Predicting the hospitalization burdens of patients with mental disease: a multiple model comparison by Lu Hou, Lu Hou, Jing Zhang, Jing Zhang, Li Li, Yelin Weng, Ziyu Yang, Zhiguo Liu

“…ML models demonstrated task-specific efficacy: ridge regression for hospitalization frequency, long short-term memory/categorical boosting regression for length of stay, and seasonal autoregressive integrated moving average with exogenous regressors/light gradient boosting machine regression for hospitalization costs. …”
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Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis by Zhenyi Zhang, Yuhao Sun, Qiangwei Peng, Tiejun Li, Peijie Zhou

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Multispheroidal model of magnetic field of uncertain extended energy-saturated technical object by B. I. Kuznetsov, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets, B. B. Kobylianskyi

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An intelligent model for predicting the behavior of soil conditions depending on external weather conditions by Antamoshkin Oleslav, Mikhalev Anton, Menshenin Andrey, Lukishin Alexander

“…This research integrates advanced machine learning models, including LSTM, Transformer, TCN, and XGBoost, to predict changes in road conditions based on meteorological and soil data. …”
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Predictive Modeling of the Softness of Facial Tissue Products: A Spectral Analysis Approach by Yong Ju Lee, Ji Eun Cha, Geon-Woo Kim, Tai-Ju Lee, Hyoung Jin Kim

“…Using seven commercial samples and an optimized multilayer perceptron model, a achieved high predictive performance (R² = 0.860) was achieved without additional measurements such as tensile modulus or surface friction. …”
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Ensemble based high performance deep learning models for fake news detection by Mohammed E.Almandouh, Mohammed F. Alrahmawy, Mohamed Eisa, Mohamed Elhoseny, A. S. Tolba

“…We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. …”
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Power Control and Voltage Regulation for Grid-Forming Inverters in Distribution Networks by Xichao Zhou, Zhenlan Dou, Chunyan Zhang, Guangyu Song, Xinghua Liu

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Enhancing Prostate Cancer Detection: Integrating Multiparametric Magnetic Resonance Imaging and 68Ga-prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomogr... by Mohammad Hossein Sadeghi, Hamed Bagheri, Mohsen Rajaeinejad, Mohammad Afshar Ardalan, Ismail Karami, Shahryar Sadeghi, Ali Mosadeghkhah, Sedigheh Sina, Farnaz KhajehRahimi, Mahboobeh Sheiki

“…Imaging data were analyzed using advanced machine learning (ML) models, including support vector machine, random forest, logistic regression, and k-nearest neighbors, to assess diagnostic accuracy. …”
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Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition by Jiachen Ge, Jianping Cai, Gaolei Zhang, Deyu Li, Lianyuan Tao

“…Prognostic genes were identified to construct predictive models through various machine learning approaches. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…The results demonstrate that, among the models used for predicting and evaluating the shelf life of broccoli, the SPA+SG+RF classification model employing fused data Type C achieves the highest accuracy. …”
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Algorithm for Selecting a Base Tractor Model to Form a Tractor Train by M. T. Toshboltaev, B. A. Kholikov

“…The existing methodological principles for optimizing the dimension range of agricultural tractors do not take into account the type of trailers. …”
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Risks and Regulations for Application of the LLaMA Model in University Future Learning Centers by QIAO Jinhua, MA Xueyun

“…Legal frameworks also need refinement to ensure clear ownership distribution for outputs of human-machine collaboration. Ultimately, optimizing the application of the LLaMA model in university future learning centers necessitates a careful balance between technological innovation and legal regulation. …”
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Effects of a lumbar exoskeleton that provides two traction forces on spinal loading and muscles by Kaicheng Qi, Kaicheng Qi, Kaicheng Qi, Zhiyang Yin, Zhiyang Yin, Zhiyang Yin, Chao Li, Chao Li, Chao Li, Jianjun Zhang, Jianjun Zhang, Jianjun Zhang, Jingke Song, Jingke Song, Jingke Song

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A Differential Biomarker Based on Recurrence Quantification Analysis of EEG Signal and Genetic Algorithm for Epilepsy Diagnosis by Vanithamani Palanisamy, A Ranichitra, Radhamani Ellapparaj

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An improved extreme learning machine algorithm for prospectivity mapping of copper deposits using multi-source remote sensing data: a case study in the North Altyn Tagh, Xinjiang,... by Boqi Yuan, Qinjun Wang, Wentao Xu, Chaokang He, Wenyue Xie

“…Traditional extreme learning machine (ELM) model suffers from instability due to random initialization of input weights and hidden-layer bias, often resulting in suboptimal predictive performance. …”
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Intelligent classification models for food products basis on morphological, colour and texture features by Narendra Veernagouda Ganganagowder, Priya Kamath

“…The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). …”
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Comparative Analysis of Machine Learning Techniques for Predicting Bulk Specific Gravity in Modified Asphalt Mixtures Incorporating Polyethylene Terephthalate (PET), High-Density P... by Bhupender Kumar, Navsal Kumar, Rabee Rustum, Vijay Shankar

“…Additionally, sensitivity analysis identified bitumen content (BC) and volume of bitumen (V_b) as the most influential parameters affecting G_mb, emphasizing the need for precise parameter optimization in asphalt mix design. This study demonstrates the effectiveness of machine learning-driven predictive modeling in optimizing sustainable asphalt mix design, offering a cost-effective, time-efficient, and highly accurate alternative to traditional experimental methods.…”
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The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes by Chaoling Wu, Wanyi Liu, Pengfei Mei, Yunyun Liu, Jian Cai, Lu Liu, Juan Wang, Xuefeng Ling, Mingxue Wang, Yuanyuan Cheng, Manbi He, Qin He, Qi He, Xiaoliang Yuan, Jianlin Tong

“…Therefore, this study aims to develop a diagnostic model for TPE using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and machine learning models. …”
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Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification by Edgar Lara-Arellano, Andras Takacs, Saul Tovar-Arriaga, Juvenal Rodríguez-Reséndiz

“…The method leverages a genetic algorithm to create an optimal feature combination for the classification task and machine learning model. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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