Research on collaborative scheduling strategies of multi-agent agricultural machinery groups by Ziyi Wang, Fan Zhang, Shiji Ma, Hailong Wang, Shunyao Zhang, Xiaozhong Gao

“…Abstract Addressing the challenges of high scheduling costs and low efficiency in the collaborative operations of agricultural machinery across multiple dispatch centers, this paper develops a scheduling model designed to minimize total costs. It introduces a Multi-Center and Multi-Machine Path Planning Algorithm Based on Deep Reinforcement Learning (MCMPP-DRL). …”
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Zoning Elastic Modulus Inversion for High Arch Dams Based on the PSOGSA-SVM Method by Bo Chen, Xiao Fu, Xuyuan Guo, Chongshi Gu, Chenfei Shao, Xiangnan Qin

“…Firstly, the measured data of multipoints with a pendulum are separated to construct the initial sample training set; then, an optimal inversion model is established to reflect the complex nonlinear relationship between the mechanical parameters of the high arch dam and the deformation of measured points; finally, the PSOGSA-SVM method is used to train and dynamically update the training set so as to realize the optimization solution of the inversion model. …”
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Intelligent Renewable Energy Agent-Based Distributed Control Design for Frequency Regulation and Economic Dispatch by Amjad Khan, Amjad Ullah Khattak, Bilal Khan, Sahibzada Muhammad Ali, Zahid Ullah, Faisal Mehmood

“…Intelligent renewable energy agents are trained through machine learning-based regression models that use root mean square error metrics for performance evaluations. …”
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Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification by Hui Li, Pan Liu, Shaoyun Liu, Bin Yang

“…However, traditional ML methods, which predominantly rely on single-property modelling, are often inadequate for addressing the practical demand for multiproperty collaborative optimization. …”
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Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…The integration of artificial intelligence (AI) and machine learning (ML) appears to be effective in optimization within the energy sector. …”
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Revolutionizing Supply Chain Management With AI: A Path to Efficiency and Sustainability by Kassem Danach, Ali El Dirani, Hassan Rkein

“…Through an in-depth analysis of various AI techniques—such as machine learning, predictive analytics, and optimization algorithms—this study offers novel insights into their applicability in solving complex supply chain problems like demand forecasting, inventory management, and logistics optimization. …”
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Suggesting a Novel Hybrid Approach for Predicting Solar Irradiance in the Qinghai Province of China by Baran Yılmaz, Rachel Samra

“…This work aims to provide a hybrid model using machine learning to accurately predict solar Direct normal irradiance with the least amount of error. …”
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A method to manage the energy consumption of cloud centers for predictability in neuro-fuzzy networks by Ying Zhang

“…The results underlined the potential of predictive models combined with optimization algorithms for significant energy savings and operational efficiency in cloud data centers.…”
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3D Reconstruction and Measurement Analysis of a Dense Point Cloud Fused with a Depth Image by Yujing Qiao, Ning Lv, Siyuan Zhang

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Development of High Accuracy Classifier for the Speaker Recognition System by Raghad Tariq Al-Hassani, Dogu Cagdas Atilla, Çağatay Aydin

“…A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), K-nearest neighbour (KNN), and support vector machine (SVM).…”
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An improved Red-billed blue magpie feature selection algorithm for medical data processing. by Chenyi Zhu, Zhiyi Wang, Yinan Peng, Wenjun Xiao

“…Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. …”
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Enhancing Review Processing in the Video Game Adaptation Domain through VADER and Rating-Based Labeling using SVM by Danita Divka Sajmira, Khothibul Umam, Maya Rini Handayani

“…A total of 2,017 English reviews of The Last of Us were gathered via web scraping from IMDb, followed by preprocessing, TF-IDF feature extraction, and hyperparameter optimization using RandomizedSearchCV. These results show that the SVM model trained on VADER-labeled data achieved an accuracy of 0.97, outperforming the model trained on manually labeled data at 0.79. …”
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Activity cliff-aware reinforcement learning for de novo drug design by Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang

“…ACARL’s primary technical contributions include the formulation of an activity cliff index to detect these critical points, and a contrastive RL loss function that dynamically enhances the generation of activity cliff compounds, optimizing the model for high-impact SAR regions. This approach demonstrates the efficacy of combining domain knowledge with machine learning advances, significantly expanding the scope and reliability of AI in drug discovery.…”
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Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment by Hyeon Cheol Kim, Si Young Ha, Jae-Kyung Yang

“…Machine learning is emerging as a promising solution to address these challenges, offering a viable alternative for predictive modeling and process control. …”
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Circular rubber aggregate CFST stub columns under axial compression: prediction and reliability analysis by Khaled Megahed, Nabil Said Mahmoud, Saad Elden Mostafa Abd-Rabou

“…The hyperparameter tuning of the introduced ML models is performed using the Bayesian Optimization technique. …”
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Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network by Feng-Ping An

“…Therefore, this paper starts from the perspective of network optimization and improves the nonlinear modeling ability of the network through optimization methods. …”
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Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks by Buse Pehlivan, Volkan Rodoplu, Engincan Tunçay, Dilara Eraslan

“…First, we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communication (mMTC) traffic. …”
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Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction by Qian Zeng, Xiaobo Li, Yixuan Chen, Minghao Yang, Xingbang Liu, Yuetian Liu, Shiwei Xiu

“…To address this limitation, this paper constructs a model based on constraints, optimization objectives, and server connection methods, determines experimental parameters and evaluation metrics, and sets up an experimental framework. …”
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Predicting photodegradation rate constants of water pollutants on TiO2 using graph neural network and combined experimental-graph features by Mahia V. Solout, Jahan B. Ghasemi

“…The efficiency of photocatalytic reactions in degrading pollutants is influenced by several factors, making parameter optimization time-consuming. In this context, machine learning techniques provide an appropriate solution for designing optimal photocatalysts. …”
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Translating microbial kinetics into quantitative responses and testable hypotheses using Kinbiont by Fabrizio Angaroni, Alberto Peruzzi, Edgar Z. Alvarenga, Fernanda Pinheiro

“…To address this issue, we introduce Kinbiont—an open-source tool that integrates dynamic models with machine learning methods for data-driven discovery in microbiology. …”
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