Graph Neural Network Classification in EEG-Based Biometric Identification: Evaluation of Functional Connectivity Methods Using Time-Frequency Metric by Roghaieh Ashenaei, Ali Asghar Beheshti Shirazi

Get full text

An introduction to Self-Aware Deep Learning for medical imaging and diagnosis by Paolo Dell’Aversana

Get full text

Evaluation of Antifibrotic Mechanisms of 3′5-Dimaleamylbenzoic Acid on Idiopathic Pulmonary Fibrosis: A Network Pharmacology and Molecular Docking Analysis by Karina González-García, Jovito Cesar Santos-Álvarez, Juan Manuel Velázquez-Enríquez, Cecilia Zertuche-Martínez, Edilburga Reyes-Jiménez, Rafael Baltiérrez-Hoyos, Verónica Rocío Vásquez-Garzón

“…Previously, 3′5-dimaleamylbenzoic acid (3′5-DMBA) was shown to exert resolving effects in IPF, offering a promising alternative for treating this disease; however, the molecular mechanisms associated with this effect have not been explored. Objetive: We evaluated the potential antifibrotic mechanisms of 3′5-DMBA by network pharmacology (NP) and molecular docking (MD). …”
Get full text

Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection by Omneya Attallah

“…To effectively integrate multiple deep features and diminish the dimensionality of features derived from each CNN, feature transformation and selection methods, including non-negative matrix factorization and Relief-F, are used leading to a reduction in classification complexity. …”
Get full text

Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs by Selene Tomassini, Damiano Duranti, Abdallah Zeggada, Carlo Cosimo Quattrocchi, Farid Melgani, Paolo Giorgini

“…Our model utilizes a pretrained VGG16, processing groups of five slices simultaneously, and features multiple end-to-end LSTM branches, each specialized in predicting one caption, subsequently combined to form the ordered reports after a BERT-based semantic evaluation. …”
Get full text

A Lightweight Multi-Scale Context Detail Network for Efficient Target Detection in Resource-Constrained Environments by Kaipeng Wang, Guanglin He, Xinmin Li

“…To meet these challenges, we propose MSCDNet (Multi-Scale Context Detail Network), an innovative and lightweight architecture designed specifically for efficient target detection in such environments. …”
Get full text

Gearbox Fault Diagnosis Based on Compressed Sensing and Multi-Scale Residual Network with Lightweight Attention Mechanism by Shihua Zhou, Xinhai Yu, Xuan Li, Yue Wang, Kaibo Ji, Zhaohui Ren

“…Subsequently, a multi-scale feature extraction (MSFE) module was designed based on multi-scale learning, with the aim of improving the feature extraction ability of the signal in noisy environments. …”
Get full text

CSSA-YOLO: Cross-Scale Spatiotemporal Attention Network for Fine-Grained Behavior Recognition in Classroom Environments by Liuchen Zhou, Xiangpeng Liu, Xiqiang Guan, Yuhua Cheng

“…To address these issues, we introduce CSSA-YOLO, a novel detection network that incorporates cross-scale feature optimization. …”
Get full text

Cross Attention-Based Multi-Scale Convolutional Fusion Network for Hyperspectral and LiDAR Joint Classification by Haimiao Ge, Liguo Wang, Haizhu Pan, Yanzhong Liu, Cheng Li, Dan Lv, Huiyu Ma

“…However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. …”
Get full text

Deep Learning for Enhanced-Resolution Reconstruction of Sentinel-1 Backscatter NRCS in China’s Offshore Seas by Xiaoxiao Zhang, Yu Du, Xiang Su, Zhensen Wu

Get full text

Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM by Vladyslav Yavtukhovskyi, Violeta Tretynyk

“…Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. …”
Get full text

A comprehensive survey on techniques, challenges, evaluation metrics and applications of deep learning models for anomaly detection by Mudita Kohli, Indu Chhabra

Get full text

Sustainable evaluation of ecotourism in the Yangtze River delta urban agglomeration: A system coordination perspective by Jiqiang Zhao, Jian Pan, Ling Tan

“…By employing network the slack-based measure (SBM) and coordination coupling degree (CCD) models, using the support vector machine recursive feature elimination (SVM-RFE) method, the study identifies the efficiency of the ETS, the coordination between ES and TS, as well as key factors affecting the sustainability of ecotourism. …”
Get full text

Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm by Peiran Lei, Mozafar Saadat, Mahdieh Gol Hassani, Chang Shu

Get full text

Development of a Preliminary Screening Tool for Predicting Polycystic Ovarian Syndrome using Machine Learning and Deep Learning Models with Non Invasive Qualitative Features: A Cas... by Hanumanth Narni, Vasudeva Rao Ananthasetty, SD Jilani

“…Machine Learning (ML) and Deep Learning (DL) models offer promising avenues for predicting probable cases of PCOS using non invasive qualitative features. Aim: To develop and compare the performance of Random Forest (RF) and Feedforward Neural Network (FFNN) models in predicting PCOS using abundant non invasive qualitative features. …”
Get full text

Diagnosis of Lung Cancer Using Endobronchial Ultrasonography Image Based on Multi-Scale Image and Multi-Feature Fusion Framework by Huitao Wang, Takahiro Nakajima, Kohei Shikano, Yukihiro Nomura, Toshiya Nakaguchi

Get full text

Resource-Constrained Specific Emitter Identification Based on Efficient Design and Network Compression by Mengtao Wang, Shengliang Fang, Youchen Fan, Shunhu Hou

“…Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) edge devices, especially in Internet of Things (IoT) applications. …”
Get full text

Modeling and Evaluating a Cache System in ICN Routers Using a Programmable Switch and Computers by Junji Takemasa, Yuki Koizumi, Toru Hasegawa

“…Information-centric networking (ICN) is one of promising networking architectures to replace IP because its notable feature, in-network caching, is expected to reduce about a one-third of the forever increasing Internet traffic. …”
Get full text

A KWS System for Edge-Computing Applications with Analog-Based Feature Extraction and Learned Step Size Quantized Classifier by Yukai Shen, Binyi Wu, Dietmar Straeussnigg, Eric Gutierrez

Get full text

Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals by Qi Li, Wei Cao, Anyuan Zhang

“…Methods Our study proposes an epilepsy detection model, CMFViT, based on a Multi-Stream Feature Fusion (MSFF) strategy that fuses a Convolutional Neural Network (CNN) with a Vision Transformer (ViT). …”
Get full text