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

“…We provide detailed statistical analyses to identify and compare poorly performed and well-executed operations across different skill levels (novice, trainee, expert) for three specific skills: stack, bands, and tower. …”
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Interpretable capsule networks via self attention routing on spatially invariant feature surfaces by Peizhang Li, Jiyuan Ru, Qing Fei, Zhen Chen, Bo Wang

“…This architecture integrates a spatially invariant feature extraction structure, facilitating pixel-level encoding of regional spatial features, and leverages self-attention to effectively capture the relative importance of different spatial regions for classification. Together, these two mechanisms constitute an interpretable classification framework. …”
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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. …”
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Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer by Nobuko Irie, Naoki Takeda, Yorifumi Satou, Kimi Araki, Masahiro Ono

“…Furthermore, analysis of wild-type Foxp3 fluorescent Timer reporter mice at different ages uncovered distinct patterns of Foxp3 expression from neonatal to aged mice, highlighting prominent thymus-like features of neonatal splenic Foxp3 + T cells. …”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…Whereas AI has countless benefits, its convoluted and sometimes vague internal operations can establish difficulties. …”
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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. …”
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Detection of Spark Erosion on Insulated Rail Joints by Deep Learning by Utku Kaya

“…In the second stage, a semantic segmentation network is applied to accurately localize and detect spark erosion regions in the detected IRJ images. The study uses two different data sets: one is a high-resolution rail image data set for IRJ classification, and the other labeled images of spark erosion. …”
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Rethinking model prototyping through the MedMNIST+ dataset collection by Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann, Christian Ledig

“…Notably, our analysis reaffirms the competitiveness of CNNs compared to ViTs, emphasizing the importance of comprehending the intrinsic capabilities of different architectures. Finally, by establishing a standardized evaluation framework, we aim to enhance transparency, reproducibility, and comparability within the MedMNIST+ dataset collection as well as future research. …”
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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. …”
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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…This network collects information from different sources for enhancing the correctness of the universal learning approach. …”
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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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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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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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