KAN-Based Tool Wear Modeling with Adaptive Complexity and Symbolic Interpretability in CNC Turning Processes by Zhongyuan Che, Chong Peng, Jikun Wang, Rui Zhang, Chi Wang, Xinyu Sun

“…Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. …”
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Achieving high precision and productivity in laser machining of Ti6Al4V alloy: A comprehensive study using a n-predictor polynomial regression model and PSO algorithm by Avinash Chetry, Sandesh Sanjeev Phalke, Arup Nandy

“…Based on R2 score and RMSE, multi-dimensional polynomial regression is decided as the most suitable regression model. Subsequently, the Particle Swarm Optimization technique has been applied to identify the optimal machining parameters for reducing angle of kerf and surface roughness, while increasing material removal rate. …”
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Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 199... by Kuijie Zhang, Xiaodong Ma, Xicheng Zhou, Gang Qiu, Chunjuan Zhang

“…A prognostic model using NPAR and SIRI optimally predicted overall survival.ConclusionThese results underscore the necessity of monitoring and managing NPAR and SIRI indicators in clinical settings for AR patients with HTN, potentially improving patient survival outcomes.…”
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Organophosphorus Pesticides Management Strategies: Prohibition and Restriction Multi-Category Multi-Class Models, Environmental Transformation Risks, and Special Attention List by Yingwei Wang, Lu Wang, Yufei Li

“…Therefore, this study retrieved the OPs restriction list and constructed eight multi-class, multi-category machine learning models for OPs restrictions. Among these, the random forest (RF) model demonstrated excellent predictive performance, as it was successfully validated and applied. …”
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1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-qu... by Ndidiamaka Martina Amadi, Joseph Okechukwu Ezeugo, Chukwunonso Chukwuzuluoke Okoye, John Ifeanyi Obibuenyi, Maduabuchi Arinzechukwu Chidiebere, Dominic Okechukwu Onukwuli, Valentine Chikaodili Anadebe

“…The comprehensive nature of this approach facilitated a more in-depth adsorption process through computational modelling based on DFT and molecular dynamics. The machine learning models aligned credibly with the experimental findings with pronounced degree of accuracy. …”
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An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation by Shuai Zhang, Lulu Li, Jingyu Wang, Yuan Li, Yongkang Zhou, Yuqing Tao, Cuntao Yu, Xiaogang Sun, Hongwei Guo, Dong Zhao, Yi Chang, Jing Sun, Xiangyang Qian

“…Subsequently, a total 50 of 10 highly associated variables were selected for model construction. By implementing the new method, the AUC value significantly improved from 0.6981 using the XGBoost model to 0.8687 with the PSO-ELM-FLXGBoost model.Conclusion In this study, machine learning methods were successfully established to predict ATAAD perioperative mortality, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.…”
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An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation by Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu

“…When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. …”
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An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory... by Jawharah Alasmari, Mohammed Alzyoudi, Masheal Alshehri, Rana Alshammari, Reyouf Aldakan

“…This study develops a machine learning model to predict narrative cohesion in children’s stories, classifying cohesion as complete, partial, or absent, using Gérard Genette’s narrative structure theory as a framework. …”
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Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

“…Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.…”
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Solving Flexible Job-Shop Scheduling Problems Based on Quantum Computing by Kaihan Fu, Jianjun Liu, Miao Chen, Huiying Zhang

“…The model is solved using a coherent Ising machine (CIM). …”
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Combining SVM and Naive Bayes Models using a Soft Voting Approach for Sentiment Analysis of Tong Tji Tea House by Fendi Pradana Saputra, Ozzi Suria

“…This study aims to analyze sentiment in Indonesian-language review texts using three machine learning models: Support Vector Machine (SVM), Naive Bayes (NB), and a combination of both through an Ensemble Soft Voting Classifier approach. …”
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ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners by Mengran Zhou, Qiqi Zhang, Feng Hu, Ling Wang, Guangyao Zhou, Weile Kong, Changzhen Wu, Enhan Cui

“…We aim at solving the problems of scarcity, single type, low accuracy and difficult construction of high‐quality data sets available for air conditioning operation characteristic models at present. This paper proposes a construction method of air conditioning operation characteristic model based on an improved seagull optimization algorithm to optimize deep belief network (ISOA‐DBN). …”
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The dual-edged potential of AI autonomously defining loss functions by Abbas Ghori

“…Abstract A loss function is one of the key components considered in machine learning as they steer the model toward the optimal performance by quantifying the discrepancy between the predicted outcome and the actual outcome. …”
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Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison by I Putu Yogi Prasetya Dharmawan, I Made Agus Dwi Suarjaya, Wayan Oger Vihikan

“…This study compares the performance of ensemble learning methods, namely Random Forest and XGBoost, with the Whisper model in classifying baby cries. The results show that the Whisper-small model has the best performance with precision 0.9115 and recall 0.9007, followed by XGBoost with slightly degraded performance after hyperparameter optimization. …”
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Intelligent design of mixture proportions of manufactured sand concrete from environmental, economical and mechanical perspectives by Junfei Zhang, Changhai Xu, Lei Zhang, Ling Wang

“…This study proposes a multi-objective optimization (MOO) method based on machine learning (ML) and the non-dominated sorting genetic algorithm II (NSGA-II) to optimize MSC mixtures. …”
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Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection by Nwamaka Georgenia Ezeji, Kingsley Ifeanyi Chibueze, Nnenna Harmony Nwobodo-Nzeribe

“…Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection.   …”
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An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory by Zhongjin Zhou, Jingbo Zhao, Jianfeng Zheng, Haimei Liu

“…The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. …”
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CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints by Amirhosein Mohammadisabet, Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…Optimization techniques, including pruning and quantization, reduced model size by 73.7%, enabling real-time deployment on resource-constrained devices. …”
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Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (U... by Hatem Ali, Arun Shroff, Karim Soliman, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. …”
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Enhanced Automatic Generation Control in Multiarea Power Systems: Crow Search Optimized Cascade FOPI‐TIDDN Controller With Integrated Renewable Solar Thermal Models and HVDC Lines... by Naladi Ram Babu, Pamarthi Sunitha, Ganesh Pardhu B. S. S., Sanjeev Kumar Bhagat, Adireddy Ramesh, Arindita Saha, Wulfran Fendzi Mbasso, Pradeep Jangir, Ahmed Hossam‐Eldin

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