The Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: by Raghavendra H. Bhalerao, Abhijeet Ashok Salunke, Shristi Sharan, Kamlesh Kumar, Priyank Rathod, Prince Kumar, Manish Chaturvedi, Nandlal Bharwani, Krupa Shah, Dhruv Patel, Keval Patel, Vikas Warikoo, Manisha Abhijeet Salunke, Shashank Pandya

“…Material and Methods In this study, a sequence of residual blocks are used to construct a 62-layer feature extraction network in lieu of ResNet50/101 in MRCNN. …”
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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach by Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti

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Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks by Deependra Rastogi, Prashant Johri, Massimo Donelli, Seifedine Kadry, Arfat Ahmad Khan, Giuseppe Espa, Paola Feraco, Jungeun Kim

“…Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. …”
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CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction by Dengpan Zhang, Wenwen Ma, Zhihao Shen, Qingping Ma

“…To address these challenges, we propose CAGNet, a novel network that combines multiscale feature aggregation and attention mechanisms. …”
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PIONet: A Positional Encoding Integrated Onehot Feature-Based RNA-Binding Protein Classification Using Deep Neural Network by Mahmood A. Rashid, Mayank Chaturvedi, Kuldip K. Paliwal

“…The CNN model processes these combined features to extract local patterns and motifs critical for RNA-protein interactions. …”
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EEG feature extraction methods in motor imagery-based brain-computer interfaces: a systematic review and network meta-analysis by Jiahao Cheng, Peng Chen, Yufeng Deng, Yi Luo, Fengyan Chen, Jiwang Ma, Fei Wang, Fen Xu, Sheng Guo, X. San Liang, Tao Zhang

“…Aim: This study systematically evaluates various feature extraction methods used in MI-based BCIs, with a specific focus on their performance in binary and multi-class classification tasks. …”
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D4Care: A Deep Dynamic Memory-Driven Cross-Modal Feature Representation Network for Clinical Outcome Prediction by Binyue Chen, Guohua Liu

“…To address these challenges, we propose a deep dynamic memory-driven cross-modal feature representation network for clinical outcome prediction. …”
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OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification by Irshad Khalil, Asif Mehmood, Hyunchul Kim, Jungsuk Kim

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Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation by Taeyeub Lee, Joram M. Posma

“…Methods We evaluated several GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and Aggregation (GraphSAGE), and Graph Isomorphism Networks (GINs), using the latest FDA DILI dataset and other molecular property prediction datasets. …”
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DSF2-NAS: Dual-Stage Feature Fusion via Network Architecture Search for Classification of Multimodal Remote Sensing Images by Shiyang Feng, Zhaowei Li, Bo Zhang, Tao Chen, Bin Wang

“…Compared to traditional feature fusion methods used for the classification of multimodal RSIs, neural architecture search (NAS) is capable of identifying the optimal network structure for multimodal RSIs and downstream tasks. …”
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Automatic Non-Urban Road Surface Point Extraction Based on Geometric Features Using Neural Networks and Raster Structure Approach by M. Dowajy, M. Fawzy, M. Fawzy, A. Barsi, T. Lovas

“…The rasterized values serve as structured inputs for a feature-based Neural Network (NN), which classifies road pixels based on intensity, density, curvature, planarity, roughness, surface variation, and verticality properties. …”
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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. …”
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DMSA-Net: a deformable multiscale adaptive classroom behavior recognition network by Chunyu Dong, Jing Liu, Shenglong Xie

“…To improve the network’s capacity for feature extraction and integration of behavior occlusion and classroom behavior at different scales, a proposal has been put forward the Multiscale Attention Feature Pyramid Structure (MSAFPS), to achieve multi-level feature aggregation after multiscale feature fusion, reducing the impact of mutual occlusion and scale differences in classroom behavior between front and back rows. …”
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Hybrid CNN-Transformer Architecture With Xception-Based Feature Enhancement for Accurate Breast Cancer Classification by Alireza Zeynali, Mohammad Ali Tinati, Behzad Mozaffari Tazehkand

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Recommendation model based on separated embedding interaction networks by FENG Shulei, JIANG Zhongyun

“…This model first uses the embedding neural network layer to convert the sparse feature vectors into dense embedding vectors, then separates the feature matrices of different dimensions for feature interaction, and explicitly controls the order of feature interaction through the number of SEIN layers. …”
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UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides by Zixin Chen, Chengming Ji, Wenwen Xu, Jianfeng Gao, Ji Huang, Huanliang Xu, Guoliang Qian, Junxian Huang

“…Specifically, we use a feature vector with 2924 values inferred by two deep learning models, UniRep and ProtT5, to demonstrate that such inferred information of peptides suffice for the task, with the help of our proposed deep neural network model composed of fully connected layers and transformer encoders for predicting the antibacterial activity of peptides. …”
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Estimating strawberry weight for grading by picking robot with point cloud completion and multimodal fusion network by Yiming Chen, Wei Wang, Junchao Chen, Jizhou Deng, Yuanping Xiang, Bo Qiao, Xinghui Zhu, Changyun Li

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Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures by Fábio Felix Dias, Moacir Antonelli Ponti, Rosane Minghim

“…The empirical results ratify that the pre-trained network learns better (accuracy up to 0.91); that using acoustic features can improve the results marginally (up to 13 percentage points of difference) depending on the time-frequency input and main architecture; and that combining spectrogram representations with acoustic features yields the best results (accuracy up to 0.91).…”
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The Comparison of Activation Functions in Feature Extraction Layer using Sharpen Filter by Oktavia Citra Resmi Rachmawati, Ali Ridho Barakbah, Tita Karlita

“…This study investigates the impact of five widely used activation functions—ReLU, SELU, ELU, sigmoid, and tanh—on convolutional neural network (CNN) performance when combined with sharpening filters for feature extraction. …”
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Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network by Zehua Li, Zehua Li, Yihui Pan, Xu Ma, Yongjun Lin, Xicheng Wang, Hongwei Li

“…Sowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and proposes a method for evaluating sowing uniformity by combining image processing methods and the ODConv_C2f-ECA-WIoU-YOLOv8n (OEW-YOLOv8n) network. …”
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