Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification by Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Convolutional neural networks, deep neural networks, and tree-based models, including Gradient Boosting Classifier and Random Forest, were trained and evaluated using standard metrics (accuracy, precision, recall, and F1 score) and learning curve convergence analysis. …”
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ACA-Net: adaptive context-aware network for basketball action recognition by Yaolei Zhang, Fei Zhang, Yuanli Zhou, Xiao Xu

“…The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. …”
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Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection by Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Le Zhange, Jinhua Wu, Ping Dai, Fei Cheng

“…Existing RGB + depth (RGB-D) salient object detection methods mainly focus on better integrating the cross-modal features of RGB images and depth maps. Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. …”
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Semantic Segmentation of Aerial Laser Point Clouds Based on Deep-Residual Enhanced Coding of Multi-Feature Information by Xin Luo, Peng Lin, Xiaoxi Li, Zuqi Wei, Hai Li

“…PointNet++ was chosen as the baseline network, and a deep-residual enhanced encoding method of multi-feature information is proposed in this work. …”
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Research on herd sheep facial recognition based on multi-dimensional feature information fusion technology in complex environment by Fu Zhang, Fu Zhang, Xiaopeng Zhao, Shunqing Wang, Yubo Qiu, Sanling Fu, Yakun Zhang

“…For multi-part detection network, The YOLOv5s path aggregation network was modified by incorporating a multi-link convolution fusion block (MCFB) to enhance fine-grained feature extraction across objects of different sizes. …”
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Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion by Zixuan Liu, Guofang Zhang, Yanguang Shen

“…Finally, feature vectors at three granularities are integrated using a gated feed-forward neural network attention mechanism (GA-FNNAtention). …”
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WormNet: A Multi-View Network for Silkworm Re-Identification by Hongkang Shi, Minghui Zhu, Linbo Li, Yong Ma, Jianmei Wu, Jianfei Zhang, Junfeng Gao

“…Additionally, we introduce a new silkworm ReID dataset for network training and evaluation. The experimental results demonstrate that WormNet achieves an mAP value of 54.8% and a rank-1 value of 91.4% on the dataset, surpassing both state-of-the-art and related networks. …”
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SVM directed machine learning classifier for human action recognition network by Dharmanna Lamani, Pramod Kumar, A Bhagyalakshmi, J. Maria Shanthi, Lakshmana Phaneendra Maguluri, Mohammad Arif, C Dhanamjayulu, Sathish Kumar. K, Baseem Khan

“…However, existing approaches such as three-dimensional convolutional neural networks (3D CNN) and two-stream neural networks (2SNN) have computational hurdles due to the significant parameterization they require. …”
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A Deep Neural Network-Based Approach to Media Hotspot Discovery by Pan Luo

“…To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. …”
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Intrusion detection in software defined network using deep learning approaches by M. Sami Ataa, Eman E. Sanad, Reda A. El-khoribi

“…Abstract Ensuring robust network security is crucial in the context of Software-Defined Networking(SDN). …”
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Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network by Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai

“…To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. …”
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Advanced intrusion detection in internet of things using graph attention networks by Aamir S. Ahanger, Sajad M. Khan, Faheem Masoodi, Ayodeji Olalekan Salau

“…In this paper, we present a novel perspective to IoT security by using a Graph-based (GB) algorithm to construct a graph that is evaluated with a graph-based learning Intrusion Detection System (IDS) incorporating a Graph Attention Network (GAT). …”
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Integrating Viewing Direction and Image Features for Robust Multi-View Multi-Object 3D Pedestrian Tracking by R. Ali, M. Mehltretter, C. Heipke

“…For each image, this directional information is combined with the 2D features extracted from that image, before 3D features are computed, using the 2D features from all images. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Lung Cancer Prediction Based on Symptomatic and Lifestyle Features by Bireswar Dutta

“…The results highlight the importance of feature selection for enhancing the model accuracy and demonstrate that the single-hidden-layer neural network, trained for 800 epochs, achieved a prediction accuracy of 92.86%, outperforming the machine learning models. …”
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Streamlining Deep Learning Network for Real-time Sea Turtle Detection by Muhamad Dwisnanto Putro, Yuliana Mose, Alex Copernikus Andaria, Jane Litouw, Vecky Canisius Poekoel, Xaverius Najoan

“…On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. …”
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Scalable machine learning framework for predicting critical links in urban networks by Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

“…The framework integrates structural, functional, and newly proposed features, offering a comprehensive representation of road network dynamics. …”
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RMFKAN: Network Spammers Detection Method Based on Improved Graph Mamba by WANG Yuzhe, YAN Jinghua, BU Fanliang, WANG Yifan, LI Jia, HAN Zhuxuan

“…The method of heterogeneous perception long-distance relationship feature extraction is used to solve the problem of feature loss in long-distance relationships across communities in large-scale social networks. …”
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Spatial Correlation Network Characteristics of Comprehensive Transportation Green Efficiency in China by Qifei Ma, Sujuan Li, Zhenchao Zhang

“…Accurately characterizing the structural features of the spatial correlation network of comprehensive transportation green efficiency (CTGE) is essential for achieving balanced regional transportation development and eliminating regional disparities. …”
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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…The best-performing hybrid model CNN-based feature extraction followed by OCIF also achieved 100% across all evaluation metrics, confirming the effectiveness and robustness of the proposed approach. …”
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YOLOFIV: Object Detection Algorithm for Around-the-Clock Aerial Remote Sensing Images by Fusing Infrared and Visible Features by Huiying Wang, Chunping Wang, Qiang Fu, Binqiang Si, Dongdong Zhang, Renke Kou, Ying Yu, Changfeng Feng

“…Moreover, the ECA attention mechanism is integrated into the feature enhancement network to amplify attention toward challenging detection scenarios. …”
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