Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold... by Zhixin Xu, Xiaoming Li, Bo Sun, Yueming Wen, Peipei Tang

“…The resulting solutions were trained in the Scikit-learn machine learning platform. After comparing machine learning models like RandomForest and XGBoost, the highest-performing XGBoost model was selected for further training. …”
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Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling by Yu Li, Xiangjun Liu, Jingwen Zhou, Fengjiao Li, Yuting Wang, Qingzhong Liu

“…The integration of advanced data analysis and nonlinear modeling capabilities of artificial intelligence (AI) is driving the transformation of TCM into precision medicine. …”
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D2LFS2Net: Multi‐class skin lesion diagnosis using deep learning and variance‐controlled Marine Predator optimisation: An application for precision medicine by Veena Dillshad, Muhammad Attique Khan, Muhammad Nazir, Oumaima Saidani, Nazik Alturki, Seifedine Kadry

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A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance by Anju Kalwar, Deepika Shekhawat, Sandeep Joshi, Amit Kumar Bairwa

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Application of smart technologies for predicting soil erosion patterns by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid, Shiva Gharibi, Jing-Cheng Han

“…This study evaluates the effectiveness of four data-driven approaches (biogeography-based optimization, earthworm optimization algorithm, symbiotic organisms search, and whale optimization algorithm) combined with artificial neural network models for the assessment of erosion susceptibility. …”
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RL–Fusion: The Large Language Model Fusion Method Based on Reinforcement Learning for Task Enhancing by Zijian Wang, Jiayong Li, Yu Liu, Xuhang Li, Cairong Yan, Yanting Zhang

“…Model fusion is a technique of growing interest in the field of machine learning, which constructs a generalized model by merging the parameters of multiple independent models with different capabilities without the need to access the original training data or perform costly computations. …”
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Query scheduling based on cloud-edge multi-data warehouse architecture and cost prediction model by GAO Xuning, YANG Song, LI Mingzhe, ZHANG Yanfeng

“…This paper designed a scheduling framework based on cloud edge multi-data warehouses, integrated the query cost prediction model with machine learning technology as the core, and realized cloud edge collaborative execution and cloud edge selective execution on multiple query granularity, so as to improve the performance and query efficiency of the whole system. …”
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Query scheduling based on cloud-edge multi-data warehouse architecture and cost prediction model by GAO Xuning, YANG Song, LI Mingzhe, ZHANG Yanfeng

“…This paper designed a scheduling framework based on cloud edge multi-data warehouses, integrated the query cost prediction model with machine learning technology as the core, and realized cloud edge collaborative execution and cloud edge selective execution on multiple query granularity, so as to improve the performance and query efficiency of the whole system. …”
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GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling by Naoya Onizawa, Takahiro Hanyu

“…This paper introduces a GPU-accelerated, open-source simulated annealing framework based on p-bits that models key device variability factors-timing, intensity, and offset-to reflect real-world device behavior. …”
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A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN by Xiang Wang, Shen Gao, Yibin Guo, Shiyu Zhou, Yonghui Duan, Daqing Wu

“…An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. …”
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Flood Image Classification using Convolutional Neural Networks by Olusogo Julius Adetunji, Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, Adedayo Aladejobi Sobowale Sobowale

“…The techniques and the developed system were implemented using a Python-based integrated development environment called “Anaconda Navigator” on Intel Core i5 with 8G Ram hardware of Window 10 operating system. The developed model achieved optimal accuracy at 200 epochs with 99.80% and corresponding loss of 0.0890. …”
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WebShield 5.0: Harnessing AI and NLP to combat web threats in Industry 5.0 by Priyanka Verma, Donna O’Shea, Thomas Newe, Ankit Vidyarthi, Deepak Gupta, Jabir Ali, Hamad Aldawsari, John G. Breslin

“…Mayfly Optimization is considered to be a variation of Particle Swarm Optimization (PSO), combining the benefits of Firefly Algorithm, Genetic Algorithm (GA), and PSO. …”
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A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

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Substantiation of the technological process for mechanical processing of roller unit parts of conveyors as a factor influencing the efficiency of their operation by L.P. Kalafatova, M.O. Babenko, S.O. Virich

“…The possibility of reducing the technological cost of manufacturing axial shafts for conveyor roller assemblies has been studied by implementing new organizational production models based on a justified selection of the optimal technological processing approach.…”
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Predictive model using systemic inflammation markers to assess neoadjuvant chemotherapy efficacy in breast cancer by Yulu Sun, Yinan Guan, Hao Yu, Yin Zhang, Jinqiu Tao, Weijie Zhang, Yongzhong Yao

“…Survival analysis demonstrated that lymph node status, NLR, and LMR were associated with prognosis. Machine learning algorithm analysis identified the random forest (RF) model as the optimal predictive model for pCR.ConclusionThis study demonstrated that lymph node status, NLR, and LMR had significant value in predicting pCR and prognosis in breast cancer patients. …”
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Enhancing drilling performance in 3D printed PLA implants application of PIV and ML models by K Shunmugesh, M Ganesh, R Bhavani, M. Adam Khan, M. Saravana Kumar, L. Rajeshkumar, Priyanka Mishra, Rajesh Jesudoss Hynes Navasingh, Angela Jennifa Sujana J, Jana Petru, Čep Robert

“…This also proves that the machine learning (ML) models offer better prediction to the optimization of drilling parameters with reference to the quality of the workpiece machined.…”
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A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm by Shuting Chen, Dapeng Tan

“…Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. …”
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Efficient Unmanned Aerial Vehicle Design: Automated Computational Fluid Dynamics Preprocessing from Geometry to Simulation by Chris Pliakos, Giorgos Efrem, Thomas Dimopoulos, Pericles Panagiotou

“…Additionally, the framework’s ability to accumulate high-quality data for machine learning enhances predictive modeling and optimization capabilities across UAV design practices.…”
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Exploring the Potential of Neural Networks to Predict Statistics of Solar Wind Turbulence by Daniel Wrench, Tulasi N. Parashar, Ritesh K. Singh, Marcus Frean, Ramesh Rayudu

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Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images by Rashid Abbasi, Farhan Amin, Amerah Alabrah, Gyu Sang Choi, Salabat Khan, Md. Belal Bin Heyat, Muhammad Shahid Iqbal, Huiling Chen

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