Electric vehicle charging station demand prediction model deploying data slotting by A.V. Sreekumar, R.R. Lekshmi

“…This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. …”
Get full text

A method for English paragraph grammar correction based on differential fusion of syntactic features. by Weiling Liu, Caijun Zhao, Yongyi Li, Chenglong Cai, Hong Liu, Ruilin Qiu, Ruoci Su, Bingbing Li

“…The new progress of deep learning and natural language processing technology has strongly promoted the development of English grammar error correction. …”
Get full text

Terahertz‐Wave Polarization Space‐Division Multiplexing Meta‐Devices based on Spin‐Decoupled Phase Control by Yuehong Xu, Yuma Takida, Tetsu Suzuki, Hiroaki Minamide

“…Additionally, the advanced meta‐devices M‐2B and M‐4B are designed to generate two‐vector and four‐vector Bessel beams with tunable spatial polarization distributions. …”
Get full text

Un fenomeno pericoloso by Attilio Selvini

“…These new techniques still require solid knowledge of topography and traditional aerial photogrammetry, along with strong skills in using computers and software. With much of the processing now automated, critical decisions—like setting flight parameters, acquiring ground control points, and choosing between vector or orthoprojection methods— directly impact the final outcome. …”
Get full text

MIMD Programs Execution Support on SIMD Machines: A Holistic Survey by Dheya Mustafa, Ruba Alkhasawneh, Fadi Obeidat, Ahmed S. Shatnawi

“…The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. …”
Get full text

A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. …”
Get full text

Multi-stage Optimization Forecast of Short-term Power Load Based on VMD and PSO-SVR by Wenwu LI, Qiang SHI, Dan LI, Qunyong HU, Yun TANG, Jinchao MEI

“…To reduce the non-linearity of the short-term load sequence and improve the prediction accuracy, a short-term load forecasting model based on multi-stage optimization variational mode decomposition (VMD) and particle swarm optimization optimize support vector regression (PSO-SVR) is proposed. In the first stage, VMD optimization and pre-processing of the original load sequence are used to decompose and obtain multiple relatively stable modal components. …”
Get full text

Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding by Kangchen Li, Huanwei Liang, Jialing Qiu, Xulan Zhang, Bobo Cai, Depeng Wang, Diming Zhang, Bingzhi Lin, Haijun Han, Geng Yang, Zhijing Zhu

“…For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. …”
Get full text

Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
Get full text

Software Implementation of the Epps-Pulley Criterion in Matlab Modeling Environment by A. A. Tipikin, A. A. Prusakov, N. A. Timoshenko

“…An assessment of the computational efficiency of the methods showed that the cyclic approach is about three times higher than the matrix-vector approach in terms of consumed time, which is presumably due to the processing of insignificant elements in triangular matrices when performing component-by-component operations. …”
Get full text

Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. …”
Get full text

A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization by Ruiqin Duan, Xiaosheng Peng, Cong Li, Zimin Yang, Yan Jiang, Xiufeng Li, Shuangquan Liu

“…In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) feature processing, bat algorithm (BA) optimization and support vector regression (SVR). …”
Get full text

Influence of high Andean grasslands on landslide reduction in Peru by Albert Franco Cerna-Cueva, Katherin Lourdes Uriarte-Barraza, Grecia Isabel Lobatón-Tarazona, Wensty Saenz-Corrales, Casiano Aguirre-Escalante, Peter Coaguila-Rodriguez, Manuel Reategui-Inga

“…The best-performing machine learning models were linear regression, Gaussian processes, random forest, and support vector machine. …”
Get full text

A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Abstract Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. …”
Get full text

Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…The collected data undergoes processing and is then sent to a remote medical server over the Internet. …”
Get full text

Computational Intelligence-Based Structural Health Monitoring of Corroded and Eccentrically Loaded Reinforced Concrete Columns by Somain Sharma, Harish Chandra Arora, Aman Kumar, Denise-Penelope N. Kontoni, Nishant Raj Kapoor, Krishna Kumar, Arshdeep Singh

“…In this article, an ML-based artificial neural network (ANN), Gaussian process regression (GPR), and support vector machine (SVM) algorithms have been applied to estimate the residual strength of corroded and eccentrically loaded RC columns. …”
Get full text

Cross-scale observation of riparian vegetation: Testing the potential of satellite-UAV-Field integrated observations for large-scale herbaceous species by Weiwei Jiang, Chenyu Li, Henglin Xiao

“…In response to the dual challenges of global change and human activities, riverine ecological management urgently requires a deep understanding of the large-scale ecological processes of dominant vegetation populations along riverbanks. …”
Get full text

Enhancing FTIR Spectral Feature Construction for Aero-Engine Hot Jet Remote Sensing via Integrated Peak Refinement and Higher-Order Statistical Fusion by Zhenping Kang, Yurong Liao, Xinyan Yang, Zhaoming Li

“…Subsequently, a multi-dimensional feature extraction vector construction algorithm was proposed, encompassing a peak feature extraction algorithm based on staged refined processing and a high-order statistical feature extraction algorithm. …”
Get full text

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…This study’s findings highlight the significant potential of hyperspectral imaging and machine learning techniques for advancing precision breeding practices, optimizing seed sorting processes, and enabling targeted agricultural input applications.…”
Get full text

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Nan Yi, Shuangyang Mo, Yan Zhang, Qi Jiang, Yingwei Wang, Cheng Huang, Shanyu Qin, Haixing Jiang

“…These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. …”
Get full text