Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery by Heshan Zhang, Xin Tan, Mengwei Fan, Cunshu Pan, Zhanji Zheng, Shuang Luo, Jin Xu

“…Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.…”
Get full text

Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…The model was developed with a new approach based on matched KID (Known Implantation Data) embryos derived from the same cohort of a stimulation cycle, both judged to be of good quality according to classical morphological criteria and morphokinetics, transferred fresh or frozen, but with a different implantation fate (clinical pregnancy vs. failure of implantation). …”
Get full text

KediNet: A Hybrid Deep Learning Architecture for Thai Dessert Recognition by Nawanol Theera-Ampornpunt, Panisa Treepong, Naphadon Phokabut, Sarawut Phlaichana

“…Toward this goal, this paper presents KediNet, a hybrid deep learning architecture that combines the strengths of convolutional neural networks (CNNs) and vision transformers (ViTs) for Thai dessert recognition. …”
Get full text

Automated assessment of simulated laparoscopic surgical skill performance using deep learning by David Power, Cathy Burke, Michael G. Madden, Ihsan Ullah

“…We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. …”
Get full text

Interpretable capsule networks via self attention routing on spatially invariant feature surfaces by Peizhang Li, Jiyuan Ru, Qing Fei, Zhen Chen, Bo Wang

“…However, current classification approaches based on convolutional neural networks often suffer from limited generalization and robustness, particularly when processing data characterized by abstract class features and pronounced spatial attributes. …”
Get full text

A novel Swin transformer based framework for speech recognition for dysarthria by Rabbia Mahum, Ismaila Ganiyu, Lotfi Hidri, Ahmed M. El-Sherbeeny, Haseeb Hassan

“…MP integrates features from different Swin phases to emphasize local information. …”
Get full text

Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer by Nobuko Irie, Naoki Takeda, Yorifumi Satou, Kimi Araki, Masahiro Ono

“…We have developed a convolutional neural network-based method that incorporates image conversion and class-specific feature visualisation for class-specific feature identification at the single-cell level. …”
Get full text

Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis. by Chao Hu, Fan Wang, Hui Xu, XiQi Dong, XiuJuan Xiong, Yun Zhang, TianCheng Zhao, YuanQiao He, LiBin Deng, XiongBing Lu

“…Based on the definition of the border region of tumor cell nests, we assessed 12 spatial indicators for different patch types within, around and outside the tumor cluster. …”
Get full text

Detection of Spark Erosion on Insulated Rail Joints by Deep Learning by Utku Kaya

“…The proposed methodology is based on a two-stage framework: in the first stage, the SqueezeNet convolutional neural network is used for the classification of rail images and the detection of IRJs. …”
Get full text

Rethinking model prototyping through the MedMNIST+ dataset collection by Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann, Christian Ledig

“…We systematically reassess commonly used Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures across distinct medical datasets, training methodologies, and input resolutions to validate and refine existing assumptions about model effectiveness and development. …”
Get full text

Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…We integrate complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose carbon emission time series into intrinsic mode functions (IMFs) capturing different frequency bands. Each IMF is processed through a hybrid convolutional neural network (CNN)–Transformer architecture: CNNs extract local features and transformers model long-range dependencies via multi-head attention. …”
Get full text

Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…It utilizes a federal learning framework which employs Convolutional Neural Networks as well as Long-Term Short Memory. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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). …”
Get full text

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

“…To achieve rapid, low-cost, and intelligent oil production prediction, we propose a multi-input deep neural network model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with an attention mechanism. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text