Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm by Mohammad Reza Komari Alaei, Reza Rostamzadeh, Kadir Albayrak, Zenonas Turskis, Jonas Šaparauskas

“…Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. …”
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Artificial Neural Network Approaches for Predicting the Heat Transfer in a Mini-Channel Heatsink with Alumina/Water Nanofluid by Mohammad Mahdi Tafarroj, Seyed Soheil Mousavi Ajarostaghi, C.J. Ho, Wei-Mon Yan

“…The optimized RBF network carried over more data with less than 2% errors as compared to the MLP. …”
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Frequency-Aware Learned Image Compression Using Channel-Wise Attention and Restormer by Hanwen Zhang, Cheolkon Jung, Xu Liu

“…Considering the training instability and convergence difficulty of Restormer, we propose a novel training strategy based on knowledge transfer to optimize compression models during training. Experimental results demonstrate that the proposed method reconstructs high quality images with outstanding coding efficiency and achieves average BD-rate gains of 9.88% and 3.24% in terms of PSNR and MS-SSIM on the Kodak24 dataset over the auto-regressive hyperprior model proposed by Cheng et al., respectively.…”
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Adaptive frequency optimization control strategy of electric vehicles participation in energy storage considering user active response margin by Li Cai, Chenxi Yang, Qingshan Xu, Leida Li, Zhen Gao, Juan Yan, Congwen Wang

“…To address a series of operational issues arising from the large percentage of distributed power supply connected to the distribution network, an adaptive frequency optimization control strategy is proposed for EVs participating in energy storage, taking into account the user’s active response margin. …”
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AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Raghad Al-Shabandar, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani

“…Two optimization algorithms—Scaled Conjugate Gradient (SCG) and Resilient Backpropagation (RBP)—were utilized to tune network weights and enhance performance. …”
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A physics-informed neural network-based method for predicting degradation trajectories and remaining useful life of supercapacitors by Lixin E, Jun Wang, Ruixin Yang, Chenxu Wang, Hailong Li, Rui Xiong

“…For this purpose, a physics-informed neural network (PINN) model is developed using Long Short-Term Memory (LSTM) as the base architecture. …”
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A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning by Yankai Shen, Xinan Liu, Xiao Ma, Hong Du, Long Xin

“…Subsequently, a path-planning model, detection units’ network model, and cost estimation are constructed. …”
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Optimizing High-Speed Railroad Timetable with Passenger and Station Service Demands: A Case Study in the Wuhan-Guangzhou Corridor by Jin Wang, Leishan Zhou, Yixiang Yue, Jinjin Tang, Zixi Bai

“…This paper aims to optimize high-speed railroad timetables for a corridor. …”
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Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network by Kun Wang, Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang, Xiaomei Chen

“…To tackle the challenges of low forecasting accuracy and high error rates caused by the long sequences, nonlinearity, and multi-scale and non-stationary characteristics of financial flow data, a forecasting model based on multi-verse expansion evolution (MVE²) and spatial–temporal fusion network (STFN) is proposed. Firstly, preprocess data for power-grid financial flow data based on the autoregressive integrated moving average (ARIMA) model. …”
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Development of optimal real‐time metro operation strategy minimizing total passenger travel time and train energy consumption by Yoonseok Oh, Ho‐Chan Kwak, Seungmo Kang

“…Moreover, owing to the fluctuating nature of passenger demand, which can change rapidly over time, its optimization becomes challenging. To address this challenge, this study develops a recurrent neural network‐based real‐time metro operation optimization model trained using data representing the moments when the trains departed from the stations. …”
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Land surface temperature retrieval from FY-3E/MERSI using an optimized water vapor scaling method by Weihan Liu, Jie Cheng, Lixin Dong

“…In situ validation showed the retrieved FY-3E/MERSI LST achieved a bias of −0.61 K and an RMSE of 2.24 K at the SURFace RADiation budget (SURFRAD) network sites. Cross-comparison with the LST product of Geostationary Operational Environmental Satellite-16 (GOES-16) showed a high degree of spatial distribution consistency, with an average bias and RMSE of −0.21 K and 1.64 K, respectively. …”
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Optimizing downlink of CR-NOMA resource allocation: A fairness index approach to balancing system performance and energy efficiency by Nahla Nur Elmadina, Rashid A. Saeed, Elmustafa Sayed Ali, Elsadig Saeid, Rania A. Mokhtar

“…This study addresses the challenge of optimizing resource allocation in downlink Non-Orthogonal Multiple Access (NOMA) systems within small-cell networks, focusing on balancing user fairness, energy efficiency, and system performance. …”
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Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng, Yi Fang

“…The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. …”
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Intelligent ESG portfolio optimization: A multi-objective AI-driven framework for sustainable investments in the Indian stock market by Apurv Gaurav, Kripamay Baishnab, Piyush Kumar Singh

“…It demonstrates on 30 randomly selected ESG-ranked Indian stocks from diverse sets, and simulates a retail portfolio scenario by leveraging advanced machine learning and optimization techniques. In first stage, a Multivariate Bidirectional Long Short-Term Memory (MBi-LSTM) network is utilized to enhance return prediction accuracy, capturing the market’s nonlinear dynamics. …”
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Integrating Machine Learning Workflow into Numerical Simulation for Optimizing Oil Recovery in Sand-Shale Sequences and Highly Heterogeneous Reservoir by Dung Bui, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Adewale Amosu, William Ampomah

“…The Artificial Neural Network (ANN) algorithm was found to provide higher field cumulative oil production compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) of 3.5% and 26.5%, respectively. …”
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Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization by Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

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Prediction of teaching quality in the context of smart education: application of multimodal data fusion and complex network topology structure by Chunzhong Li, Chenglan Liu, Wenliang Ju, Yuanquan Zhong, Yonghui Li

“…The prediction model based on attention mechanism optimized deep neural network achieved an average accuracy of 94.16% in the first test; the average F1 score was 90.60%; the AUC (Area Under the Curve) value was 0.975; the average mean square error was 0.271. …”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…In order to address the issues of low accuracy and poor interpretability in existing HFMD incidence prediction models, in this paper, we propose an interpretable prediction model, namely, ARIMA–LSTM–XGBoost, which integrates multiple meteorological factors with Autoregressive integrated moving average model (ARIMA), Long short-term memory (LSTM), Extreme gradient boosting (XGBoost), Grey wolf optimizer (GWO), Genetic algorithm (GA) and Shapley additive explanations (SHAP). …”
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²¹⁰Po and ²¹⁰Pb content in the certain types of foodstuffs. Optimization of the analytical method by M. V. Kaduka, L. N. Basalaeva, T. A. Bekyasheva, S. A. Ivanov, N. V. Salazkina, V. V. Stupina, A. N. Kaduka

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Network Analysis of Volatility Spillovers Between Environmental, Social, and Governance (ESG) Rating Stocks: Evidence from China by Miao Tian, Shuhuai Li, Xianghan Cao, Guizhou Wang

“…The study finds that as ESG ratings decrease from AAA to C, the network’s average shortest path length and average connectedness strength decreases, indicating that highly rated companies play a central role in the network and maintain their ESG ratings through close connections, positively affecting market stability. …”
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