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65 articles for “Unsupervised”
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Dimensionality Reduction using Eigenfaces and Fisherfaces for Face Recognition Applications using Kernel LMS Algorithm
Abstract: AbstractFace recognition is an intricate numerical technology in computer systems that can support recognition of human faces using methods such as principal component analysis (PCA), linear discriminant analysis (LDA), etc. by comparing the given facial uniqueness with the already available face database. PCA and LDA are classical feature extraction and data representation techniques used in face recognition algorithm involving unsupervised statistical methods. The 2-D facial image can be transformed into …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 2, 2015 · pp. 29–55 Read article
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Text Analysis in Health Care Study Using IoT
Abstract: The Internet of Things (IoT) should be a recent buzzword that has steadily acquired acceptance (IoT). Organizations, governments, and the scientific community have already acknowledged the significance of the Internet of Things as a result of a surge in publications over the past few years. Despite scant research on goals and potential breakthroughs, IoT in health had a significant role in this development. In this study, the only method used …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 11–18 Read article
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An Experimental Approach of Machine Learning Algorithms to Detect Botnet DDoS Attacks
Abstract: Botnets are one of the threats in a network to hamper the quality of the network by disrupting theresources of the network. These Botnets can be controlled remotely by Botmaster. The Machine Learning Algorithms play a major role to detect and control the Botnets that cause to DDoS attacks, malwares and phishing attacks that are vulnerable to network resources. The DDoS attacks are most dangerous malware events that disrupt whole …
Published in Journal Of Network security · Vol. 10, Issue 3, 2022 · pp. 7–14 Read article
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Impact of Pelvic-Floor Muscle Training on Stress Urinary Incontinence – A Narrative Review
Abstract: Background: Urinary incontinence (UI), characterized by the unintentional loss of urine, is a common health issue among women globally. Stress urinary incontinence (SUI) is the most frequently reported type and can adversely affect daily activities and overall well-being. Various contributing factors, including obesity, pregnancy, and advancing age, are associated with its occurrence. Pelvic-floor muscle training (PFMT) is commonly advocated as a primary conservative management strategy for SUI. Objective: To evaluate …
Published in Research and Reviews: A Journal of Health Professions · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
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Well Integrity and Reservoir Behavior under Sudden Field Shutdowns During Armed Conflicts: A Comprehensive Technical Review
Abstract: In the oil and gas industry, the sudden and unplanned cessation of operations resulting from armed conflicts or pandemics typically poses complex and multifaceted technical challenges to both well integrity and reservoir behavior. Minimal or no opportunity for proper pre-shutdown preservation procedures in unplanned shutdowns, unlike planned shutdowns that follow established operational protocols. The mechanisms and consequences of well integrity degradation and reservoir performance alteration under such conditions are synthesized …
Published in Journal of Petroleum Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 40–52 Read article
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Machine Learning Approaches for Intelligent Network Traffic Management in 6G
Abstract: The increasing adoption of smart devices, Internet of Things (IoT) technologies, cloud-based services, autonomous applications, and multimedia platforms has greatly enhanced the demands and complexity of modern wireless communication networks. To meet these evolving requirements, upcoming sixth-generation (6G) communication systems are anticipated to deliver extremely high data transmission speeds, support a vast number of connected devices, ensure minimal communication delays, enable intelligent network management, and provide seamless real-time connectivity for …
Published in Journal of Communication Engineering & Systems · Vol. 16, Issue 2, 2026 · pp. 19–32 Read article
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AI-Driven Cyber Threat Detection and Response Framework for Higher Educational Institutions (HEIs)
Abstract: Higher educational institutions (HEIs) are increasingly exposed to complex cyber threats due to their open-access digital ecosystems, diverse user populations, and extensive research infrastructures. Conventional signature-based intrusion detection systems often fail to detect zero-day and evolving attacks, creating an urgent need for intelligent and adaptive security mechanisms. This research introduces an AI-based framework for detecting and responding to cyber threats, specifically designed for HEIs. The framework employs an unsupervised machine …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 · pp. 28–34 Read article
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Study and Research Survey of Machine Learning on Different Field for Next Research Directions
Abstract: Machine learning (ML) is a part of artificial intelligence (AI). ML is a brain of AI, which works to same to same as human creature. ML used to several learning algorithms is a part of related learning techniques as supervised, unsupervised, reinforcement, deep and deep-reinforcement learning techniques. ML used on various research fields as, cyber security, big data, IOT, big code naturalness, flood prediction, ultra sound computer-aided diagnostic system, medicine …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 7, Issue 2, 2020 · pp. 46–55 Read article
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Analysis of Various Diabetes Prediction Techniques Based on Machine Learning
Abstract: AbstractData mining is a method of mine from the underdone data for maintains useful information. In organize to obtain fundamental knowledge it is important to remove bulky quantity of data. This method is consist of many important components like data cleaning, data selection, data integrity, pattern evaluation with data mining engine and database with graphical user interface. Diabetes is a chronic disease that is also known as Non‐Insulin Dependent Diabetes …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 8, Issue 1, 2021 · pp. 17–22 Read article
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Comparative Analysis of Proposed FCM Clustering Integrated Enhanced Firefly-Optimized Algorithm (En-FAOFCM) for MR Image Segmentation and Performance Evaluation
Abstract: Image segmentation has a significant responsibility in diagnosis and treatment of diseases. To scan patients and determine the severity of certain injuries in hospitals, magnetic resonance imaging (MRI) method is normally used. This paper emphasizes on comparative study of segmentation techniques for segmenting MRI brain images. In this regard, to group the pixels of images in the intensity space, unsupervised clustering techniques are used. Here enhanced firefly algorithm is used …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 1, 2016 · pp. 32–44 Read article
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Colour Based Segmentation of a Landsat Image Using K-Means Clustering Algorithm
Abstract: AbstractImage segmentation is one of the typical vintage subjects in image processing and it acts as a bulls-eye of the image processing technique. By definition, image segmentation means identifying the similar regions in the image; or in other words, identifying the homogenous pixels in the image and grouping all these pixels based on the homogeneity condition considered. This homogeneity condition can be like, colour, texture, size, compactness etc. It is …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 4, Issue 3, 2017 · pp. 31–38 Read article
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A Survey on Different Clustering Algorithms with Their Major Features
Abstract: Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. Clustering is used to organize data for efficient retrieval. The aim is to create homogeneous subgroups of examples. The individuals in the same subgroup are similar; the individuals in different subgroups are as different as possible. One of the problems in clustering is the identification of clusters in given data. A popular technique …
Published in Journal of Web Engineering & Technology · Vol. 1, Issue 3, 2014 · pp. 19–24 Read article
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Credit Card Fraud Detection Using Deep Learning Techniques
Abstract: Credit card frauds have become very common nowadays and lot of cases has been reported in the recent past with the increase in cybercrimes. Online credit card frauds can be efficiently detected exploiting deep-learning techniques. This paper proposes two unsupervised learning algorithms namely Auto encoder and RBM (Restricted Boltzmann Machine) that acquire information from new transactions and estimate anomalous patterns from these transactions to accurately predict number of credit card …
Published in Journal of Web Engineering & Technology · Vol. 7, Issue 1, 2020 · pp. 30–47 Read article
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Improving DBSCAN using Parallel Computing Approaches
Abstract: AbstractData mining has become the buzz word of computational research due to its enormous economical and social significance. Clustering is subset of data mining operations and is a type of learning using observation techniques. Clustering uses on unsupervised learning model and does not require any training data to generate the clustering model. Clustering enables grouping of similar and dissimilar type of data in separate groups. DBSCAN is on emerging and …
Published in Recent Trends in Parallel Computing · Vol. 6, Issue 2, 2019 · pp. 20–26 Read article
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Scalability Analysis of Semantic Relational Key Terms based Document Clustering Using Parsing Text Calcification
Abstract: Abstract: Document agglomeration, one among the standard info mining ways, is an unsupervised learning worldview wherever agglomeration techniques endeavor to tell apart-inborn groupings of the text documents, with the goal that a meeting of clusters is made during which clusters show high intra-cluster closeness and low bury cluster likeness. The importance of document agglomeration rises out of the big volumes of matter documents created. Albeit varied document agglomeration techniques are …
Published in Recent Trends in Programming languages · Vol. 6, Issue 3, 2019 · pp. 33–40 Read article
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A Taxonomy of Machine Learning Techniques
Abstract: Learning is any process by which a system improves performance from experience. Training a model simply means learning (i.e., determining) good values for all the weights and therefore the bias from labeled examples. Machine learning (ML) is a sub-field of artificial intelligence that creates machines which exhibits learning capability and improve their performance from experience without being explicitly programmed. ML focuses on the creation of computer programs that can access …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 3, 2021 · pp. 18–25 Read article
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Review of Deep Learning in Medical Imaging
Abstract: In the past few years, computer-aided analysis has fallen quickly behind. Organ categorization and segmentation is a key stage in computer-aided diagnosis. In recent years, there has been a lot of interest in the division of abdominal organs such as the liver, stomach, kidney, pancreas, and bladder from various picture modalities. Radiologists or medical specialists analyze abdominal pictures the majority of the time. Due to subjectivity, intricacy of the picture, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 9, Issue 3, 2022 · pp. 16–24 Read article
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Empowering Machine Learning: Unraveling Algorithms, Real-world Use Cases, and Evolving Research Trends
Abstract: Machine learning has brought about a revolutionary transformation across numerous industries, offering automated solutions to intricate problems. This article aims to delve into the realm of machine learning algorithms, exploring their real-world applications and the evolving research directions in this rapidly advancing field. The paper presents a comprehensive overview of popular machine learning algorithms, encompassing supervised learning, unsupervised learning, and reinforcement learning, while elucidating the underlying principles that govern them. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 2, 2023 · pp. 70–79 Read article
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Enhancing Online Security: Machine Learning Methods for Phishing Detection—Literature Survey
Abstract: Phishing attacks represent a substantial online security threat as they mimic authentic websites to trick users into disclosing sensitive information. Detecting these malicious websites is crucial for safeguarding user data and maintaining digital trust. This summary provides insight into the application of machine learning methods in the identification of phishing websites. Machine learning, which falls under the umbrella of artificial intelligence, has proven to be a robust instrument for automating …
Published in Journal of Web Engineering & Technology · Vol. 10, Issue 3, 2023 · pp. 29–34 Read article
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An Analytical Review on Word Sense Disambiguation
Abstract: Due to high Semantic Ambiguity that is associated with language, Word Sense Disambiguation is an open problem of Natural Language Processing and Ontology in Computer Linguistics. For instance, the word Head can mean the part of body and a person in charge of organization. The task of automatically assigning the most suitable meaning to a polysemous word can be defined as Word Sense Disambiguation. Several approaches have been done, from …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 1, 2018 · pp. 32–37 Read article