Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture by Xu LUO, Peixia WU, Weiming HAO, Yinhong QU, Han CHEN

“…MethodsA vestibular function calibration test recognition model with cross-modal feature fusion was constructed by integrating vision transformer (ViT) and a modified ConvNeXt convolutional network. The model utilized trajectory pictures and spatial distribution maps as inputs, employed a multi-task learning framework to classify calibration data, and to directly evaluate undershoot waveform. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…All imaging model architectures used convolutional neural networks or a variation thereof. …”
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Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. …”
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The analysis of acquisition system for electronic traffic signal in smart cities based on the internet of things by Yue Ma, Chenglong Wang, Tianlei Fu, Ziting Meng

“…Second, an improved Multi-Task Convolutional Neural Network (MT-CNN) model, called Attention-Mechanism Multi-Modal Feature Fusion GooGleNet (AM-MMFF-GooGleNet), is proposed. …”
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Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. …”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

“…Our research proposes a new framework instigated and developed to improve crop disease detection and classification by multifaceted analysis. …”
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scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder by Xiaoxu Cui, Renkai Wu, Yinghao Liu, Peizhan Chen, Qing Chang, Pengchen Liang, Changyu He

“…By integrating innovative computational techniques, the SMD model provides a powerful framework for unraveling cellular complexities, enhancing our understanding of biological processes, and elucidating disease mechanisms. …”
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MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer by Van-Nhan Tran, Hoanh-Su Le, Piljoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon

“…In this study, we propose a model called MEViT, which integrates the EfficientNet Vision Transformer with a meta-learning framework to enhance generalization in deepfake detection. …”
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Unraveling trends in schistosomiasis: deep learning insights into national control programs in China by Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Zhijie Zhang, Yi Hu

“…METHODS We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. …”
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Development of a CNN-based decision support system for lung disease diagnosis using chest radiographs by B. T. Magar, M. A. Rahman, P. K. Saha, M. Ahmad, M. A. Rashid, H. Higa

“…This study presents CXRNet, a novel, efficient convolutional neural network (CNN)-based framework designed for multi-class classification of common chest diseases, including cardiomegaly, COVID-19, pneumonia, tuberculosis, and normal. …”
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AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling by Prema Nedungadi, G. Veena, Kai-Yu Tang, Remya R. K. Menon, Raghu Raman

“…Although AI techniques and multimodal frameworks have significantly improved content personalization, challenges like algorithmic bias and echo chambers remain. …”
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Learning Power Systems Waveform Incipient Patterns Through Few-Shot Meta-Learning by Lixian Shi, Qiushi Cui, Yang Weng, Yigong Zhang, Shilong Chen, Jian Li, Wenyuan Li

“…To resolve these problems, a few-shot meta-learning framework for incipient fault detection (FSMLF-IFD) is proposed in this paper. …”
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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…The integration of Graph Convolutional and Time Aware models enables the model to recognize the spatiotemporal flood characteristics, overcoming limitations of prevailing methods and ensuring long‐term forecast accuracy. …”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n’s backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n’s neck structure. …”
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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine by Andre F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

“…Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.…”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…This study suggests a hybrid classification model of deep learning, integration of convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN), optimized by Optuna to predict the stability of NS stabilized infinite slope. …”
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Topic Words-Based Multilingual Hateful Linguistic Resources Construction for Developing Multilingual Hateful Content Detection Model Using Deep Learning Technique by Naol Bakala Defersha, Kula Kekeba Tune, Solomon Teferra Abate

“…Finally, their performance was compared by integrating them into deep learning-based low-resource Ethiopian languages’ hateful content detection framework. Among applied deep learning algorithms with Ethiopian language linguistic resources, word2vec-based multilingual lexicons with convolutional neural network (CNN) outperform than others. …”
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Tricho-Vision: The use of computer vision in trichotaxonomy for enhancing wildlife conservation of priority species by Alloy Das, Priyanka Banerjee, Sanket Biswas, Manokaran Kamalakannan, Joydev Chattopadhyay, Dhriti Banerjee, Tanoy Mukherjee

“…The proposed Tricho-Vision framework offers significant applications in biodiversity monitoring and wildlife crime investigation, facilitating accurate species identification from forensic hair samples. …”
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Underground helmet detection algorithm based on improved YOLOv8s by Jiaru YANG, Yinan QIN, Tianxu LI, Han ZHUANG

“…Then, the SPD-Conv convolution module is added, and the non-step convolutional layer is used to reduce the situations that the redundant information of small targets is filtered and fine-grained information is lost. …”
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Accelerating spin Hall conductivity predictions via machine learning by Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi‐Chi Zhang, Junlin Li, Xing‐Qiu Chen, Peitao Liu

“…Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. …”
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