Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. …”
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Improving inverter efficiency for electric vehicles: Experimental validation of the neural network-based SHE technique using RT-LAB by Seyf Eddine Bechekir, Mokhtaria Jbilou, Mostefa Brahami, Fatima Zohra Boudjella, Imen Souhila Bousmaha, Mimouna Oukli, Said Nemmich

“…This approach improves inverter performance and offers new optimization possibilities for various applications, including electric vehicles. …”
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Reinforcement learning-based assimilation of the WOFOST crop model by Haochong Chen, Xiangning Yuan, Jian Kang, Danni Yang, Tianyi Yang, Xiang Ao, Sien Li

“…Our results demonstrate that the Time-Series Driven RL model achieved assimilation accuracy comparable to the SUBPLEX optimization algorithm, with an average MAE of 0.65 compared to 0.76 for SUBPLEX, and a slight decrease in RMSE, while significantly reducing the computational burden by 365 times. …”
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Decoding On-Field Roles in Football: A Sociological Analysis Using Intelligent MCDM Algorithm With Fuzzy Information for Player Positioning and Technical-Tactical Behavior by Lu Yan, Nannan Wang, Rui Chen

“…So, football strategy demands a decision-making model that considers these details and assists in achieving optimal team performance. …”
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Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty by M. Siva Ramkumar, Jaganathan Subramani, M. Sivaramkrishnan, Arunkumar Munimathan, Goh Kah Ong Michael, Mohammad Mukhtar Alam

“…In this study, a new hybrid algorithm is used for system modelling and low-cost, optimal management of Micro Grid (MG) networked systems. …”
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GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network With High Renewables by Dinesh Kumar Mahto, Mahipal Bukya, Rajesh Kumar, Akhilesh Mathur, Vikash Kumar Saini

“…The proposed GAT model outperformed the state-of-the-art MPGCN and DNN models, achieving improvement of 86.33% and 62.71%, respectively, under 60% DG penetration condition. …”
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A novel ensemble of hybrid starling murmuration optimized stacked gated Ghostnets for robust classification of DDoS attacks by P. Mamatha, S. Balaji, S. Sai Anuraghav

“…In this regard, this research article introduces an improved framework for identifying the DDoS threats utilising the novel Hybrid Gated Attention Evoked Ghostnets with its hyper-parameters tuned by the Starling Murmuration Optimization (SMO) technique to increase the detection accuracy with the high robustness and reduced overfitting problems. …”
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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. …”
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Performance Optimization of Synchronous Reluctance Motor using Adaptive MTPA Curve Fitting with Anti-Windup Current Control by Alex V, Rammohan A

“…Conventional scalar control methods typically rely on precomputed lookup tables or iterative optimization algorithms, which introduce computational overhead and require significant memory resources. …”
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The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network by Qi Yang, Dequan Zhang, Chongxin Liu, Le Xu, Shaobo Li, Xiaochun Zheng, Li Chen

“…This study successfully combined widely targeted lipidomic with a back propagation (BP) neural network optimized based on a particle swarm algorithm to identify the authenticity of Yanchi Tan lamb. …”
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Research on low carbon welding scheduling based on production process by Rong Hua Meng, Zan Yang Wang, Wen Hui Zeng, Feng Guan, Ding Kun Lei, Zheng Jia Wu, Shao Hua Deng

“…Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. …”
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Optimization of Cocoa Pods Maturity Classification Using Stacking and Voting with Ensemble Learning Methods in RGB and LAB Spaces by Kacoutchy Jean Ayikpa, Abou Bakary Ballo, Diarra Mamadou, Pierre Gouton

“…Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. …”
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Modified invasive weed optimization MPPT approach for PV system interfaced with BLDC motor for water pumping system by S. Nagaraja Rao, B. M. Kiran Kumar, B. M. Manjunatha, A. Suresh Kumar

“…Therefore, the modified invasive weed optimization (MIWO) approach integrated with P&O approach to improve performance under PSC. …”
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Optimal sleep time controller based on traffic prediction and residual energy in duty-cycled wireless sensor networks by Haibo Luo, Minghua He, Zhiqiang Ruan, Xiaxia Zeng

“…In duty-cycled wireless sensor networks, energy efficiency and packet latency are two most important metrics in the design of medium access control and routing algorithms. …”
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Cost-effectiveness of FIT and a FIT-based model to optimise symptomatic diagnosis of colorectal cancer: health economic modelling for the COLOFIT project by Linda Sharp, Sarah Bailey, Colin Rees, Willie Hamilton, Dawn Craig, Joe West, David Humes, Gary Abel, James Turvill, Brian Nicholson, Chloe Thomas, Jim Chilcott, Colin Crooks, Olena Mandrik, John Whelpton

“…We assessed the cost-effectiveness of COLOFIT compared with FIT and investigated optimal risk thresholds.Methods An individual patient-level simulation model was developed, with synthetic populations constructed from data used to validate COLOFIT. …”
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Neural network inspired efficient scalable task scheduling for cloud infrastructure by Punit Gupta, Arnaav Anand, Pratyush Agarwal, Gavin McArdle

“…In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. …”
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Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine by Shan Guan, Tingrui Dong, Long-kun Cong

“…Abstract In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. …”
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Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine by Hanliang Bian, Zhongxun Sun, Jiahan Bian, Zhaowei Qu, Jianwei Zhang, Xiangchun Xu

“…Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. …”
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Algorithmic Generation of Realistic 3D Graphics for Liquid Surfaces Within Arbitrary-Form Vessels in a Virtual Laboratory and Application in Flow Simulation by Dimitrios S. Karpouzas, Vasilis Zafeiropoulos, Dimitris Kalles

“…Moreover, the process of surface discretization with finite triangular elements is analyzed with the aim of optimizing their representation during the algorithmic implementation. …”
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Simultaneous Optimization of Traffic Signal Control and Vehicle Platooning Scheme Based on Connected and Automated Vehicle (CAV) Technology by Jiwon Kang, Yongjun Choi, Keemin Sohn

“…A reinforcement learning (RL) algorithm is devised to simultaneously optimize both the traffic signal control and the platooning scheme in a unified modelling framework. …”
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