Wavelet-Based Topological Loss for Low-Light Image Denoising by Alexandra Malyugina, Nantheera Anantrasirichai, David Bull

“…The effectiveness of this proposed method was evaluated by training state-of-the-art denoising models on the BVI-Lowlight dataset, which features a wide range of real noise distortions. …”
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A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution. by Sheng Miao, Guoqing Ni, Guangze Kong, Xiuhe Yuan, Chao Liu, Xiang Shen, Weijun Gao

“…This study explores the application of Three-Dimensional Convolutional Neural Networks (3DCNN) in spatial interpolation to evaluate soil pollution. …”
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Supervised Semantic Segmentation of Urban Area Using SAR by Joanna Pluto-Kossakowska, Sandhi Wangiyana

“…SAR sensors register features of urban areas that, when further processed, such as textures, can help in automatic recognition. …”
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Dual-Path Interactive U-Net for Unsupervised Hyperspectral Image Super-Resolution by Wenchen Deng, Jianjun Liu, Jinlong Yang, Zebin Wu, Liang Xiao

“…Previous studies have demonstrated that U-shaped networks can capture spatial structural features within images. …”
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Deep Learning for Vehicle Deceleration Detection Using UWB CIR and Doppler Shift by Kyungbo Lee, Jiwoong Park, Young-Bae Ko

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The Effect of Utilitarian and Social Motives on the Perception of the Quality of the Mobile Phone Application Brand through the Value and Type of Use by Tourists (Snap Food Case St... by Yazdan Shirmohammadi, Neda Sotoudeh

“…Abstract Today, mobile phones have become one of the main features of modern life, and mobile phone manufacturers have provided more features to facilitate communication in the new era. …”
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Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning by Zhe Chen, Dong Zhang, Chunxiang Liu, Hui Wang, Xinyao Jin, Fengwen Yang, Junhua Zhang

“…We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. Results: The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN), and attention mechanism-based LSTM (LSTM-attention) models and achieved superior results in model performance and predictive classification of most TCM syndromes. …”
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Exploring the Feasibility of a 5-Week mHealth Intervention to Enhance Physical Activity and an Active, Healthy Lifestyle in Community-Dwelling Older Adults: Mixed Methods Study by Kim Daniels, Sharona Vonck, Jolien Robijns, Kirsten Quadflieg, Jochen Bergs, Annemie Spooren, Dominique Hansen, Bruno Bonnechère

“…Future enhancements should focus on better calendar visibility, workout customization, and integrating social networking features to foster community and support. …”
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Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM by Lihai Chen, Xiaolong Bai, Yonghui He, Dong Jia, Yican Li, Zhenshui Li

“…Therefore, it is crucial for the reliable operation of mechanical equipment to evaluate the health status of bearings. It combines IDBO (Improved Dung beetle optimizer) optimised VMD (Variational mode decomposition) and CNN-BiLSTM (convolutional neural network-Bi-directional Long Short-Term Memory) to achieve rolling bearing conformity fault diagnosis and damage assessment. …”
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A Combined Windowing and Deep Learning Model for the Classification of Brain Disorders Based on Electroencephalogram Signals by Dina Abooelzahab, Nawal Zaher, Abdel Hamid Soliman, Claude Chibelushi

“…Methods: The model consists of three key components: data selection, feature extraction, and classification. Data selection employs a windowing technique, while the feature extraction and classification stages use a deep learning framework combining a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) network. …”
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Toward Inclusive Smart Cities: Sound-Based Vehicle Diagnostics, Emergency Signal Recognition, and Beyond by Amr Rashed, Yousry Abdulazeem, Tamer Ahmed Farrag, Amna Bamaqa, Malik Almaliki, Mahmoud Badawy, Mostafa A. Elhosseini

“…Logistic Regression yielded the highest accuracy of 86.5% for the vehicle fault dataset (DB1) using compact features, while neural networks performed best for datasets DB2 and DB3, achieving 88.4% and 85.5%, respectively. …”
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Multi-source Data-driven Analysis of Deformation and Influencing Factors for Expansive Soil Canal Slopes by ZHANG Yuhan, HU Jiang, LI Xing

“…Furthermore, a self-explaining neural network (SENN) model incorporating an attention mechanism is developed to predict canal slope deformation. …”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. …”
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Incorporating Attention Mechanism Into CNN-BiGRU Classifier for HAR by Ohoud Nafea, Wadood Abdul, Ghulam Muhammad

“…The proposed methodology uses convolutional neural networks (CNN) and recurrent neural networks (RNN) to extract the spatial and temporal features. …”
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Enhancing forest biodiversity indicators in inventories through harmonized protocols by Moreno-Fernández D, Breidenbach J, Cañellas I, Chirici G, D’Amico G, Ferretti M, Giannetti F, Puliti S, Schnell S, Shackleton R, Skudnik M, Alberdi I

“…In this study, we present the forest biodiversity monitoring results and lessons from a cross-country study to support large-scale monitoring systems. We developed, evaluated, and discussed harmonized protocols, mainly focused on birds and mammals, which extend beyond the traditional features captured in large-scale forest inventories. …”
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Experimental Modeling of Face Emotion Recognition Using Machine Learning Classification (SVM, KNN, Random Forest) and Deep Learning CNN by Shane Ardyanto Baskara, Nina Setiyawati

“…Images were preprocessed and used to train and evaluate each model. Traditional ML models relied on extracted features, while CNN learned features directly from images. …”
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Investigation on potential bias factors in histopathology datasets by Farnaz Kheiri, Shahryar Rahnamayan, Masoud Makrehchi, Azam Asilian Bidgoli

“…Abstract Deep neural networks (DNNs) have demonstrated remarkable capabilities in medical applications, including digital pathology, where they excel at analyzing complex patterns in medical images to assist in accurate disease diagnosis and prognosis. …”
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Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction by Lise M. Bjerre, Cayden Peixoto, Rawan Alkurd, Robert Talarico, Rami Abielmona

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The burden of rare cancers among adults in the Canton of Geneva, Switzerland, from 2011 to 2020 by Nathalie Bot, Evelyne Fournier, Marie-Laure Amram, Laura Botta, Alice Bernasconi, Elisabetta Rapiti

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An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms by Yanbin Weng, Jie Yang, Changfan Zhang, Jing He, Cheng Peng, Lin Jia, Hui Xiang

“…Firstly, the lightweight MobileNetV2 network is employed to replace the Xception feature extraction network, thereby reducing the number of model parameters. …”
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