Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Convolutional Neural Networks are used to extract spatial features from agricultural data, while Long Short-Term Memory networks processed time series data. To enhance model performance, the slime mould algorithm is employed for parameter optimization. …”
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Risk Assessment and Spatial Zoning of Rainstorm and Flood Hazards in Mountainous Cities Using the Random Forest Algorithm and the SCS Model by Zixin Xie, Bo Shu

“…Through the Random Forest (RF) algorithm and the Soil Conservation Service (SCS) model, this study aimed to assess and demarcate rainstorm and flood hazard risks in Huangshan City. …”
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Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation by Gina Sprint, Maureen Schmitter-Edgecombe, Diane Cook

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Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…Among the seven forecasting models constructed by MLAs, the accuracy of the Light Gradient Boosting Machine (LightGBM) model was the highest, indicated that the LightGBM algorithms might perform the best for predicting 3-year risk of DKD onset.Conclusions Our study could provide powerful tools for early DKD risk prediction, which might help optimize intervention strategies and improve the renal prognosis in T2DM patients.…”
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Modeling of 3 SAT discrete Hopfield neural network optimization using genetic algorithm optimized K-modes clustering by Xiaojun Xie, Saratha Sathasivam, Hong Ma

“…This approach minimized the likelihood of redundant searches and reduced the risk of getting trapped in local minima, thus improving search efficiency. Experimental tests on benchmark datasets showed that the proposed model outperformed traditional DHNN-3SAT models, DHNN-3SAT models combined with genetic algorithms, and DHNN-3SAT models combined with imperialist competitive algorithms across four evaluation metrics. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions by Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, Gregory Conde, Wenzhuo Wang, Shengnan Zhu, Yiqing Shao, Jinyu Meng

“…This study introduces an innovative two-step hybrid runoff prediction framework tailored for the headwater region of the Yellow River Basin (YRB) to improve prediction accuracy and elucidate the runoff modeling process. …”
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A Driver Behavior Detection Model for Human-Machine Co-Driving Systems Based on an Improved Swin Transformer by Junhua Cui, Yunxing Chen, Zhao Wu, Huawei Wu, Wanghao Wu

“…The results show that the proposed model algorithm has a better performance in 10 classifications of driver behavior detection, with an accuracy of 99.42%, which is improved by 3.8% and 1.68% compared to Vgg16 and MobileNetV2, respectively. …”
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SC-PA: A spot-checking model based on Stackelberg game theory for improving peer assessment by Jia Xu, Panyuan Yang, Teng Xiao, Pin Lv, Minghe Yu, Ge Yu

“…To this end, this paper proposes a novel spot-checking based peer assessment model, named SC-PA, to improve students’ motivation in peer assessment. …”
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Adaptive Virtual Inertia and Voltage Estimation: Enabled Model Predictive Control for Improved Performance in Islanded DC Microgrids by Salisu Abdullahi, Khaled Eltag, Lei Weining, Chen Xiaohu

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A study on method for bearing residual life prediction based on optimized Bray-Curtis dissimilarity and PSO algorithms by LI Quanfu, JIA Chen, SONG Dongli, XU Xiao

“…Then, the bearing degradation model was established based on the improved PSO algorithm. …”
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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

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Improvement of quality of 3D printed objects by elimination of microscopic structural defects in fused deposition modeling. by Evgeniy G Gordeev, Alexey S Galushko, Valentine P Ananikov

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Improving the Solving of Optimization Problems: A Comprehensive Review of Quantum Approaches by Deborah Volpe, Giacomo Orlandi, Giovanna Turvani

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Multi-strategy improved runge kutta optimizer and its promise to estimate the model parameters of solar photovoltaic modules by Serdar Ekinci, Rizk M. Rizk-Allah, Davut Izci, Emre Çelik

“…By aligning experimental and model-based estimated data, our approach seeks to reduce errors and improve the accuracy of PV system performance. …”
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Fresh Tea Leaf-Grading Detection: An Improved YOLOv8 Neural Network Model Utilizing Deep Learning by Zejun Wang, Yuxin Xia, Houqiao Wang, Xiaohui Liu, Raoqiong Che, Xiaoxue Guo, Hongxu Li, Shihao Zhang, Baijuan Wang

“…The empirical findings indicate that the enhanced YOLOv8 algorithm has achieved a marked improvement in metrics such as Precision, Recall, F1, and mAP, with increases of 3.39%, 0.86%, 2.20%, and 2.81% respectively, when juxtaposed with the original YOLOv8 model. …”
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An improved MP-NMO correction method based on multiparameter fitting and its application by Xiaohui YANG, Pengfei YU, Jiawei ZHANG

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MEchain—A novel mode to improve blockchain's real‐time and throughput by Yunwei Cao, Ting Yang, Yu Wang, Gang Mao

“…In this paper, the authors propose a novel method, called MEchain, based on the Proof of Time Series Algorithm. MEchain consists of two models: the multi‐chain model and the elastic‐chain model. …”
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Research on a dynamic self-learning efficient intrusion detection model by YANG Wu1, ZHANG Bing2, ZHOU Yuan2, WANG Wei1

“…A dynamic self-learning efficient intrusion detection model was proposed based on inductive reasoning.Ap-plying the method of inductive reasoning into intrusion detection,an incremental inductive reasoning algorithm for intru-sion detection was proposed.This model produced by this algorithm can make self-learning over the ever-emerged new network behavior examples and dynamically modify behavior profile of the model,which overcomes the disadva-ntage that the traditional static detecting model must relearn over all the old and new examples,even can not relearn because of limited memory size.And at the same time,the learning efficiency and detecting efficiency of intrusion detection model are improved greatly.…”
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Educational process improvement of oil and gas complex specialists training by Irina V. Vereshchagina, Svetlana A. Gulina

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