Knowledge Distillation‐Based Zero‐Shot Learning for Process Fault Diagnosis by Yi Liu, Jiajun Huang, Mingwei Jia

“…Information under both normal and fault conditions is then effectively extracted from process data by the well‐trained teacher model. Subsequently, knowledge distillation is used to transfer only the data of normal conditions in the teacher model to the student model. …”
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Knowledge, Attitude and Practices for HBV and HCV (Hepatitis B Virus and Hepatitis C Virus) among the Students of a Central University in South Delhi (India) and Strategies for Pre... by Anjum Ara, Deep Inder, Pawan Kumar, Kafil Akhtar

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Can Deep Learning Models Acquire Specialist Knowledge? Identifying Carotid Bruits in Type 2 Diabetes Using an Electronic Stethoscope by Aref Miri Rekavandi, Mohammed Bennamoun, Farid Boussaid, Wendy A. Davis, Timothy M. E. Davis

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A Cement Bond Quality Prediction Method Based on a Wide and Deep Neural Network Incorporating Embedded Domain Knowledge by Rengguang Liu, Jiawei Yu, Luo Liu, Zheng Wang, Shiming Zhou, Zhaopeng Zhu

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K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain by Vivek Kumar, Diego Reforgiato Recupero, Rim Helaoui, Daniele Riboni

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Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning by Yongguang Liang, Jingru Yang, Shuoyang Wei, Yanfei Liu, Shumeng He, Kang Zhang, Jie Qiu, Bo Yang

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Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography by Haoyang Cui, Chen Wang, Paul Calle, Yunlong Liu, Qinghao Zhang, Sinaro Ly, Justin Reynolds, Feng Yan, Ke Zhang, Ronghao Liu, Junyuan Liu, Kar-Ming Fung, Zhongxin Yu, Ajay Jain, Qinggong Tang, Chongle Pan

“…However, OCT image analysis using deep learning is hampered by limited diverse training data to meet performance requirements and high inference latency for real-time applications. …”
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Lightweight CNN for Resource-Constrained BCD System Using Knowledge Distillation by Falmata Modu, Rajesh Prasad, Farouq Aliyu

“…To address this, we propose a lightweight convolutional neural network (CNN) for BCD, leveraging knowledge distillation (KD) to transfer knowledge from a complex teacher model (TM) to a smaller student model (SM). …”
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Deep learning-based detection of indicator species for monitoring biodiversity in semi-natural grasslands by Deepak H. Basavegowda, Inga Schleip, Paul Mosebach, Cornelia Weltzien

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A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation by Hamada Rizk, Ahmed Elmogy, Mohamed Rihan, Hirozumi Yamaguchi

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Knowledge-Beliefs and Conviction as the Core Landmark of Russian Youth Education and Upbringing by Valeriy V. Likholetov, Asat G. Abdullin

“…The aim of this research is to analyze the problems of educating and training Russian youth and to identify effective means of forming reliable knowledge, convictions, and beliefs in young people. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). …”
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A multi-task deep reinforcement learning framework based on curriculum learning and policy distillation for quadruped robot motor skill training by Liang Chen, Bo Shen, Jiale Hong

“…In this paper, a multi-task deep RL framework based on curriculum learning and policy distillation is proposed, which aims to enhance the quadruped robot's motor performance across multiple continuous tasks by leveraging knowledge from expert skill teachers. …”
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UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification by Zeyu Ren, Xiangyu Kong, Yudong Zhang, Shuihua Wang

“…<italic>Goal:</italic> Deep learning techniques have made significant progress in medical image analysis. …”
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Physics-augmented deep learning models for improving evapotranspiration estimation in global land regions by Binrui Liu, Xinguang He, Wenkai Lyu, Lizhi Tao

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Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments by Zhongyuan Jing, Ruyan Wang

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The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications by Luis F. F. M. Santos, Miguel Ángel Sánchez-Tena, Cristina Alvarez-Peregrina, José-María Sánchez-González, Clara Martinez-Perez

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Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

“…We then employ the LoRA finetuning method to adapt the pre‐trained model to gas saturation prediction with meager labeled data while preserving pre‐trained knowledge. …”
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Efficient network attack path optimization method based on prior knowledge-based PPO algorithm by Qiuxiang Li, Jianping Wu

“…To address the issues of excessive invalid actions and poor training effect of current deep reinforcement learning-based attack path optimization methods, we propose a Prior Knowledge-based Proximal Policy Optimization (PKPPO) algorithm. …”
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Multi-Scale Contrastive Learning with Hierarchical Knowledge Synergy for Visible-Infrared Person Re-Identification by Yongheng Qian, Su-Kit Tang

“…Different from these methods, we propose a multi-scale contrastive learning network (MCLNet) with hierarchical knowledge synergy for VI-ReID. MCLNet is a novel two-stream contrastive deep supervision framework designed to train low-level details and high-level semantic representations simultaneously. …”
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