Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

“…The improved reliability of the Bayesian-optimized regressor demonstrates its superiority over traditional, non-optimized regressors, indicating that incorporating Bayesian optimization can significantly advance experimental variogram modelling, thus offering a more accurate and intelligent solution, combining geostatistics and artificial intelligence specifically machine learning for experimental variogram modelling.…”
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Attention-based hybrid deep learning model with CSFOA optimization and G-TverskyUNet3+ for Arabic sign language recognition by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam Tejani

“…In addition, employing a novel metaheuristic algorithm, the Crisscross Seed Forest Optimization Algorithm, which combines the Crisscross Optimization and Forest Optimization algorithms to determine the best features from the extracted texture, color, and deep learning features. …”
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Energy Management of a Semi-Autonomous Truck Using a Blended Multiple Model Controller Based on Particle Swarm Optimization by Mohammad Ghazali, Ishaan Gupta, Kemal Buyukkabasakal, Mohamed Amine Ben Abdallah, Caner Harman, Berfin Kahraman, Ahu Ece Hartavi

“…The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. …”
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Methods and Algorithms for Decision-Making in Agro-Industrial Environmental Management by Olga Ivashchuk, Vyacheslav Fedorov, Zhanat Kenzhebayeva, Oleg Ivashchuk, Moldir Allaniyazova, Kaiyrbek Makulov, Gulnar Kaziyeva

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LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT by Farouk Noumich, Abouchabaka Jaafar, Amrani Ayoub

“…The proposed hybrid model incorporates two deep learning architectures: long short-term memory (LSTM) and artificial neural networks (ANN), with a genetic algorithm (GA) applied as an optimization method to simultaneously optimize the parameters of the model structure. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models.   …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…The objective was to apply a classification method to predict WQI using Kinta River data in Malaysia and improve on existing models&#x2019; <inline-formula> <tex-math notation="LaTeX">$70-95\%$ </tex-math></inline-formula> accuracy range. …”
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A Novel Seal Packaging Structure Applied in Sub Miniature Push-On Connector Based on Ni47Ti44Nb9 Shape Memory Alloy Seal Ring by Yang Hai, Cong Lu

“…Finally, to reduce the seal packaging leakage rate, the structural parameters of the seal ring are further optimized with an improved Adaptive Genetic Algorithm (IAGA), and the efficiency of the IAGA is verified through comparison with the conventional Adaptive Genetic Algorithm (AGA).…”
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SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy consideration by Han Wei

“…Empirical results demonstrate the optimized SMOTE algorithm’s superiority over six comparison models, such as random over-sampling, under-sampling, etc. …”
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Quality of service optimization algorithm based on deep reinforcement learning in software defined network by Cenhuishan LIAO, Junyan CHEN, Guanping LIANG, Xiaolan XIE, Xiaoye LU

“…Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms.…”
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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…Integrating these predictive models into clinical practice could support early identification of high-risk patients and optimize clinical decision-making.…”
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A GD-PSO Algorithm for Smart Transportation Supply Chain ABS Portfolio Optimization by Yingjia Sun, Hongfeng Ren

“…In this article, a penalty function based on graph density (GD) was introduced to the particle swarm optimization algorithm (PSO), and a GD-PSO algorithm was proposed. …”
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Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm by Tawfiq Hasanin, Aisha Alsobhi, Adil Khadidos, Ayman Qahmash, Alaa Khadidos, Gabriel Ayodeji Ogunmola

“…Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. …”
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A Learning-Enhanced Metaheuristic Algorithm for Multi-Zone Orienteering Problem with Time Windows by Hongwu Li, Yongqi Luo, Yanru Chen, Yangsheng Jiang

“…The HACO algorithm combines the global search capabilities of a population-based algorithm with the parallel decision-making abilities of the Pointer Network learning model. …”
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Fruit-Fly-Optimized Weighted Averaging Algorithm for Data Fusion in MEMS IMU Array by Ting Zhu, Gao Peng, Jianping Li, Jiawei Xuan, Jingbei Tian

“…In this study, an optimal weighted averaging algorithm based on the fruit fly optimization algorithm (FOA) is proposed by analyzing the data fusion mechanism of the MEMS IMU array. …”
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Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features by Qu FZ, Ding J, An XF, Peng R, He N, Liu S, Jiang X

“…Finally, a correlation analysis was conducted to examine the relationships between these features and other significant clinical features.Results: The logistic regression (LR) model was determined to be the optimal machine learning algorithm in this study, achieving an accuracy of 0.64, a precision of 0.45, a recall of 0.72, an F1 score of 0.51, and an AUC of 0.81 in the training set and 0.91 in the testing set. …”
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Load optimization of cogeneration units based on intuitive multi-objective fish swarm algorithm by Xueqiang Shen, Jiaxin Wang

“…Convergence direction is adaptively adjusted using intuitionistic fuzzy entropy, with Pareto frontier solutions determining optimal load allocation. Evaluated via the Zitzler-Deb-Thiele (ZDT) benchmark functions, IFEMOAFSA achieves a 42.63% comprehensive performance improvement over four benchmark algorithms, verified by Mean Inverted Generational Distance (MIGD) and Mean Hypervolume Metric (MHV). …”
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Optimizing space heating efficiency in sustainable building design a multi criteria decision making approach with model predictive control by Zheng Qi, Nan Zhou, Xianwei Feng, Sama Abdolhosseinzadeh

“…The research question explores how advanced control strategies can balance heating costs and thermal comfort efficiently. A novel Model Predictive Control (MPC) framework integrates Long Short-Term Memory (LSTM) neural networks for energy demand prediction and the Ant Nesting Algorithm (ANA) for multi-objective optimization. …”
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Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest by Dongdong Yang, Shixuan Lü, Junming Wei, Lijun Zheng, Yunguang Gao

“…The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). …”
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Quadrotor Robust Fractional-Order Control Based on a Recent Bonobo Optimization Algorithm by Faycal Ziani, Aissa Benhammou, Mohamed Amine Hartani, Hegazy Rezk

“…The five fractional parameters for each engine are also improved using the Bonobo Optimization (BO) algorithm. …”
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