Offline Arabic handwritten word recognition: A transfer learning approach by Mohamed Awni, Mahmoud I. Khalil, Hazem M. Abbas

“…In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. …”
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A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model by Juanru Zhao, Mei Yuan, Yiwen Cui, Jin Cui

“…Intelligent fault diagnosis (IFD) for mechanical equipment based on small and imbalanced datasets has been widely studied in recent years, with transfer learning emerging as one of the most promising approaches. Existing transfer learning-based IFD methods typically use data from different operating conditions of the same equipment as the source and target domains for the transfer learning process. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…To address this issue, this paper introduces a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with an attention mechanism designed to forecast day-ahead electricity demand in Australia. …”
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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

“…Feature selection is carefully addressed via a combination of 14 statistical methods, tree-based methods, bio-inspired methods, and regularization methods so that only the most relevant features for modelling are chosen and included. …”
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Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach by Chandra Wadde, Gayatri Routhu, Mark Clemente-Arenas, Surya Prakash Gummadi, Rupesh Kumar

“…The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. …”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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Development of a Baby Cry Identification System Using a Raspberry Pi-Based Embedded System and Machine Learning by Mohcin Mekhfioui, Wiam Fadel, Fatima Ezzahra Hammouch, Oussama Laayati, Marouan Bouchouirbat, Nabil El Bazi, Amal Satif, Tarik Boujiha, Ahmed Chebak

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A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images by Mishmala Sushith, A. Sathiya, V. Kalaipoonguzhali, V. Sathya

“…These enriched spatial features are then fed into an RNN with attention mechanism to capture temporal dependencies so that most relevant data aspects can be considered for analysis. …”
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Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection by Degang Wang, Longfei Ren, Xu Sun, Lianru Gao, Jocelyn Chanussot

“…Hyperspectral anomaly detection (HAD) aims to locate targets deviating from the background distribution in hyperspectral images (HSIs) without requiring prior knowledge. Most current deep learning-based HAD methods struggle to effectively distinguish anomalies due to limited utilization of supervision information and intrinsic nonlocal self-similarity in HSIs. …”
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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

“…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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Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform by Sara Bagherzadeh, Mohammad Reza Norouzi, Amirhesam Ghasri, Pouya Tolou Kouroshi, Sepideh Bahri Hampa, Fatemeh Farokhshad, Ahmad Shalbaf

“…This system was optimized through a series of experiments to identify the most accurate model. The experiments employed a pre-trained convolutional neural network, ResNet18, fine-tuned on time–frequency synchrosqueezed wavelet transform (SSWT) images derived from EEG signals. …”
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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin, Chunhou Zheng

“…IL-6-inducing peptides are critical for the development of immunotherapy and diagnostic biomarkers for some diseases. Most existing methods for predicting IL-6-induced peptides use traditional machine learning methods, whose feature selection is based on prior knowledge. …”
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Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction by Deyong Guan, Na Ren, Ke Wang, Qi Wang, Hualong Zhang

“…Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. …”
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Efficient and Motion Correction-Free Myocardial Perfusion Segmentation in Small MRI Data Using Deep Transfer Learning From Cine Images: A Promising Framework for Clinical Implement... by German Garcia-Jara, Angel Jimenez-Molina, Esteban Reyes, Nicolas Tapia-Rivas, Cristobal Ramos-Gomez, Jose De Grazia, Matias Sepulveda

“…Deep learning-based methods, the most accurate to accomplish this task, still rely on expensive motion correction steps and require large labeled datasets. …”
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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

“…Despite extensive studies using S-1 data for SOC mapping, most focus on either single or multi-date periods without achieving satisfactory results. …”
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CAFU-Net: A Context-Aware Feature Aggregation Network for Lung Nodule Segmentation by Jiachen Hou, Yingqi Lu, Zhougui Ling, Tao Li, Xiangsuo Fan, Yanna Qin, Qingnan Huang

“…On the MID-FAHGMU dataset, CAFU-Net achieves an IoU of 63.52%, a Dice coefficient of 76.21%, an F1-score of 76.47%, an F2-score of 77.06%, and an F0.5-score of 77.74%, exceeding most comparative methods in several metrics. These experimental results fully validate the superiority and robustness of CAFU-Net in the task of pulmonary nodule segmentation. …”
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Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study by Haoyue Cao, Jianqiang Han, Weiming Hou, Juxiang Yuan

“…Conclusions We found that most greenhouse gases and air pollutants increase the risk of contracting scrub typhus, mainly driven by CH4, NOx, and NMVOC. …”
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Deep Learning Framework for Predicting Transonic Wing Buffet Loads Due to Structural Eigenmode-Based Deformations by Rebecca Zahn, Moritz Zieher, Christian Breitsamter

“…The hybrid ROM is defined by a series connection of a convolutional autoencoder (CNN-AE) and a long short-term memory (LSTM) neural network. …”
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WiCNNAct: Wi-Fi-Based Human Activity Recognition Utilizing Deep Learning on the Edge Computing Devices by Venkata Raghava Shashank Viswanathuni, Rakesh Reddy Yakkati, Sreenivasa Reddy Yeduri, Linga Reddy Cenkeramaddi

“…Comprehensive systems, however, mostly rely on wearables, video cameras, and ambient sensors, which might be expensive and difficult to deploy or cause privacy issues. …”
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Advancing Breast Cancer Detection: SE-Conformer Framework for Malignancy Detection in Histopathology Images by Lekha S. Nair, K. R. Amarnath, Jyothisha J. Nair

“…Globally, breast cancer is the second most lethal form of cancer among women, and has high rates of incidence and mortality. …”
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