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78 articles for “App segmentation”
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User App Segmentation for Better Understanding of Reviews and Password Resets Using Regression
Abstract: In today’s digital landscape, understanding users’ needs and behaviors is key to improving app experiences. This paper focuses on how we can better understand user reviews and password resets in mobile apps through a method called user segmentation. By dividing users into groups based on their feedback and password reset patterns, we aim to uncover insights that can enhance app design and security. Using a mix of user reviews and …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 1–9 Read article
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Refining Retinal Layer Segmentation in OCT Imaging with Advanced Techniques and Clinical Applications
Abstract: Segmenting retinal layers from Optical Coherence Tomography (OCT) pictures entails locating and separating different retinal layers to offer comprehensive anatomical and pathological information. Age-related macular degeneration, diabetic retinopathy, and glaucoma are among the retinal illnesses for which this procedure is crucial for diagnosis and follow-up. By utilizing preprocessing techniques to improve image quality and applying advanced algorithms—such as intensity-based, gradient-based, and texture-based methods—alongside deep learning approaches, clinicians can accurately measure …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 2, 2024 · pp. 01–06 Read article
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Non-Contact Quantification of Swelling-Induced Deformation in Polymer Hydrogels Using Image Analysis
Abstract: Swelling of polymer hydrogels governs transport, mechanics, and functional performance in biomedical systems, yet it is often reported using bulk ratios that conceal spatially heterogeneous deformation and boundary-driven instabilities. This study presents a non-contact image-analysis framework to quantify swelling-induced deformation by tracking shape and boundary evolution from time-lapse imaging. The approach segments the hydrogel region, extracts a sub-pixel refined contour, and computes boundary displacement descriptors including mean and upper-percentile normal …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1499–1509 Read article
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Implementation of Human Gesture Recognition Using CNN
Abstract: A gesture popularity system based entirely on convolutional neural networks (CNNs). Preprocessing techniques include segmentation, polygonal approximation, contour construction, morphological filters, and resource characteristic extraction. Various convolutional neural networks are employed for training and testing, with results compared to existing architectures and protocols. All generated measurements and convergence graphs produced at any point during education are examined and contested in order to verify the reliability of the approach offered. Our …
Published in Journal of Control & Instrumentation · Vol. 15, Issue 2, 2024 · pp. 24–37 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article
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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 Read article
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Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation
Abstract: Artificial intelligence (AI) refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilizing experience, learned facts, and flexible rules to solve problems that may not fit the standard outlines for a normal algorithm. From this follows the utilization of AI in various sectors, such as the information technology (IT) industry, media, healthcare and medicine, logistics, environmental sustainability, finance, business, …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 34–39 Read article
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A Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection
Abstract: In recent years, deep learning techniques have shown remarkable performance in various image analysis applications, particularly in the domain of medical image processing. Among these, image segmentation plays a critical role, as it helps in isolating and analyzing specific regions within medical images. The proposed study focuses on segmenting the corpus callosum, a vital structure in the human brain, using a novel optimization technique known as the Split and Merge …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Geo AI-Powered Urban Footprints
Abstract: In the contemporary era, building footprints are of paramount importance for accurate and current inventories in the development of infrastructure and geospatial analysis. Traditional methods, relying on manual digitization, were largely unsustainable as the urban regions were growing rapidly. Manual digitization was expensive and lacked geometric precision. This paper introduces an automated, end-to-end GEO AI-powered framework for high-end fidelity building footprint extraction from Google Satellite Data. Our approach for this …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Experimental Study on Load Transfer Mechanism in Columns Retrofitted by Bonding Precast Segments and FRP Wrapping
Abstract: An experimental study was conducted to investigate the load transfer mechanism in columns retrofitted with precast segments and Fiber-Reinforced Polymer (FRP) wrapping. As the demand for structural retrofitting grows, it becomes crucial to comprehend the behavior and effectiveness of retrofit techniques to ensure the safety and integrity of existing infrastructure. The study involved subjecting a series of column specimens to axial compression loads in order to evaluate the load transfer …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 153–165 Read article
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Artificial Intelligence in Image Recognition: Context of Machine Vision
Abstract: The machine learning discipline is as old as decades, but some problems such as image recognition, location detection, image classification, image generation, speech recognition, and natural language processing cannot be solved. Image classification studies are another basic, most classic and essential line of research in deep learning. Computer intelligent recognition of the images technology has enabled a gradual reaction (updating) to foreign measurement trends, which promotes advancement of different areas …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 01–06 Read article
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Polyurethane: Chemistry, Production, Applications, and Future Prospects—An Overview
Abstract: Polyurethane (PU), a class of versatile polymers, has emerged as one of the most significant materials in modern industry owing to its remarkable mechanical strength, elasticity, durability, and resistance to abrasion, chemicals, and environmental degradation. Its wide range of tunable properties has made PU indispensable across multiple sectors, including fashion, automotive, manufacturing, biomedical, coatings, and construction. This review emphasizes the diverse applications of polyurethane and explores how its unique chemistry …
Published in Journal of Thin Films, Coating Science Technology & Application · Vol. 12, Issue 3, 2025 · pp. 17–25 Read article
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A Comprehensive Review of FinFet Technology: Advantages, Challenges and Applications
Abstract: This has been complemented by improvements on the new layers that have surpassed the manufactures planar transistor by the new FinFET (Fin Field-Effect Transistor), which enhance modern demands on high performance, small size and low power consumption. This review is for a timely information on the latest development on FinFET technology, such as by curbing leakage currents by 50 percent and enhancing its speed by 30 percent proving that FinFETs …
Published in Journal of Semiconductor Devices and Circuits · Vol. 11, Issue 3, 2024 · pp. 1–12 Read article
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Face Aging Using Generative Adversarial Network
Abstract: This project addresses the challenge of predicting how a person may look in the future or how they appeared in the past using a single photograph. While existing methods mainly focus on altering texture, they often neglect changes in head shape that naturally occur during the aging process, limiting their effectiveness, especially when applied to images of children. To tackle this issue, a novel approach is introduced that employs a …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 41–52 Read article
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Data-Driven Predictive Analytics and Decision- Making in FinTech Using MongoDB and High-Throughput Data Pipelines
Abstract: This paper examines the implementation of MongoDB and high-throughput data pipelines within the financial technology (FinTech) sector to drive data-informed predictive analytics and decision-making. The study focuses on the architectural components, scalability, and challenges of integrating NoSQL databases into real-time data ingestion and analytics pipelines. The transformative potential of these technologies in modern financial systems is highlighted through practical use cases such as fraud detection, credit scoring, and personalized financial …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 1–15 Read article
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Detection of Brain Tumors from MRI Images Based On Development of Thinking Computer Systems Techniques
Abstract: Brain tumors are one of the common diseases of the nervous system and have great harm to human health, and even lead to death. The detection, segmentation, and extraction of contaminated tumour regions from Magnetic Resonance Imaging (MRI) pictures are major problems; yet, a repetitive and time-consuming task performed by radiologists or clinical experts relies on their experience. The many anatomical structures of the human organ can be imagined using …
Published in Current Trends in Signal Processing Read article
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Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods
Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, so …
Published in Recent Trends in Sensor Research & Technology Read article
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A Comprehensive Survey on Detection of Video Transitions
Abstract: Video shot boundary detection (SBD) is a fundamental task in the field of video processing and analysis. It plays a critical role in various video applications such as content-based video retrieval, video indexing, editing, summarization, and browsing. Identifying shot boundaries helps segment a continuous video stream into distinct shots, each representing a meaningful visual unit. This segmentation is essential for organizing and interpreting video data efficiently. This study provides an …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 3, 2025 · pp. 17–26 Read article
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Fibre Orientation and Void Distribution Analysis in Polymer Composite Structures Using Image Processing
Abstract: Fibre orientation and void distribution are critical microstructural features that govern the mechanical performance and reliability of polymer composite materials. Accurate and simultaneous characterisation of these features remains challenging due to their complex spatial interactions and dependence on processing conditions. In this work, an integrated image-processing framework is proposed for the quantitative analysis of fiber orientation and void distribution in polymer composite structures. High-resolution composite microstructure images are processed through …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 925–933 Read article