Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss by Xiaoming Xu, Xian Li, Na Chen, Dongjie Zhao, Chunmei Chen

“…To address these challenges, we propose an enhanced DRL framework that leverages a Dynamic Huber loss function tailored for UAV autonomous obstacle avoidance. …”
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A novel elite guidance-based social learning particle swarm optimization algorithm by QI Cheng, XIE Junwei, WANG Xue, FENG Weike, ZHANG Haowei

“…Finally, 12 benchmark test function sets covering unimodal, multimodal and rotated-multimodal functions are used to validate the performance of the proposed algorithm. …”
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Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction by P. Nancy, Prasad Raghunath Mutkule, Kalpana Sunil Thakre, Ajay S. Ladkat, S.B.G. Tilak Babu, Sunil L. Bangare, Mohd Naved

“…The support vector machine with radial basis function (SVM RBF) and random forest algorithms are used here for data classification. …”
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Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles by Ingo Dierking, Adam Moyle, Gabriele Maria Cepparulo, Katherine Skingle, Laura Hernández, Juhan Raidal

“…Machine learning-based image recognition is employed to investigate the annihilation dynamics of umbilic defects induced in systems of nematic liquid crystals doped with nanoparticles. …”
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Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning by Ning Shan, Yu Shang, Yaowu He, Zhe Wen, Shangwei Ning, Hong Chen

“…Statistical analyses revealed that CCL2 was negatively correlated with lung function in IPF patients and decreased after mycophenolate mofetil (MMF) treatment in SSc patients. …”
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Simulation Study: Trajectory Tracking Control of Parafoil System Based on Deep Reinforcement Learning by Xuepu Zhang

“…However, the Markov property of reinforcement learning offers a new possibility for controlling the parafoil system. …”
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Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks by Stefan Hildebrand, Sandra Klinge

“…An extendable, efficient and explainable Machine Learning approach is proposed to represent cyclic plasticity and replace conventional material models based on the Radial Return Mapping algorithm. …”
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KPMapNet: Keypoint Representation Learning for Online Vectorized High-Definition Map Construction by Bicheng Jin, Wenyu Hao, Wenzhao Qiu, Shanmin Pang

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A Deep Reinforcement Learning Approach for Portfolio Management in Non-Short-Selling Market by Ruidan Su, Chun Chi, Shikui Tu, Lei Xu

“…Reinforcement learning (RL) has been applied to financial portfolio management in recent years. …”
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Electromagnetic Source Imaging With a Combination of Sparse Bayesian Learning and Deep Neural Network by Jiawen Liang, Zhu Liang Yu, Zhenghui Gu, Yuanqing Li

“…In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. …”
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Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

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A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning by Xibin Wang, Zhenyu Dai, Hui Li, Jianfeng Yang

“…And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. …”
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Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms by Shanping Shi, Chao Huang, Xiaojian Tang, Hua Liu, Weiwei Feng, Chen Chen

“…Further analysis revealed the important role of USP14 in muscle function, cellular growth factor response, and maintenance of chromosome structure, and its close association with various immune cell functions. …”
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Deep learning method for cucumber disease detection in complex environments for new agricultural productivity by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Xin Liu

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Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry by Toufique Ahmed

“…This study found that fabric allowance can be predicted from required gray fabrics by using logarithmic function. Similarly, required gray fabrics can be calculated from a linear equation with 99% goodness of fit. …”
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Machine Learning‐Assisted Pd‐Au/MXene Sensor Array for Smart Gas Identification by Yiheng Chen, Jiawang Hu, Nanlin Hu, Shikai Wu, Yuan Lu

“…Owing to the electron sensitization and catalysis function of noble metal sites, the Pd‐Au/MXene nanocomposite demonstrates enhanced gas‐sensing characteristics, with a response up to 2.73 times the response of pristine Ti3C2Tx and a response speed 1.81 times as the pristine Ti3C2Tx. …”
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Hybrid Supervised and Reinforcement Learning for Motion-Sickness-Aware Path Tracking in Autonomous Vehicles by Yukang Lv, Yi Chen, Ziguo Chen, Yuze Fan, Yongchao Tao, Rui Zhao, Fei Gao

“…The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). …”
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Machine learning-based multiphysics model for corrosion fatigue crack propagation in aluminum alloy by Hongkun Wang, Zhenshuang Wu, Dongmei Jiang, Haitao Wang, En-Hou Han

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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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Research on Binary Mixed VOCs Gas Identification Method Based on Multi-Task Learning by Haixia Mei, Ruiming Yang, Jingyi Peng, Keyu Meng, Tao Wang, Lijie Wang

“…Traditional volatile organic compounds (VOCs) detection models separate component identification and concentration prediction, leading to low feature utilization and limited learning in small-sample scenarios. Here, we realize a Residual Fusion Network based on multi-task learning (MTL-RCANet) to implement component identification and concentration prediction of VOCs. …”
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