Multi‐Wound Classification: Exploring Image Enhancement and Deep Learning Techniques by Prince Odame, Maxwell Mawube Ahiamadzor, Nana Kwaku Baah Derkyi, Kofi Agyekum Boateng, Kelvin Sarfo‐Acheampong, Eric Tutu Tchao, Andrew Selasi Agbemenu, Henry Nunoo‐Mensah, Dorothy Araba Yakoba Agyapong, Jerry John Kponyo

“…The approaches used included Contrast Limited Adaptive Histogram Equalization (CLAHE) with machine and deep learning models, Discrete Wavelet Transformations (DWT) with a novel Gated Wavelet Convolutional Neural Network (CNN) model, and FixCaps, an improved version of Capsule Networks utilizing Convolutional Block Attention Module (CBAM) to reduce spatial information loss. …”
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Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater by Areej Alhothali, Hifsa Khurshid, Muhammad Raza Ul Mustafa, Kawthar Mostafa Moria, Umer Rashid, Omaimah Omar Bamasag

“…This study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total organic carbon (TOC) in produced water (PW) using tea waste biochar (TWBC). …”
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Skin Lesion Image Segmentation Algorithm Based on MC-UNet by Guihua Yang, Bingxing Pan

“…Aiming at the situation of dermatoscopic images with fuzzy lesion boundaries, variable morphology and high similarity to background, this paper proposes a skin lesion segmentation algorithm that achieves higher segmentation accuracy by combining existing convolutional neural network methods. The algorithm begins by using a Multiscale Residual Block (MRB) with different-sized convolutional kernels to enlarge the receptive field and extract multi-scale features of dermatoscopic images. …”
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POTENTIAL OF MACHINE TRANSLATION IN MUSEUM MEDIA DISCOURSE: ANALYSIS OF MODERN BROWSER SYSTEMS by Vera A. Mityagina, Anna A. Novozhilova, Anna P. Naumova

“…The functional capabilities of machine translation systems, optimized by neural network technologies, are viewed as denotative equivalence provision, correct transference of the majority of proper names and adequate actualization of the lexical units meanings with their reference to the context. …”
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Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission by Sandeep Sachdeva, Maninder Singh, U. P. Singh, Ajat Shatru Arora

“…To reduce the error of load forecasting, fuzzy method has been used with Artificial Neural Network (ANN) and OFDM transmission is used to get data from outer world and send outputs to outer world accurately and quickly. …”
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Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM by A. Tianjian Yang, B. Xing Wang, C. Siyi Cheng, D. You Chen, E. Xi Zhang

“…On this basis, the cooperative jamming decision schemes and their corresponding cooperative jamming effectiveness values are solved at different locations in the target space, and the results are detected as outliers using box plots, thus constructing sample data for cooperative jamming effectiveness evaluation. Subsequently, a neural network based on the extreme learning machine methodology is developed, with its initial weights and biases fine-tuned through an improved particle swarm optimization, which is termed IPSO-ELM. …”
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Rapid Fluid Velocity Field Prediction in Microfluidic Mixers via Nine Grid Network Model by Qian Li, Yuwei Chen, Taotao Sun, Junchao Wang

“…Using this theory, we developed and trained an artificial neural network (ANN) to predict the fluid dynamics within microfluidic mixers. …”
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Analysis and Dynamic Prediction of Bus Dwell Time Under Rainfall Conditions by Baoyun SUN, Yaping YANG, Lei DONG, Honglin LU, Zimin WANG

“…Support vector machine, k-nearest neighbour and backpropagation (BP) prediction models were established, and the BP neural network model, having the best prediction effect, was optimised using a genetic algorithm (GA). …”
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Susceptibility modeling of hydro-morphological processes considered river topology by Nan Wang, Mingxiao Li, Hongyan Zhang, Weiming Cheng, Chao Du, Luigi Lombardo

“…A graph-based deep neural network improves the predictive and interpretability of HMP susceptibility modeling using embedding learning techniques. …”
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Vision transformers for automated detection of diabetic peripheral neuropathy in corneal confocal microscopy images by Chaima Ben Rabah, Ioannis N. Petropoulos, Rayaz A. Malik, Ahmed Serag

“…The ViT model's performance was also compared to ResNet50, a convolutional neural network (CNN) previously applied for DPN detection using CCM images. …”
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Deep learning-enhanced defects detection for printed circuit boards by Van-Truong Nguyen, Xuan-Thuc Kieu, Duc-Tuan Chu, Xiem HoangVan, Phan Xuan Tan, Tuyen Ngoc Le

“…., a type of convolutional neural network (CNN)) model. The proposed algorithm is tested in three different lighting conditions: low light, normal light, and high light conditions. …”
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Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling by Lu-Kai Song, Guang-Chen Bai, Cheng-Wei Fei, Jie Wen

“…To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. …”
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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…To verify the effectiveness of the proposed model, we extensively apply it to neural network-based models. We compare and analyze the performance of the proposed model with the comparisons using actual electricity usage data for 4710 households. …”
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Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods by Zhenhai Guo, Xia Xiao

“…These approaches are improvements on the power curve modeling that is originally fitted by the single layer feed-forward neural network (SLFN) in this paper; in addition, a data quality check and outlier detection technique and the directional curve modeling method are adopted to effectively enhance the original model performance. …”
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Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm by Nana Zhang, Qi An, Shuai Zhang, Huanhuan Ma

“…The prediction performance of the Light gradient boosting machine model is evaluated by comparing it against multiple benchmark models (ARIMA, decision tree, random forest, support vector machine, XGBoost, and artificial neural network) in terms of accuracy, generalizability, and robustness on different datasets and under different time windows. …”
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Multi-omic spatial effects on high-resolution AI-derived retinal thickness by V. E. Jackson, Y. Wu, R. Bonelli, J. P. Owen, L. W. Scott, S. Farashi, Y. Kihara, M. L. Gantner, C. Egan, K. M. Williams, B. R. E. Ansell, A. Tufail, A. Y. Lee, M. Bahlo

“…We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. …”
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Ultrasonic-assisted extraction of luteolin from peanut shells using ionic liquid and its molecular mechanism by Liwei Niu, Siwen Zhang, Xiaoyu Si, Yuhan Fang, Shuang Wang, Lulu Li, Zunlai Sheng

“…Further optimization of the extraction conditions was performed using response surface methodology and neural network analysis, resulting in a significantly enhanced luteolin yield of 3.71 ± 0.06 mg/g. …”
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Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation by Canhui Lin, Ke Xu, Yingguang Li, Xu Liu, Chenli Zhou

“…This paper introduced an implicit neural representation for parametric design of stress-oriented cellular structure, which exhibited great potential to resolve the aforementioned issues by harnessing the universal approximation and resolution invariant capability of a neural network. The structure was implicitly trained to smoothly align with the principal stress field of the input geometry under arbitrary loading condition. …”
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Balanced coarse-to-fine federated learning for noisy heterogeneous clients by Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

“…However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. …”
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Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors by Akiyasu Yamamoto, Akinori Yamanaka, Kazumasa Iida, Yusuke Shimada, Satoshi Hata

“…Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. …”
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