Search
827 articles for “Classify”
-
Natural Language Processing in Everyday Applications: From Classrooms to Clinics
Abstract: Natural Language Processing (NLP) is transforming everyday sectors like healthcare and education by creating new opportunities for efficiency, accessibility, and early intervention. This paper investigates the function of natural language processing (NLP) in computer education. Tools like automated grading systems, intelligent tutoring assistants, and multilingual translation platforms are revolutionizing traditional teaching methods. Despite its benefits, adoption is still being slowed down by problems like data privacy, integration with education, and …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 Read article
-
A Comparative Study to Assess the Fetal Outcome Among Normal and Anemic Mothers at Sultania Zanana Hospital at Bhopal
Abstract: Anemia during pregnancy is a significant public health concern, especially in developing countries like India. It is a clinical condition characterized by a deficiency in hemoglobin levels, often due to inadequate intake of essential nutrients such as iron, folic acid, and vitamins. The World Health Organization classifies anemia during pregnancy as having hemoglobin levels under 11 g/dL, with specific thresholds varying by trimester. Maternal anemia not only affects the mother's …
Published in International Journal of Midwifery Nursing And Practices · Vol. 3, Issue 2, 2025 · pp. 41–74 Read article
-
Low-cost Machine Learning-Based Sensor-based Activity Recognition for Patients with Financial Difficulties
Abstract: Elderly and schizophrenic patients are compelled to obtain treatment at home due to a lack of resources, putting them at risk for patient neglect and other health issues. This is particularly troublesome for prescription yoga or fitness programs, which are hard for doctors to keep an eye on all the time. We have looked into an automated method that tracks patients’ everyday behaviors using machine learning- based techniques in order …
Published in Recent Trends in Sensor Research & Technology · Vol. 12, Issue 3, 2025 · pp. 7–17 Read article
-
A Retrospective Study to Assess the Incidence of Anaemia Among Pregnant Women Admitted in a Tertiary Care Centre, Jalandhar, Punjab
Abstract: Background:Anemia during pregnancy is a common and preventable condition that contributes significantly to adverse maternal and neonatal outcomes, including preterm delivery, low birth weight, and increased maternal morbidity. Despite the implementation of various national health programs in India, the burden of anemia among pregnant women remains high, particularly in rural regions. Objective: To assess the incidence and severity of anemia among pregnant women admitted to a tertiary care center in …
Published in International Journal of Women's Health Nursing And Practices · Vol. 3, Issue 2, 2025 · pp. 38–42 Read article
-
SIBR/Aloe barbadenis Mill.: A Review on Medicinal Utility from the Perspective of Unani Medicine
Abstract: Aloe barbadensis Mill., known as Sibr in Unani medicine, is a time-honored medicinal plant belonging to the family Liliaceae. Revered across cultures and healing systems, it has been extensively described in Unani literature under various names such as Elwa, Musabbar, and Ghikwar. This review aims to comprehensively explore the medicinal utility of Sibr from an Unani perspective while integrating evidence from modern pharmacognosy, phytochemistry, and pharmacology. Traditionally, the dried juice …
Published in Research & Reviews : A Journal of Unani, Siddha and Homeopathy · Vol. 12, Issue 3, 2025 · pp. 22–29 Read article
-
Smart Patient Monitoring and Motion Tracking System
Abstract: The integration of smart technologies in healthcare has revolutionized patient monitoring and diagnostics. This paper presents a Smart Patient Monitoring and Motion Tracking System designed for hospitals, leveraging EEG (Electroencephalogram) signals to track patient movements and monitor neurological health. The proposed system combines motion tracking with real time EEG signal analysis to enhance patient safety, especially for individuals prone to seizures, neurological disorders, or other mobility-related risks. The system employs …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 2, 2025 · pp. 9–23 Read article
-
EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
-
A Review of Literature on Breast Cancer: Molecular Insights, Clinical Approaches, and Future Perspectives
Abstract: Breast cancer is the leading cause of morbidity and mortality in women globally. Although both men and women can get the condition, women are more likely to get it, and its prevalence has been rising globally in recent years. The epidemiology, risk factors, pathophysiology, diagnosis, treatment options, and survival rates of breast cancer are all examined in this review of recent research. Breast cancer can show up as a lump …
Published in International Journal of Oncological Nursing and Practices · Vol. 3, Issue 2, 2025 · pp. 15–20 Read article
-
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 · pp. 1–9 Read article
-
Acoustic Sensing for City Flow: Quasi-Supervised Recognition of Sirens and Traffic for Urban Mobility Intelligence
Abstract: This paper frames environmental audio as a mobility telemetry source, extending a benchmark urban-sound corpus with transportation-critical classes—ambulance, firetruck, police, and traffic—and training spectrogram-based models under a quasi-supervised regime to support real-time city operations; leveraging 10-fold protocols, class-weighted objectives, and audiospecific augmentations (time stretch, pitch shift, SpecAugment, PatchAugment), the system benchmarks multiple CNN backbones combined with self-supervised learning paradigms enable the extraction of rich, discriminative acoustic representations, achieving strong multi-class …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 42–50 Read article
-
Lab Reagents and Their Importance in Bio-chemistry
Abstract: Biochemical reagents are essential substances in the study of life at the molecular level. These reagents help scientists detect, identify, and quantify bio-molecules such as proteins, carbohydrates, nucleic acids, and enzymes in biological systems. They play a vital role in understanding metabolic reactions and physiological processes within living organisms. Biochemical reagents are generally classified into analytical, diagnostic, enzymatic, chromogenic, and buffer reagents, each serving a specific purpose in laboratory and …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 16, Issue 1, 2026 · pp. 1–4 Read article
-
ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
-
Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
-
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
-
A Study on “Clean" in Beauty: A Machine LearningApproach to Ingredient Transparency and ConsumerTrust
Abstract: The burgeoning "clean beauty" market, while driven by consumer demand for safer and more sustainable products, is plagued by ambiguous definitions and the pervasive challenge of "greenwashing". This ambiguity hinders informed consumer choices and complicates brand authenticity. This study addresses these complexities by developing a novel machine learning (ML) framework designed to objectively analyze cosmetic ingredient lists, classify products based on their "cleanliness" profile, and identify key ingredient attributes that …
Published in Recent Trends in Cosmetics · Vol. 3, Issue 1, 2026 · pp. 1–12 Read article
-
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
-
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
-
Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
-
Securing Web Applications: A Machine Learning Approach for SQL Injection Threats
Abstract: The rapid evolution and widespread adoption of the internet have significantly transformed the world, leading to an increased number of cyberattacks. Cybersecurity has become one of the most critical challenges for society, incurring substantial financial losses annually. This research focuses on SQL injection attacks, the specific threat to web applications, aiming to detect malicious queries designed to exploit vulnerabilities and access sensitive data. In recent years, the frequency of SQLi …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 16–22 Read article
-
AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article