Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning by Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Somaye Mohammadi, Len Gelman

“…This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults using time-domain signals, frequency-domain spectra, and envelope frequency spectrum analysis. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…The model outperformed baseline methods with an R2 of 0.9165 and mean squared error (MSE) of 0.6893, significantly outperforming conventional approaches for optimizing production in variable growing conditions.…”
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A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade by Junjia Zhang, Zhuorui Li, Enzhi Wang, Bin Yu, Jiangping Li, Jun Ma

“…Secondly, we used a Time2Vec module to extract periodic information from the copper smelting process variables, incorporates time series processing directly into the prediction model. …”
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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…Within this period, optimal feature subsets were extracted using variable selection algorithms. The performance of the partial least squares regression, random forest, and convolutional neural network–long short-term memory (CNN-LSTM) models was evaluated using a 10-fold cross-validation approach. …”
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Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models by Zishuai Wang, Wangchang Li, Zhonglin Tang

“…Deep learning (DL) methods like multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. …”
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Dynamic deep learning based super-resolution for the shallow water equations by Maximilian Witte, Fabrício R Lapolli, Philip Freese, Sebastian Götschel, Daniel Ruprecht, Peter Korn, Christopher Kadow

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CNN-based remote dental diagnosis model for caries detection with grad-CAM by Donghyeok Kim, Jangkyum Kim, Seong Gon Choi

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Potato plant disease detection: leveraging hybrid deep learning models by Jackson Herbert Sinamenye, Ayan Chatterjee, Raju Shrestha

“…This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). …”
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DP-FWCA: A Prompt-Enhanced Model for Named Entity Recognition in Educational Domains by Zhenkai Qin, Dongze Wu, Jiajing He, Jingming Xie, Aimin Wei

“…However, the inherent complexity and contextual variability of educational texts, compounded by a limited supply of domain-specific annotated data, impose formidable challenges on conventional NER methods. …”
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Deep multi-task learning framework for gastrointestinal lesion-aided diagnosis and severity estimation by Zenebe Markos Lonseko, Dingcan Hu, Kaixuan Zhang, Helen Haile Hayeso, Tao Gan, Jinlin Yang, Nini Rao

“…Traditional methods for diagnosing lesions face challenges in accurately estimating severity due to requiring interpretable biomarkers, inter-observer variability, and overlapping lesions. Moreover, existing deep-learning models treat lesion classification and severity estimation as separate tasks, complicating diagnosis. …”
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Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

“…Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. …”
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Fault diagnosis method for rolling bearings based on CWT-IDenseNet by Guangfei JIA, Hanwen LIANG, Jinqiu YANG, Zhe WU, Yuxin HAN

“…Aiming at the problems of incomplete information contained in one-dimensional signals and overfitting of the DenseNet under variable working conditions, a rolling bearing fault diagnosis method based on continuous wavelet transform (CWT) time-frequency images and an improved densely connected convolutional network (IDenseNet) was proposed. …”
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Deep Learning Method for Bearing Fault Diagnosis by LIU Xiu, MA Shan-tao, XIE Yi-ning, HE Yong-jun

“…In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. …”
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PDCG-Enhanced CNN for Pattern Recognition in Time Series Data by Feng Xie, Ming Xie, Cheng Wang, Dongwei Li, Xuan Zhang

“…This study compares the effectiveness of three methods—Fréchet Distance, Dynamic Time Warping (DTW), and Convolutional Neural Networks (CNNs)—in detecting similarities and pattern recognition in time series. …”
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Military Training Aircraft Structural Health Monitoring Leveraging an Innovative Biologically Inspired Feedback Mechanism for Neural Networks by Tarek Berghout

“…Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue. …”
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A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics by Hunter Dlugas, Seongho Kim

“…The datasets varied widely in size, mass spectrometry method, and response variable. <b>Results</b>: With respect to AUC on test data, BNN, CNN, FNN, KAN, and SNN were the top-performing models in 4, 1, 5, 3, and 4 of the 17 datasets, respectively. …”
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IoUT-Oriented an Efficient CNN Model for Modulation Schemes Recognition in Optical Wireless Communication Systems by M. Mokhtar Zayed, Saeed Mohsen, Abdullah Alghuried, Hassan Hijry, Mona Shokair

“…However, accurate modulation recognition in these systems remains a significant challenge due to the variable nature of underwater channels. This paper explores the application of Convolutional Neural Networks (CNNs) for modulation recognition in the OWC systems, focusing specifically on 64-QAM (Quadrature Amplitude Modulation) and 32-PSK (Phase Shift Keying). …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mechanism is utilized for regression which extracts temporal and spatial complex features. …”
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Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI by Reem Ibrahim Alkanhel, Hager Saleh, Ahmed Elaraby, Saleh Alharbi, Hela Elmannai, Saad Alaklabi, Saeed Hamood Alsamhi, Sherif Mostafa

“…Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. …”
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CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems by Yafei Li, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, Zelin Wang

“…In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). …”
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