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118 articles for “clustering approach”
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A PCA Based K-Means Clustering Algorithm for Wireless Sensor Nodes
Abstract: This paper presents a novel and improved approach for K-Means Clustering of wireless sensor networks, by using Principal Component Analysis for data reduction on the raw data. A wireless sensor network consisting of 100 nodes is classified into three different clusters using PCA based K-Means Algorithm. Davies-Bouldin Index is used as a parameter to check the effectiveness of the clustering algorithm. Experimental results demonstrate that the PCA based K-Means Algorithm …
Published in Journal of Web Engineering & Technology · Vol. 2, Issue 2, 2015 · pp. 6–10 Read article
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Ranking of Image Search by Click-Based Uniformity and Typicality
Abstract: AbstractAccording to the image search re-ranking, apart from the semantic gap and intent gap, which is the gap between the representation of user’s query and the real objective of the users, is forming a major issue in restricting the evolution of image retrieval. In order to reduce human effects, the method use image click-through data, which can be viewed as the implicative feedback from users, to help overcome the intention …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 3, 2016 · pp. 39–46 Read article
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A Clustering Based CPU Scheduling Algorithm for Real Time Systems
Abstract: CPU scheduling is a fundamental function of operating system. The performance and efficiency of multitasking operating systems mainly depend upon the uses of CPU scheduling algorithm. When an operating system wants to execute a process, it does not know the execution time it needs. After running, the exact execution time of that process would appear. Here we present a new scheduling approach using the Partitioning Around Medoid (PAM) clustering algorithm …
Published in Current Trends in Information Technology · Vol. 1, Issue 1, 2011 · pp. 1–7 Read article
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An Improved K-means Clustering Algorithm for Classification of Odor/Gas Sensor Data Using Normalized Cosine Distance Parameter
Abstract: This paper presents a novel approach of K-means Clustering for classification of odors/gases (E-nose) using cosine distance as a distance parameter. A sensor array constituting five sensors is exposed to four different types of gases to extract data. The problem of classifying the data into respective classes in considered as a K-means Clustering task. To quantify the amount of similitude between the data corresponding to same classes; usually euclidean distance …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 2, Issue 2, 2015 · pp. 56–60 Read article
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Feature Extraction and Classification for Using Mammographic Images
Abstract: AbstractOne of the greatest threats prevailing among women is the breast cancer. According to the National Cancer Institute, 22% of new cases occur every year and it is considered to be the second most frequent type of cancer, worldwide. Mammogram is very important imaging technique that used diagnosis in early stages of breast cancer. Mammography is in this case the best diagnostic technique for screening, the radiology tool which gives …
Published in Journal of Advances in Shell Programming · Vol. 4, Issue 2, 2017 · pp. 10–18 Read article
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Performance Analysis of BEEM using Fuzzy Logic in Wireless Sensor Network
Abstract: Advances in wireless sensor network technology provides the availability of small and low-cost sensor nodes which has capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. This sensing capability results in extremely large amount of application areas. However, the characteristics of wireless sensor networks require more effective method for data forwarding and processing. To upgrade the energy utilization, clustering is one of the prototypical …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 7, Issue 1, 2020 · pp. 31–38 Read article
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An Approach for Travel Pattern Analysis Using HDBSCAN and Apriori Algorithms
Abstract: Most mega-city regions around the world are suffering from an ongoing increase in the number of commuting trips. Understanding commuting patterns is crucial for both public and authority planners. The understanding of travel patterns helps passengers to know about the places and the time where they could get vacant transport and also helps authority planners in laying out a new transport service. The traditional way of understanding travel patterns includes …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 2, 2023 · pp. 1–9 Read article
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Improvement of Energy Consumption in Wireless Sensor Networks
Abstract: AbstractA WSN is a set of nodes structured to form a network which are wireless in nature. Routing is a significant technology in this network. A sensor network is composed with vast number of little sensors. All sensor nodes are well-defined with specific parameters in word of energy. Data transmission in wireless communication takes more power than data processing. When the nodes are sending additional number of data, proportionately their …
Published in Journal of Computer Technology & Applications · Vol. 8, Issue 2, 2017 · pp. 1–6 Read article
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Gray Wolf Optimization Based Clustering Using Network Parameters for WSN Applications
Abstract: Modern communication systems need to use energy. This is very important for the Internet of things which’s a big network of smart devices that talk to each other. The Internet of Things is becoming very popular because it is useful. This paper is about a way to send data in a Wireless Sensor Network. We made a routing protocol that works well in a simulated environment. Our approach is to …
Published in Journal of Communication Engineering & Systems · Vol. 16, Issue 2, 2026 · pp. 33–42 Read article
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Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach
Abstract: The present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 7–18 Read article
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Graph based Word Sense Disambiguation for Gujarati
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 Gujarati word પુવૅ (Purva) can mean East and Past. 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 Dictionary-and Knowledge-based method that uses Lexical …
Published in Journal of Advancements in Robotics · Vol. 5, Issue 2, 2018 · pp. 35–43 Read article
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Heuristic Based Transportation Routing and Sizing
Abstract: Employee transportation is one of the most concerned issues and a part of HR policy in today’s work culture. Optimization of these routes and analysis of scenario needs to be done to leverage cost benefits for the organizations of today. Traditional studies focus on goods delivery and pickup, but employees are a totally different entity, which change the type of problem to be solved. As the entity to be considered …
Published in Journal of Production Research & Management · Vol. 5, Issue 3, 2015 · pp. 20–27 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
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Intrinsic Evaluation of Graph Embeddings: Assessing Clustering and Community Detection Performance
Abstract: This paper presents an intrinsic evaluation of some graph embedding techniques on clustering and community detection tasks. We analyze a diverse set of embedding methods, ranging from traditional techniques such as Laplacian eigenmaps to more recent approaches like graph autoencoders, high-order proximity preserved embedding (HOPE), and graph attention network (GAT), using two widely studied datasets, Cora and CiteSeer. Our evaluation relies on two main metrics: Silhouette score with respect to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 40–48 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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Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species
Abstract: The rebuilding of phylogenetic trees for marine species shows major computing problems because of the massive genomic data and the huge biodiversity inherent in ocean ecosystems. Traditional phylogenetic methods are accurate but become more expensive when they are processing with thousands of marine taxa parallelly. This article shows a critical analysis of parallel greedy algorithms as an adaptable solution for large-scale marine phylogenetics. It examines the main principles of greedy …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 33–45 Read article
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Various Routing Algorithms in Wireless Sensor Networks for Energy Minimization
Abstract: The past few years have witnessed increased interest in the potential use of wireless sensornetworks (WSNs) in a wide range of applications and it has become a hot research area. Lot ofresearchers are showing interest in WSN for disaster management where prediction ofupcoming disaster is required well in advance before it happens and in critical areas wherehuman presence is not possible directly; wired communication dose not suit well for suchapplications. …
Published in Recent Trends in Sensor Research & Technology · Vol. 7, Issue 3, 2020 · pp. 26–37 Read article
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Morphological Clustering of Diagnostic Images by Advanced Hard C Means
Abstract: In the proposed approach, morphology technique is used to find the area affected in the diagnostic image and extract the same using adaptive threshold technique. To get more accurate segmentation, Hars C means algorithm is used.Here in this project compare and analyze the qualitative and quantitative performance of different operators. Adaptive canny operator have better performance than sobel and Roberts operator. By using the proposed method can improve PSNR, Noise …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 5, Issue 2, 2018 · pp. 25–33 Read article
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Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations
Abstract: K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 30–37 Read article
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A Novel Clustering Techniques Evaluation using Monte Carlo Simulation
Abstract: In Machine Learning clustering is one the most significant method. Today, we have data in rich from many sources but in order to get meaningful information from it is very boring task. Machine learning clustering algorithms to create cluster to decode the meaningful information from the data, this analysis approach has gained much popularity in recent years. This paper explores evaluation performance of frequently used existing clustering techniques such as …
Published in Research & Reviews : Journal of Statistics · Vol. 10, Issue 3, 2021 · pp. 1–23 Read article