Classification of Diabetic Retinopathy Based on Efficient Computational Modeling by Jiao Xue, Jianyu Wu, Yingxu Bian, Shiyan Zhang, Qinsheng Du

“…Convolutional neural networks (CNN) and Vision Transformers (ViT) have long been the main backbone networks for visual classification in the field of deep learning. …”
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Multi-Scale Contextual Coding for Human-Machine Vision of Volumetric Medical Images by Jietao Chen, Weijie Chen, Qianjian Xing, Feng Yu

“…While classical methods predominantly employing lossless compression are increasingly constrained by the limits of compression ratios, lossy 3D medical image compression methods are emerging as a promising alternative. Different from the existing 3D convolutional compression algorithms oriented only for human vision, this paper proposes a Multi-scale Contextual Autoencoder (MCAE) architecture that recurrently incorporates anatomical inter-slice context to optimize the compression of the current slice for both human and machine vision. …”
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Hot question prediction in Stack Overflow by Li Xian Zhao, Li Zhang, Jing Jiang

“…The authors propose the VSAF method which analyses the View amount changes, Answer amount changes and Score changes soon after questions' creation based on Fully convolutional neural network. The performance of the VSAF method based on a training set and two different test sets has been evaluated. …”
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GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information by Kusal Debnath, Pratip Rana, Preetam Ghosh

“…We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). …”
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Grade Identification of Raw Nongxiangxing Baijiu Based on Fused Data of Near Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry by ZHANG Wei, ZHANG Guiyu, TUO Xianguo, FU Ni, LI Xiaoping, PANG Tingting, LIU Kecai

“…Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. …”
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Oilfield Production Prediction Method Based on Multi-Input CNN-LSTM With Attention Mechanism by Lihui Tang, Zhenpeng Wang, Yajun Gao, Hao Wu, Wenbo Zhang, Xiaoqing Xie

“…Additionally, to quantify the impact of different input features on production, we adopt a random forest algorithm to assess feature importance and optimize data input through assigned weights. …”
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Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images by R. Raja Subramanian, R. Raja Sudharsan, Bavithra Vairamuthu, Deshinta Arrova Dewi

“…Our research focuses on the advanced classification of oral ulcer stages using a convolutional neural network (CNN). To evaluate performance comprehensively, we developed and tested three custom models, comparing their effectiveness in distinguishing between different stages of oral ulcers. …”
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Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification by Lehel Dénes-Fazakas, Levente Kovács, György Eigner, László Szilágyi

“…<b>Methods:</b> We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses a machine learning method to realize the fast prediction of ship resistance corresponding to different bulbous bows. To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. …”
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How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting by Zinnet Duygu Aksehir, Erdal Kilic

“…In literature, the convolutional neural networks (CNN) models were used for stock market forecasting and gave successful results. …”
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AI-Based Point Cloud Upsampling for Autonomous Driving Systems by Nicolás Salomón, Claudio A. Delrieux, Damián A. Morero, Leandro E. Borgnino

“…This work is based on 3 main axes: firstly, the analysis of available LiDAR data and its representation; secondly, the development and implementation of an interpolation technique based on 1D convolutional layers integrated with fully connected layers, in order to analyse data coming from a sliding window; and finally, the comparative evaluation of the results between different state-of-the-art interpolation techniques, using object detection networks in point clouds. …”
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Hand Gesture Recognition in Indian Sign Language Using Deep Learning by Harsh Kumar Vashisth, Tuhin Tarafder, Rehan Aziz, Mamta Arora, Alpana

“…ISL uses a combination of one-handed and two-handed gestures, which makes it fundamentally different from other common sign languages like American Sign Language (ASL). …”
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Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model by Abdoul-Aziz Maiga, Edwin Ataro, Stanley Githinji

“…Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. …”
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Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection by Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, the CMS-HCAL Collaboration

“…Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. …”
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A Simple but Effective Way to Handle Rotating Machine Fault Diagnosis With Imbalanced-Class Data: Repetitive Learning Using an Advanced Domain Adaptation Model by Donghwi Yoo, Minseok Choi, Hyunseok Oh, Bongtae Han

“…By employing pseudo-labeling, weighted random sampling, and time-shifting, the proposed repetitive learning method generates pseudo-augmented source and target fault data. Deep convolutional domain adaptation networks are followed to extract features by minimizing different losses. …”
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RFSoC Modulation Classification With Streaming CNN: Data Set Generation &#x0026; Quantized-Aware Training by Andrew Maclellan, Louise H. Crockett, Robert W. Stewart

“…Furthermore, we explore quantised-aware training, producing three modulation classification models with different fixed-point weight precisions (16-bit, 8-bit, and 4-bit). …”
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CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images by Jianming Hu, Yangyu Wei, Wenbin Chen, Xiyang Zhi, Wei Zhang

“…To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. …”
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A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Information by Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

“…The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability to perceive local features through residual convolutional blocks and squeeze-and-excitation (SE) mechanisms. …”
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Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates by Chayanon Sub-r-pa, Rung-Ching Chen

“…Ablation studies reveal that the number of keypoints (K) impacts the performance, with K = 3 providing the most suitable balance for the overall alignment accuracy, although the performance varies across different template geometries. GeoTemplateKPNet offers a foundational self-supervised solution for the robust geometric analysis of templates, which is crucial for downstream alignment tasks and applications.…”
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Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework by Suresh Neethirajan

“…Controlled exposure to realistic auditory stimuli (dog barking) and visual stimuli (umbrella opening) across different developmental stages enabled a critical comparative evaluation of vocal stress responses within a commercial-like experimental setup. …”
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