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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CNN Issues in Skin Lesion Classification: Data Distribution and Quantity by Giuliana Ramella, Luca Serino

“…This challenge is commonly overlooked in most skin lesion classification papers, which mainly rely on weighted classification techniques. …”
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Adaptive Token Mixer for Hyperspectral Image Classification by Shuhan Lei, Meng Zhang, Yuhang Wang, Nan Tang, Ni Jia, Lihua Fu

“…In addition, we introduce a cross-shaped convolutional operator (COSTCO) to enhance local spatial feature extraction. …”
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Development of approach to an automated acquisition of static street view images using transformer architecture for analysis of Building characteristics by Seunghyeon Wang

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Predictive identification of oral cancer using AI and machine learning by Saraswati Patel, Dheeraj Kumar

“…The results demonstrated that normalization, specifically min-max scaling, was the most effective method, leading to the highest accuracy (94 %) and the lowest MSE (0.013) for CNN models. …”
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Enhance differential privacy mechanisms for clinical data analysis using CNNs and reinforcement learning by Rakesh Batchala, Priyank Jain, Manasi Gyanchandani, Sanyam Shukla, Rajesh Wadhvani

“…The results demonstrate that DQN performs well under most privacy settings, and A2C performs better in certain configurations, which indicates the need to match the RL strategy with specific privacy characteristics. …”
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PoI+NBU: A Feasibility study in Generating High-Resolution Adversarial Images with a Black Box Evolutional Algorithm based Attack by Enea Mancellari, Ali Osman Topal, Franck Leprévost

“…Adversarial attacks in the digital image domain pose significant challenges to the robustness of machine learning models. Trained convolutional neural networks (CNNs) are among the leading tools used for the automatic classification of images. …”
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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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A new approach to estimate neighborhood socioeconomic status using supermarket transactions and GNNs by Eduardo Cruz, Monica Villavicencio, Carmen Vaca, Lisette Espín-Noboa, Nervo Verdezoto

“…The model was trained with spectral and spatial convolutional filters using cross-validation to select the best approach for the prediction. …”
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Improved Multi-Grained Cascade Forest Model for Transformer Fault Diagnosis by Yiyi Zhang, Yuxuan Wang, Jiefeng Liu, Heng Zhang, Xianhao Fan, Dongdong Zhang

“…However, due to the limited number of DGA data, most deep learning models will be overfitted and the classification accuracy cannot be guaranteed. …”
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Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures by Weijie Zhang, Veronika Belcheva, Tatiana Ermakova

“…Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. …”
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Artificial Vision System for Autonomous Mobile Platform Used in Intelligent and Flexible Indoor Environment Inspection by Marius Cristian Luculescu, Luciana Cristea, Attila Laszlo Boer

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A Novel Context-Aware Feature Pyramid Networks With Kolmogorov-Arnold Modeling and XAI Framework for Robust Lung Cancer Detection by Yajnaseni Dash, Sudhir C. Sarangi, Vinayak Gupta, Naween Kumar, Ajith Abraham

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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…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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Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images by S. Reka, T. Suriya Praba, Mukesh Prasanna, Vanipenta Naga Nithin Reddy, Rengarajan Amirtharajan

“…GAN (Generative Adversarial Networks) and CNN (Convolutional Neural Networks) are the most recent cutting-edge innovations that have supported the system in attaining the expected result. …”
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