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118 articles for “clustering approach”
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Energy Efficient Data Aggregation of Heterogeneous Network using Metaheuristic Approach
Abstract: The development of wireless sensor network results in faster technological development and its fundamental problem of network life time in the given energy constraints leads to several research study and its optimization. Sensor networks revolutionize the paradigm of collecting and processing information from diverse environments. In this paper proposed the distributed clustering optimization to determine the better network performance using the meta-heuristic approach based on Improved Harmony Search Algorithm (IHSA) …
Published in Recent Trends in Sensor Research & Technology · Vol. 1, Issue 1, 2014 · pp. 8–15 Read article
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Implementation of K-Means clustering algorithm with technical indicators to identify Profitable stocks
Abstract: In the ever-changing world of stock market trading, accurately predicting price movements is key to maximizing profits. Technical analysis, which looks at past price data to predict future trends, provides valuable insights for investors. This paper delves into using machine learning methods, particularly the K-Means clustering algorithm, along with moving average data, to categorize daily trading patterns. By breaking down the market into clusters and examining the main patterns within …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 8–14 Read article
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Image Preprocessing and Analysis on Eye Fundus Images Segmentation by Using Density Clustering Methods
Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, so …
Published in Recent Trends in Sensor Research & Technology · Vol. 9, Issue 4, 2022 · pp. 11–18 Read article
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Image Preprocessing And Analysis On Eye Fundus Images Segmentation By Using Density Clustering Methods
Abstract: Abstract: In order to do an automated evaluation of various retinal illnesses such as Diabetic retinopathy, Glaucoma, and Macular Edema, fundus images must be pre-processed first. For many reasons, it's difficult to accurately detect the optic disc. Many blood vessels cross the optic disc, making it difficult to discern the disc's boundaries in fundus images. Lesion regions in diabetic retinopathy look very much like an optic disc's colour and texture, …
Published in Recent Trends in Sensor Research & Technology · Vol. 8, Issue 3, 2021 · pp. 11–18 Read article
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A Comparative Review of Parallel Computing Techniques for High-Performance Computing
Abstract: High-Performance Computing (HPC) has become an essential technology for solving computationally intensive problems in scientific computing, engineering, artificial intelligence, weather forecasting, computational biology, financial modelling, and large-scale data analytics. The increasing complexity and heterogeneity of modern HPC systems have created a strong requirement for efficient parallel computing techniques and portable programming models. Several programming approaches, including Message Passing Interface (MPI), Open Multi-Processing (OpenMP), Compute Unified Device Architecture (CUDA), OpenACC, SYCL, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 2, 2026 · pp. 16–26 Read article
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Relay Node Selection Based Reduced Energy Consumption in Heterogeneous Wireless Sensor Network
Abstract: Energy conserving optimization is of chief concern while scheming a wireless sensor network (WSN) as most of the sensor nodes are furnished with limited power supply. In this paper, we focus on minimizing energy consumption and maximizing network lifetime in three-tiered network architecture. For this obvious goal, clustering techniques are a boon with a hierarchical arrangement to boost the performance of the network as well as reduce the consumption of …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 3, Issue 2, 2016 · pp. 7–14 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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Unleashing the Power of Mathematics in Natural Language Processing: Sentiment Analysis Perspectives
Abstract: Sentiment analysis (SA), which utilizes natural language processing (NLP), computational linguistics, text analysis, image processing, and video processing to extract and analyze subjective information from the internet, social media, and other sources, is becoming increasingly popular in both the business world and the scientific community. It is even possible to model it so that it focuses on polarity, sentiments and emotions, urgency, and even goals. It is able to distinguish …
Published in Recent Trends in Programming languages · Vol. 10, Issue 2, 2023 · pp. 1–9 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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Adaptive Huffman Algorithm for Data Compression Using Text Clustering and Multiple Character Modification
Abstract: Adaptive Huffman algorithm is a popular data compression technique that creates a variable-length binary code for each symbol in a message. However, the original algorithm may not be efficient in compressing text data, particularly when dealing with long sequences of repeated characters. In this study, we propose a novel approach to enhance the compression ratio of the Adaptive Huffman algorithm by utilizing text clustering and multiple character modification. The proposed …
Published in Recent Trends in Programming languages · Vol. 10, Issue 1, 2023 · pp. 30–40 Read article
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A Novel Sentence Ranking Clustering Based Algorithm for Text Summarization
Abstract: Text Summarization is the process of generating reduced text from source document by extraction or generation. A novel approach will be presented for extractive text summarization. The proposed technique performs filtering and then ranking on sentences based on NLP text mining techniques. This method uses the synonyms extracted from online thesaurus dictionary to identify similar sentences and then clusters these sentences to summarize. The sentences are ranked based on sentence …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 2, Issue 2, 2015 · pp. 1–6 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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Representative Feature Selection for Efficient Clustering
Abstract: AbstractClustering is one of the widely used data mining techniques. In machine learning, it is an unsupervised learning method that needs training data to be used. The problem with clustering is that it is computationally expensive and takes more time for grouping high-dimensional data. Therefore it is necessary to reduce number of features in the high-dimensional data in order to make the search space reduced. Many techniques came into existance …
Published in Journal Of Network security · Vol. 5, Issue 2, 2017 · pp. 19–27 Read article
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Modeling Galaxy Formation in a Hierarchical Universe: A Fiducial Approach and Comparison with Observational Data
Abstract: We have developed a detailed model to understand how galaxies form in the framework of hierarchical theories of structure formation. Our model accounts for key processes like the formation and merging of dark matter halos, the heating and cooling of gas inside these halos, the regulation of star formation driven by energy from evolving stars and supernovae, galaxy mergers, and the changes in star populations over time. This approach is …
Published in International Journal of Universe · Vol. 1, Issue 1, 2025 · pp. 30–36 Read article
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A Novel Approach for Adjustment of CH in Energy Efficient routing Protocol (LEACH) for Wireless Sensor Network
Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. In this paper, author develop and analyze low-energy adaptive clustering hierarchy distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating …
Published in Journal Of Network security · Vol. 1, Issue 1, 2013 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 Detailed Survey on Crime Detecting and Clustering Technique in Data Mining
Abstract: AbstractData mining is the extraction of useful information or the knowledge from the set of data or it’s the process analyzing the data from the various perspectives and summarizing into information which is useful. Detection of crime begins with discovery of crime scene, and proceeds from side to side the process of evidence collected works, identification and the analysis. It is called as an act that is being committed or …
Published in Journal of Advanced Database Management & Systems · Vol. 4, Issue 2, 2017 · pp. 19–26 Read article
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Optimizing Sampling Techniques Using Fuzzy Set Theory: A Comprehensive Approach
Abstract: Sampling is a critical process in statistics, used to estimate population parameters without needing to examine the entire population. Traditional sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, face limitations when applied to complex or heterogeneous populations with imprecise boundaries. These methods often fail to accurately represent populations with overlapping characteristics or missing data, resulting in sampling bias and reduced accuracy. To address these challenges, this …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 29–43 Read article
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Comparing UWDBCSN Clustering with LNCA for Node Replication Attack
Abstract: AbstractClustering in WSN divides sensor nodes into different clusters. Each node is headed by a cluster head. Instead of direct routing, the communication can take place between cluster heads that increases the lifetime of sensor network. Some security issues in clustering may arise because of the presence of attacker node(s) in the network. Attacker nodes may disrupt the whole cluster by introducing attacks like node replication, denial of service, wormhole, …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 4, Issue 2, 2017 · pp. 41–49 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article