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1975 articles for “Mean” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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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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Determination of The Mean Fetal Biometrics and The Mean of Ratio of biometrics in 2nd and 3rd Trimester of Pregnancy in Correlation with Fetal Gestational Age: As a Predictive Biometric Parameters
Abstract: Introduction: Fetal cerebellar diameter in normal gestation is also highly correlated with fetal growth indices. Objective: To determine to mean fetal biometric ratios in 2nd and 3rd trimester of pregnancy on ultrasound as a predictive biometric parameter of gestational age. Study Design: Cross-sectional study. Material and Methods: A total of 140 pregnant women in their second and third trimester were included in this study. BPD, AC, FL and TCD were …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 7, Issue 3, 2018 · pp. 12–16 Read article
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Auditory Discrimination for Meaningful and Non-meaningful Minimal Pairs in Persons with Aphasia and Neurotypical Population
Abstract: The present study was carried out with the aim of investigating auditory discrimination in persons with aphasia. Neurotypical participants, persons with anomic aphasia and persons with Broca’s aphasia served as the participants of the study. Auditory discrimination task was carried out on all the participants. The participants were instructed to denote if the pair of words was alike or different by pressing the designated keys on a game pad. Neurotypical …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 10, Issue 1, 2020 · pp. 16–19 Read article
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Rating Based Sentimental prediction: A Six Gram Statistical Mean-Median Approach
Abstract: Revolution in internet has opened a new platform for expressing the user opinions on various aspects openly in various Business domains. The expressed views include very useful information with which analysis of the user expectation and requirements can be done. Analyzing the user views and its prediction will help the business people to enhance their services to the customers as per user requirement. In this paper a novel approach is …
Published in Journal of Advanced Database Management & Systems · Vol. 5, Issue 2, 2018 · pp. 33–42 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 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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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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Circular Retiming technique to design Optimized Least Mean square architecture for adaptive filter
Abstract: Circular retiming improves performance and power consumption of the Least Mean Square (LMS) adaptive filter. It is implemented for weight updated block and error computational block to minimize the adaptive filter error without violating the functional equivalence output. Here, filter multiplication block is updated in a circular convolution format and is retimed to reduce the dynamic power consumption. Additional retime flip-flops shows better performance result compared with the existing state-of-the-art …
Published in Journal of VLSI Design Tools and Technology · Vol. 8, Issue 3, 2018 · pp. 1–9 Read article
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Extracting Alphabets and Numbers from License Plate using Mean Value
Abstract: Segmentation plays a significant role in computer vision and its related applications of image processing. Segmentation is actually a process of partitioning a digital image into multiple segments (sets of pixels) to simplify and/or change the representation of an image into more meaningful and easier to analyze. This paper presents a method of license plate extraction using segmentation method and mean value of the images. We extracted the numbers and …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 1, Issue 3, 2014 · pp. 1–8 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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Parallel Algorithms for Mean, Variance, and Standard Deviation over a 2D-Mesh Network
Abstract: Abstract: Due to advanced technology and usage of internet at worldwide scale, there has always been a challenge to handle and manage the huge amount of data in an efficient way. There are so many statistical and non-statistical methods applied to data to find result and conclusions that can later be used to solve many other complex problems in an easy and fast way, which also reduces the cost. In …
Published in Recent Trends in Parallel Computing · Vol. 6, Issue 3, 2019 · pp. 17–22 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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Fractional Riemannian Fuzzy C-Means with Time-Series Regularization for Economic Manifold Forecasting
Abstract: A fractional Riemannian fuzzy c-means framework is proposed for uncertain economic forecasting on non-Euclidean data domains. Observations are represented on a Riemannian manifold, cluster centres are intrinsic prototypes, and a latent fuzzy regime signal is regularised by both autoregressive and fractional-memory penalties. The resulting objective couples geometric clustering with time-series consistency, thereby discouraging partitions that are locally plausible but temporally incoherent. Closed-form membership updates, exponential-map centre updates, normal equations for …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 49–55 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 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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Eye Disease Classification Using K-means Clustering Algorithm and Ensemble Classification Approach
Abstract: In this study, we present a comprehensive approach for the classification of eye diseases, specifically targeting normal, cataract, glaucoma, and diabetic retinopathy conditions. This research uses a dataset from Kaggle, which provides a wide and varied collection of retinal images to ensure good representation. The methodology encompasses advanced image processing and machine learning techniques to ensure accurate diagnosis and prediction. The preprocessing phase involves a series of image enhancement techniques …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 2, 2025 · pp. 15–27 Read article
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Fault Diagnosis of Air Compressor (AC) System using Local Mean Decomposition (LMD) and Logistic Regression (LR) Machine Learning Classifier
Abstract: This article presents a detailed and systematic procedure for performing fault diagnosis in an air compressor (AC) system by analyzing the audio signals generated during its operation. The analysis covers both normal (healthy) conditions and seven distinct types of faults, including bearing failure, flywheel malfunction, inlet valve leakage, outlet valve leakage, non-return valve failure, piston ring defect, and rider belt issues. To acquire the acoustic signals, the researchers utilized a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 416–427 Read article
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Hardware Design using Flexible Hk-Means
Abstract: High performance hardware is required to meet the necessities of consumer electronics. Color quantization and image partition are an inevitable part for different applications, and HKMeans is the main algorithm that is used for color quantization and image partition owing to its low cost, less computational time and the hardware area required. The computational time and the hardware area increases as the quantization number increases. Hierarchical K-means or HK-Means is …
Published in Journal of Electronic Design Technology · Vol. 6, Issue 1, 2015 · pp. 30–35 Read article
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Identification of Thyroid Disease Severity using Fuzzy C-Means
Abstract: Clustering is a primary data description method in data mining which group’s most similar data. Data clustering is one of the important problems in the fields of data mining, bio-informatics and pattern recognition. Various algorithms are used to solve this problem. This paper presents the performance analysis of k-means clustering algorithm and compares it with fuzzy C- Means (FCM) algorithm on Thyroid disease data set. We also find the accuracy …
Published in Journal of Advanced Database Management & Systems · Vol. 1, Issue 1, 2014 · pp. 13–19 Read article