Toward Model-Independent Separative Training for Deep Hyperspectral Anomaly Detection With Mask Guidance by Xi Su, Xiangfei Shen, Haijun Liu, Lihui Chen, Gemine Vivone, Xichuan Zhou

“…To address this limitation, we propose a model-independent separative training strategy for DNNs, named DeepSeT. Our method introduces a latent binary mask to identify the potential anomalies and background to guide the training. …”
Get full text

A Physics‐Informed Deep Learning Framework for Estimating Thermal Stratification in a Large Deep Reservoir by Yuan He, Xiaofan Yang

“…To overcome these limitations, this study proposes a hybrid multi‐parameter scientific knowledge‐guided neural network (MP‐KgNN) for solving 1‐D lake temperature governing equation trained using both simulations of the WRF‐Lake model and onsite LWT measurements based on a novel training framework called physics‐informed deep learning (PIDL) framework and simulates the thermodynamics in a large deep reservoir located in eastern China from 1960 to 2021. …”
Get full text

Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function by Marko Ruman, Tatiana V. Guy

“…In contrast, using standard Generative Adversarial Networks or Cycle Generative Adversarial Networks led to worse performance than training from scratch in the majority of cases. The results demonstrate that the proposed method ensured enhanced knowledge generalization in deep reinforcement learning.…”
Get full text

Initiating a Full Fulfilment Curriculum-Based Deep Learning: Integration of Knowledge and Character in Learning by Aulia Fitri, Khairul Amin, Zuhri Tauhid, Aulia Ramadona, Wismanto

“…Based on these needs, the full fulfilment curriculum was developed to combine aspects of academic knowledge with character formation through deep learning approaches, namely Mindful Learning, Meaningful Learning, and Joyful Learning. …”
Get full text

A Multi-Robot Collaborative Exploration Method Based on Deep Reinforcement Learning and Knowledge Distillation by Rui Wang, Ming Lyu, Jie Zhang

“…To this end, we propose to view the robot team as an agent and obtain a policy network that can be centrally executed by training with an improved SAC deep reinforcement learning algorithm. …”
Get full text

Accelerated and Interpretable Flood Susceptibility Mapping Through Explainable Deep Learning with Hydrological Prior Knowledge by Jialou Wang, Jacob Sanderson, Sadaf Iqbal, Wai Lok Woo

Get full text

Tracing Student Learning Outcome at Historically Black Colleges and Universities via Deep Knowledge Tracing by Ming-Mu Kuo, Xiangfang Li, Pamela H. Obiomon, Lijun Qian, Xishuang Dong

“…Deep Knowledge Tracing (DKT) has emerged as an advanced approach to enhancing higher education outcomes by enabling personalized learning experiences and more accurate assessments of student knowledge mastery. …”
Get full text

Theoretical Model, Model Innovation, and Important Implications of DeepSeek Empowering Library Knowledge Services by ZHANG Xingwang, LI Jie, LI Sifan, WANG Xiaopei

“…At the same time, it can bring many problems, such as model security, intellectual property risks, knowledge illusions, and information cocoons. From the existing public information, DeepSeek can provide important technical support and core driving force for library knowledge service innovation in the era of artificial intelligence from four aspects: technical algorithms, training cost, open source ecology, and local lightweight deployment. …”
Get full text

A Prior Knowledge-Enhanced Deep Learning Framework for Improved Thermospheric Mass Density Prediction by Ling Li, Changyong He, Dunyong Zheng, Shaoning Li, Dong Zhao

“…Accurate thermospheric mass density (TMD) prediction is critical for applications in solar-terrestrial physics, spacecraft safety, and remote sensing systems. While existing deep learning (DL)-based TMD models are predominantly data-driven, their performance remains constrained by observational data limitations. …”
Get full text

Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images by Onur Sevli

“…Transfer learning is to transfer the knowledge of a pre-trained neural network to a similar model in case of limited training data or the goal of reducing the workload. …”
Get full text

A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms by Fanping Min

“…In order to improve the talent training effect of electrical courses, this paper proposes a talent training model for electrical courses considering diverse constraint models and knowledge recognition algorithms. …”
Get full text

Enhancing Knowledge and Beliefs: The Impact of a Gender-transformative Training Program on Tuberculosis Care in Southern Nigeria by Daniel C. Oshi, Patrick O. Amah, Isaac N. Alobu, Sarah N. Oshi, Sherline Brown, Kingsley N. Ukwaja

“…This study aimed to assess changes in knowledge and beliefs following a training program on gender-transformative TB programming among stakeholders in Southern Nigeria. …”
Get full text

Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models by Vasundhara Acharya, Bülent Yener, Gillian Beamer

“…It is also feasible to distill the knowledge acquired by deep neural networks to smaller student models through knowledge distillation (KD), achieving goals such as model compression and performance enhancement. …”
Get full text

Improving Student Learning Outcome Tracing at HBCUs Using Tabular Generative AI and Deep Knowledge Tracing by Ming-Mu Kuo, Xiangfang Li, Pamela Obiomon, Lijun Qian, Xishuang Dong

“…To overcome this challenge, this study explores the application of generative artificial intelligence models to generate synthetic data, augmenting real datasets to improve student learning outcome tracing at these colleges and universities using Deep Knowledge Tracing techniques, which potentially offers actionable insights to identify at-risk students and enables proactive interventions to enhance retention and graduation rates in Science, Technology, Engineering and Math education. …”
Get full text

Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge by Ke DENG, Shuaijun WANG, Hua YU, Jian ZHANG, Junfan CHEN, Zhouping WU

“…To mitigate the time-frequency doubly-selective fading in such underwater acoustic rapidly time-varying channels and reduce the bit error rate(BER) of OFDM systems, this paper proposed an underwater acoustic rapidly time-varying channel equalization method that combined deep learning with domain knowledge. Different from regarding the outcomes of traditional channel estimation and equalization detection as preprocessing results or supplementary information sources for deep neural networks(DNNs), this paper employed the structured information from classical frequency-domain equalization models to assist in the training and inference of DNN models, so as to counteract the adverse effects of ICI and adapt to scenarios where there is a mismatch between the actual deployment channel environment and the training channel environment. …”
Get full text

Exploring the Dilemma of AI Use in Medical Research and Knowledge Synthesis: A Perspective on Deep Research Tools by Ariel Yuhan Ong, David A Merle, Siegfried K Wagner, Pearse A Keane

“…Amidst all these, OpenAI’s latest tool, Deep Research, stands out for its potential to revolutionize how researchers engage with the literature. …”
Get full text

Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge‐Based Framework and Deep Learning by Parnian Ghaneei, Ehsan Foroumandi, Hamid Moradkhani

Get full text

Knowledge and Practices Regarding Deep Venous Thrombosis (DVT) Prevention Among Nurses in Jeddah, Saudi Arabia by Ruba M. Alharazi, Raiannah H. Alqahtani, Rahaf A. Alanazi, Walaa Alharbi, Shmokh M. Alshenen, Aisha Alhofaian, Afnan Tunsi, Loujain Sharif

“…This study aimed to assess the knowledge and practices regarding deep venous thrombosis prevention among nurses in Ministry of Health hospitals and King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia. …”
Get full text

Multimodal Deep Learning for Violence Detection: VGGish and MobileViT Integration With Knowledge Distillation on Jetson Nano by Mohammed, Antara Labiba Swapnil, Marilyn Dip Peris, Istiaque Hasan Nihal, Riasat Khan, Mohammad Abdul Matin

“…With multimodal VGGish + MobileViT, the classification accuracy and F1 score have been enhanced to 97.13% and 0.97, respectively. The knowledge distillation technique has been employed by transferring the backbone knowledge from a fine-tuned ViT model (teacher) to a MobileViT (student), focusing on training only the task head of the student model. …”
Get full text

Predicting Subsurface Layer Thickness and Seismic Wave Velocity Using Deep Learning: Knowledge Distillation Approach by Amir Moslemi, Anna Briskina, Jason Li, Zubeka Dang, Peyman Moghaddam

“…This study introduces a deep learning-based approach enhanced by knowledge distillation (KD) to predict subsurface layer thickness and seismic wave velocity. …”
Get full text