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387 articles for “clustering”
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An Innovative Fuzzy-Enhanced Black Widow Spider Optimization for Energy-Efficient Cluster Communication Through the Selection of the Ideal Cluster Head
Abstract: This paper presents a novel approach to enhance energy efficiency in cluster-based wireless sensor networks (WSNs) by integrating fuzzy logic with the black widow spider optimization (BWSO) algorithm. The focus of the suggested fuzzy-enhanced BWSO approach is on cluster head selection, which is essential for lowering energy consumption and enhancing network performance. The program uses fuzzy logic to assess a number of factors, including network density, node proximity, and energy …
Published in International Journal of Wireless Security and Networks · Vol. 2, Issue 2, 2024 · pp. 28–34 Read article
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Fuzzy Based Clustering Algorithm Rebroadcast with Ant Colony Based Cluster Head Selection for Minimizing the Routing Overhead in Mobile Ad-Hoc Networks
Abstract: Mobile Ad-hoc Networks (MANET) are built with no sort of management, rendering the quality of servicedespite the routing overhead. With MANET, mobile hosts cannot interact with other hosts in a single hop.In the case of multi-hop, packets transmitted by the source host get relayed with the help of multipleintermediate hosts prior to its arrival at the destination host. Owing to the node movement in MANETs,path outages and route discoveries happen …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 8, Issue 2, 2021 · pp. 12–27 Read article
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An Application of Recommendation Systems to Cluster-based Pattern Recognition
Abstract: Information overload is a common problem faced by internet users; so, the recommendation system is becoming increasingly important to this service. Collective filtering is used by recommendation systems to offer recommendations to individuals or for products based on their shared interests. Data or people should be grouped into clusters so that they have more in common with those in the same cluster and stand out from those in other clusters. …
Published in Recent Trends in Parallel Computing · Vol. 10, Issue 2, 2023 · pp. 28–36 Read article
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A Case Study of Scissor Cluster in Meerut City
Abstract: Usually small scale industrial clusters grow due to certain type of expertise of a community located at a location. It develops and grows due to the condition of the local market, availability of raw material and technical expertise of local people. Government policies help in the development of the industrial clusters at various locations. Some of the successful industrial clusters are sports goods industrial cluster in Meerut, hosiery industrial cluster …
Published in Journal of Production Research & Management · Vol. 2, Issue 1-2-3, 2012 · pp. 1–17 Read article
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Study on Genetic Diversity in Relation to Number of Clusters in Wheat (Triticum aestivum L.)
Abstract: The present investigation was conducted to examine the genetic diversity for several morphological traits in bread wheat (Triticum aestivum L.). The experiment was conducted at the Department of Genetics and Plant Breeding, Janta Vedic College, Baraut, Baghpat (UP), India. The experimental materials consisted of 50 cultivars of bread wheat grown in the randomized block design (RBD) with three replications. Multivariate analysis using D2 statistics [1] was used for assessing the …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 6, Issue 3, 2017 · pp. 18–23 Read article
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Performance Analysis of Hierarchical and Non Hierarchical Clustering Techniques
Abstract: Clustering is a procedure to organizing the objects in to groups or clustered together, based on the principle of minimizing the intra-class similarity. The various clustering algorithms are analyzed and compare the performance of clustering algorithms on aspect for validity indices to build the model. The aim is to judge the efficiency of different clustering techniques on wine dataset and determine the optimum algorithm. The results show that complete linkage …
Published in Research & Reviews : Journal of Statistics · Vol. 9, Issue 2, 2020 · pp. 54–71 Read article
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Country Cluster Visualization Based on Agricultural Imports: Unsupervised Learning Approach
Abstract: The clustering algorithm used in this analysis makes it easier for policymakers to understand performance metrics. K-means clustering has been demonstrated to be a tool for analyzing countries' agricultural imports and a visualization tool for interpreting the results in this study. Authors have used agricultural imports from 190 nations from Knoema, a web-based open data platform. Cereals, meat, and coffee imports from 190 nations in 2016 are included in the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
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Improving Node Efficiency in Wireless Sensor Networks Using Advanced Clustering and Routing Techniques
Abstract: Many Internet of Things (IoT) applications, such as disaster relief, smart buildings, smart farming, and healthcare monitoring, have made use of wireless sensor networks (WSN). It is one of the alternatives to address different IoT difficulties in different contexts. One of the main problems with sensor networks is power efficiency. WSN operated on the Client-Server (CS) architecture in the past, however researchers suggested Mobile Agent (MA) based WSN to increase …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 2, 2024 · pp. 35–64 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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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
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Genetic Diversity Studies of Eggplant (Solanum melongena L.) Genotypes
Abstract: Twenty-two genotypes of eggplant were evaluated to assess the genetic diversity within the genotypes at Horticulture Research Centre (HRC) field, Regional Agricultural Research Station (RARS), Bangladesh Agricultural Research Institute (BARI), Jamalpur, Bangladesh during the winter season of 2013-2014. Genetic diversity among 22 eggplant genotypes was estimated using Mahalanobis’s D2 statistic. Altogether five clusters were formed. Cluster II enclosed the highest number of genotypes (6) and cluster IV contained the lowest …
Published in Research & Reviews : Journal of Botany · Vol. 8, Issue 1, 2019 · pp. 42–57 Read article
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Effects of Cluster Computing on Big Data Analysis and Network Topology
Abstract: The rapid expansion of big data has posed substantial difficulties for conventional computing systems. As a result, cluster computing has grown to be a potent method for effective large data processing. Cluster computing involves multiple interconnected nodes functioning as a unified system, pooling together their processing, storage, and memory resources. These nodes are typically connected through high-speed networks such as ethernet or InfiniBand, facilitating efficient data sharing and communication among …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 1, 2023 · pp. 31–39 Read article
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Efficient Clustering Techniques for Data Stream Mining
Abstract: Data mining mainly works on a massive database for storing heavy amount of data. It is generally essential for extracting the meaning insights from the massive, continuously growing database. The traditional method often struggles with sheer volume and the dynamic nature of the modern data. Data stream mining allows for the real-time analysis, means insights are generated as the data arrives, and not after the long batch process. This continuous …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 26–32 Read article
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Extracting High Dimensional Data by Using Fast Clustering Based Feature Subset Selection System
Abstract: AbstractThis paper presents the feature assortment involve identifying a subset of the majority valuable features with the intention of produce companionable results the same as the inventive complete set of features. A feature assortment system could exist evaluate commencing both the effectiveness and efficiency points of analysis. Although the effectiveness concerns the point in time necessary in the direction of locate a subset of features, the efficiency is linked toward …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 3, Issue 3, 2016 · pp. 1–13 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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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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Comparative Analysis of Distributed Energy Efficient Clustering Protocols in Heterogeneous Wireless Sensor Network
Abstract: Wireless Sensor Networks have huge applications in the area of military, health and infrastructure protection etc. The various features of WSN such as dynamic topology, application specific and self configurable made it to be application to vast areas, but a limitation is also there which is power constraint nature of WSNs. There evolved many transmission approached to estimate cost of energy consumption in WSNs, out of which cluster based scheme …
Published in Journal Of Network security · Vol. 8, Issue 2, 2020 · pp. 1–7 Read article
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Numerical Simulation and Design of Efficient Brain Tumor Segmentation with Hybrid Fuzzy K-Means Clustering Technique
Abstract: The goal of this study is to detect brain cancers from MRI images using a Matlab GUI interface. Using the GUI, this application may employ numerous combinations of segmentation, filters, and other image processing methods to produce the best results. We begin by applying the Prewitt horizontal edge-emphasizing filter on the image. “Watershed pixels” are the next step in detecting tumours. The most essential aspect of this project is that …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 3, 2021 · pp. 8–17 Read article
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Quasi-Sobol-Based Optimization and Sigmoid Fuzzy Logic for Efficient Task Clustering in Decentralized Edge-Cloud Architectures
Abstract: Decentralized edge-cloud systems face a lot of issues in efficient task clustering, resource allocation, and real-time decision-making. Conventional methods mostly fail to work well under dynamic workloads and uncertain conditions. This study intends to optimize task clustering through quasi-Sobol-based optimization and Sigmoid Fuzzy Logic for resource allocation, enhancing decision-making accuracy and achieving efficiency in the system at the edge-cloud environment. A hybrid technique that has incorporated optimization via Quasi-Sobol sequences …
Published in Trends in Machine design · Vol. 12, Issue 2, 2025 · pp. 9–18 Read article
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A Review on Algorithms of Cluster Head in Mobile Ad Hoc Networks
Abstract: AbstractMobile ad hoc networks consists of mobile nodes that communicates with each different with none mounted topology. Cluster head choice, energy consumption and security may be a great challenge for MANET recently. Optimizing cluster head choice minimizes the energy consumption during a specific network and in addition reduces the information transmission overheads. There are several algorithms for cluster head choice that are supported many parameters. For higher utilization of energy, …
Published in Current Trends in Signal Processing · Vol. 8, Issue 3, 2018 · pp. 1–6 Read article