IEDSFAN: information enhancement and dynamic-static fusion attention network for traffic flow forecasting by Lianfei Yu, Ziling Wang, Wenxi Yang, Zhijian Qu, Chongguang Ren

“…However, traffic flow forecasting still faces serious challenges. Most of the existing traffic flow forecasting methods are static graph convolutional networks based on prior knowledge, ignoring the special spatial–temporal dynamics of spatial–temporal data. …”
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Segmentation of dermatoscopic images of skin lesions. Comparison of methods by A. F. Smalyuk, M. S. Dzeshka, I. D. Kupchykava

“…Segmentation is necessary as the first stage of most methods of computer diagnostics of malignancy of neoplasms. …”
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Fine-Tuning-Based Transfer Learning for Building Extraction from Off-Nadir Remote Sensing Images by Bipul Neupane, Jagannath Aryal, Abbas Rajabifard

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<p><strong>Deep-learning techniques for symptoms' detection of <em>Aculops lycopersici</em></strong> <strong>(Acari: Eriophyidae) and <em>Tuta absoluta</em></strong> (<strong>Lepid... by Alireza Shabani Nezhad, Maryam Aleosfoor

“… Accurate pest detection is the most fundamental requirement in the management of damage-causing factors. …”
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Breast cancer image classification by using HCNN and LeNet5 by Pramoda Patro, Shaik Honey Fathima, R. Harikishore, Aditya Kumar Sahu

“…Breast cancer (BC) is one of the most common cancers in women; the death rate is high and holds the second position next to lung cancer. …”
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A New Analysis of Web Customer Service Text Classification of Alexa Virtual Assistant Commands Using a Deep Learning Model by Seidakhmet Nurpatsha

“…To evaluate performance, four deep learning-based models are employed: a Simple Neural Network (SNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). …”
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Shifted Hexpo activation function: An improved vanishing gradient mitigation activation function for disease classification by Joseph Otoo, Suleman Nasiru, Irene Dekomwine Angbing

“…Additionally, Grad-CAM visualizations highlight SHexpo’s capability to enhance interpretability by localizing the most relevant image regions contributing to model predictions. …”
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Graph-Based Feature Crossing to Enhance Recommender Systems by Congyu Cai, Hong Chen, Yunxuan Liu, Daoquan Chen, Xiuze Zhou, Yuanguo Lin

“…In recommendation tasks, most existing models that learn users’ preferences from user–item interactions ignore the relationships between items. …”
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A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording by Marcin Kołodziej, Marcin Jurczak, Andrzej Majkowski, Andrzej Rysz, Bartosz Świderski

“…Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture alongside simultaneous recording of facial and neck EMG signals. …”
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A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification by Qing Zhang, Jialu Chen, Baohua Yuan

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Enhancing Real Estate Listings Through Image Classification and Enhancement: A Comparative Study by Eyüp Tolunay Küp, Melih Sözdinler, Ali Hakan Işık, Yalçın Doksanbir, Gökhan Akpınar

“…A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). …”
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A review of plant leaf disease identification by deep learning algorithms by Junmin Zhao, Laixiang Xu, Zizhen Ma, Juncai Li, Xiaowei Wang, Yunchang Liu, Xiaojie Du

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Node Classification Based on Kolmogorov-Arnold Networks by YUAN Lining, FENG Wengang, LIU Zhao

“…Most graph deep learning methods extract feature information from graph data by using learnable weights and specific activation functions. …”
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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques by Chinna Gopi Simhadri, Hari Kishan Kondaveeti, Valli Kumari Vatsavayi, Alakananda Mitra, Preethi Ananthachari

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Artificial Intelligence and Hand Hygiene Accuracy: A New Era in Infection Control for Dental Practices by Salwa A. Aldahlawi, Amr H. Almoallim, Ibtesam K. Afifi

“…Material and Method The AI model utilized a pretrained convolutional neural network (CNN) and was fine‐tuned on a custom data set of videos showing dental students performing alcohol‐based hand rub (ABHR) procedures. …”
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Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

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A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat by Runzhi Yang, Shanshan Li, Bing Zhang, Quanjun Jiao, Dailiang Peng, Songlin Yang, Ruyi Yu

“…In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. …”
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MeetSafe: enhancing robustness against white-box adversarial examples by Ruben Stenhuis, Dazhuang Liu, Yanqi Qiao, Mauro Conti, Manos Panaousis, Kaitai Liang

“…Convolutional neural networks (CNNs) are vulnerable to adversarial attacks in computer vision tasks. …”
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Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations by Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye, Yubo Yuan, Tianrui Ma, Tao Da

“…Solar radiation forecasting is the basis of building a robust solar power system. Most ground-based forecasting methods are unable to consider the impact of cloud changes on future solar radiation. …”
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Multimodal driver emotion recognition using motor activity and facial expressions by Carlos H. Espino-Salinas, Huizilopoztli Luna-García, José M. Celaya-Padilla, Cristian Barría-Huidobro, Nadia Karina Gamboa Rosales, David Rondon, Klinge Orlando Villalba-Condori

“…The research applies machine learning to identify the most relevant motor activity signals for emotion recognition. …”
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