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73 articles for “Feature Extraction”
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Blood Cell Subtype Classification Using Deep Learning
Abstract: A vital component of human bodies is blood. Despite making up only 1% of the human body, blood cells have a greater impact on disease and infection detection. These blood cell subtype classifications also aid in ensuring that patients receive the quickest-acting medical treatment and recover swiftly. To get the findings, many machine learning techniques are used, including the Feature Extraction methods (K* classifier and Decision table) and the Image …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 37–45 Read article
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Blood Group Prediction Using Fingerprint
Abstract: There is a widespread need worldwide for a cost-effective solution to measure blood groups, especially critical in underprivileged regions. Leveraging Image Processing, a prevalent technology in both affluent and resource-limited areas, presents a viable option for addressing this demand. This project advocates for the use of Image Processing algorithms and detection models to non-invasively ascertain blood groups. Its goal is to offer workable solutions for creating a point-of-care instrument that …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 9, Issue 2, 2023 · pp. 1–6 Read article
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Facial Image Analysis for Identification of Gender: A Machine Learning based Approach
Abstract: Facial biometrics for gender classification is known to significantly improve the efficiency of person identification in biometric access control models. It not only increases the speed but also improves the accuracy. It reduces the effort of finding a match for a face in the databases to almost a half. The process like any pattern recognition framework needs to extract the useful features. A novel gender identification algorithm is proposed which …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 7, Issue 1, 2021 · pp. 23–29 Read article
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Depression Detection System
Abstract: Traditionally, depression was identified through in-depth clinical interviews, during which the psychologist would analyze the subject’s responses to ascertain his or her mental condition. With the prevalence of depression increasing, some automated and trustworthy methods of depression recognition are needed. Our Objective it to design a model that can extract relevant features automatically as an input using efficient machine learning algorithms and further detect the depressive state of the user …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 28–37 Read article
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Short Communication on Fingerprint Image Enhancement and its Future Scope
Abstract: The fingerprint images are rarely of perfect quality, due to the reasons like variations in impression condition, skin condition, scanning devices or may be due to non-co-operative attitude of the subject. This degraded quality of image can result in a significant number of spurious minutiae being created and genuine minutiae being ignored. A vital step in studying the statistics of fingerprint minutiae is to reliably extract the minutiae feature from …
Published in International Journal of Software Computing and Testing · Vol. 2, Issue 1, 2016 · pp. 7–8 Read article
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Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons
Abstract: Emotion recognition from facial manifestations increases the interaction between humans and computers by enabling the interaction between humans and computers to effectively explain human feelings. This study presents a comparative analysis of three approaches to the emotional recognition of faces: a custom Convolutional Neural Network (CNN) model, Traditional Machine Learning (ML) methods, and other deep learning architectures such as VGG, ResNet, and Hybrid CNN–RNN models. The proposed CNN-based system, developed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 23–31 Read article
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Forecasting Trends in the Stock Market by Using Deep Learning Approaches
Abstract: Accurately predicting stock market trends remains a significant challenge in the field of finance. Deep learning, a subset of machine learning, has emerged as a powerful tool for capturing complex relationships within data. What sets deep learning apart is its ability to handle vast amounts of data, recognize intricate features, and adapt to evolving patterns, all while uncovering hidden insights. At its core, deep learning uses artificial neural networks that …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 2, 2024 · pp. 22–30 Read article
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AI-Integrated Precision Agriculture System for Fertilizer Optimization, Multimodal Crop Yield Prediction
Abstract: Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 1, 2026 · pp. 11–17 Read article
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Advanced Diabetic Retinopathy Detection Using Deep Learning Classifiers
Abstract: Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 1, 2025 · pp. 26–33 Read article
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Maculopathy Detection and Recommendation using Machine Learning
Abstract: Maculopathies, a diverse group of eye diseases affecting the central portion of the retina, the macula, are a leading cause of vision loss globally. Early detection and accurate diagnosis are critical for timely intervention and preventing irreversible vision loss. However, traditional methods such as manually detecting the disease are time-consuming, tiring, and often lack sensitivity and specificity, hindering optimal patient care. Here we delve into the applications of machine learning …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 10, Issue 1, 2024 · pp. 10–16 Read article
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Brain Tumor Detection using Machine Learning Methods-KNN, SVM, DT, RF
Abstract: Brain tumor is a major contributor to illness and death worldwide, and timely diagnosis is essential for successful treatment and improved outcomes Recent research has looked into the possibility of machine learning algorithms for detecting and classifying brain tumours in medical pictures. In this study, four commonly used machine learning algorithms (K-Nearest Neighbours, Decision Trees, Support Vector Machines, and Random Forests) were evaluated for their effectiveness in brain tumor detection. …
Published in International Journal of Chemical Engineering and Processing · Vol. 9, Issue 2, 2023 · pp. 48–59 Read article
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Criminal Face Identification Using Deep Learning and Machine Learning
Abstract: Criminal face identification is a crucial task for law enforcement agencies to prevent and solve crimes. The traditional methods for identifying criminals using human expertise and eyewitness testimonies have several limitations, such as subjectivity, inaccuracy, and unreliability. Machine learning and deep learning techniques have yielded promising gains in facial recognition and identification applications in recent years. In this study, we propose a criminal face identification system that combines machine learning …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 9, Issue 1, 2023 · pp. 28–34 Read article
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Detecting COVID_19 in X-ray images using artificial neural network
Abstract: In this paper, we propose a method for detecting COVID-19, which is most important for now days in lowering mortality rates. COVID-19 had a rapid impact on our daily lives, businesses, and global trade and transportation. Chest CT scans and CT x-rays reveal particular radiographic abnormalities in people with COVID-19 by means of faster and more efficient representation of the patient's lung that enables for the detection of viral phenomena. …
Published in International Journal of Broadband Cellular Communication · Vol. 8, Issue 1, 2022 · pp. 32–38 Read article
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Ballistic Impact Performance of Composite Armor Using Ultrasonic Nde and Ansys Autodyn
Abstract: The world is currently under the threat of terrorist attacks and wars. This will be very critical because the scientists have developed advanced weapons (guns, bullet) for the awesome attacks. Therefore, the safety of soldiers and vehicles of the army has become a major concern. Armors are used in defense applications to provide protection for soldiers and military vehicles. In general, armor is made from natural materials, composite materials and …
Published in International Journal of Mechanics and Design · Vol. 9, Issue 1, 2023 · pp. 1–23 Read article
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Classification of Fetal ECG Arrhythmia by Using Advance Hybrid Deep Learning Technique CNN-LSTM
Abstract: Fetal heart abnormalities, particularly fetal heart arrhythmias, are becoming an increasing concern in maternal healthcare. Early and accurate detection remains challenging due to limitations in conventional diagnostic techniques and inadequate signal processing methods. Existing studies have applied various machine learning and deep learning approaches; however, capturing the complex characteristics of fetal electrocardiogram (ECG) signals remains a significant gap. This study proposes an advanced deep learning-based approach for detecting fetal heart …
Published in International Journal of Cardiovascular Nursing · Vol. 12, Issue 1, 2026 · pp. 27–36 Read article
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Detection of Brain Tumor Using K-means Clustering Algorithm
Abstract: A brain tumor is a dangerous medical illness that, if not treated promptly, can be fatal. Therefore, it becomes essential to find the tumor early on in order to organize therapy as soon as possible. A brain tumor is the result of the growth of abnormal cells in the brain. Brain tumors come in many different forms. The majority of brain tumors are malignant (cancerous); however, some are benign (noncancerous). …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 9, Issue 2, 2023 · pp. 1–9 Read article
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A Conceptual study of Metabolism wsr to metabolism of Carbohydrate,Protein and Fat
Abstract: Energy homeostasis is as critical as other vital process of body.It is produced in body by various chemical reaction that take place in each cell of living organism .Unique feature of living organism by which they extract energy from the environment and use it to carry out activities such as growth,development and reproduction.It is a common belief that Thin people have higher metabolic rate and Fat people have lower metabolic …
Published in International Journal of Nursing Critical Care · Vol. 7, Issue 2, 2021 · pp. 22–29 Read article
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Simultaneous Analysis of Radar Look Angel and Placement of Features to Identification of Terrain Feature (Mianrahan Basin in West of Iran
Abstract: In this study, the effect of look angle and placement of feature than radar sensor to detection of the effects of geomorphological features was conducted simultaneously. In this study, the C-band of satellite images Sentinel -1 was used. The images were captured in two different look angles and two different look directions. Four features such as valley, blade, alluvial fans and debris was extracted by visual interpretation of the images, …
Published in International Journal of Landscape Planning and Architecture · Vol. 3, Issue 1, 2017 · pp. 1–10 Read article
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Detection of Phishing Sites Using Machine Learning
Abstract: The content of phishing websites and online-based material provides a variety of indicators. One of the criminal attacks that are successful in the online world is phishing sites that direct users to a phishing website that looks and acts like a legitimate website in order to steal the victim's personal and sensitive information. The Extreme Learning Machine-based version that was suggested was excellent at spotting phishing websites. Internet page types …
Published in International Journal of Software Computing and Testing · Vol. 9, Issue 1, 2023 · pp. 33–39 Read article
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Multi-class Fault Diagnosis of Rolling Element Bearing Based on Empirical Mode Decomposition and Entropy Features
Abstract: Rolling element bearings are one of the most commonly used machine elements in engineering industry. Fault detection and diagnosis of rolling element bearings is essential for prevention of malfunction and failure during operation. The present work deals with rolling element bearing fault diagnosis by using Support Vector Machine (SVM) and Artificial Neural Network (ANN). Four types of bearing conditions are considered in the present analysis: inner race fault (IR), outer …
Published in International Journal of Manufacturing and Materials Processing · Vol. 2, Issue 1, 2016 · pp. 01–10 Read article