Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…FMs were recorded from 51 typically developing participants. We compared three different sensor modalities (pressure, inertial, and visual sensors). …”
Get full text

Advanced deep learning techniques for automated extraction of non-debris-covered areas of glaciers in High-Mountain Asia using time-series remote sensing data by Gexia Qin, Ninglian Wang, Bo Jiang, Yuwei Wu, Yanchao Yin, Zhijie Li

“…Deep learning approaches have gained prominence for automatic glacier boundary extraction due to their localized nature of convolutional operations, potentially leading to incomplete or fragmented glacier pixel representations. …”
Get full text

Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
Get full text

Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. …”
Get full text

CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model by Shanwei Liu, Shuaipeng Wang, Wei Zhang, Tao Zhang, Mingming Xu, Muhammad Yasir, Shiqing Wei

“…Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. …”
Get full text

Beyond averaging: A transformer approach to decoding event related brain potentials by Philipp Zelger, Manuel Arnold, Sonja Rossi, Josef Seebacher, Franz Muigg, Simone Graf, Antonio Rodríguez-Sánchez

“…During the sound presentation, EEG signals were recorded.A convolutional transformer was trained to categorize the EEG data into the two classes (”not too loud” and ”too loud”). …”
Get full text

Efficient one-stage detection of shrimp larvae in complex aquaculture scenarios by Guoxu Zhang, Tianyi Liao, Yingyi Chen, Ping Zhong, Zhencai Shen, Daoliang Li

“…This paper proposes an efficient one-stage shrimp larvae detection method, FAMDet, specifically designed for complex scenarios in intensive aquaculture. Firstly, different from the ordinary detection methods, it exploits an efficient FasterNet backbone, constructed with partial convolution, to extract effective multi-scale shrimp larvae features. …”
Get full text

Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence by Xuezhi Zhou, Jing Dai, Yizhan Lu, Qingqing Zhao, Yong Liu, Chang Wang, Zongya Zhao, Chong Wang, Zhixian Gao, Yi Yu, Yandong Zhao, Wuteng Cao

“…Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. …”
Get full text

Clouds Detection in Polar Icy Terrains: A Deformable Attention-Based Deep Neural Network for Multispectral Polar Scene Parsing by Shaojin Dong, Cailan Gong, Yong Hu, Long Cheng, Yang Wang, Fuqiang Zheng

“…Considering these challenges, we introduce a deep convolutional neural network model called DLACD-Net. …”
Get full text

MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images by Kai Du, Yi Ma, Zhongwei Li, Rongjie Liu, Zongchen Jiang, Junfang Yang

“…The framework uses parallel branches, including a convolutional neural network and a vision transformer, to extract surrounding spatial features and central spectral features from the fused data. …”
Get full text

Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. …”
Get full text

Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with sparsely annotated data by Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen

“…While DL approaches have been proposed to automate cartilage segmentation, most such models have limited accuracy and generalizability, especially across data from different embryonic age groups. To address these limitations, we propose novel DL methods that can be adopted by any DL architectures—including Convolutional Neural Networks (CNNs), Transformers, or hybrid models—which effectively leverage age and spatial information to enhance model performance. …”
Get full text

EBSSPA: Efficient Deep Learning Model for Enhancing Blockchain Scalability and Security Through Fusion Pattern Analysis by Anuradha Hiwase, Amit Pimpalkar, Barkha Dange, Nitin Thakre, Sakshi Jaiswal, Tejaswini Mankar

“…Background: Blockchain technologies have come a long way, and integration of blockchain technologies into different fields is flourishing; however, there is a lack of blockchain platforms to manage the high network loads and more sophisticated security threats. …”
Get full text

Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach by Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md. Mahbubur Rahman, Hafiz Md. Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder

“…The performance of different ML and DL models is evaluated, with Logistic Regression (LR) achieving an accuracy of 99.16% and a 0.008% error rate. …”
Get full text

Rice Leaf Disease Image Enhancement Based on Improved CycleGAN by YAN Congkuan, ZHU Dequan, MENG Fankai, YANG Yuqing, TANG Qixing, ZHANG Aifang, LIAO Juan

“…However, rice disease image recognition faces challenges such as limited availability of datasets, insufficient sample sizes, and imbalanced sample distributions across different disease categories. To address these challenges, a data augmentation method for rice leaf disease images was proposed based on an improved CycleGAN model in this reseach which aimed to expand disease image datasets by generating disease features, thereby alleviating the burden of collecting real disease data and providing more comprehensive and diverse data to support automatic rice disease recognition.MethodsThe proposed approach built upon the CycleGAN framework, with a key modification being the integration of a convolutional block attention module (CBAM) into the generator's residual module. …”
Get full text

Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…In order to further overcome the time asynchronization and uneven spatial distribution of rainfall in the two regions, the convolutional neural network CNN optimizers (RMSP, ADAM and SGD) are used to establish the contrast-target region rainfall relationship model based on the grid data of natural rainfall plane. …”
Get full text

Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets by Saleh Nagi Alsubari, Sachin N. Deshmukh, Mosleh Hmoud Al-Adhaileh, Fawaz Waselalla Alsaade, Theyazn H. H. Aldhyani

“…Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. …”
Get full text

Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms by Raka Thoriq Araaf, Arkar Minn, Tofael Ahamed

“…All labeled images were used to train the YOLOv5 and YOLOv8 algorithms through the convolutional neural network (CNN). The trained model was tested with a test dataset, a digital mirrorless camera image dataset (100 images), a phone camera dataset (100 images), and real-time detection with a coffee leaf rust image dataset. …”
Get full text

Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study by Soonil Kwon, SooMin Chung, So-Ryoung Lee, Kwangsoo Kim, Junmo Kim, Dahyeon Baek, Hyun-Lim Yang, Eue-Keun Choi, Seil Oh

“…A hold-out test dataset was constructed using a different recruitment period. Five-fold cross-validation and calibration plots were used to evaluate performance. …”
Get full text

Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: A case study on orca (Orcinus orca) vocalizations by Fabio Frazao, Ruth Joy, Michael Dowd

“…The spectrogram is well-suited for many such pattern recognition algorithms, including those developed for computer vision, such as convolutional neural networks. However, while it emphasizes some aspects of the signal, it downplays others. …”
Get full text