Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events by Xuemei Dai, Jing Zhou, Xu Zhang, Kaifeng Zhang, Wei Feng

“…First, a two-layer graph neural network (GNN) is employed to predict the probability distribution of system outages caused by SEWEs. …”
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Enhancing early lung cancer detection with MobileNet: A comprehensive transfer learning approach by Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Sofia Uribe-Toscano

“…This study investigates the application of MobileNetV2, a state-of-the-art, lightweight convolutional neural network, for the accurate classification of lung adenocarcinoma (LAC), benign lung tissue (BLT), and lung squamous cell carcinoma (LUSC). …”
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Multitask Learning for Estimation of Magnetic Parameters Using Pattern Recognition by Anubha Sehgal, Shipra Saini, Hemkant Nehete, Kunal Kranti Das, Sourajeet Roy, Brajesh Kumar Kaushik

“…Using these techniques, we introduce a multi-task convolutional neural network (CNN) model and support vector regression (SVR) model that is intended to precisely estimate two important parameters of magnetic systems such as the Dzyaloshinskii-Moriya interaction (DMI) constant and the exchange constant (A_ex). …”
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Enhancing Particulate Matter Estimation in Livestock-Farming Areas with a Spatiotemporal Deep Learning Model by Dohyeong Kim, Heeseok Kim, Minseon Hwang, Yongchan Lee, Choongki Min, Sungwon Yoon, Sungchul Seo

“…Using a 200 m × 200 m prediction grid, forecasts were generated for both 1 h and 24 h intervals using the Graz Lagrangian model (GRAL) and a one-dimensional convolutional neural network combined with the long short-term memory algorithm (1DCNN-LSTM). …”
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TXtreme: transformer-based extreme value prediction framework for time series forecasting by Hemant Yadav, Amit Thakkar

“…This paper proposes a TXtreme framework that uses Long-Short memory network, feed-forward neural network, and transformer to improve time series forecasting under extreme values. …”
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Study of Microstructure and Wear Resistance of AA5052/B4C Nanocomposites as a Function of Volume Fraction Reinforcement to Particle Size Ratio by ANN by D. Dinesh Kumar, A. Balamurugan, K. C. Suresh, R. Suresh Kumar, N. Jayanthi, T. Ramakrishnan, S. K. Hasane Ahammad, S. Mayakannan, S. Venkatesa Prabhu

“…This research examines a model function developed from an artificial neural network (ANN). AA5052/B4C composites bent using a powder metallurgy technique to hardness and ball-on-disc wear testing. …”
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Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes by Andong Guo, Yuqing Zhang, Qing Hao

“…In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. …”
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A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy by Feng You, Yunbo Gong, Haiqing Tu, Jianzhong Liang, Haiwei Wang

“…First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. …”
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Characterization of Neural Interaction During Learning and Adaptation from Spike-Train Data by Liqiang Zhu, Ying-Cheng Lai, Frank C. Hoppensteadt, Jiping He

“…Our computation and analysis indicated that theadaptation tends to alter the connection topology of theunderlying neural network, yet the average interaction strength inthe network is approximately conserved before and after theadaptation. …”
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A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale by Ziyao Zhao, Yi Zhang, Yi Zhang

“…Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. …”
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Polarimetric image recovery method with domain-adversarial learning for underwater imaging by Fei Tian, Jiuming Xue, Zhedong Shi, Hongling Luo, Wanyuan Cai, Wei Tao

“…In this paper, we collect, to the best of our knowledge, the richest polarization color image dataset with different water types and present a specially designed neural network UPD-Net firstly employing the domain-adversarial learning strategy to recover the degraded color images. …”
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A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances by Guang Yang, Junjie Wang, Feng Zhang, Shiwen Zhang, Cheng Gong

“…The real-time feature and self-adaptability of the method are attributed to the combinational use of Genetic Algorithm (GA) and Deep Neural Network (DNN). The decision system for proposing solutions, which contains multiple DNN cells with same structures, is trained by GA results. …”
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Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN by Krishna Prakash A, Jane Helena H, Paul Oluwaseun Awoyera

“…This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. …”
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Tea Disease Recognition Based on Image Segmentation and Data Augmentation by Ji Li, Chenyi Liao

“…Finally, an Inception Embedded Pooling Convolutional Neural Network (IDCNN) is developed for disease recognition. …”
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Design and Evaluation of a Leader–Follower Isomorphic Vascular Interventional Surgical Robot by Pengfei Chen, Yutang Wang, Dapeng Tian

“…The classification process includes time-frequency domain feature extraction, feature selection based on the Relief method and random forest (RF) method, and a BP neural network (NN) classifier. The results indicate that this method can achieve accuracy of 94%. …”
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EScope: Effective Event Validation for IoT Systems Based on State Correlation by Jian Mao, Xiaohe Xu, Qixiao Lin, Liran Ma, Jianwei Liu

“…EScope selects informative and representative sensors using an Neural-Network-based (NN-based) sensor selection component and extracts a verification sensor set for event validation. …”
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An Estimation Model on Electricity Consumption of New Metro Stations by Zhao Yu, Yun Bai, Qian Fu, Yao Chen, Baohua Mao

“…It was demonstrated that the proposed model could outperform the traditional methods which use a back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations.…”
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A Diagnostic and Performance System for Soccer: Technical Design and Development by Alberto Gascón, Álvaro Marco, David Buldain, Javier Alfaro-Santafé, Jose Victor Alfaro-Santafé, Antonio Gómez-Bernal, Roberto Casas

“…Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. Compared to existing systems, this approach significantly reduces costs and enhances practicality by minimizing the number of sensors required while ensuring real-time evaluation capabilities. …”
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BERT-BiGRU-Senti-GCN: An Advanced NLP Framework for Analyzing Customer Sentiments in E-Commerce by Muhammad Rizwan Rashid Rana, Asif Nawaz, Saif Ur Rehman, Muhammad Ali Abid, Mubariz Garayevi, Jana Kajanová

“…The key steps in this framework include data collection, NLP-enhanced feature extraction using BERT-BiGRU, and final classification using a Graph Neural Network-based finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback from the e-commerce industry. …”
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An Improved Predictor-Corrector Guidance Algorithm for Reentry Glide Vehicle Based on Intelligent Flight Range Prediction and Adaptive Crossrange Corridor by Mingjie Li, Chijun Zhou, Lei Shao, Humin Lei, Changxin Luo

“…In this paper, according to residual network (ResNet)’s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. …”
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