Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction by Xin Zan, Jasmine Siu Lee Lam

“…In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific scale. …”
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Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network by Xinhai Li, Yong Liang, Zhenhao Yang, Jie Li

“…For individual feature modeling, we propose the Spatio-Temporal Weighted Graph Attention Network (STWGAT) branch, which incorporates a newly developed directed social attention function to explicitly capture both the direction and intensity of pedestrian interactions. …”
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Individual tree extraction through 3D promptable segmentation networks by Tianyu Xiu, Hanwen Qi, Jiabo Xu, XinLian Liang

“…Existing end‐to‐end deep learning‐based methods for extracting individual trees typically rely on extracting instance‐sensitive features and clustering techniques. In this paper, inspired by the Segment Anything Model (SAM) and prompt‐driven paradigm, we propose a novel approach to forest point cloud instance segmentation, called the 3D Promptable Segmentation Network (3DPS‐Net). …”
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Application of Recurrent Neural Networks in Uncertainty Analysis of Sheet Metal Forming by Cruz Daniel, Parreira Tomás, Marques Armando, Prates Pedro, Oliveira Marta, Neto Diogo, Santos Abel, Amaral Rui, Barbosa Manuel, Pereira André

“…The results from the simulated tests can be considered as sequential data, allowing their evaluation using recurrent neural networks (RNNs), which are particularly suited for modelling temporal or ordered datasets. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. …”
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Detection and diagnosis of air compressor faults using weightless neural networks by Anubhab Bhattacharyya, Naveen Venkatesh Sridharan, Aravinth Sivakumar, Sugumaran Vaithiyanathan

“…Following this, the features were divided into separate sets to evaluate the validation, training, and testing accuracies of the WNNs using the WiSARD classifier. …”
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Identifying feature genes of chickens with different feather pecking tendencies based on three machine learning algorithms and WGCNA by Jiying Wen, Shenglin Yang, Jinjin Zhu, Ai Liu, Qisong Tan, Yifu Rao

“…Finally, the discriminant value of the feature genes was evaluated by manipulating the receiver operating curve (ROC) in the external dataset GSE10380.…”
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Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

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A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting by Altaf Hussain, Zulfiqar Ahmad Khan, Tanveer Hussain, Fath U Min Ullah, Seungmin Rho, Sung Wook Baik

“…Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. …”
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Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…Image processing extracts critical features from retinal images, acting as early warning signs for DR. …”
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Detection algorithm of electronic disguised voice based on convolutional neural network by Hongwei XU, Diqun YAN, Fan YANG, Rangding WANG, Chao JIN, Li XIANG

“…An electronic disguised voice detection algorithm based on the statistical features of MFCC and the convolution neural network was proposed.Firstly,the statistical features of MFCC were extracted and reconstructed as the input of convolution neural network.Considering the convolution kernel size,the number of convolution kernels and the pooling size,24 different network structures were evaluated in this work.Finally,the convolution neural network structure which could be effectively used for electronic disguised voice detection was determined.The experimental results show that the proposed algorithm can effectively detect the trace of electronic disguising.Meanwhile,the specific forgery operation of the electronic disguised voice can also be estimated.…”
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Multi-label Bird Species Classification Using Transfer Learning Network by Xue HAN, Jianxin PENG

“…Several pre-trained convolutional neural networks (CNNs), including InceptionV3, ResNet50, VGG16, and VGG19, were evaluated for extracting deep features from audio signals represented as mel spectrograms. …”
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An Improved Software Source Code Vulnerability Detection Method: Combination of Multi-Feature Screening and Integrated Sampling Model by Xin He, Asiya, Daoqi Han, Shuncheng Zhou, Xueliang Fu, Honghui Li

“…The key innovations include (i) utilizing abstract syntax tree (AST) representation of source code to extract potential vulnerability-related features through multiple feature screening techniques; (ii) conducting analysis of variance (ANOVA) and evaluating feature selection techniques to identify representative and discriminative features; (iii) addressing class imbalance by applying an integrated over-sampling strategy to create synthetic samples from vulnerable code to expand the minority class sample size; (iv) employing outlier detection technology to filter out abnormal synthetic samples, ensuring high-quality synthesized samples. …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…A comprehensive set of ML features have been proposed. However, until now, no work has systematically evaluated the performance of various categories of gait features for alcohol intoxication detection task using traditional machine learning algorithms. …”
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Dense dynamic convolutional network for Bel canto vocal technique assessment by Zhenyi Hou, Xu Zhao, Shanggerile Jiang, Daijun Luo, Xinyu Sheng, Kaili Geng, Kejie Ye, Jiajing Xia, Yitao Zhang, Chenxi Ban, Jiaxing Chen, Yan Zou, Yuchao Feng, Xin Yuan, Guangyu Fan

“…To accurately reflect a performer’s singing proficiency, it is essential to quantify and evaluate their vocal technical execution precisely. …”
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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications by Suneet Gupta, Praveen Gupta, Bechoo Lal, Aniruddha Deka, Hirakjyoti Sarma, Sheifali Gupta

“…The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. …”
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Cross-subject affective analysis based on dynamic brain functional networks by Lifeng You, Tianyu Zhong, Erheng He, Xuejie Liu, Qinghua Zhong

“…We then extracted three network attribute features—global efficiency, local efficiency, and local clustering coefficients—to achieve emotion classification based on dynamic brain network features.ResultsThe proposed method is evaluated on the DEAP dataset through subject-dependent (trial-independent), subject-independent, and subject- and trial-independent experiments along both valence and arousal dimensions. …”
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Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods by Xiuping Xuan, Mingjin Sun, Donghui Hu, Chunli Lu

“…Receiver Operating Characteristic analysis was applied to evaluate the accuracy of the feature genes. Pearson’s correlation analysis was used to calculate the correlations between feature genes and immune cell infiltration. …”
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Artificial neural networks applied to somatosensory evoked potentials for migraine classification by Gabriele Sebastianelli, Daniele Secci, Francesco Casillo, Chiara Abagnale, Cherubino Di Lorenzo, Mariano Serrao, Shuu-Jiun Wang, Fu-Jung Hsiao, Gianluca Coppola

“…Although these insights into migraine pathophysiology have been valuable, they are not currently used in clinical practice. This study aims to evaluate the potential of Artificial Neural Networks (ANNs) in distinguishing migraine patients from healthy individuals using neurophysiological recordings. …”
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Persistent Homology Combined with Machine Learning for Social Network Activity Analysis by Zhijian Zhang, Yuqing Sun, Yayun Liu, Lin Jiang, Zhengmi Li

“…This paper first creates an ego network for each user, encodes the higher-order topological features of the ego network as persistence diagrams using persistence homology, and computes the persistence entropy. …”
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