Pretraining-improved Spatiotemporal graph network for the generalization performance enhancement of traffic forecasting by Xiangyue Zhang, Chao Li, Ling Ji, Yuyun Kang, Mingming Pan, Zhuo Liu, Qiang Qi

“…To address these issues, various sophisticated modules are embedded into different models. However, this approach increases the computational cost of the model. …”
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Detection of microfibres in wastewater sludge with deep learning by Félix Martí-Pérez, Ana Domínguez-Rodríquez, Carlos Monserrat, Cèsar Ferri, María-José Luján-Facundo, Eva Ferrer-Polonio, Amparo Bes-Piá, José-Antonio Mendoza-Roca

“…This study presents a novel approach utilising advanced deep learning techniques to enhance the detection of MFi in sewage sludge samples using two different filtration support (fibreglass and cellulose acetate). …”
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Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing by Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas, Wallace A. Martins, Geoffrey Eappen, Saed Daoud, Osvaldo Simeone, Bipin Rajendran, Symeon Chatzinotas

“…To benchmark the performance of the proposed model, we implement conventional Convolutional Neural Networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. …”
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Laparoscopic Suture Gestures Recognition via Machine Learning: A Method for Validation of Kinematic Features Selection by Juan M. Herrera-Lopez, Alvaro Galan-Cuenca, Antonio J. Reina, Isabel Garcia-Morales, Victor F. Munoz

“…For that purpose, this work models the laparoscopic suturing manoeuvre as a set of simpler gestures to be recognized and, using the ReliefF algorithm on the JIGSAWS dataset’s kinematic data, presents a study of significance of the different kinematic variables. To validate this study, three classification models based on the multilayer perceptron and on Hidden Markov Models have been trained using both the complete set of variables and a reduced selection including only the most significant. …”
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Bitemporal Remote Sensing Change Detection With State-Space Models by Lukun Wang, Qihang Sun, Jiaming Pei, Muhammad Attique Khan, Maryam M. Al Dabel, Yasser D. Al-Otaibi, Ali Kashif Bashir

“…This article investigates the impact of different scanning mechanisms within Mamba, evaluating five mainstream methods to optimize its performance in change detection. …”
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FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. …”
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Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Additionally, data perturbation techniques were employed to enhance the zero-shot segmentation capability of the model and ensure robust performance across different data sources. Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. …”
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Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis by Hisaki Makimoto, Takayuki Okatani, Masanori Suganuma, Tomoyuki Kabutoya, Takahide Kohro, Yukiko Agata, Yukiyo Ogata, Kenji Harada, Redi Llubani, Alexandru Bejinariu, Obaida R. Rana, Asuka Makimoto, Elisabetha Gharib, Anita Meissner, Malte Kelm, Kazuomi Kario

“…We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. …”
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Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. …”
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Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning by Yeongho Choi, Ki Hong Kim, Yoonjic Kim, Dong Hyun Choi, Yoon Ha Joo, Sae Won Choi, Kyoung Jun Song, Sang Do Shin

“…We developed prediction models using convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks. …”
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Beef Traceability Between China and Argentina Based on Various Machine Learning Models by Xiaomeng Xiang, Chaomin Zhao, Runhe Zhang, Jing Zeng, Liangzi Wang, Shuran Zhang, Diego Cristos, Bing Liu, Siyan Xu, Xionghai Yi

“…Combining the analysis of 52 elements and the stable carbon isotope ratio with machine learning algorithms enables effective tracing and origin prediction of beef from different regions. Key factors influencing beef origin were identified as Fe, Cs, As, δ¹³C, Co, V, Sc, Rb, and Ru.…”
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A Novel Spectral-Spatial Attention Network for Zero-Shot Pansharpening by Hailiang Lu, Mercedes E. Paoletti, Juan M. Haut, Sergio Moreno-Alvarez, Guangsheng Chen, Weipeng Jing

“…The experiments were conducted on the public dataset PAirMax, which have challenging scenes captured by different sensors. Compared to some state-of-the-art traditional and DL-based methods, <monospace>ZSPNet</monospace> demonstrates superior performance in both quantitative assessments and visual results.…”
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A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction by Shengyou Wang, Chunfu Shao, Yajiao Zhai, Song Xue, Yan Zheng

“…The majority of studies on road traffic flow prediction have focused on the flow of passenger cars or the flow of traffic as a whole, which ignore the significant impact of trucks with different sizes and operational characteristics on traffic flow efficiency. …”
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Effect of natural and synthetic noise data augmentation on physical action classification by brain–computer interface and deep learning by Yuri Gordienko, Nikita Gordienko, Vladyslav Taran, Anis Rojbi, Sergii Telenyk, Sergii Telenyk, Sergii Stirenko

“…The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. …”
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Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight by Wenbo Xiao, Qiannan Han, Gang Shu, Guiping Liang, Hongyan Zhang, Song Wang, Zhihao Xu, Weican Wan, Chuang Li, Guitao Jiang, Yi Xiao

“…This study introduces an innovative deep learning-based model leveraging multimodal data—2D RGB images from different views, depth images, and 3D point clouds—for the non-invasive estimation of duck body dimensions and weight. …”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…However, a systematic evaluation of the skill and robustness of different ML approaches is an active field of research that requires more investigation. …”
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Dual attention mechanisms with patch-level significance embedding for ischemic stroke classification in brain CT images by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Nithin Raj, J. Pooja, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…The framework is evaluated on a dataset of 2023 CT of four different classes (i.e., acute: 361, chronic: 267, subacute: 382, and normal: 1013 images), employing both four and nine non-overlapping patch configurations. …”
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Multistep PV power forecasting using deep learning models and the reptile search algorithm by Sameer Al-Dahidi, Hussein Alahmer, Bilal Rinchi, Abdullah Bani-Abdullah, Mohammad Alrbai, Osama Ayadi, Loiy Al-Ghussain

“…However, this is the first study to evaluate MGU in the context of PV forecasting, and its performance may vary under different case studies. It is shown that TFT and RSA offer superior accuracy and generalization across forecast horizons. …”
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MWFNet: A multi-level wavelet fusion network for hippocampal subfield segmentation by Xinwei Li, Linjin Wang, Weijian Tao, Hongying Meng, Haiming Li, Jiangtao He, Yue Zhao, Jun Hu, Zhangyong Li

“…Additionally, we developed a Multi-scale Attention Residual Block (MARB) that leverages convolutional kernels of different sizes to facilitate multi-scale feature extraction. …”
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GL-ST: A Data-Driven Prediction Model for Sea Surface Temperature in the Coastal Waters of China Based on Interactive Fusion of Global and Local Spatiotemporal Information by Ning Song, Jie Nie, Qi Wen, Yuchen Yuan, Xiong Liu, Jun Ma, Zhiqiang Wei

“…The spatiotemporal multimodal variations in sea surface temperature refer to its diverse changes across different temporal and spatial scales. Understanding and predicting these variations are crucial for climate research and marine ecosystem conservation. …”
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