Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques by Christian Salamea-Palacios, Melissa Montalvo-López, Raquel Orellana-Peralta, Javier Viñanzaca-Figueroa

“…A comparison between the performance of the algorithms revealed that the best combination achieved a mean absolute error of 9.85 mg/dL and a correlation of 0.94 between the estimated concentration and the real glucose values. …”
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Improved K-means clustering and adaptive distance threshold for energy reduction in WSN-IoTs by Azamuddin Ab Rahman, Sakib Iqram Hamim

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Flexible Parsimonious Mixture of Skew Factor Analysis‎ ‎Based‎ ‎on‎ ‎Normal‎ ‎Mean--Variance Birnbaum-Saunders by Farzane Hashemi, Jalal Askari, Saeed Darijani

Subjects: “…normal mean-variance distribution‎…”
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Application of K-means supported by clustered systems in big data association rule mining by Lihua Liu

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APPLICATION OF QUADRATIC PROGRAMMING ON PORTFOLIO OPTIMIZATION USING WOLFE’S METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM by Syaripuddin Syaripuddin, Fidia Deny Tisna Amijaya, Wasono Wasono, Shanaz Tulzahrah, Rara Suciati

“…In this research, the classical method uses Wolfe’s method, while the heuristic method uses the particle swarm optimization (PSO) algorithm. This research aims to determine optimal results in portfolio problems using two methods, namely Wolfe’s method and the PSO algorithm. …”
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Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization by Feiyue QIU, Bowen CHEN, Tieming CHEN, Guodao ZHANG

“…To address problems that the effectiveness of feature learned from real noisy data by classical nonnegative matrix factorization method,a novel sparsity induced manifold regularized convex nonnegative matrix factorization algorithm (SGCNMF) was proposed.Based on manifold regularization,the L<sub>2,1</sub>norm was introduced to the basis matrix of low dimensional subspace as sparse constraint.The multiplicative update rules were given and the convergence of the algorithm was analyzed.Clustering experiment was designed to verify the effectiveness of learned features within various of noisy environments.The empirical study based on K-means clustering shows that the sparse constraint reduces the representation of noisy features and the new method is better than the 8 similar algorithms with stronger robustness to a variable extent.…”
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Zero Watermarking Algorithm for Hyperspectral Remote Sensing Images Considering Spectral and Spatial Features by Bingbing Yang, Haowen Yan, Liming Zhang, Qingbo Yan, Zhaoyang Hou, Xiaolong Wang, Xinyu Xu

“…Most existing zero-watermarking algorithms for remote sensing images are designed for panchromatic or multispectral data. …”
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An optimized public opinion communication system in social media networks based on K-means cluster analysis by Mingchao Qi, JunQiang Zhao, Yan Feng

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An intelligent algorithm to fast and accurately detect chaotic correlation dimension by Mengyan Shen, Miaomiao Ma, Zhicheng Su, Xuejun Zhang

“…Therefore, it is necessary to propose a fast and intelligent algorithm to solve the above problem. This study implies the distinct windows tracking technique and fuzzy C‐means clustering algorithm to accurately identify the scaling range and estimate the correlation dimension values. …”
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Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization by C. Jaspin Jeba Sheela, G. Suganthi

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Walking detection for Parkinson’s disease patients and healthy control subjects measured with a smartphone accelerometer using mean amplitude deviation algorithm by Milla Juutinen, Jari Ruokolainen, Juha Puustinen, Anu Holm, Mark van Gils, Antti Vehkaoja

“…The goal of this study was to validate mean amplitude deviation for detecting gait in Parkinson’s disease patients and healthy controls. …”
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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. …”
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Optimal design of Halbach magnetized magnetic screw for wave energy converters based on KELM network optimized by weighted mean of vectors algorithm by Qiongfang Zhang, Haitao Yu, Yulei Liu, Qinghai Qin, Jiahui Zhang

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Enhancing Customer Segmentation Through Factor Analysis of Mixed Data (FAMD)-Based Approach Using K-Means and Hierarchical Clustering Algorithms by Chukwutem Pinic Ufeli, Mian Usman Sattar, Raza Hasan, Salman Mahmood

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Image Segmentation Based on the Optimized K-Means Algorithm with the Improved Hybrid Grey Wolf Optimization: Application in Ore Particle Size Detection by Xinyi Chai, Zijun Wu, Wei Li, Haowei Fan, Xinyang Sun, Jing Xu

“…In this paper, a novel image segmentation algorithm is proposed, combining the K-means algorithm with a hybridized IGK-means. …”
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A Sparse Variable Step-Size Least-Mean-Square Algorithm for Impulsive Noise in a Code-Division Multiple Access System by Mohammad Salman, Ahmed A. F. Youssef, Fahmi Elsayed, Mostafa Rashdan

“…The conventional least-mean-square (LMS) algorithm has a poor performance when the input autocorrelation&#x2019;s eigenvalue spread is quite large. …”
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Low complexity hybrid iterative algorithm based signal detection in massive MIMO system by Shufeng ZHAO, Bin SHEN, Furong YANG

“…Among the uplink signal detection algorithms for massive MIMO systems,the minimum mean square error (MMSE) algorithm can achieve the near-optimal linear detection performance.However,conventional MMSE usually involves high complexity due to the required matrix inversion of large-size matrix,which makes it hard to implement in realistic applications.Based on joint steepest descent (SD) algorithm and Gauss-Seidel iteration,a low complexity hybrid iterative detection algorithm was proposed.The SD algorithm was employed to obtain an efficient searching direction for the following Gauss-Seidel to speed up convergence.Meanwhile,an approximated method was also proposed to compute the bit log-likelihood ratio (LLR) for soft channel decoding.Simulation results verify that the proposed algorithm can converge rapidly and achieve its performance quite close to that of the MMSE algorithm with only a small number of iterations.Meanwhile,the complexity is reduced by an order of magnitude,which is kept consistently of O(K ²).…”
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High-Throughput Satellite Precoding Algorithm Based on Non-Ideal Channel State Information by Yingzhe DOU, Guanchang XUE, Mingchuan YANG

“…In order to suppress co-channel interference caused by frequency reuse, high-throughput satellite communication systems have begun to study ground interference suppression technologies, including multi-user detection technology applied in the reverse link and precoding technology applied in the forward link.A forward link model was established that took into account the eff ects of free space loss, rain attenuation, and beam gain.Based on the non-ideal channel state information model, the minimum mean square error precoding algorithm was improved, and an improved minimum mean square error precoding algorithm suitable for non-ideal channel state information was proposed.The satellite precoding algorithm considered adopted a more accurate channel model with irrational channel information, and incorporated the non-ideal channel state information into the precoding algorithm and eliminated it.The simulation results showed that the high-throughput satellite precoding algorithm could signifi cantly improved the throughput of the system under the condition of non-ideal channel state information, and also improved the robustness of the system.…”
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SIMULATION OF RANDOM ERRORS IN IONOSPHERIC PARAMETERS ESTIMATION OBTAINED BY MEANS OF INCOHERENT SCATTERING by Aleksandr Viktorovich Bogomaz

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Variable step-size LMS algorithm with hybrid weight coefficients based on the Rayleigh distribution curve by CHEN Dongwei, LIU Weidong, ZHANG Mingyi, JIN Mengzhe, FANG Qingyuan

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