Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Furthermore, we leverage the autoregressive model to solve the scale insensitive problem of the neural network, making our model more robust. Specifically, we combine the OMNI data measured at Lagrangian Point 1 (L1) with the extreme ultraviolet images observed by the Solar Dynamics Observatory satellite to predict the solar wind speed at L1. …”
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Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
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Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering by Shuhui Yi, Yinglong Diao, Junjie Liu, Tian Fang, Xiaodong Yin

“…The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.…”
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A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification by Arafat Rahman, Nazmun Nahid, Björn Schuller, Md Atiqur Rahman Ahad

“…After intermediate and final feature extraction, we use an ensemble of a random forest classifier and a stacked convolutional neural network (S-CNN) model to detect activities and users. …”
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Predicting Geostationary (GOES) 4.1–30 keV Electron Flux Over All MLT Using LEEMYR Regression Models by L. E. Simms, N. Y. Ganushkina, M. van deKamp, M. W. Liemohn

“…We reduce predictors to an optimal set using stepwise regression, resulting in models with validation comparable to a neural network. Models predict 1, 3, 6, 12, and 24 hr into the future. …”
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Machine learning prediction of combat basic training injury from 3D body shape images. by Steven Morse, Kevin Talty, Patrick Kuiper, Michael Scioletti, Steven B Heymsfield, Richard L Atkinson, Diana M Thomas

“…Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.…”
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RETRACTED: Optimizing electric vehicle charging schedules and energy management in smart grids using an integrated GA-GRU-RL approach by Xinhui Zhao, Guojun Liang

“…However, the main challenge faced by smart grids is the efficient scheduling of electric vehicle charging and effective energy management within the grid.Methods: To address this issue, we propose a novel approach for intelligent grid electric vehicle charging scheduling and energy management, integrating three powerful technologies: Genetic Algorithm (GA), Gated Recurrent Unit (GRU) neural network, and Reinforcement Learning (RL) algorithm. …”
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EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION by Milind PARSE, Dhanya PRAMOD

“…The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. …”
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A decision support system based on classification algorithms for the diagnosis of periodontal disease by Abdulrahman Alshehri, Mohammed Dahman, Mousa Assiri, Abdulkarim Alshehri, Sharifah Alqahtani, Mohammed Shaiban, Bashyer Alqahtani, Sabah Althbyani, Hatem Alhefdi, Khalid Hakami, Abdulbari Ali, Abdullah Saeed

“…Aims: The purpose of this study was to develop and evaluate a decision support system (DSS) based on selected classification algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and logistic regression for the periodontal disease diagnosis. …”
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Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media by Jiao-huang Luo, Ai-hua Yang

“…To solve the problem, we regard ADR detection as a question-answer problem and introduces an innovative neural network framework with multiple GRU layers designed for extracting ADR-related information from tweets. …”
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Social Risk Early Warning of Environmental Damage of Large-Scale Construction Projects in China Based on Network Governance and LSTM Model by Junmin Fang, Dechun Huang, Jingrong Xu

“…Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. …”
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Speech Enhancement Using Joint DNN-NMF Model Learned with Multi-Objective Frequency Differential Spectrum Loss Function by Matin Pashaian, Sanaz Seyedin

“…We propose a multi-objective joint model of non-negative matrix factorization (NMF) and deep neural network (DNN) with a new loss function for speech enhancement. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. …”
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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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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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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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Marked point process variational autoencoder with applications to unsorted spiking activities. by Ryohei Shibue, Tomoharu Iwata

“…Our model defines the joint mark intensity as a latent variable model, where a neural network decoder transforms a shared latent variable into states and marks. …”
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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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