Construction of an AI Literacy General Education Curriculum Based on "Knowledge-Skills" Navigation by Baiyang LI, Rong SUN

“…The foundational cognition level systematically organizes the four key knowledge modules involved in generative artificial intelligence: Machine Learning, Neural Networks, Deep Learning, and Natural Language Processing, helping students to build an initial cognitive framework for GenAI. …”
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Complexification through gradual involvement and reward Providing in deep reinforcement learning by E. V. Rulko

“…Training a relatively big neural network within the framework of deep reinforcement learning that has enough capacity for complex tasks is challenging. …”
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Large language models for depression recognition in spoken language integrating psychological knowledge by Yupei Li, Shuaijie Shao, Manuel Milling, Manuel Milling, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller

“…Depression is a growing concern gaining attention in both public discourse and AI research. While deep neural networks (DNNs) have been used for its recognition, they still lack real-world effectiveness. …”
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Transformer Fault Diagnosis Based on Knowledge Distillation and Residual Convolutional Neural Networks by Haikun Shang, Yanlei Wei, Shen Zhang

“…Subsequently, the Sparrow Optimization Algorithm (SSA) is applied to optimize the hyperparameters of the ResNet50 model, which is trained on DGA data as the teacher model. Finally, a dual-path distillation mechanism is introduced to transfer the efficient features and knowledge from the teacher model to the student model, MobileNetV3-Large. …”
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Digital Holographic Reconstruction Based on Deep Learning Framework With Unpaired Data by Da Yin, Zhongzheng Gu, Yanran Zhang, Fengyan Gu, Shouping Nie, Jun Ma, Caojin Yuan

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Deep Reinforcement-Learning-Based Air-Combat-Maneuver Generation Framework by Junru Mei, Ge Li, Hesong Huang

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Novel distinguisher for SM4 cipher algorithm based on deep learning by Huijiao WANG, Xin ZHANG, Yongzhuang WEI, Lingchen LI

“…A method was proposed to construct a deep learning distinguisher model for large state block ciphers with large-block and long-key in view of the problem of high data complexity, time complexity and storage complexity of large state block cipher distinguishers, and the neural distinguishers were constructed for SM4 algorithm.Drawing inspiration from the idea that ciphertext difference could improve the performance of distinguishers, a new input data format for neural distinguisher was designed by using partial difference information between ciphertext pairs as part of the training data.The residual neural network model was used to construct the neural distinguisher.The training dataset for large blocks was preprocessed.Additionally, an improved strategy for model relearning was proposed to address the high specificity and low sensitivity of the constructed distinguisher.Experimental results show that the proposed deep learning model for SM4 can achieve 9 rounds neural distinguisher.The accuracy of 4～9 rounds distinguishers can reach up to 100%, 76.14%, 65.20%, 59.28%, 55.89% and 53.73% respectively.The complexity and accuracy of the constructed differential neural distinguisher are significantly better than those of traditional differential distinguishers, and it is currently the best neural distinguisher for the block cipher SM4 to our knowledge.It also proves that the deep learning method is effective and feasible in the security analysis of block cipher of large block.…”
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Deep learning based fine-grained recognition technology for basketball movements by Lin Zhang

“…The design of the study contributes to the digital development of teaching and training in basketball and enriches the theoretical body of knowledge on deep learning and action recognition.…”
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Comprehensive Review of Physics-Guided Deep Learning: Advancements, Challenges, and Perspectives by CHEN Chong, ZHU Xiaoyu, WANG Fang, XU Yaqian, ZHANG Wei

“…Therefore, a novel framework called physics-guided deep learning has emerged which enhances the performance, explainability, and physical consistency of deep learning by integrating domain-specific physical knowledge into the construction and training process of deep learning models. …”
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A phenotypic drug discovery approach by latent interaction in deep learning by Tat Wai Billy Yu

“…Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.…”
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Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification by Lochana Mendis, Debjyoti Karmakar, Marimuthu Palaniswami, Fiona Brownfoot, Emerson Keenan

“…Efforts to address this issue have focused on data-driven deep-learning methods to detect fetal compromise automatically. …”
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Analyzing the Impact of Data Augmentation on the Explainability of Deep Learning-Based Medical Image Classification by Xinyu (Freddie) Liu, Gizem Karagoz, Nirvana Meratnia

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Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics by Sándor Szénási, Gábor Légrádi, Gábor Kovács

“…A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. …”
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Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems by Kosmas Alexopoulos, Panagiotis Mavrothalassitis, Emmanouil Bakopoulos, Nikolaos Nikolakis, Dimitris Mourtzis

“…The objective of this work is to design and implement a framework for the modeling and deployment of a deep reinforcement learning (DRL) agent to support short-term production scheduling. …”
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The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review by Jingguo Qu, Xinyang Han, Man-Lik Chui, Yao Pu, Simon Takadiyi Gunda, Ziman Chen, Jing Qin, Ann Dorothy King, Winnie Chiu-Wing Chu, Jing Cai, Michael Tin-Cheung Ying

“…Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. …”
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Automatic classification of fungal-fungal interactions using deep leaning models by Marjan Mansourvar, Jonathan Funk, Søren Dalsgård Petersen, Sajad Tavakoli, Jakob Blæsbjerg Hoof, David Llorente Corcoles, Sabrina M. Pittroff, Lars Jelsbak, Niels Bjerg Jensen, Ling Ding, Rasmus John Normand Frandsen

“…We used our model to categorize the outcome of interactions between the plant pathogen Fusarium graminearum and individual isolates from a collection of 38,400 fungal strains. The authors trained multiple deep learning architectures and evaluated their performance. …”
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Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil–environment relationships by Lei Zhang, Lin Yang, Yuxin Ma, A-Xing Zhu, Ren Wei, Jie Liu, Mogens H. Greve, Chenghu Zhou

“…Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. …”
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TranGDeepSC: Leveraging ViT knowledge in CNN-based semantic communication system by Tung Son Do, Thanh Phung Truong, Quang Tuan Do, Sungrae Cho

“…This paper introduces TranGDeepSC, a lightweight CNN-based deep semantic communication (DeepSC) system that leverages Vision Transformer (ViT) knowledge through co-training to enhance image transmission. …”
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KDFF-DETR: Knowledge-Driven Feature Fusion DETR for Spaceborne Infrared Ship Detection by Yifei Peng, Ze Tao, Jian Zhang, Rongchang Zhao, Chao Liu, Hui Sun

“…Besides, a contribution-balanced feature fusion (CBFF) method for query selection is proposed, dynamically adjusting the weight distribution of handcrafted and deep features during different training stages to improve feature fusion accuracy and generalization ability. …”
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IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network by Yuexu Jiang, Manish Sridhar Immadi, Duolin Wang, Shuai Zeng, Yen On Chan, Jing Zhou, Dong Xu, Trupti Joshi

“…Methods: The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. …”
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