Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model by Guowei Li, Gang Tang, Jingyu Zhang, Qun Sun, Xiangjun Liu

“…The PSO algorithm is then employed to optimize the network structure and hyperparameters of the convolutional neural network. …”
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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment by C. Labesh Kumar, Suresh Betam, Denis Pustokhin, E. Laxmi Lydia, Kanchan Bala, Rajanikanth Aluvalu, Bhawani Sankar Panigrahi

“…For the DDoS attack classification process, the attention mechanism with convolutional neural network and bidirectional gated recurrent units (CNN-BiGRU-AM) is employed. To ensure optimal performance of the CNN-BiGRU-AM model, hyperparameter tuning is performed by utilizing the seagull optimization algorithm (SOA) model to enhance the efficiency and robustness of the detection system. …”
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Heating, Ventilation, and Air Conditioning (HVAC) Temperature and Humidity Control Optimization Based on Large Language Models (LLMs) by Xuanrong Zhu, Hui Li

“…Simulated experimental results demonstrate that the improved control strategy, optimized through LLM-enhanced Model Predictive Control (MPC), significantly enhances the energy efficiency and stability of HVAC system control. …”
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P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s by Zhiping Tan, Zhiping Tan, Dapeng Ye, Jiancong Wang, Wenxiang Wang

“…Secondly, after analyzing the image set characteristics to be detected in this research, the point-line distance bounding box loss function is utilized to calculate the coordinate distance of the prediction box and target box, and aimed at improving detection speed. Subsequently, a convolutional block attention module (CBAM) and optimized anchor boxes are employed to reduce the false detection rate of the model. …”
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Optimal Fuzzy Deep Neural Networks-Based Plant Disease Detection and Classification on UAV-Based Remote Sensed Data by M. Pajany, S. Venkatraman, U. Sakthi, M. Sujatha, Mohamad Khairi Ishak

“…Moreover, this model’s scalability and efficiency improve its value for precision agriculture, optimizing resource usage and promoting sustainable farming practices. …”
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Optimizing Distribution Grid Performance through Electric Vehicle Integration and Stochastic Modeling in Extreme Weather Conditions by A. Niknami, M. Askari, M. Amirahmadi, M. Babaeinik

“…Monte Carlo simulation is employed to model uncertainties, while a multi-objective optimization algorithm is used to solve the problem. …”
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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“… The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. …”
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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…For the optimal subset of features, the spotted hyena optimization algorithm (SHOA) model is used. …”
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Improved FraSegNet-Based Rock Nodule Identification Method and Application by Yanbo Zhang, Guanghan Zhang, Qun Li, Xulong Yao, Hao Zhou

“…The algorithm was validated with the Barton–Bandis model and Hoek–Brown criterion. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. To further improve the model’s performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. …”
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Deep Reinforcement Learning Algorithm Based on Graph Weight Multi-Pointer Network for Solving Multiobjective Traveling Salesman Problem by Xiaoyu Fu, Shenshen Gu

“…To this end, this paper proposes a deep reinforcement learning algorithm based on graph weight multi-pointer network (GWMPN), which only needs to perform forward propagation on the GWMPN model to obtain the solution of MOTSP, thus significantly improving the solution efficiency. …”
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A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation by Jeridi Ahmed, Moulahi Mohamed Hedi, Khaterchi Hechmi, Zaafouri Abderrahmen

“…This study introduces a novel hybrid approach, WSO-HO, which integrates the strengths of the war strategy optimization (WSO) and Hippopotamus Optimization (HO) algorithms, enhanced by the Newton-Raphson (NR) method, to achieve precise parameter estimation for PV models. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). …”
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Optimization of State Clustering and Safety Verification in Deep Reinforcement Learning Using KMeans++ and Probabilistic Model Checking by Ryeonggu Kwon, Gihwon Kwon

“…Counterexample analysis identifies critical failure paths, offering actionable insights for policy improvement. Experimental results demonstrate that the optimal number of clusters balances state space reduction with accurate failure analysis, enabling scalable verification. …”
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A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model by Xueyu Huang, Renjie Pan, Jionghui Wang

“…Key improvements include: 1) To enhance the extraction of global feature information from graphite mine data, a global average pooling branch is incorporated into the Inception-resnet architecture. 2) Incorporating a <inline-formula> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula> convolutional layer at the tail of the model to control channel dimensions and employing the LeakyReLU activation function to address the limitations of the ReLU activation function. 3) Designing an LDP-Conv structure to replace certain <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> convolutions and incorporating a channel attention mechanism to improve feature capture. 4) Optimizing the Stem module to expand the early-stage receptive field and reconstructing the network architecture. …”
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RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention by Huiqing Wei, Fei Han, Qing Liu, Henry Han

“…However, most of these methods use attention mechanisms and their variants, which have room for further improvement in the speed of solving MOCO problems. In this paper, following the idea of decomposition strategy and neural combinatorial optimization, a novel fast-solving model for MOCO based on retention is proposed. …”
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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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Performance Optimization Method of Steam Generator Liquid Level Control Based on Hybrid Iterative Model Reconstruction by Xiaoyu Li, Xiangsong Kong, Changqing Shi, Jinguang Shi, Zean Yang

“…After that, the particle swarm optimization algorithm is used to calculate the optimal point of the current valid model, and the optimization process is controlled by establishing the iteration termination judgment based on the historical iteration data. …”
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Engineering oriented shape optimization of GHT-Bézier developable surfaces using a meta heuristic approach with CAD/CAM applications by Samia BiBi, Md Yushalify Misro, Muhammad Abbas, Asnake Birhanu, Kenjiro T. Miura, Mohd Nadhir Ab Wahab, Emad E. Mahmoud

“…The shape control parameters are considered as optimization variables. So, our aim is to find the optimal shape control parameters by applying the I-GWO algorithm to the optimization models through an iterative process. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…AI techniques are primarily employed for modeling, monitoring, optimization, and management of complex systems influenced by multiple parameters within industrial processes. …”
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