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216 articles for “Image Analysis”
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Synthesis of Activated Carbon from Corn Cob and Factors Affecting its Adsorption and Desorption Efficiency of Cationic Brilliant Green Dye
Abstract: Extreme dye pollutants released from textile, leather, and pulp industries are becoming a major concern to the water. These pollutants are highly toxic to living things. The major objective of this study was to prepare low-cost and efficient activated carbon from corncob (CCAC) adsorbent through the H2SO4 activation method and characterize its suitability for removing brilliant green dye (BGD) from aqueous solution. The adsorption and desorption efficiency of the prepared …
Published in Journal of Water Pollution & Purification Research · Vol. 12, Issue 3, 2025 · pp. 27–44 Read article
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Viscoelastic Behavior and Wrinkle Formation in Cotton- Polyester Garments: A Data-Driven Approach for Textile Care
Abstract: This study investigates the wrinkle behavior of cotton-polyester blended fabrics by analyzing data from over 1,200 store-handled garments. Integrating concepts from polymer chemistry and computer vision, it aims to establish a smart textile care framework based on fiber-specific wrinkle characteristics. The research identifies how cotton’s hydrophilic and non-elastic structure results in increased wrinkling, while polyester’s thermoplastic and crystalline properties enhance wrinkle resistance. Elastomeric fibers like Lycra contribute to wrinkle recovery …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 50–60 Read article
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Assessing of Forest Structure Using Earth Observation Data: A Case Study in Munessa Forest, Oromia Region, Ethiopia
Abstract: Understanding forest structure is crucial for estimating carbon emissions associated with forests, assessing forest degradation, and evaluating the success of forest restoration efforts. However, forest structure quantification is limited to the area of interest without considering the whole forest coverage. Forest structure may be easily assessed over a wide area using data from remote sensing. Thus, by combining ground observation with satellite-based light detection and ranging (LiDAR) and Sentinel 2 …
Published in International Journal of Land · Vol. 2, Issue 1, 2025 · pp. 28–37 Read article
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Plant Disease Detection Using Machine Learning
Abstract: Plant diseases significantly threaten global crop yields and affect both nutritional safety and farmer income. Accurate and early detection of plant diseases is essential for effective intervention and treatment. In this study, we used the CNN model (convolutional neural network) to explore a deep learning-based approach for plant disease classification. The model was trained and evaluated on a large dataset encompassing 38 different classes of plant disease, including healthy leaves. …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 2, 2025 · pp. 07–19 Read article
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Emerging Role of Drone Technologies in Environmental Research: Trends, Applications, and Future Directions
Abstract: Unmanned aerial vehicles (UAVs), commonly called drones, have rapidly transformed environmental research and management over the past decade. Their flexibility, improving sensor payloads, and ability to collect high-resolution spatial and temporal data make them powerful tools across disciplines — from biodiversity monitoring and precision agriculture to water quality assessment and disaster response. UAVs bridge the gap between ground-based surveys and satellite remote sensing by offering near-real-time, fine-scale data acquisition that …
Published in International Journal on Drones · Vol. 1, Issue 2, 2025 · pp. 34–42 Read article
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AI-Based Outfit Rating and Suggestion System
Abstract: The increasing demand for personalised fashion advice in the digital era has highlighted the need for intelligent, automated styling solutions. The AI-Based Outfit Rating and Suggestion System is a web- based platform that assists users in evaluating and improving their clothing choices through intelligent image analysis. Unlike conventional fashion applications that merely identify garment categories or suggest purchases, this system performs a holistic assessment of complete outfits by analysing colour …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 2, 2026 Read article
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ASSESSING OF FOREST STRUCTURE USING EARTH OBSERVATION DATA: ACASE STUDY IN MUNESSA FOREST, OROMIA REGION, ETHIOPIA
Abstract: Forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. However, forest structure quantification is only limited to the specific area of interest without considering the whole forest coverage. Remote sensing data can easily deliver a large area to assess forest structure. Therefore, this study aims to assess forest structure of Munessa Natural Forest by integrating satellite based light detection …
Published in International Journal of Land Read article
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Advancements in Image Processing Techniques for Computer Vision Applications
Abstract: The fast-developing field of computer vision is transforming how people perceive and comprehend pictures and movies. Autonomous systems, robotics, healthcare, and surveillance are just a few of the many applications that have been made possible by recent significant advances in image and video processing. An overview of current developments in computer vision approaches, algorithms, and techniques for image and video analysis is given in this abstract. In conclusion, the analysis …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 1, 2024 · pp. 27–32 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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Path Lab-AI: An Autonomous Framework for Error-Free Histopathology Slide Interpretation
Abstract: Path Lab-AI represents a fully autonomous platform for the analysis of histopathology slides with circumscribed structures, designed to obtain highly accurate results using diagnostic methods and avoiding the usual limitations of standard microscopy-based pathology. Leveraging recent deep learning and whole slide image (WSI) analysis innovations, our system takes advantage of automated WSI ingestion along with pre-processing steps to account for staining variability, remove artifacts, and localize tissue from background. Such …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 19–30 Read article
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Disaster Impact Assessment Using Multi-Sensor Satellite Data: An AI-Based Remote Sensing Approach
Abstract: Natural disasters such as floods, earthquakes, and wildfires cause significant damage to human life and infrastructure every year. Rapid and accurate assessment of the affected areas is essential for effective disaster response and recovery planning. Traditional image-based analysis using single-sensor data often fails under adverse conditions such as cloud cover, smoke, or poor lighting. To overcome these limitations, this study proposes a novel framework for disaster impact assessment using multi-sensor …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 10–22 Read article
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Detection and Classification of Diabetic Retinopathy Using Deep Learning Techniques
Abstract: This project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 64–69 Read article
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Integrated Dam Automation: Real-Time Monitoring and Controlling Using IoT
Abstract: Dam automation is a critical area in water resource management, especially given the rising demand for sustainable and safe water control systems. An integrated approach to dam automation involves implementing advanced sensors and monitoring systems to improve structural safety, water quality, and resource management. This paper presents a comprehensive automation model that combines crack detection, convolutional neural networks (CNNs), water level monitoring, turbidity sensing, and rainfall data to ensure real-time …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 1, 2025 · pp. 31–38 Read article
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Multi-Sensor System for Underwater Pothole Detection to Enhance Road Safety During Monsoon Seasons
Abstract: Monsoon seasons across India and similar tropical regions severely compromise road safety by causing water accumulation that conceals dangerous potholes beneath stagnant pools, leading to frequent vehicle damage, tire punctures, and fatal accidents. Traditional detection methods relying on smartphone accelerometers, ultrasonic sensors, or machine vision fail under flooded conditions due to acoustic signal reflection at water surfaces and optical distortions from glare and turbidity. This research proposes an innovative multi-sensor …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 4, Issue 1, 2026 · pp. 25–32 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 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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Identification of Papaya Fruit Ripening Process Using AI
Abstract: Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 13, Issue 2, 2024 · pp. 23–30 Read article
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Lung Cancer Detection and Classification Using Deep Learning
Abstract: Lung cancer is a disease that can be effectively treated if detected early. Various technologies, such as magnetic resonance imaging, isotopes, X-rays, and computed tomography scans, are employed for diagnosis. One of the most crucial strategies in combating cancer is early detection, which greatly enhances a patient’s likelihood of survival; this is where artificial intelligence plays a significant role. The approach proposed in this study leverages historical medical data to …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 13, Issue 3, 2024 · pp. 11–17 Read article
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U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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Enhancing Image Classification Performance with Deep Neural Networks
Abstract: Classifying images is useful in many domains, including the study of plant diseases and the analysis of human expressions. Image categorization employing the idea of a “deep neural network” helps to compact otherwise cumbersome photos. It is possible to classify images by using the idea of a “deep neural network”. Self-driving cars, medical diagnosis, automatic translation, etc., all make use of Deep Neural Networks. Recently, excellent results have been achieved …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 1, 2024 · pp. 13–23 Read article