Analytical approach for modelling of thread crimp in jacquard woven two-dimensional fabrics by Brigita Kolcavová Sirková, Iva Mertová

“…The parameter responds both to changes in areas in the input image and to changes in the interlacing in the weave. An algorithm has been built in the ProTkaTex software that records the transition of both warp and weft threads in the pattern’s weave. …”
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Research on load labeling method for big data in novel power system by ZHANG Ximing, XU Huan, YANG Qiuyong

“…The synthetic dataset is compared with the real dataset, and the smart grid machine learning algorithm is trained and tested on this basis. …”
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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

“…The inclusion of the DBI in the k-means++ clustering algorithm facilitated an objective evaluation of cluster quality, ensuring that the selected number of clusters accurately represents the underlying data patterns. …”
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A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing by Yuchen Liang, Yuqi Wang, Anping Li, Chengyi Gu, Jie Tang, Xianjuan Pang

“…This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. …”
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Describing the performance of U.S. hospitals by applying big data analytics. by Nicholas S Downing, Alexander Cloninger, Arjun K Venkatesh, Angela Hsieh, Elizabeth E Drye, Ronald R Coifman, Harlan M Krumholz

“…Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. …”
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Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67.Discussion: In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. …”
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Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis by Bing Zhou, Jialing Zhang, Mingfeng Guo

“…Cox regression and 101 machine learning algorithms were employed to identify prognostic genes and construct a risk model, which was validated using TCGA, ICGC, and GEO datasets. …”
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Toward Hand Gesture Recognition Using a Channel-Wise Cumulative Spike Train Image-Driven Model by Yang Yu, Zeyu Zhou, Yang Xu, Chen Chen, Weichao Guo, Xinjun Sheng

“…Recognizing hand gestures from neural control signals is essential for natural human–machine interaction, which is extensively applied to prosthesis control and rehabilitation. …”
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High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data by Annemarie F. Laudanski, Arne Küderle, Felix Kluge, Bjoern M. Eskofier, Stacey M. Acker

“…Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. …”
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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. …”
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N6-methyladenine identification using deep learning and discriminative feature integration by Salman Khan, Islam Uddin, Sumaiya Noor, Salman A. AlQahtani, Nijad Ahmad

“…A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. …”
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Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data by Tasmiah Haque, Md Asif Bin Syed, Srinjoy Das, Imtiaz Ahmed

“…This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. …”
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Frequency-Based Density Estimation and Identification of Partial Discharges Signal in High-Voltage Generators via Gaussian Mixture Models by Krissana Romphuchaiyapruek, Sarawut Wattanawongpitak

“…However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. …”
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A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster by Ruochen Liu, Haoyuan Lv, Ping Yang, Rongfang Wang

“…Comprehensive experiments show that the proposed algorithm can improve the resource utilization of clusters, reduce the number of physical machines, and effectively meet resource constraints and high availability requirements.…”
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Beyond accuracy: Multimodal modeling of structured speaking skill indices in young adolescents by Candy Olivia Mawalim, Chee Wee Leong, Guy Sivan, Hung-Hsuan Huang, Shogo Okada

“…The experimental results demonstrate that fusing interpretable features, including prosody, action units, and turn-taking, significantly enhances the accuracy of spoken English skill prediction, achieving an overall accuracy of 83% when a machine learning model based on the light gradient boosting algorithm is used. …”
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Hybrid deep learning for IoT-based health monitoring with physiological event extraction by Sivanagaraju Vallabhuni, Kumar Debasis

“…It further enhanced the True Positive Rate (TPR) by 25% through using an online learning algorithm with Incremental Gradient Descent with Momentums. …”
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PV<i>k</i>NN: A Publicly Verifiable and Privacy-Preserving Exact <i>k</i>NN Query Scheme for Cloud-Based Location Services by Jingyi Li, Yuqi Song, Chengliang Tian, Weizhong Tian

“…The <i>k</i>-nearest- neighbor (<i>k</i>NN) algorithm is crucial in data mining and machine learning, yet its deployment on large-scale datasets within cloud environments presents significant security and efficiency challenges. …”
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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

“…The models are optimized using the Whale Optimization Algorithm (WOA) to fine-tune hyperparameters, enhancing their predictive performance. …”
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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…Underwater acoustic target classification has become a key area of research for marine vessel classification, where machine learning (ML) models are leveraged to identify targets automatically. …”
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