Impact of Channel and System Parameters on Performance Evaluation of Frequency Extrapolation Using Machine Learning by Michael Neuman, Daoud Burghal, Andreas F. Molisch

“…This paper presents an extensive investigation of the impact of various parameters, both of the system and of the underlying channels, on the performance of three types of ML algorithms, namely K-nearest neighbor (KNN), convolutional multilayer perceptron (CNN/MLP), and autoencoder structures (AE). …”
Get full text

Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model by Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu, Wenyong Yuan

“…Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. …”
Get full text

RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network by Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu, Zhiming Hong

“…This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. …”
Get full text

Deep Learning-Based Invalid Point Removal Method for Fringe Projection Profilometry by Nan He, Jiachun Huang, Shaoli Liu, Sizhe Fan, Jianhua Liu, Jia Hu, Hao Gong

“…In this paper, we propose a novel invalid point removal framework that consists of two aspects: (1) A convolutional neural network (CNN) is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions. (2) A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects. …”
Get full text

Enhanced Mask R-CNN Incorporating CBAM and Soft-NMS for Identification and Monitoring of Offshore Aquaculture Areas by Jiajun Zhang, Yonggui Wang, Yaxin Zhang, Yanxin Zhao

“…In this study, a violation monitoring framework for marine aquaculture areas based on image recognition using an enhanced Mask R-CNN architecture incorporating a convolutional block attention module (CBAM) and soft non-maximum suppression (Soft-NMS) is proposed and applied in Sandu’ao. …”
Get full text

Cyberbullying detection of resource constrained language from social media using transformer-based approach by Syed Sihab-Us-Sakib, Md. Rashadur Rahman, Md. Shafiul Alam Forhad, Md. Atiq Aziz

“…For deep learning models, we explored Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). …”
Get full text

A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons by Caizhi Zheng, Ronghan Hong, Hao-Jie Hu, Qing Huo Liu

“…The proposed BSE-VoxNets consists three parts: transformer feature extractor, convolutional upsampling block and feature fusion block. 10000 BSE samples were used for training BSE-VoxNets, with 200 samples for testing, and the optimal cross-union ratio (IoU) of the test dataset reached 0.953. …”
Get full text

Optical Flow Magnification and Cosine Similarity Feature Fusion Network for Micro-Expression Recognition by Heyou Chang, Jiazheng Yang, Kai Huang, Wei Xu, Jian Zhang, Hao Zheng

“…Recent advances in deep learning have significantly advanced micro-expression recognition, yet most existing methods process the entire facial region holistically, struggling to capture subtle variations in facial action units, which limits recognition performance. …”
Get full text

Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …”
Get full text

Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation by Hyun Bin Kim, Hong Qi Tan, Wen Long Nei, Ying Cong Ryan Shea Tan, Yiyu Cai, Fuqiang Wang

“…For CT scans, two different approaches with convolutional neural network were utilized to tackle the 3D scan entirely or tackle it slice by slice. …”
Get full text

ED‐Autoformer: A New Model for Precise Global TEC Forecast by Jiawei Zhou, Hongtao Cai, Xu Yan, Hong‐wen Xu, Kun Hu, Chao Xiong

“…For the geomagnetic storm on 20 September 2015, the RMSE remained below 3.50 TECu for most periods, peaking at 5.50 TECu during the main phase. …”
Get full text

AI-based estimation of forest plant community composition from UAV imagery by Lindo Nepi, Giacomo Quattrini, Simone Pesaresi, Adriano Mancini, Roberto Pierdicca

“…This study utilizes deep learning models, namely Vision Transformer (VIT-B16 and VIT-H14) and Convolutional Neural Networks (VGG19 and Resnet101), to quantify the abundance of tree species from RGB images captured by drones in multiple areas of central Italy. …”
Get full text

Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis by Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori, Birgit Betina Ertl-Wagner, Farzad Khalvati

“…Abstract Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. …”
Get full text

Electricity theft detection in integrated energy systems considering multi-energy loads by Wenlong Liao, Dechang Yang, Leijiao Ge, Yixiong Jia, Zhe Yang

“…The significant progress has been made in electricity theft detection, but most classic works focus on electricity theft detection in residential environments, neglecting other locations such as hotels, industrial plants, and street lights. …”
Get full text

Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review by Rongjie He, Wengang Zhang, Jie Dou, Nan Jiang, Huaixian Xiao, Jiawen Zhou

“…Among the algorithms, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two models that are most widely used with satisfactory results in landslide risk assessment. …”
Get full text

DSC-Net: Enhancing Blind Road Semantic Segmentation with Visual Sensor Using a Dual-Branch Swin-CNN Architecture by Ying Yuan, Yu Du, Yan Ma, Hejun Lv

“…However, the core challenge in the semantic segmentation of blind roads lies in the effective extraction of global context and edge features. Most existing methods rely on Convolutional Neural Networks (CNNs), whose inherent inductive biases limit their ability to capture global context and accurately detect discontinuous features such as gaps and obstructions in blind roads. …”
Get full text

Toward AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension by Kamlesh Narwani, Hongzhi Lin, Sandeep Pirbhulal, Mir Hassan

“…Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. …”
Get full text

Sentence type identification-based product review sentiment analysis using BeDi-DC and Log-Squish CNN by Neetesh Kumar Nema, Vivek Shukla, Rohit Miri, Praveen Chouksey, Rohit Raja, Kamred Udham Singh, Ankit Kumar, Mohd Asif Shah

“…Thus, this study performs sentence-type assessment-based product review sentiment analysis using beta divergence divide and conquer (BeDi-DC) and Log-Squish Convolutional Neural Network (Log-Squish CNN). Initially, the input product review data were preprocessed, followed by word count extraction. …”
Get full text

Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement by Tomonari Yamada, Takaaki Yoshimura, Shota Ichikawa, Hiroyuki Sugimori

“…Using DeepLab v3+, a convolutional neural network model optimized for segmentation accuracy, the method achieved an average Dice Similarity Coefficient (DSC) of 0.9615 and an Intersection over Union (IoU) of 0.9261 across five-fold cross-validation. …”
Get full text

Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…Based on the results of the experiment, the most effective model and preprocessing technique were proposed.…”
Get full text