Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…Recently, the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. …”
Get full text

FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

Get full text

Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict the risk of cardiovascular disease (CVD) by analyzing patient data. This study evaluates seven binary classification algorithms, including Random Forests, Logistic Regression, Naive Bayes, K-Nearest Neighbors (kNN), Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, to understand their effectiveness in predicting CVD. …”
Get full text

Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification by Nuan Cui, Yingjie Wu, Guojiang Xin, Jiaze Wu, Liqin Zhong, Hao Liang

“…Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. …”
Get full text

Emotion classification with multi‐modal physiological signals using multi‐attention‐based neural network by Chengsheng Zou, Zhen Deng, Bingwei He, Maosong Yan, Jie Wu, Zhaoju Zhu

“…It features two kinds of attention modules, feature‐level, and semantic‐level, which drive the network to focus on the information‐rich features by mimicking the human attention mechanism. …”
Get full text

MCPA: multi-scale cross perceptron attention network for 2D medical image segmentation by Liang Xu, Mingxiao Chen, Yi Cheng, Pengwu Song, Pengfei Shao, Shuwei Shen, Peng Yao, Ronald X. Xu

“…This structure gradually shifts the segmentation focus of MCPA network training from large-scale structural features to more sophisticated pixel-level features. …”
Get full text

Detection of tuberculosis using cough audio analysis: a deep learning approach with capsule networks by Sakthi Jaya Sundar Rajasekar, Anu Rithiga Balaraman, Deepa Varnika Balaraman, Saleem Mohamed Ali, Kannan Narasimhan, Narayanasamy Krishnasamy, Varalakshmi Perumal

“…These recordings were processed into spectral images, and HOG features were extracted. Various models, including Capsule Networks + FCNN, CNN, VGG16, and ResNet50 were trained and evaluated. …”
Get full text

Enhanced Face Detection Using Multi-Cascade Face Detection and Deep Ladder Neural Network by Pande Anshul, Voditel Priti

“…Through deep learning-based imputation, we can effectively reconstruct missing facial features. The Labelled Faces in the Wild (LFW) dataset evaluate by comprising 13,000+ images of 5,749 individuals. …”
Get full text

A prediction method for anti-cancer drug combinations synergy based on graph attention network by QIN Weiqi;BAO Xin;CHEN Xiao;QIU Jianlong;WANG Donglin

“…It then employs a graph attention network(GAT) and multilayer perceptron(MLP) to extract features from both drug and cell line data, fusing these multi-source features to predict combination synergy. …”
Get full text

Classification of melanoma skin Cancer based on Image Data Set using different neural networks by Rukhsar Sabir, Tahir Mehmood

“…Additionally, we aim to demonstrate their effectiveness in contributing to the critical task of saving lives through early and accurate melanoma diagnosis.Our methodology involves a multi-stage process, which includes image normalization and augmentation, followed by segmentation, feature extraction, and classification. Notably, the neural network models underwent rigorous evaluation, with EfficientNet-B0 exhibiting exceptional performance as the winning model. …”
Get full text

GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein–Ligand Binding Affinity by Somanath Dandibhotla, Madhav Samudrala, Arjun Kaneriya, Sivanesan Dakshanamurthy

“…During external validation with the DUDE-Z v.2023.06.20 dataset, GNNSeq attained an average area under the curve (AUC) of 0.74, demonstrating its ability to distinguish active ligands from decoys across diverse ligand–receptor pairs. To further evaluate its performance, we combined GNNSeq with two additional specialized models that integrate structural and protein–ligand interaction features. …”
Get full text

Resilience assessment of mobile emergency generator-assisted distribution networks: A stochastic geometry approach by Chenhao Ren, Rong-Peng Liu, Wenqian Yin, Qinfei Long, Yunhe Hou

“…In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks affected by extreme weather events through investigation of structural features. …”
Get full text

A Random Degradation Aggregation Network With Temporal-Spatial Attention for Satellite Video Super-Resolution by Lu Li, Mi Wang, Yingdong Pi

“…Second, we employ a high-order grid propagation mechanism combined with a bidirectional recurrent neural network to propagate extracted features across the entire video sequence. …”
Get full text

Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer by Ping Jiang, Sijia Wu, Wenjian Qin, Yaoqin Xie

“…In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field. …”
Get full text

An Integrated Risk Assessment of Rockfalls Along Highway Networks in Mountainous Regions: The Case of Guizhou, China by Jinchen Yang, Zhiwen Xu, Mei Gong, Suhua Zhou, Minghua Huang

“…Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. …”
Get full text

A novel network with enhanced edge information for left atrium segmentation from LGE-MRI by Ze Zhang, Zhen Wang, Xiqian Wang, Kuanquan Wang, Yongfeng Yuan, Qince Li

“…To intensify edge information within image features, this study introduces an Edge Information Enhancement Module (EIEM) to the foundational network. …”
Get full text

Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues by Yihan Zhou, Xiaokang Yu, Zixi Huang, Feierdun Palati, Zeyu Zhao, Zihan He, Yuan Feng, Yuxi Luo

Get full text

BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. …”
Get full text

SlowFast-TCN: A Deep Learning Approach for Visual Speech Recognition by Nicole Yah Yie Ha, Lee-Yeng Ong, Meng-Chew Leow

“…Temporal Convolutional Network (TCN) is used as the backend architecture to learn the features from the frontend to perform the classification. …”
Get full text

Stream‐breeding salamander use of headwater stream networks in managed forests of western Washington, USA by Reed Ojala‐Barbour, Aimee P. McIntyre, Eric M. Lund, Marc P. Hayes

“…Under these rules, non‐fish‐bearing headwater streams receive buffers on at least 50% of the stream length, including FP Sensitive Sites that receive 15–17 m radius no‐cut patch buffers. We evaluated how torrent (Rhyacotriton spp.) and giant (Dicamptodon spp.) salamander relative abundance is influenced by headwater stream network features that correspond to FP Sensitive Sites. …”
Get full text