A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system by Amna Ikram, Sunnia Ikram, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Adil Hussain, Amal H. Alharbi, Marwa M. Eid, Marwa M. Eid

“…This study proposes a Fuzzy-Optimized Hybrid Ensemble Model (FOHEM), integrating stacked ensemble machine learning algorithms with a fuzzy inference system (FIS) to improve yield prediction. …”
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Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces by K. Balamurugan, G. Sudhakar, Kavin Francis Xavier, N. Bharathiraja, Gaganpreet Kaur

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The Design and Testing of a Combined Operation Machine for Corn Straw Crushing and Residual Film Recycling by Jiuxin Wang, Wuyun Zhao, Xiaolong Liu, Fei Dai, Ruijie Shi, Keping Zhang, Xiaoyang Wang, Wenhui Zhang, Jiadong Liang

“…The key components of the combined operation machine were designed based on an agronomic model for corn planting and the mechanized operation requirements in the Hexi irrigation area. …”
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Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation by Siqi Liao, Wanting Sun, Haoran Zheng, Qiyang Xu

“…In this study, we explore the application of advanced machine learning models in optimizing the dual-column distillation process for wood vinegar production, such as Random Forest algorithms. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical Information System (GIS) and Machine Learning (ML) models in Baitarani River Basin, Odisha by Abhijeet Das

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Integrating Metaheuristics and Machine Learning for Enhanced Vehicle Routing: A Comparative Study of Hyperheuristic and VAE-Based Approaches by Kassem Danach, Louai Saker, Hassan Harb

“…In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. …”
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A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems by Udit Mamodiya, Indra Kishor, Priyam Ganguly, Isha Mukherjee, Nithesh Naik

“…This paper introduces COMLAT (Climate-Optimized Machine Learning Adaptive Tracking), an AI solar tracking system that employs climate prediction using CNN-LSTM for climate prediction, XGBoost for estimation of energy yield, and Deep Q-Learning (DQL) for real-time tracking control for solar efficiency optimization. …”
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Application model of “Internet+Marketing service” virtual robot under dung beetle optimizer algorithm by HE Wei, ZHOU Yutian, YU Yang, KANG Yumeng, ZHU Meng, QIAN Xusheng

“…The optimized model learning was completed through training. …”
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A Dynamic Neural Network Optimization Model for Heavy Metal Content Prediction in Farmland Soil by Kun Cao, Cong Zhang, Liangliang Li, Shuaifeng Li

“…Through comparison with support vector machine(SVM), light gradient boosting machine(LightGBM), RBFNN, and genetic algorithm optimizes the radial basis function neural network(GA-RBFNN), the experimental results demonstrate that the DNNOM is closer to the real value than the other four models, and the four error indicator values are also significantly lower than those of the other comparison models, which have higher prediction accuracy. …”
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Edge computing based english translation model using fuzzy semantic optimal control technique. by Na Wang

“…Some issues, including ambiguity in English translation and improper word choice in translation techniques, must be addressed to enhance the quality of the English translation model and accuracy based on the corpus. Hence, an edge computing-based translation model (FSRL-P2O) is proposed to improve translation accuracy by using huge bilingual corpora, considering Fuzzy Semantic (FS) properties, and maximizing the translation output using optimal control techniques with the incorporation of Reinforcement Learning and Proximal Policy Optimisation (PPO) techniques. …”
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Identification of the Optimal Model for the Prediction of Diabetic Retinopathy in Chinese Rural Population: Handan Eye Study by Shanshan Jin, Xu Zhang, Hanruo Liu, Jie Hao, Kai Cao, Caixia Lin, Mayinuer Yusufu, Na Hu, Ailian Hu, Ningli Wang

“…Background. To identify an optimal model for diabetic retinopathy (DR) prediction in Chinese rural population by establishing and comparing different algorithms based on the data from Handan Eye Study (HES). …”
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Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients by Zhenyi Liu, Yihao Huang, Long Li, Yisha Xu, Peng Wu, Zhigang Zhang, Tingyong Han, Liangjie Zhang, Ming Zhang

“…ObjectiveTo address the limitations of conventional delirium prediction models in emergency polytrauma care, this study developed an interpretable machine learning (ML) framework incorporating trauma-specific biomarkers and advanced optimization algorithms for risk stratification of delirium in emergency polytrauma patients.MethodsThis multi-center retrospective observational cohort study was conducted across six hospitals in the Ya’an region. …”
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OPTIMIZATION STRATEGIES AND COMPUTATIONAL MODELING IN THE DESIGN AND PERFORMANCE EVALUATION OF GREEN POROUS OIL ADSORBENT MATERIALS by Haoran Zhang, Sagdat Mederbekovna Tazhibayeva

“…Material development is systematic to improve oil spill cleanup solutions' scalability, performance, and environmental impact. Experimental optimization, computational modeling, machine learning prediction, and multi-criteria decision analysis used for high-performance oil spill adsorbents. …”
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OPTIMIZATION STRATEGIES AND COMPUTATIONAL MODELING IN THE DESIGN AND PERFORMANCE EVALUATION OF GREEN POROUS OIL ADSORBENT MATERIALS by Haoran Zhang, Sagdat Mederbekovna Tazhibayeva

“…Material development is systematic to improve oil spill cleanup solutions' scalability, performance, and environmental impact. Experimental optimization, computational modeling, machine learning prediction, and multi-criteria decision analysis used for high-performance oil spill adsorbents. …”
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Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models by Ruturaj U. Kakade, Nitin Khedkar, Amol Dalavi

“…Material characterization was performed on samples with the highest and lowest TS to evaluate the correlation between microstructure and strength. Machine learning models Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) were applied to predict TS based on welding parameters.• The SVR model achieved the best predictive performance, with an R² of 0.8750 and a model accuracy of 96.73 %.• The results confirm the potential of SVR for accurately forecasting TS in A-MIG welded EN10028, facilitating process optimization in pressure applications…”
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Collection Efficiency of Cyclone Separators: Comparison between New Machine Learning-Based Models and Semi-Empirical Approaches by Edoardo Bregolin, Piero Danieli, Massimo Masi

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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

“…Four machine learning algorithms were utilized, with the optimal model undergoing hyperparameter optimization to evaluate the significance of predictive factors.ResultsA total of 1,092 CKD patients were included, with 231 (21.2%) diagnosed with sarcopenia. …”
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Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum by Zhichao Sheng, Jian Xiong, Zhijing Zhang, Taiyu Su, Min Zhang, Qimuge Saren, Xiao Chen

“…Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Lung Cancer Prediction Based on Symptomatic and Lifestyle Features by Bireswar Dutta

“…Lung cancer remains a leading cause of global mortality, with early detection being critical for improving the patient survival rates. However, applying machine learning and deep learning effectively for lung cancer prediction using symptomatic and lifestyle data requires the careful consideration of feature selection and model optimization, which is not consistently addressed in the existing research. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

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