Estimation of Silage Maize Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods by Xuchun Li, Jixuan Yan, Caixia Huang, Weiwei Ma, Zichen Guo, Jie Li, Xiangdong Yao, Qihong Da, Kejing Cheng, Hongyan Yang

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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…This leads to significant economic and time costs, while the complexity of instruments and algorithms further raises the barrier for DLD design and optimization. To address this challenge, this paper proposes a novel integrated approach that combines Dissipative Particle Dynamics (DPD) simulation with various machine learning (ML) models to rapidly predict the critical diameter of DLD devices with arbitrary pillar shapes considering fluid dynamics. …”
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Bio-Magneto Sensing and Unsupervised Deep Multiresolution Analysis for Labor Predictions in Term and Preterm Pregnancies by Ejay Nsugbe, Oluwarotimi Williams Samuel, Jose Javier Reyes-Lagos, Dawn Adams, Olusayo Obajemu

“…DWS is combined with select pattern-recognition-based prediction machines in order to assemble a clinical decision pipeline for the prediction of the states of various pregnancies, with a greater degree of machine intelligence. …”
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Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features by Bolaji A. Omodunbi, David B. Olawade, Omosigho F. Awe, Afeez A. Soladoye, Nicholas Aderinto, Saak V. Ovsepian, Stergios Boussios

“…Early and accurate diagnosis is critical for effective management and care. Leveraging machine learning (ML) techniques, this study aimed to develop a robust prediction system for PD using a stacked ensemble learning approach, addressing challenges such as imbalanced datasets and feature optimization. …”
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Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data by Jian Li, Junrui Kang, Ji Qi, Jian Lu, Hongkun Fu, Baoqi Liu, Xinglei Lin, Jiawei Zhao, Hengxu Guan, Jing Chang, Zhihan Liu

“…Compared to the best traditional machine learning model (support vector regression), <italic>R</italic>² increased by 52.96% and RMSE decreased by 26.05%, and relative to the best deep learning baseline model (long short-term memory), <italic>R</italic>² and RMSE improved by 7.04% and 7.04%, respectively. …”
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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…The study highlights the effectiveness of feature engineering and model optimization in enhancing DDoS detection accuracy, making machine learning a viable solution for real-time cybersecurity applications.…”
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Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction. by Ruiyu Zhan

“…To tackle this issue, we present an innovative method that harmonizes the Grey Wolf Optimizer (GWO) with Levy flight to optimize the weights and biases of a Backpropagation (BP) neural network-a prominent machine learning model extensively employed in classification tasks. …”
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Apple phenotyping using deep learning and 3D depth analysis: An experimental study on fruitlet sizing during early development by Giorgio Checola, Damiano Moser, Paolo Sonego, Cristian Iob, Franco Micheli, Pietro Franceschi

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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…The performance of the optimal model was then compared to radiologists' assessments. …”
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Comparison of LSTM and Transformer Models in Predicting NVIDIA Stock Closing Prices and the Application of Rule-based Trading Strategies by Muhammad Irfan Abdul Gani, Putry Wahyu Setyaningsih

“…For future work, the use of methods such as Prophet, ARIMA, and hybrid ensemble approaches is recommended to enhance prediction accuracy, improve market adaptability, and deliver a more robust stock forecasting system leveraging advanced machine learning techniques for more optimal investment decisions.…”
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Preassembly Cryogenic Drilling in Carbon Fiber Sandwich Sheets with Inner Foam Core by Rosario Domingo, Marta M. Marín, Néstor Rodríguez-Padial, Roberto Álvarez-Fernández

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Synthesize Evaluation Method of Characteristic Defects in Hole Drilling of Carbon Fiber Reinforced Polymer by Siyu Liang, Guangjun Liu, Zhongguo Guan

“…Then, based on the experimental results, a non-linear cutting parameter optimization model is established, which effectively suppresses the orifice defects to ensure the accuracy of the hole size, roundness, and hole wall roughness. …”
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Fast jet tagging with MLP-Mixers on FPGAs by Chang Sun, Jennifer Ngadiuba, Maurizio Pierini, Maria Spiropulu

“…We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. …”
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Computational simulation and mathematical modelling of thermal performance and energy enhancement of integrated infrared with hot air heated system by Hany S. El-Mesery, Ahmed H. ElMesiry, Oluwasola Abayomi Adelusi, Zicheng Hu, Sara Elhadad

“…The study also revealed that increased air temperature, infrared intensity, and reduced airflow rates enhanced energy indices. Among the 11 machine learning models evaluated, the Kucuk and Midilli models best fit the drying curves, making them most suitable for predicting peppermint drying behavior. …”
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TransNN-MHA: A Transformer-Based Model to Distinguish Real and Imaginary Motor Intent for Assistive Robotics by Tipu Sultan, Guangping Liu, Pascal Sikorski, Madi Babaiasl

“…In this article, we utilize the EEG Motor Movement/Imagery Dataset, consisting of 4087 training samples and 818 test samples, to develop TransNN-MHA, a new Transformer-based Neural Network that incorporates Multi-Head Attention (MHA) mechanisms for classifying real and imaginary motor actions. The proposed model employs a minimalist architecture that omits decoders and positional encodings to optimize EEG classification. …”
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Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach by Rasool Jader, Sadegh Aminifar

“…The suggested model uses the clustering KMeans technique for data reduction and the elbow method to find the optimal k value and the Mahalanobis distance method to find more related cluster to new samples, and the classification methods such as decision tree, random forest, SVM, KNN, logistic regression, and Naïve Bayes are used for prediction. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

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An artificial intelligence platform for predicting postoperative complications in metastatic spinal surgery: development and validation study by Weihao Jiang, Juan Zhang, Weiqing Shi, Xuyong Cao, Xiongwei Zhao, Bin Zhang, Haikuan Yu, Shengjie Wang, Yong Qin, Mingxing Lei, Yuncen Cao, Boyu Zhu, Yaosheng Liu

“…The larger cohort (70%) was used to train machine learning-based models, while the smaller cohort (30%) served to internally validate the models. …”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

“…We then utilized grid search to train each model on their respective optimized hyperparameters. …”
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Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review by Rongjie He, Wengang Zhang, Jie Dou, Nan Jiang, Huaixian Xiao, Jiawen Zhou

“…The generalization ability, sampling training strategies, and hyper-parameters optimization of these models are crucial and should be carefully considered. …”
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