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56 articles for “Robust K-means”
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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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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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A Critical Review of the Limitations of the Arithmetic Mean and the Robustness of the Median in Statistical Analysis
Abstract: Arithmetic mean is an extremely popular measure of central tendency used in statistical analyses, primarily because it is so easy to calculate and has many positive mathematical characteristics. However, when there are outliers, skewed distributions or heterogeneous spread of data, the reliability of the arithmetic mean diminishes greatly. This paper includes a critical review of the limitations of the arithmetic mean and an assessment of the robustness of the median …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 16, Issue 2, 2026 · pp. 21–29 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 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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Visual Features based Paddy Leaf Disease Recognition, its Severity Detection and Remedy Prediction using K-means Clustering and AdaBoost
Abstract: AbstractAgriculture is a vital part of an economy. Paddy is one of the main food crops which play a major role in agricultural field. The gross national income of a country depends on paddy cultivation. But the production of paddy is damaged due to different types of paddy leaf diseases. Generally, farmers and agricultural experts identify diseases manually which is very ineffective and time consuming. So effective recognition, severity detection, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 7, Issue 3, 2020 · pp. 41–52 Read article
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Spatiotemporal Analysis of Mean-Field Coupled Lorenz Oscillators for Applications in Electronic Network Design and Chaotic Synchronization
Abstract: This study investigates the spatiotemporal behavior of a network of 100 coupled Lorenz oscillators interacting through mean-field coupling with coupling strength κ = 0.1 and explores its relevance to electronic system design and nonlinear network architectures. While each oscillator follows classical Lorenz dynamics, the coupling mechanism enables collective behavior that resembles synchronization phenomena observed in distributed electronic and communication systems. Numerical simulations reveal rich dynamical characteristics including partial synchronization, emergent …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 1–9 Read article
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Noise-Driven Collective Behavior in Mean-Field Coupled Lorenz Oscillator Networks
Abstract: This work investigates the spatiotemporal dynamics of an ensemble of 100 identical Lorenz oscillators coupled through a mean-field scheme in the presence of additive noise. Building on prior studies of spatiotemporal chaos in coupled Lorenz arrays and mean-field coupled chaotic oscillators, the focus is on how the competition between deterministic coupling and stochastic forcing shapes collective behavior, including complete synchronization, desynchronization, clustered states, and noise-modified spatiotemporal chaos. The governing equations …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article
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A Secure and Transparent Voting System Leveraging Ubiquitous Computing and Blockchain Technology
Abstract: Ubiquitous computing and blockchain technology are two pivotal innovations driving significant changes in the digital world. This article explores the intersection of these technologies, analyzing how their combination can unlock new opportunities and address existing challenges. Ubiquitous computing envisions seamlessly integrating computing capabilities into everyday objects and environments, aiming to enhance user experience and efficiency. Meanwhile, blockchain technology, known for its decentralized and secure nature, offers robust solutions for transparency, …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 2, 2024 · pp. 9–13 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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A Comprehensive Examination of Cloudflare's IP-based Distributed Denial of Service Mitigation
Abstract: This study dives deep into the world of DDoS (Distributed Denial of Service) attack prevention, with a keen focus on Cloudflare. As a powerhouse in the cybersecurity world, Cloudflare uses smart tactics, like acting as a reverse proxy, to keep online services safe from DDoS attacks. What does Cloudflare do exactly? It acts as a bouncer, checking all incoming server traffic to weed out harmful requests. This unique position lets …
Published in Recent Trends in Parallel Computing · Vol. 10, Issue 2, 2023 · pp. 45–54 Read article
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Robust Quadratic Discriminant Rule using Comedian
Abstract: Discriminant analysis aims to get rules that separate the groups of observations. Moreover, it can be used to classify new observations into one of the known groups. The classical discriminant techniques are based on the classical estimates of mean vector and variance covariance matrix. Unfortunately, these classical estimates are highly influenced by the extreme observations and hence any analysis based on these estimates will provide misleading results when the data …
Published in Research & Reviews : Journal of Statistics · Vol. 8, Issue 2, 2019 · pp. 41–47 Read article
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Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.
Abstract: This research investigates predictive modelling and optimization technique for the tensile and flexural strength of PlA (Poly Lactic Acid) in Fused Deposition Modelling (FDM) 3D printing. Employing Decision Trees and Bayesian Optimization enhances comprehension and control of 3D printing process. Precise model predicts PLA material properties based on input parameters. Methodology involves rigorous data preprocessing, encompassing, cleaning, transformation, and normalization. Hyperparameter optimization via grid search systematically explores configurations, optimizing model …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 203–214 Read article
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Drug-likeness and 3D-QSAR Analysis of 1,2,3-triazoles as Novel Potent HIV-1 Vif Inhibitors: An approach in the development of Leads in the Treatment of HIV Infection
Abstract: AbstractThe human immunodeficiency virus type 1 (HIV-1) Vif protein is one of various primate lentiviral accessory proteins that act as a virulence factor to advance viral duplication and persistence. 3D quantitative structure-activity relationship (QSAR) modeling of thirty-six (36) newly discovered 1,2,3-triazole analogs as potent novel inhibitors of HIV-1 Vif to predict their biological activity (pIC50), to try to improve/optimize it using some numerical data of HIV-1 Vif inhibitors based on …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 9, Issue 3, 2022 · pp. 23–37 Read article
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Investigative Study of Adaptive Fault Tolerance in Optical Networks
Abstract: Optical networks have become the backbone of modern telecommunications infrastructure, enabling high-speed data transmission across global networks. However, these networks face significant reliability challenges due to component failures, signal degradation, and environmental factors. This investigative study examines adaptive fault tolerance mechanisms in optical networks, focusing on emerging technologies and methodologies that enhance network resilience. The research analyzes various fault detection techniques, including machine learning-based approaches, self-healing protocols, and dynamic reconfiguration …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 2, 2025 · pp. 24–30 Read article
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Development of System for Speech Enhancement using Combinational Adaptive LMS on Reconfigurable Platform
Abstract: Low cost and robustness makes the least mean square (LMS) algorithm most popular. Improved adaptive filter performance can be obtained by combinational approach. In this paper, for speech enhancement, we used one of such approaches known as ‘filter combination’. As our simulation results show, Fixed-Sign LMS algorithm overcome other algorithms in terms of SNR and mean square error. Keywords: Adaptive filter, speech enhancement, windows, convex combination
Published in Journal of VLSI Design Tools and Technology · Vol. 6, Issue 3, 2016 · pp. 28–34 Read article
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Comparative Analysis of Digital Image Watermarking using DCT and DWT
Abstract: AbstractWatermarking is a technique to hide information into digital data and provides security against intentional and unintentional attack. It is a very important and emerging field which helps to protect ownership of digital information. Watermarking is the process of inserting or hiding information behind cover data. In this paper we discussed about two digital watermarking techniques, Discrete Cosine Transform (DCT) and discrete wavelet Transform (DWT). The parameters to test robustness …
Published in Journal of Communication Engineering & Systems · Vol. 4, Issue 1, 2014 · pp. 1–5 Read article
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Impact of Big Data Analytics for Efficient Consumer Behavior Prediction
Abstract: Big data analytics plays a critical role in predicting customer behavior by analyzing large and complex data sets from various sources. By applications of predictive modelling principles, it will be acting as a weapon for uncovering patterns and insightful for leveraging from the consumer’s point of view. In our study, we incur sincere efforts to discuss the current trends in the domain of prediction methods involved in consumer behavior. Personalization …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 1, 2023 · pp. 15–22 Read article
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A IoT-Enabled Predictive Intelligence for Real-Time Failure and Damage Evolution Monitoring of Polymer Composites
Abstract: Damage assessment of carbon-fibre-reinforced polymer composites is still challenging since the damage occurs as a combination of matrix cracking, interfacial debonding, delamination and fibre fracture. The present work proposes a framework for predictive-intelligence based on IoT for multiaxial fatigue and compression-after-impact (CAI) CFRP experiments, employing publicly available acoustic-emission (AE) datasets. A causal CNN–GRU attention model is developed by integrating time-domain, spectral, wavelet, loading-history and trend features to estimate the damage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Method Development and Validation for the Simultaneous Estimation of Ertugliflozin and Sitagliptin in Bulk and Tablet Dosage Form by using HPLC.
Abstract: A simple, Accurate, precise method was developed for the simultaneous estimation of the Ertugliflozin and Sitagliptin in bulk and tablet dosage form. Chromatogram was run through DIKMA Spursil C18 column (4.6 mm x 150mm, 3.0 µm). Mobile phase containing KH2PO4: Methanol (45: 55) was pumped through column at a flow rate of 1ml/min. Temperature was maintained at Ambient. Optimized wavelength for Ertugliflozin and Sitagliptin was 225 nm. Retention time of …
Published in Trends in Drug Delivery · Vol. 8, Issue 3, 2023 · pp. 40–49 Read article