An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings by Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

“…For improved performance, these models were then combined using a voting classifier technique. …”
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Construction and Application Analysis of an Intelligent Distribution Network Identification System Based on Deep Neural Networks by Yu Ma

“…In addition, the Cross-Validation Grid Search Algorithm (GSA) was used to optimize the hyperparameters to ensure that the model can achieve the optimal performance on different data sets. …”
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A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture by Hussain Mobarak Albarakati, Muhammad Attique Khan, Ameer Hamza, Faheem Khan, Naoufel Kraiem, Leila Jamel, Latifah Almuqren, Roobaea Alroobaea

“…In addition, we proposed a quantum hippopotamus optimization algorithm for the best feature selection. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The dataset was then split into training and test sets with an 80/20 ratio. Machine-learning models were trained, with hyperparameters optimized via grid search. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…The results showed that tree-based boosting algorithms consistently outperformed other models in accuracy, precision, F1-score, and ROC-AUC, while the kernel-based approach excelled in recall. …”
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FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis by Guojun Liu, Yan Shi, Hongxu Huang, Ningkun Xiao, Chuncheng Liu, Hongyu Zhao, Yongqiang Xing, Lu Cai

“…It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. …”
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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…Additionally, a computational tool for predicting CRC risk based on the optimal model was developed, designed for researchers with programming experience. …”
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Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis by Tengfei Zhou, Tengfei Zhou, Xinming Tian, Wei Wang, Zhe Chu

“…A total of 29 predictive models were developed, comprising 10 optimal models, primarily utilizing logistic regression or machine learning algorithms. …”
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Towards an Efficient Remote Sensing Image Compression Network with Visual State Space Model by Yongqiang Wang, Feng Liang, Shang Wang, Hang Chen, Qi Cao, Haisheng Fu, Zhenjiao Chen

“…Furthermore, in comparison to traditional codecs and learned image compression algorithms, our model achieves BD-rate reductions of −4.48%, −9.80% over the state-of-the-art VTM on the AID and NWPU VHR-10 datasets, respectively, as well as −6.73% and −7.93% on the panchromatic and multispectral images of the WorldView-3 remote sensing dataset.…”
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Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients by Indika Rajakaruna, Mohammad Hossein Amirhosseini, Mike Makris, Mike Laffan, Yang Li, Deepa J. Arachchillage

“…The SVC model also led in precision (0.98), recall (0.97), and F1 score (0.97), and recorded the lowest log-loss score (0.112 on the test dataset), reflecting better model convergence and an improved fit to the data. …”
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Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism. by Yutong Liu, Shile Zhang

“…The research uses the Conference on Natural Language Learning (CoNLL)-2014 dataset as an experimental benchmark, which contains a rich collection of grammatical error samples and is a standard resource in linguistic research. During model training, the Adam optimization algorithm is employed, and the model's performance is enhanced by introducing a customized dependency self-attention mechanism for parameter optimization. …”
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Study on Finite Element Model Modification of Long-Span Suspension Bridge Based on BPANN-GA by Zi-Xiu Qin, Xi-Rui Wang, Wen-Jie Liu, Zi-Jian Fan

“…In order to improve the reliability of the finite element analysis model of long-span suspension bridges, this paper proposes a finite element model (FEM) modification method by the hybrid algorithm of backpropagation artificial neural network (BPANN) and genetic algorithm (GA) based on field measurements and vibration modal analysis. …”
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Building Information Modelling- (BIM-) Based Generative Design for Drywall Installation Planning in Prefabricated Construction by Jose Daniel Cuellar Lobo, Zhen Lei, Hexu Liu, Hong Xian Li, SangHyeok Han

“…The integration of BIM with other analytical algorithms also allows optimization of designs, such as the generative design that can parametrize the design. …”
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Development of a machine learning-based surrogate model for friction prediction in textured journal bearings by Yujun Wang, Georg Jacobs, Shuo Zhang, Benjamin Klinghart, Florian König

“…This enhancement is achieved through an architecture design based on cross-validation and further optimization utilizing the genetic algorithm. Eventually, the average prediction accuracy is improved to 98.81% from 95.89%, with the maximum error reduced to 3.25% from 13.17%. …”
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Inversion Study of Hydrogeological Parameters for Metro Foundation Pit Confined Aquifiers Based on Surrogate Modeling by YE Ru, DI Honggui, ZHU Zhitai, ZHU Yilong, JIANG Bin, XIANG Longsheng, CHAI Dongsheng, YAO Qiyu

“…The use of deep learning-based surrogate modeling combined with optimization algorithms enables efficient and accurate inversion analysis of groundwater parameters.…”
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A multiobjective continuation method to compute the regularization path of deep neural networks by Augustina Chidinma Amakor, Konstantin Sonntag, Sebastian Peitz

“…For linear models, it is well known that there exists a regularization path connecting the sparsest solution in terms of the ℓ1 norm, i.e., zero weights and the non-regularized solution. …”
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Exponential Squared Loss-Based Robust Variable Selection with Prior Information in Linear Regression Models by Hejun Wei, Tian Jin, Yunquan Song

“…Experimental results demonstrate that our model significantly improves estimation robustness compared to existing methods, even in outlier-contaminated scenarios.…”
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Interactive cardiovascular disease prediction system using learning techniques: Insights from extensive experiments by Purnima Pal, Harsh Vikram Singh, Veena Grover, R. Manikandan, Rasoul Karimi, Mohammad Khishe

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Bayesian Time-Domain Ringing Suppression Approach in Impulse Ultrawideband Synthetic Aperture Radar by Xinhao Xu, Wenjie Li, Haibo Tang, Longyong Chen, Chengwei Zhang, Tao Jiang, Jie Liu, Xingdong Liang

“…This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. …”
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Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models by Mohamed Salah Benkhalfallah, Sofia Kouah, Saad Harous

“…In particular, accurate regression-based energy forecasting of the energy consumption in various sectors plays a key role in informed decision-making, efficiency improvements, and resource allocation. This paper examines the application of artificial intelligence and supervised machine learning techniques to modeling and predicting the energy consumption patterns in the smart grid sector of a commercial building located in Singapore. …”
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