Developments and Prospects
Abstract: Camelids, including llamas, alpacas, vicuñas, and guanacos, have been central to South American culture, economy, and ecosystems for centuries. Known for their adaptability to harsh Andean climates, these species provide valuable resources, such as luxurious fiber, nutritious meat, and durable leather. In modern times, their importance has grown due to increasing demand for sustainable and ethically sourced products, coupled with their minimal environmental impact and significant contributions to rural livelihoods. …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 14, Issue 2, 2025 · pp. 35–38 Read article
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Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities
Abstract: The exponential growth of digital healthcare data has spurred innovative applications of data mining techniques in medical research and practice. Among these, association rule mining stands out for its ability to uncover meaningful correlations within diverse datasets, such as electronic health records, imaging data, and genetic information. This paper reviews the application of association rule mining in predicting heart diseases, emphasizing its potential to enhance early detection, risk stratification, and …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 29–34 Read article
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Leafguard: Smart Plant Health Detection
Abstract: Machine learning techniques, including traditional (shallow) ML, deep learning (DL), and augmented learning (AL), are being increasingly utilized for leaf disease classification. These methods involve feature extraction, data augmentation, and transfer learning to enhance model effectiveness and reduce the need for labeled data. The success of machine learning approaches in this domain hinges on the quality and quantity of data available. LeafGuard is a cutting-edge device with intelligent sensing systems …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 32–39 Read article
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Pathways in Drug Discovery and Development: From Molecular Targets to Market Approval
Abstract: A complicated, multidisciplinary, and resource-intensive process, the discovery and development of new pharmacological drugs is essential to the advancement of contemporary medicine. Finding and optimising lead chemicals comes after a disease-relevant biological target has been identified and validated. Through preclinical research in animal models, these leads are thoroughly assessed for toxicity, pharmacokinetics, safety, and efficacy. Clinical trials, which are carried out in several stages to evaluate safety, efficacy, ideal dosage, …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 13, Issue 1, 2026 · pp. 01–04 Read article
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Multivariant Disease Detection from Different Plant Leaves and Classification
Abstract: Agricultural growth is significant in Indian GDP which is based on yield of crops, quality of the plants and procedure of the plants taken. To maintain good quality of plant, the plant diseases should be identified and then given proper suggestions to farmers for specific fertilizers and pesticides to be used. The use of specific fertilizers or pesticides makes plant more health with good quality so that farmers can get …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 27–35 Read article
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A Comparative Study of different Techniques to predict Maternal Morbidity and Mortality Model
Abstract: Artificial intelligence (AI) encompasses a range of techniques, including machine learning and deep learning, which are increasingly utilized in the healthcare sector for tasks such as disease diagnosis and drug discovery. To achieve accurate disease diagnosis through AI, it is essential to integrate data from multiple medical sources, including ultrasound imaging, magnetic resonance imaging (MRI), mammography, genomics, and computed tomography (CT) scans, among others. This article presents a comprehensive review …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 1, 2025 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 Read article
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Changes of the Function of Interstitial Cells of Cajal and Fecundity During Chronic Salpingitis: Experimental Study
Abstract: We have conducted this study to clarify the changes of the function of interstitial cells of Cajal and fecundity in chronic salpingitis white rat models which are made by infecting its vaginal cavity with Chlamydia. Background: Chlamydial infection is a sexually transmitted disease which is caused by Chlamydia trachomatis. 75% of the fallopian tube disease is thought to be due to anamnesis of asymptomatic Chlamydial infection and those who are …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 1, 2024 · pp. 23–26 Read article
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Antimicrobial Peptides in Fiddler Crabs: Structural Analysis and Potential Applications
Abstract: Crustaceans represent the largest and most ecologically and economically significant group of marine and aquatic arthropods. Their biomass and critical role in ecosystems highlight their importance. Among crustaceans, decapods are frequently used as model organisms in studies of immune responses due to their significant commercial value and the need to mitigate disease outbreaks in shellfish aquaculture. Antimicrobial host-defense peptides (AMPs) are pivotal in metazoan immunity, especially for invertebrates lacking adaptive …
Published in International Journal of Cheminformatics · Vol. 2, Issue 2, 2024 · pp. 26–32 Read article
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Early Detection of Heart Disease using Machine Learning Techniques
Abstract: Coronary illness stays one of the main sources of death around the world. Exact expectations of coronary illness can altogether work on quiet results by empowering early intercession and customized treatment plans. Throughout the course of many recent years, AI (ML) methods have been extensively investigated for anticipating coronary illness, attribuFig to their remarkable capacity to analyze complex data patterns and generate precise predictions based on historical clinical records. With …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 34–45 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