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270 articles for “Extraction time”
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A Review of Automated Pomegranate Disease Detection and Classification Using Machine Learning
Abstract: The abstract outlines a research study focused on developing an automated system for detecting and classifying diseases that affect pomegranate fruits. Pomegranates, like many other crops, are vulnerable to several types of diseases that appear as visible colored spots on the fruit’s surface. These visible symptoms, such as lesions or discoloration, can significantly impact the fruit’s quality, market value, and yield. Therefore, timely and accurate identification of such diseases is …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 01–13 Read article
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Synthesis and Characterization of Green Synthesized Iron Nano Particle (GInP) Using Eucalyptus globulus for Lead Removal
Abstract: The application of green synthesized iron nanoparticle for the removal of lead is researched in this study. The FT-IR and XRD results reveal that a nanoparticle with 80 nm size could be synthesized using Eucalyptus globulus leaf extract. The specific surface area of the nanoparticle was found to be 59 m2/g. The lead removal capability of the green synthesized iron nanoparticle (GInP) was investigated for lead concentration ranging from 50–250 …
Published in Journal of Materials & Metallurgical Engineering · Vol. 10, Issue 3, 2020 · pp. 20–25 Read article
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Design and Development of Screw Detection System : A case study
Abstract: This study explores the design of a vision-based screw detection and orientation system for industrial automation, inspection, and robot disassembly. By integrating machine learning algorithms like region-based convolutional neural networks (R-CNN) with traditional image processing and impedance sensing, the system performs real-time screw presence detection, head type identification, and alignment. Three key technologies—deep learning classification, edge-based geometric analysis, and impedance verification—are integrated into a single modular system. The findings indicate …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 30–36 Read article
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Computational Analysis of Non-Newtonian Blood Flow through Bifurcated Coronary Artery: Insights into Hemodynamics and Wall Shear Stress
Abstract: This abstract presents a study on the computational fluid dynamics (CFD) simulations of blood flow through a bifurcated coronary artery using non-Newtonian fluid model. The objective of this study is to investigate the hemodynamic characteristics in Bifurcated Coronary artery. The methodology involved the utilization of ANSYS SpaceClaim software for creating a geometric model of the bifurcated coronary artery. A mesh independent study was conducted to ensure the accuracy and reliability …
Published in Journal of Polymer & Composites · Vol. 11, Issue 13, 2023 · pp. 160–168 Read article
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Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
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Detection of Active Compounds in Ulothrix sp. Algae
Abstract: The study included the identification of some active chemical compounds from blue-green algae Ulothrix and the production of many active compounds using algae extract. Algae are a talented cause of defiant corpulence mediators, as plump positions are an international hazard to human wellbeing. It is also linked to metabolic syndrome, type 2 diabetes, heart disease and blood vessels, anti-obesity agents from algae are represented by four main compounds with (Phlorotannins, …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 14, Issue 3, 2024 · pp. 39–43 Read article
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Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review
Abstract: To overcome the challenges in fashion trend forecasting, researchers have introduced several advanced and data-driven approaches. One such method uses a long short-term memory (LSTM) model combined with an encoder-decoder architecture to extract meaningful fashion content and recognize styles from product images. This model achieves higher accuracy in predicting upcoming fashion trends by incorporating varying price intervals and has shown impressive results when evaluated on the Amazon fashion dataset. Another …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 35–41 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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Troubleshooting CSS: Common Issues and Effective Solutions for Web Development
Abstract: Many modern websites suffer from bloated and ineffective stylesheets, despite the fact that CSS is crucial for user experience and web performance. The effects of CSS optimization strategies, including minification, modularization, critical CSS extraction, lazy loading, and unused rule elimination, are simulated and assessed in this study on four representative web platform types: news portals, e-learning platforms, e-commerce sites, and SaaS dashboards. Using a controlled experimental environment and common auditing …
Published in Journal of Web Engineering & Technology · Vol. 12, Issue 3, 2025 · pp. 44–57 Read article
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IOT in Supply Chain and Logistics: A Critical Review
Abstract: Abstract Internet of Things (IoT) is a major component now-a-days in the industrial scenario. From procurement to supply chain, logistics every process can be made more organized and less time consuming as well as increasing the efficiency of the processes. This paper critically reviews the till date research on IoT in Logistics and supply chain and tries to obtain a logical conclusion of any research gap if any. In this …
Published in Journal of Communication Engineering & Systems · Vol. 8, Issue 2, 2018 · pp. 11–16 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article
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Advances in Analytical Techniques for Water Quality Assessment
Abstract: Assessing the quality of water is essential for preserving ecological balance and public health. This study evaluates new developments in analytical methods that are meant to improve the accuracy, effectiveness, and reach of water quality monitoring. While fundamental, conventional procedures like spectrophotometry and chromatography have drawbacks in terms of sensitivity and real-time capability. By allowing quick and precise pollutant and contaminant identification, emerging technologies such as sensor networks, remote sensing, …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 2, Issue 1, 2024 · pp. 1–7 Read article
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SkinSight: Design and Implementation of an Intelligent Skin Type Detection System
Abstract: Identifying an individual’s skin type accurately is essential for creating personalized dermatological treatments and formulating skincare products that genuinely meet user needs. In this project, a real- time skin type classification system is developed using a combination of convolutional neural networks (CNNs) and modern computer vision techniques. The system processes live video streams, isolates the facial region through Haar cascade–based detection, and applies a series of preprocessing steps to enhance …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 35–45 Read article
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VolleyNexis AI: A Multimodal Artificial Intelligence Framework for Opponent Strategy Prediction, Tactical Intelligence, and Athlete Performance Optimization in Volleyball
Abstract: The rapid advancement of Artificial Intelligence (AI) has profoundly transformed sports analytics, enabling deeper insights, real-time data analysis, and enhanced performance predictions. Noticeable results have been seen by enabling automated analysis of complex gameplay patterns along with athlete performance. Volleyball is a dynamic and strategic sport, which requires continuous tactical adjustments and constant monitoring of the player’s performance. This paper presents VolleyNexis AI, which is a multimodal artificial intelligence framework …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 2, 2025 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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Rare Representation of Three Mesiodentes: A Supernumerary Challenge
Abstract: Supernumerary teeth are commonly occurring anomaly which is characterised by the presence of additional teeth compared to the conventional dentition. Mesiodens is the most typical supernumerary tooth, located within the premaxilla between the two central incisors. Mesiodentes is seen in a rare condition when mesiodens erupt in multiple numbers. The etiology behind its incidence is obscure and numerous; however the foremost accepted theory is the hyperactivity of the dental lamina. …
Published in Research and Reviews: A Journal of Dentistry · Vol. 8, Issue 2, 2017 · pp. 21–25 Read article
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An Effective Convolutional Neural Network for Identifying Cancer Blood Disorder Cells Using Microscopic Images
Abstract: Blood, bone marrow, and lymphatic systems are all impacted by hematological cancer is known as a cancer blood disorder. Blood malignancies and various blood disorders pose significant health challenges across all age groups. Early disease detection is essential for effective cancer blood disorder treatment and management. If a blood cancer is not identified in time, it may be hazardous. It results in abnormal white blood cell production by the bone …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 13, Issue 2, 2024 · pp. 29–35 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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CNN-Based Diagnosis of Skin Cancer from Dermoscopic Images
Abstract: Skin cancer has become one of the diseases widely spread over the globe, with melanoma becoming a severe threat to one’s health. Detection of such diseases at the initial stage saves an individual from drastic damage. Using a Convolutional Neural Network (CNN) for detecting skin cancer through image classification as benign or malignant provides significant support to dermatological practice and reduces dependence solely on subjective visual examination. Dermatologists often face …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 1, 2026 · pp. 37–42 Read article
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OpenCV, AI, and Haar Cascade File: A Review
Abstract: Real-time image processing applications using OpenCV encompass a diverse and crucial array of tasks in modern technology. OpenCV's robust capabilities enable the implementation of object detection and tracking, which are vital for surveillance systems to monitor and analyze activities in real time. In the realm of security, face recognition technology, powered by OpenCV, provides accurate and efficient identification and authentication, enhancing safety measures. Gesture recognition, another significant application, facilitates intuitive …
Published in Journal of Open Source Developments · Vol. 11, Issue 2, 2024 · pp. 39–46 Read article