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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A New Evaluation Method of Total Organic Carbon for Shale Source Rock Based on the Effective Medium Conductivity Theory by Jian Fu, Xuesong Li, Yonghe Sun, Qiuli Huo, Ting Gao, Li Fu, Yuchen Liu, Suxing Dong, Haijun Fan

“…At present, the commonly used methods for assessing TOC include △logR and neural network method. However, practice shows that these methods have limitations in the application of unconventional intervals of sand-shale interbeds, and they cannot sufficiently reflect the variation of TOC in the vertical direction. …”
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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

“…Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. …”
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A Deep Q-Learning Algorithm With Guaranteed Convergence for Distributed and Uncoordinated Operation of Cognitive Radios by Ankita Tondwalkar, Andres Kwasinski

“…To address this challenge, this work presents the uncoordinated and distributed multi-agent DQL (UDMA-DQL) technique that combines a deep neural network with learning in exploration phases, and with the use of a Best Reply Process with Inertia for the gradual learning of the best policy. …”
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A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator by Joshi Kumar V, Vinodh Kumar Elumalai

“…This paper puts forward a policy feedback based deep reinforcement learning (DRL) control scheme for a partially observable system by leveraging the potentials of proximal policy optimization (PPO) algorithm and convolutional neural network (CNN). Although several DRL algorithms have been investigated for a fully observable system, there has been limited studies on devising a DRL control for a partially observable system with uncertain dynamics. …”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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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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ESTIMATED ELECTRICITY BILLING SYSTEM AND ITS EFFECTS ON CONSUMERS IN RESIDENTIAL AND BUSINESS CENTRES IN WUKARI METROPOLIS, TARABA STATE by Abubakar Ahmadu, Vyonkhen Tanko Nacho

“…It is recommended among others the adoption of Artificial Neural Network (ANN) to gauge consumers’ energy consumption pending the provision of prepaid meters and that National Electricity Regulatory Commission (NERC) should intensify efforts in the provision of free and/or subsidized prepaid meters to consumers. …”
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A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images by Jiang Xie, Jinzhu Wei, Huachan Shi, Zhe Lin, Jinsong Lu, Xueqing Zhang, Caifeng Wan

“…In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. …”
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Machine Learning for Promoting Environmental Sustainability in Ports by Meead Mansoursamaei, Mahmoud Moradi, Rosa G. González-Ramírez, Eduardo Lalla-Ruiz

“…The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. …”
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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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Automated Fillet Weld Inspection Based on Deep Learning from 2D Images by Ignacio Diaz-Cano, Arturo Morgado-Estevez, José María Rodríguez Corral, Pablo Medina-Coello, Blas Salvador-Dominguez, Miguel Alvarez-Alcon

“…This work presents an automated welding inspection system based on a neural network trained through a series of 2D images of welding seams obtained in the same study. …”
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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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Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model by A. Shamila Ebenezer, S. Deepa Kanmani, V. Sheela, K. Ramalakshmi, V. Chandran, M. G. Sumithra, B. Elakkiya, Bharani Murugesan

“…The convolutional neural network has become a standard tool for organizing and recognizing images. …”
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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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A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework by Zhenbo Lu, Wei Zhou, Shixiang Zhang, Chen Wang

“…In this framework, a residual neural network (ResNet) combined with attention modules was proposed to extract crash-related appearance features from urban traffic videos (i.e., a crash appearance feature extractor), which were further fed to a spatiotemporal feature fusion model, Conv-LSTM (Convolutional Long Short-Term Memory), to simultaneously capture appearance (static) and motion (dynamic) crash features. …”
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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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An interpretable and stacking ensemble model for predicting heat and mass transfer of desiccant wheel by Mengyang Li, Liu Chen

“…The model uses an integration approach, Light Gradient Boosting Machine, Random Forest and Back Propagation Neural Network models are used as the first-level base models to learn the data, and the Linear Regression model as a meta-model integrates the output of the base model to obtain the final prediction results. …”
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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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