Cinemeducation: A Novel Way of Teaching–Learning Method – Time to Integrate It into the Mainstream Curriculum by Nikunja Kumar Das, Sahjid Mukhida, Rajashri Patil, Pankaj Das

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Research on deep learning-based fracture network inversion method for shale gas reservoirs by CHEN Weiming, JIANG Lin, LUO Tongtong, LI Yue, WANG Jianhua

“…To address this, a shale gas reservoir fracture network inversion method based on deep learning was proposed. The core of this method is to quantitatively analyze the fracturing curve characteristic parameters based on the site fracturing curve data, using strongly correlated indicators of fracture network parameters as inputs and microseismic monitoring fracture network parameters (including length, width, height, and volume) as target outputs. …”
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Cognitive Electronic Jamming Decision-Making Method Based on Improved Q-Learning Algorithm by Huiqin Li, Yanling Li, Chuan He, Jianwei Zhan, Hui Zhang

“…In this paper, a cognitive electronic jamming decision-making method based on improved Q-learning is proposed to improve the efficiency of radar jamming decision-making. …”
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A Few-shot Learning Method for Intent Analysis of Air Combat Confrontation Behaviors by PAN Ming, ZHENG Jingsong, LI Jinliang, FANG Long, YANG Yang, ZHAO Shijie

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Modelling of energy demand prediction system in potato farming using deep learning method by Riswanti Sigalingging, Nasha Putri Sebayang, Noverita Sprinse Vinolina, Lukman Adlin Harahap

“…A system using deep learning methods, specifically the Convolutional Neural Network (CNN), was also developed to accurately predict the classification of potato plant growth phases using image data. …”
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A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule by YiZhe Zhang, YunYi Zhang, GuoHui Zhou, Wei Zhang, KangLe Li, QuanQi Mu, Wei He, Kai Tang

“…Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. …”
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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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A Secure Data Collection Method Based on Deep Reinforcement Learning and Lightweight Authentication by Yunlong Wang, Jie Zhang, Guangjie Han, Dugui Chen

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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

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Retracted: News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model by null Complexity

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Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning by Changfan Zhang, Yuxuan Wang, Jing He

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Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders by Cengiz Gunay, Krishan Bhalsod

“…We have faced challenges applying this method and we are planning to present these in our poster. …”
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Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method by Fangzhou Lin, Shang Gao, Yichuan Tang, Xihan Ma, Ryo Murakami, Ziming Zhang, John D. Obayemi, Winston O. Soboyejo, Haichong K. Zhang

“…Additionally, training data is not always accessible for learning-based methods. In this work, we propose a Spectroscopic Photoacoustic Denoising (SPADE) framework using hybrid analytical and data-free learning method. …”
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End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning by Pengyuan Wang, Pinle Qin, Rui Chai, Jianchao Zeng, Pengcheng Zhao, Zuojun Chen, Bingjie Han

“…In this paper, we propose an end-to-end, unsupervised deep-learning framework that jointly performs real-time video stabilization and stitching. …”
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A Novel Learning-Based MPC Method via Basic-Residual Cooperative Model by Yuesheng Liu, Zhongxian Xu, Ning He, Lile He, Fuan Cheng

“…Compared to existing learning-based MPC methods that rely on a single network model as prediction models for either static feature capture or dynamic adaptation, which often result in insufficient adaptability or compromised computational efficiency, the proposed method integrates a dual-network architecture: a Long Short-Term Memory (LSTM) network to capture static system features, and a self-attention feed-forward neural network to adapt to dynamic aspects. …”
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STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning by Yi Lai, Ye Zhu, Li Li, Qing Lan, Yizheng Zuo

“…To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. …”
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A method for spatial interpretation of weakly supervised deep learning models in computational pathology by Abhinav Sharma, Bojing Liu, Mattias Rantalainen

“…Abstract Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). …”
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N V.V. THE METHOD OF ESTIMATION OF THE ECOSYSTEMS STABILITY LEVEL BY USE OF AUTOMATIC LEARNING by V. N. Exzaryan, Rukavitsi

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Intelligent Counterforce Allocation Method Using Multi-Agent Reinforcement Learning for Ground Operations by Kiwoong Park, Sangheun Shim

“…This study proposes the Intelligent Counterforce Allocation for Ground Operations (ICAGO) method, which leverages multi-agent reinforcement learning (MARL) to support command-level decision-making in ground warfare. …”
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Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots by Weimin Zhang, Guoyong Wang

“…A reinforcement learning-based continuous action space path planning method for mobile robots is proposed in this article. …”
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