Modelling the Isotope composition of groundwater using hydrochemical properties in eastern Saudi Arabia: Implementation of innovative data intelligence techniques by Mohammed Benaafi, Waleed M. Hamanah, Ebrahim Al-Wajih

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Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules by Linlin Shao, Lili Zhang, Lifang Liu, Fangfang Sun, Hongyu Li, Tongfeng Liu, Feng Hu, Lirong Zhao

“…The optimal classifier was used to build US-only, US+CEUS, and clinical+US+CEUS models. …”
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Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease by Weizhe Zhen, Yu Wang, Hongjun Zhen, Weihe Zhang, Wen Shao, Yu Sun, Yanan Qiao, Shuhong Jia, Zhi Zhou, Yuye Wang, Leian Chen, Jiali Zhang, Dantao Peng, Dantao Peng

“…The linear regression curves of the AD and non-AD groups had completely opposite trends. Through a machine learning approach, we constructed an AD diagnostic model with excellent performance based on the selected features.…”
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Deep learning-based text generation for plant phenotyping and precision agriculture by Li Zhu, Long Tang, Shan Ren, Shan Ren

“…Our approach incorporates three key elements. A hybrid generative model is used to capture complex spatial and temporal phenotypic patterns. …”
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SmartScanPCOS: A feature-driven approach to cutting-edge prediction of Polycystic Ovary Syndrome using Machine Learning and Explainable Artificial Intelligence by Umaa Mahesswari G, Uma Maheswari P

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Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model by Xing Zhao, Chenxi Li, Xueting Zou, Xiwang Du, Ahmed Ismail

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Application of real‐time nonlinear model predictive control for wave energy conversion by Ali S. Haider, Ted K.A. Brekken, Alan McCall

“…The proposed strategy features code generation and deployment on the real‐time target machines for industrial applications. The simulations and experiments confirm the success of the proposed approach in achieving the feasible operation of the NMPC and an optimal power capture by the wave energy converters.…”
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Development of a fundamental model for pelleting efficiency of an innovative hybrid fish feed processing system by Daniel C., Nnadi, John Chijioke, Edeh, Offiong Alexander, Aniekan, Aniekan, Offiong

“…The development of a fundamental model for predicting pelleting efficiency at variable feed rates and number of orifices was central to optimizing the performance of an innovative hybrid fish feed processing system. …”
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Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation by Liheng Yan, Qiulin Ye, Baole Shi, Juanjuan Liu, Yuexin Hu, Ouxuan Liu, Xiao Li, Bei Lin, Yue Qi

“…At the same time, five machine learning-based models—random forest (RF), logistic regression (LR), gradient boosting (GB), multilayer perceptron (MLP), and support vector machine (SVM)—were utilized to construct recurrence prediction models. …”
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Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model by Yaoping WANG, Te LI, Kaihua JIANG, Wenhui LI, Qiang WU, Yu WANG

“…Based on the natural pollution test data of Taizhou City, the basic ESDD prediction model was established by using extreme learning machine (ELM), and its initial weights and thresholds were optimized by the mind evolution algorithm (MEA). …”
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Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation by Zeguo Zhang, Zeguo Zhang, Zeguo Zhang, Liang Cao, Liang Cao, Liang Cao, Jianchuan Yin, Jianchuan Yin, Jianchuan Yin

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Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity by Zhi Gao, Zhi Gao, Zhi Gao, Yongfang Jiang, Yongfang Jiang, Yongfang Jiang, Mengxuan Chen, Mengxuan Chen, Mengxuan Chen, Weihang Wang, Weihang Wang, Weihang Wang, Qiyao Liu, Qiyao Liu, Qiyao Liu, Jing Ma, Jing Ma, Jing Ma

“…This study aimed to develop an interpretable machine learning algorithm for the effective prediction of mNGS results in patients with FUO.MethodsA clinical dataset from a large medical institution was used to develop and compare the performance of several predictive models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), and Random Forest, and the Shapley additive explanation (SHAP) method was employed to interpret and analyze the results.ResultsThe mNGS-positive rate among 284 patients with FUO reached 64.1%. …”
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An artificial intelligence model integrating culprit lesion diagnosis and risk assessment for acute coronary syndromeResearch in context by Peng Peng Xu, Bin Hu, Fan Zhou, Zhi Han Xu, Qian Chen, Tong Yuan Liu, Bang Jun Guo, Chang Sheng Zhou, Xin Wei Tao, Hong Yan Qiao, Jia Ni Zou, Xiang Ming Fang, Wen Cai Huang, Long Jiang Zhang

“…The time-dependent AUC curve further indicated that the optimal validity period for the RF model derived from the culprit lesion was 2 years. …”
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Improvement of metaphor understanding via a cognitive linguistic model based on hierarchical classification and artificial intelligence SVM by Dongmei Zhu

“…It proposes a metaphor recognition algorithm that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM). First, the text is transformed into numerical features using a pre-trained word embedding model. …”
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Measurement-Based Neural Network Technique for Modeling the Low-Frequency Electric Field Radiated Behavior of Satellite Units by Anna N. Lampou, Anargyros T. Baklezos, Konstantinos K. Spyridakis, Dimitrios A. Rigas-Papakonstantinou, Ioannis O. Vardiambasis, Christos D. Nikolopoulos

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MRP-YOLO: An Improved YOLOv8 Algorithm for Steel Surface Defects by Shuxian Zhu, Yajie Zhou

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Simulating of Changes in Water Distribution Uniformity Coefficient in Classic Stationary Sprinkler Irrigation Using Data-Mining Models by Fariborz Ahmadzadeh-Kaleybar, Shahram Shahmohammadi Kalalagh, Sina Fard Moradinia

“…The values of the evaluation indices (RMSE, MAE, R2) for the most optimal SVM model in the test and training steps were obtained (4.8917, 4.2704, 0.7884) and (2.6790, 2.4113, 0.9185) respectively. …”
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D-optimal candexch algorithm-enhanced machine learning UV-spectrophotometry for five-analyte determination in novel anti-glaucoma formulations and ocular fluids: four-color sustain... by Omkulthom Al kamaly, Lateefa A. Al-Khateeb, Michael K. Halim, Noha S. katamesh, Galal Magdy, Ahmed Emad F. Abbas

“…A strategic multi-level, multi-factor experimental design creates a 25-mixture calibration set for four models (PLS, GA-PLS, PCR, and MCR-ALS). The key novelty was using the D-optimal design generated by MATLAB's candexch algorithm to construct a robust validation set, overcoming random data splitting limitations in machine learning chemometric methods and ensuring unbiased evaluation across concentrations. …”
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Machine learning-driven prognostic prediction model for composite small cell lung cancer: identifying risk factors with network tools and validation using SEER data and external co... by Fei Li, Mengfan Zhao, Linlin Cao, Shuai Qie

“…Initially, we employed the Cox proportional hazards model for rigorous variable selection. Subsequently, through 10-fold cross-validation and grid search for optimal parameters, we selected the XGBoost model as the best-performing one among four candidates. …”
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Machine learning-driven risk prediction in severe acute kidney injury and increased mortality risk with early sodium bicarbonate use: a model-based post hoc analysis by Wanqiu Xie, Gerd Walz, Toma Antonov Yakulov

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