Hierarchical Deep Learning Model Optimization Using Enhanced Evolutionary-based Approach for Fake News Detection by Deepti Nikumbh, Anuradha Thakare

“…This work introduces the Deep Learning Model with Evolutionary Computing Approach (DLECA), a novel method for compressing and optimizing hierarchical deep learning models (HDLM). …”
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Modelling the effect of vaccination on the spread of COVID-19 via a novel evolutionary ensemble learning algorithm by Mohammad Hassan Tayarani Najaran

“…To build the model, an ensemble learning algorithm is proposed, which is a combination of different learning algorithms. …”
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Leaky ReLU-ResNet for Plant Leaf Disease Detection: A Deep Learning Approach by Smitha Padshetty, Ambika

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Sim-to-Real Reinforcement Learning for a Rotary Double-Inverted Pendulum Based on a Mathematical Model by Doyoon Ju, Jongbeom Lee, Young Sam Lee

“…The training process adopts the Truncated Quantile Critics (TQC) algorithm, with a reward function specifically designed to reflect the nonlinear characteristics of the system. …”
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Robust DOA Estimation via a Deep Learning Framework with Joint Spatial–Temporal Information Fusion by Yonghong Zhao, Xiumei Fan, Jisong Liu

“…In this paper, we propose a robust deep learning (DL)-based method for Direction-of-Arrival (DOA) estimation. …”
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An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause by Sheng Li, Yang‐Yi Sun, Chieh‐Hung Chen

“…Abstract In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD). …”
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An Iterative Learning Scheme-Based Fault Estimator Design for Nonlinear Systems with Randomly Occurring Parameter Uncertainties by He Jun, Wei Shanbi, Chai Yi

“…On the other hand, a novel optimal function using expectation is presented to ensure the uniform convergence of the fault estimation scheme, thus reducing the impact of randomly occurring parameter uncertainties. …”
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Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques by Luis Hernán Ochoa Gutierrez, Carlos Alberto Vargas Jiménez, Luis Fernando Niño Vásquez

“…During training stages of SVMs, several combinations of kernel function exponent and complexity factor were applied to time signals of 5, 10 and 15 seconds along with earthquake magnitudes of 2.0, 2.5, 3.0 and 3.5 ML. …”
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A deep learning physics-informed neural network (PINN) for predicting drilled shaft axial capacity by M.E. Al-Atroush

“…To bridge this gap, a novel Deep Learning–Physics-Informed Neural Network (DL-PINN) framework is proposed. …”
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Solute Energetics in Aqueous Xanthan Gum Solutions: What Can Be Learned from a Fluorescent Probe? by Mark P. Heitz, Emmanuel M. Nsengiyumva, Paschalis Alexandridis

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Deep reinforcement learning based online lifting path planning for tower cranes in unknown dynamic environments by Kai Wang, Jing Li, Zhiyuan Yin, Jiankang Zhang, Xin Ma

“…Moreover, a novel reward function is introduced to optimize the smoothness of the lifting path, which improves the success rate and optimizes the energy and time cost. …”
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Transfer learning-enhanced physics informed neural network for accurate melt pool prediction in laser melting by Qingyun Zhu, Zhengxin Lu, Yaowu Hu

“…It contains the enhanced PINN (EPINN) and transfer learning framework. The EPINN model integrates the heat transfer law and boundary condition to loss function, imposing strong physical constraints on data. …”
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Multi-Agent Reinforcement Learning With Cross-Layered Adaptive Wireless Video Streaming for Road Traffic Monitoring by Aung Myo Htut, Hideya Ochiai, Chaodit Aswakul

“…To promote cooperation and fairness among agents, a multi-agent architecture with independent learners employing a social welfare function as a joint reward is implemented. The learning agents are trained and evaluated under various scenarios, and their performance is compared to a baseline without learning agents. …”
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Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications by Bibhu Prasad Ganthia, Sudheer Hanumanthakari, Hemachandra Gudimindla, Harishchander Anandaram, M. Siva Ramkumar, Monalisa Mohanty, S. Raja Gopal, Atul Sarojwal, Kibrom Menasbo Hadish

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Three-Dimensional Shape Reconstruction from Digital Freehand Design Sketching Based on Deep Learning Techniques by Ding Zhou, Guohua Wei, Xiaojun Yuan

“…The implementation begins by extracting features from the FDS using the self-supervised learning model DINO, followed by the continuous Signed Distance Function (SDF) regression as an implicit representation through a Multi-Layer Perceptron network. …”
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Deep Reinforcement Learning-Based Two-Phase Hybrid Optimization for Scheduling Agile Earth Observation Satellites by Guanghui Zhou, Zhicheng Jin, Dongning Liu

“…To balance solution quality and computational efficiency, a deep reinforcement learning (DRL)-based algorithmic framework is proposed. …”
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Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention by Elad Yoshai, Gil Goldinger, Tatiana Kogan, Anna Zakharov, Miki Haifler, Natan T. Shaked

“…Here, we present a generative deep learning approach to enhance frozen section images by leveraging guidance from permanent sections. …”
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Large Language Model-Assisted Deep Reinforcement Learning from Human Feedback for Job Shop Scheduling by Yuhang Zeng, Ping Lou, Jianmin Hu, Chuannian Fan, Quan Liu, Jiwei Hu

“…However, it still has challenges in reward function design and state feature representation, which makes it suffer from slow policy convergence and low learning efficiency in complex production environments. …”
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Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm by Fang Liu, Xiaoli Liu, Changyou Yin, Hongrong Wang

“…It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. …”
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Particle Swarm Optimization – Extreme Learning Machine with Decreasing Inertia Weight for COVID-19 Prediction in Surabaya by Mohamad Handri Tuloli, Syaiful Anam, Nur Shofianah

“…The best MAPE is achieved using the sigmoid activation function with the number of hidden layer nodes around.…”
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