Two-Stage Unsupervised Hyperspectral Band Selection Based on Deep Reinforcement Learning by Yi Guo, Qianqian Wang, Bingliang Hu, Xueming Qian, Haibo Ye

“…To address this issue, this paper proposes a two-stage unsupervised band selection method based on deep reinforcement learning. First, we performed noise estimation preprocessing to filter out bands with high noise levels to reduce the interference in the agent’s learning process. …”
Get full text

Non-line-of-sight Visible Light Positioning System based on Deep Learning by HUANG Weijie, LIN Bangjiang, DING Yongqi, LUO Jiabin, HUANG Tianming

“…However, traditional VLP systems rely on Line-of-Sight (LOS) paths and cannot function properly when obstructed by obstacles.【Methods】To address this issue, we propose a novel Non-Line-of-Sight (NLOS) VLP system based on deep learning. …”
Get full text

Movement-related theta rhythm in humans: coordinating self-directed hippocampal learning. by Raphael Kaplan, Christian F Doeller, Gareth R Barnes, Vladimir Litvak, Emrah Düzel, Peter A Bandettini, Neil Burgess

“…Recent evidence suggests that the hippocampus might function as a network hub for volitional learning. …”
Get full text

Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning by Jing Zhang, Yong-feng Fu, Hao Shen, Quan Liu, Li-ning Sun, Li-guo Chen

“…Raw images are utilized as the “state”, with voltage adjustments representing the “actions.” Deep reinforcement learning is employed to learn the focusing strategy directly from captured images, achieving end-to-end autofocus. …”
Get full text

Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks by Thomas Nowotny, James P Turner, James C Knight

“…We implemented Eventprop in the GPU-enhanced neural networks framework (GeNN) and used it for training recurrent SNNs on the Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC) datasets. We found that learning depended strongly on the loss function and extended Eventprop to a wider class of loss functions to enable effective training. …”
Get full text

Efficient Integration of Reinforcement Learning in Graph Neural Networks-Based Recommender Systems by Abdurakhmon Sharifbaev, Mikhail Mozikov, Hakimjon Zaynidinov, Ilya Makarov

“…To address this limitation, reinforcement learning (RL) has emerged as a promising solution. …”
Get full text

Knowledge Graph Representation Learning Model Based on Capsule Network and Information Fusion by Chu Zhao, Gilja So, Rui Chen

“…In recent years, knowledge representation learning has played a key role in intelligent recommendation, intelligent question-answering, and intelligent retrieval, and has been widely concerned. …”
Get full text

RIS-Assisted CR-MEC Systems Using Deep Reinforcement Learning Approach by Pham Duy Thanh, Hoang Thi Huong Giang, Ic-Pyo Hong

“…A Markov decision process problem is formulated and then is firstly solved by a proposed deep policy gradient scheme, in which the system directly learns the policy from gradients of actions. To obtain higher stability, we subsequently propose a deep Q-learning scheme to derive a proper solution by maximizing the state-action value function. …”
Get full text

Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites by Wen Jiang, Eric J. Jaehnig, Yuxing Liao, Zhiao Shi, Tomer M. Yaron-Barir, Jared L. Johnson, Lewis C. Cantley, Bing Zhang

“…Abstract Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and function of most phosphosites hampers the extraction of meaningful biological insights. …”
Get full text

FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation by Tuan Dung Kieu, Charles Fonbonne, Trung-Kien Tran, Thi-Lan Le, Hai Vu, Huu-Thanh Nguyen, Thanh-Hai Tran

“…Although federated learning enhances data privacy, it faces challenges related to data quality and client behavior. …”
Get full text

Inferring the equation of state from neutron star observables via machine learning by N.K. Patra, Tuhin Malik, Helena Pais, Kai Zhou, B.K. Agrawal, Constança Providência

Get full text

A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment by Nwadher Suliman Al-Blihed, Dina M. Ibrahim

“…We built FL models, and conducted experiments based on multiple client divisions—namely, 2, 5, or 10 clients—using both model types. The deep learning models used were Convolutional Neural Network (CNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), and the performance measures used to evaluate these FL models were the Loss function and Accuracy. …”
Get full text

Risk assessment of tunnel water inrush based on Delphi method and machine learning by Leizhi Dong, Qingsong Wang, Weiguo Zhang, Yongjun Zhang, Xiaoshuang Li, Fei Liu

“…Then, the Radial Basis Function (RBF) network, improved by the Locally Linear Embedding (LLE) algorithm and the Particle Swarm Optimization (PSO), is applied to predict the risk level. …”
Get full text

Hyperspectral RGB Imaging Combined With Deep Learning for Maize Seed Variety Identification by Jian Li, Fan Xu, Shaozhong Song, Qi Ji, Junling Liu

“…It aims to reconstruct RGB images from hyperspectral data and employ deep learning techniques to identify the varieties of corn seeds. …”
Get full text

News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model by Ningfeng Sun, Chengye Du

“…Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. …”
Get full text

DCLMA: Deep correlation learning with multi-modal attention for visual-audio retrieval by Jiwei Zhang, Hirotaka Hachiya

“…Furthermore, our objective function supervised model learns discriminative and modality-invariant features between samples from different semantic categories in the mutual latent space. …”
Get full text

Unsupervised Learning for Reference Signals Overhead Reduction in 3GPP MIMO Systems by Omar M. Sleem, Mohamed Salah Ibrahim, Akshay Malhotra, Mihaela Beluri, Constantino M. Lagoa

“…Toward this end, this paper proposes a machine learning-based approach that enables reference signal-free data channel demodulation. …”
Get full text

Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping by Giacomo Quattrini, Simone Pesaresi, Nicole Hofmann, Adriano Mancini, Simona Casavecchia

“…This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. …”
Get full text

A Novel YOLO Algorithm Integrating Attention Mechanisms and Fuzzy Information for Pavement Crack Detection by Qingqing Li, Tianshu Wu, Tingfa Xu, Jianmei Lei, Jiu Liu

“…This model integrates fuzzy logic and fuzzy membership functions to handle uncertainty in crack detection. …”
Get full text

Cognitive–Motor Coupling in Multiple Sclerosis: Do Chronological Age and Physical Activity Matter? by Brenda Jeng, Peixuan Zheng, Robert W. Motl

“…<b>Results</b>: The bivariate correlation analyses indicated that cognitive function had moderate-to-strong associations with motor function (range of <i>r_s</i> between 0.433 and 0.459). …”
Get full text