CABAD: A video dataset for benchmarking child aggression recognition by Shehzad Ali, Md Tanvir Islam, Ik Hyun Lee, Mohammad Hijji, Khan Muhammad

“…Leveraging CABAD, we propose CABA_Net, a multi-stage deep-learning framework integrating MobileViT for spatial feature extraction, Temporal Convolutional Networks (TCN) for sequential modeling, and an Attention LSTM for refined temporal attention on behavioral patterns. …”
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Decoupled pixel-wise correction for abdominal multi-organ segmentation by Xiangchun Yu, Longjun Ding, Dingwen Zhang, Jianqing Wu, Miaomiao Liang, Jian Zheng, Wei Pang

“…Notably, our findings indicate that DPC-Net, when equipped with convolutional attention, surpasses those networks utilizing Transformer attention mechanisms on multi-organ segmentation tasks. …”
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Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization by Amnah Alshahrani, Mukhtar Ghaleb, Hany Mahgoub, Achraf Ben Miled, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Hasan Beyari, Sultan Alanazi

“…Abstract Nowadays, cost-sensitive customers need customized products that demand consumption-based production. …”
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Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model by Zheng Gao, Yuqing Yang, Zhiqiang Yang, Xinyue Zhang, Chao Liu

“…Methods and results A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network‐long short‐term memory (CNN‐LSTM) DLM was employed. …”
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A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning by Cheng Wang, Zhen Yu, Zhou Long, Hui Zhao, Zhenwei Wang

“…Therefore, the methods include: (1) Data augmentation of the original DFU images by using geometric transformations and random noise; (2) Deep ResNet models selection based on different convolutional layers comparative experiments; (3) DFU classification model training with transfer learning by using the selected pre-trained ResNet model and fine tuning. …”
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Research on anti-occlusion tracking method for underground mine personnel based on adaptive link optimization by LU Yang, DONG Lihong, YE Ou

“…Additionally, after performing time-domain block processing on the trajectory pair input, a channel prior convolutional attention mechanism was added to enhance the time-domain representation capability. …”
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Optimizing the automated recognition of individual animals to support population monitoring by Tijmen A. deLorm, Catharine Horswill, Daniella Rabaiotti, Robert M. Ewers, Rosemary J. Groom, Jessica Watermeyer, Rosie Woodroffe

“…The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. …”
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BCI‐control and monitoring system for smart home automation using wavelet classifiers by Amer Al‐Canaan, Hicham Chakib, Muhammad Uzair, Shuja‐uRehman Toor, Amer Al‐Khatib, Majid Sultan

“…The designed wavelet‐based BCI system consists of analogue EEG signal acquisition and machine‐learning modules, which consist of deep‐learning Multi‐layer perceptron (MLP) classifiers and linear discriminant analysis (LDA) as well as other classifier models for comparison including convolutional neural networks (CNN). The deep learning and LDA classifiers models produced the best performance with average accuracy of 95.6% and 96% for both training and testing data sets.…”
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Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments by Nejib Ben Hadj-Alouane, Arav Dhoot, Monia Turki-Hadj Alouane, Vinod Pangracious

“…<b>Methods:</b> Leveraging deep learning techniques, our approach synthesizes Skeleton Energy Images (SEIs) from gait sequences and employs three advanced models—a Convolutional Neural Network (CNN), a Residual Network (ResNet), and a Vision Transformer (ViT)—to analyze these images. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. …”
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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…The ML model will utilise convolutional neural networks (CNN) for feature extraction and risk prediction. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…But most of them had the time cost exceeding 80ms, making them could not perform real-time calculations. …”
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ADEPNET: A Dynamic-Precision Efficient Posit Multiplier for Neural Networks by Aditya Anirudh Jonnalagadda, Uppugunduru Anil Kumar, Rishi Thotli, Satvik Sardesai, Sreehari Veeramachaneni, Syed Ershad Ahmed

“…Recent research has found that the performance of deep neural network models saturates beyond a certain level of accuracy of multipliers used for convolutions. Therefore, the extra hardware cost of developing precise arithmetic circuits for such applications becomes an unnecessary overhead. …”
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LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection by Shijian Huang, Yunong Tian, Yong Tan, Zize Liang

“…Additionally, we propose the Aggregated Downsampling Convolution (ADown-Conv) module, a dual-path compression unit that enhances feature representation while efficiently reducing spatial dimensions. …”
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Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning by Zihan Wang, Jinjin Wu, Chenbei Li, Bing Wang, Qingxia Wu, Lan Li, Huijie Wang, Chao Tu, Jianhua Yin

“…Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. …”
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The Use of Artificial Intelligence in Sturgeon Aquaculture by Dragoș Sebastian Cristea, Alexandru Adrian Gavrilă, Ștefan Mihai Petrea, Dan Munteanu, Sofia David, Cătălin Octavian Mănescu

“…It was found that the LAB colour space provided superior results in terms of precision and efficiency, but maximum accuracy was achieved using convolutional neural networks (YOLACT). The analysis of the project results confirms the significant advantages of using the AI system for biomass monitoring, advantages consisting of the reduction of unit costs with labour and feed, improvement of water quality, active optimisation of sturgeon growing conditions. …”
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A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis by Jie Xu, Erkang Jing, Yidong Chai

“…We segmented each cup and disc using a fully convolutional neural network and then calculated the cup size, disc size, and cup-to-disc ratio of each quadrant. …”
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Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping by Yue‐Lin Dong, Zhen‐Jie Zhang

“…Abstract Accurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. …”
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Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms by Xin Zeng, Guang-Peng Su, Wen-Feng Du, Bei Jiang, Yi Li, Zi-Zhong Yang

“…MM-IDTarget integrates some cutting-edge deep learning techniques such as graph transformer, multi-scale convolutional neural networks (MCNN), and residual edge-weighted graph convolutional network (EW-GCN) to extract sequence and structure modal features of drugs and targets. …”
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Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest by Majdi Fanous, Jonathan M. Eden, Juntao Yang, Simon See, Vasile Palade, Alireza Daneshkhah

“…Traditional numerical models struggle in such settings due to high computational cost, the need for extensive mesh generation, and difficulties in assimilating heterogeneous data sources. …”
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