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235 articles for “Data Extraction”
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Structure Property Correlation of Polymer Dielectrics Using Electrical Response Data
Abstract: Polymer dielectrics are foundational to insulation, capacitors, embedded passives, and flexible electronics, where performance is governed by the frequency-dependent electrical response rather than a single dielectric constant. This study presents a spectroscopy-aware structure–property correlation framework that transforms dielectric response data into physically interpretable spectral fingerprints and learns mappings from polymer descriptors to these fingerprints for prediction and interpretation. Broadband spectra are standardized on a log-frequency grid and parameterized using relaxation-informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 315–324 Read article
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Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS
Abstract: Coastal landscapes are among the most dynamic environments on Earth, undergoing continuous transformation due to both natural processes and anthropogenic activities. In India, particularly along the southern coastal regions of Andhra Pradesh, Tamil Nadu, and Kerala, shoreline morphology and sediment transport patterns are significantly influenced by seasonal monsoons, cyclones, storm surges, waves, tides, and changing river discharges. These factors contribute to varying rates of coastal erosion, accretion, inundation, and land- …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 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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A SEIR-Informed Stacked Fusion of Prophet, XGBoost, and LSTM for Ward-Level Epidemic Forecasting in Amravati Municipal Corporation
Abstract: Municipal epidemic preparedness depends on accurate short-horizon forecasts at fine spatial granularity. Ward-level incidence series are typically nonstationary due to changing contact patterns, interventions, reporting delays, and heterogeneous demographic and environmental factors. This paper presents a mathematically formulated hybrid forecasting architecture designed for Amravati Municipal Corporation (AMC). The method decomposes observed incidence into (i) a mechanistic SEIR baseline that enforces epidemiological structure and (ii) a data-driven residual learned using Prophet …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 1, 2026 · pp. 17–23 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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A Systematic Review of the Herbal Medicinal Plants With Anti-Obesity Potential
Abstract: Obesity is a chronic, multifactorial metabolic condition characterized by abnormal fat accumulation. It is mainly driven by the intricate influence of genetic, environmental, hormonal, metabolic and lifestyle determinants. The burden of obesity has risen considerably over the past few years, making it one of the major health concerns in both developed and developing countries alike. The rapid increase globally in obesity prevalence is linked with numerous health complications such as …
Published in Research & Reviews : Journal of Herbal Science · Vol. 15, Issue 2, 2026 Read article
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Eye Disease Classification Using K-means Clustering Algorithm and Ensemble Classification Approach
Abstract: In this study, we present a comprehensive approach for the classification of eye diseases, specifically targeting normal, cataract, glaucoma, and diabetic retinopathy conditions. This research uses a dataset from Kaggle, which provides a wide and varied collection of retinal images to ensure good representation. The methodology encompasses advanced image processing and machine learning techniques to ensure accurate diagnosis and prediction. The preprocessing phase involves a series of image enhancement techniques …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 15–27 Read article
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Fault Diagnosis of Air Compressor (AC) System using Local Mean Decomposition (LMD) and Logistic Regression (LR) Machine Learning Classifier
Abstract: This article presents a detailed and systematic procedure for performing fault diagnosis in an air compressor (AC) system by analyzing the audio signals generated during its operation. The analysis covers both normal (healthy) conditions and seven distinct types of faults, including bearing failure, flywheel malfunction, inlet valve leakage, outlet valve leakage, non-return valve failure, piston ring defect, and rider belt issues. To acquire the acoustic signals, the researchers utilized a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 416–427 Read article
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Fabrication, Numerical Simulation and Compact Modeling of Ph-BTBT-C10 Organic Thin Film Transistor
Abstract: Flexible and cost-effective electronics have been necessitated by the advent of organic thin-film transistors (OTFTs). This study aims to study the performance of OTFT using a 2-decyl-7-phenyl-[1]benzothieno[3,2-b][1]benzothiophene (Ph-BTBT-C10) organic semiconductor. The paper also explore accurate device modeling for technology optimization and circuit design that supports device improvement. This research includes device fabrication, numerical simulation using TCAD, compact modeling, and parameter extraction. By combining temperature-dependent bandgap narrowing with existing theories, this …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 1–18 Read article
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Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection
Abstract: Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by the presence of abnormal hemoglobin, resulting in the distinctive sickle shape of red blood cells. Timely and accurate identification of Sickle Cell Anemia (SCA) is essential for effective management and treatment. This study presents a new method that utilizes Convolutional Neural Networks (CNNs), a deep learning model particularly effective for image analysis. The process involves using microscopic images …
Published in Research and Reviews: A Journal of Medicine · Vol. 14, Issue 3, 2024 · pp. 9–15 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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Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials
Abstract: The performance of polymer composite materials is intrinsically governed by their microstructural architecture, which is shaped by manufacturing conditions and constituent interactions. Despite extensive experimental characterization efforts, establishing transparent and quantitative structure–property relationships from microstructural images remains a challenge. In this study, an explainable image-driven framework is developed to systematically correlate microstructural features with composite property indicators. Microstructure images are processed to identify voids, fibers, and filler phases, from which …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 188–196 Read article
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The Strength of Dry, Fresh and Decomposed Raffia Palm Trunk in the Bioremediation of Oil-based Drill Cutting
Abstract: In this research, the impact of dry drill cuttings and compost tea on the environment's ability to degrade oil-based drill cutting contamination is investigated. The experiment was conducted in the research center located at the workshop of Agricultural and Environmental Engineering Department, Rivers State University, Port Harcourt. Oil-based drill cutting samples were placed in bulk in eleven reactors (T1, T2, T3-T11) with four replications. The original drill cuttings' physiochemical characteristics …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 15, Issue 3, 2024 · pp. 26–33 Read article
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Assessing the Performance of DL Methods in Handwritten Digit Recognition
Abstract: Handwritten digit recognition is a computer vision task that involves the automatic identification and classification of hand-written digits. The objective is to develop models capable of accurately recognizing and distinguishing digits handwritten by humans. With the development of machine learning and deep learning techniques, this field has advanced remarkably. The convolutional neural network (CNN) is the most often used technique for this purpose. By utilizing CNN, the model can learn …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 1, 2023 · pp. 25–32 Read article
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Academia to Industry: The Impact of AI on Information Retrieval Technologies
Abstract: Artificial intelligence (AI) has significantly reshaped the field of information retrieval (IR), bridging theoretical advancements from academia with practical applications across various industries. This article explores the transformative impact of AI on IR technologies, highlighting key contributions from academic research and how they have been adapted for industry-scale implementations. Academic innovations, such as neural ranking models and semantic search techniques, have improved the accuracy and relevance of search results by …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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Plants Disease Detection Using TensorFlow and OpenCV
Abstract: Growing healthy and productive crops is crucial in the global battle for food security. To minimize crop losses and apply timely control measures, early and precise diagnosis of plant diseases is essential. Conventional illness detection techniques are subjective, labor-intensive, and complicated; they frequently rely on eye inspection. The TensorFlow and OpenCV libraries are used in this study to explore the use of Convolutional Neural Networks (CNNs) for plant disease discovery. …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 1, 2024 · pp. 31–38 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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Herbal Medicines and Complementary Therapies: Their Role, Types and Applications
Abstract: Medicinal plants are used in herbal medicine to treat and prevent illnesses. This includes the use of standardized and titrated botanical extracts in addition to common and traditional medicines from different cultures. The safety of a treatment may be suggested by its long history and deep cultural roots in traditional medical systems, but this does not always imply that it is effective. This is especially important in herbal medicine, since …
Published in Emerging Trends in Personalized Medicines · Vol. 2, Issue 1, 2025 · pp. 40–53 Read article
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In silico analysis of Abrus precatorius L. as a potential agonist of PPARγ : A novel approach to the treatment of diabetes
Abstract: Diabetes mellitus is a chronic disorder that is characterized by a lack of insulin secretion by the pancreas or a defect in insulin action, or both. If diabetes is not controlled, over time it can cause damage to blood vessels and nerves, leading to life-threatening complications such as, kidney damage, heart disease and vision impairment. It can also weaken the immune system, making patients more prone to infection. Although treatments …
Published in International Journal of Molecular Biotechnological Research · Vol. 3, Issue 2, 2025 Read article
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Advancement in Image Classification: Media Player Control Using Hand Gestures
Abstract: We explore the development of picture categorization methods in this paper, with an emphasis on how they are used to manipulate media players with hand gestures. Our investigation focuses on the development of machine learning techniques, particularly on supporting vector machines (SVM) and convolutional neural networks (CNN). SVMs are used to identify and authenticate people from digital photos or video clips, but CNNs are great at face detection, which is …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article