FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture by Wenyu Wang, Jie Ding, Xin Shu, Wenwen Xu, Yunzhi Wu

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CGFTNet: Content-Guided Frequency Domain Transform Network for Face Super-Resolution by Yeerlan Yekeben, Shuli Cheng, Anyu Du

“…The network features a channel attention-linked encoder-decoder architecture with two key components: the Frequency Domain and Reparameterized Focus Convolution Feature Enhancement module (FDRFEM) and the Content-Guided Channel Attention Fusion (CGCAF) module. …”
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Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis by Narges Norouzkhani, Hesam Mobaraki, Shirin Varmazyar, Hadis Zaboli, Zhina Mohamadi, Golnaz Nikeghbali, Kamyar Bagheri, Newsha Marivany, Mirmehdi Najafi, Mahdiyeh Nozad Varjovi, Mohamed Abouzeid, Hanieh Zeidi Baghrabad, Pooya Eini, Aida Azhdarimoghaddam, Farbod Khosravi, Mahsa Asadi Anar

“…Methods This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. …”
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AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images by Sen Wang, Huiping Jiang, Jixiang Yang, Xuan Ma, Jiamin Chen

“…Additionally, the bidirectional adaptive feature pyramid network (BAFPN) is proposed for cross-scale feature fusion, integrating semantic information and enhancing adaptability. …”
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YOLO-Ssboat: Super-Small Ship Detection Network for Large-Scale Aerial and Remote Sensing Scenes by Yiliang Zeng, Xiuhong Wang, Jinlin Zou, Hongtao Wu

“…Additionally, it employs a high-resolution feature layer and incorporates a Multi-Scale Weighted Pyramid Network (MSWPN) to enhance feature diversity. …”
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Simulation-Based Electrothermal Feature Extraction and FCN–GBM Hybrid Model for Lithium-ion Battery Temperature Prediction by Luyan WANG, Hongliang HAO, Zhongkang ZHOU, Huimin MA, Jin ZHAO, Zeyang LIU, Qiangqiang LIAO

“…This study proposes a hybrid temperature prediction model that integrates Fully Connected Networks (FCN) and Gradient Boosting Machines (GBM) to capture temperature evolution under varying discharge rates. …”
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Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness by Shuiping Li, Yueyue Chen, Xiaobo Zhang, Junbo Wang, Xuanxiang Gao, Yunhong Jiang, Zhaojun Ban, Cunkun Chen

“…The potential of employing hyperspectral imaging (HSI) in the near-infrared (NIR) range (386.82−1,004.50 nm) for predicting the firmness of 'Fuji' apples cultivated in Aksu has been evaluated. The performance of seven preprocessing algorithms and two feature selection algorithms was evaluated. …”
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MCADNet: A Multi-Scale Cross-Attention Network for Remote Sensing Image Dehazing by Tao Tao, Haoran Xu, Xin Guan, Hao Zhou

“…In order to overcome this difficulty, we propose the multi-scale cross-attention dehazing network (MCADNet), which offers a powerful solution for RSID. …”
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AI-based tool wear prediction with feature selection from sound signal analysis by Viet Q. Vu, Tien-Ninh Bui, Minh-Quang Tran

“…Finally, an artificial neural network (ANN) model is designed to estimate tool wear levels. …”
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MSTNet: a multi-stage progressive network with local–global transformer fusion for image restoration by Ruyu Liu, Lin Wang, Jie He, Jiajia Wang, Jianhua Zhang, Xiufeng Liu, Chaochao Wang, Haoyu Zhang, Sheng Dai

“…We also introduce a fusion module to combine the features from different Transformer branches and obtain a comprehensive and accurate feature representation. …”
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Research on Pantograph Defect Classification Based on Vibration Signals by Vytautas Gargasas, Kęstas Rimkus, Mindaugas Alekna, Andrius Knyš, Mindaugas Žilys, Algimantas Valinevičius

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An efficient approach for diagnosing faults in photovoltaic array using 1D-CNN and feature selection Techniques by Yousif Mahmoud Ali, Lei Ding, Shiyao Qin

“…A simple and accurate one-dimensional convolutional neural network (1D-CNN) model is developed to classify the faults based on the selected features. …”
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Osteoarthritis Classification Using Hybrid Quantum Convolutional Neural Network by Devansh Tikariha, Abdul Moomin, D. Jeyamani, P. Rukmani

“…Using a QCNN, this model harnesses the ability of quantum computing to represent high-dimensional data transformations, a novel approach that complements classical CNN layers by exploring patterns that are not captured in traditional networks. The initial results showed a high classification accuracy of 97.26%, suggesting that quantum-enhanced layers can significantly bolster feature extraction and classification in medical diagnostics. …”
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FMCNN: Raw-Data Type Identification Using Feature Matrix and CNN by Eunsu Lee, Hoon Yoo

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Adversarial Threats to Cloud IDS: Robust Defense With Adversarial Training and Feature Selection by Hariprasad Holla, Shashidhar Reddy Polepalli, Arun Ambika Sasikumar

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Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance by M. Elhoseny, Deepak Dasaratha Rao, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

“…Data fusion is then carried out utilizing an enhanced RPN (region proposal network). The enhanced RPN also has a loss function (object detection loss, bounding box loss and target classification loss), an estimate of ROI and feature extraction network (FEN). …”
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Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning by M. Vimala, SatheeshKumar Palanisamy, Sghaier Guizani, Habib Hamam

“…A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. …”
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Fault diagnosis in photovoltaic arrays: A robust and efficient approach using feature engineering and 1D-CNN by Yousif Mahmoud Ali, Lei Ding

“…To overcome these challenges and limitations, this study proposes a robust and efficient method based on feature engineering and one-dimensional convolutional neural networks (1D-CNN). …”
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An approach to arousal disorder classification using deformable convolution and adaptive multiscale features in EEG signals by Andia Foroughi, Fardad Farokhi, Fereidoun Nowshiravan Rahatabad, Alireza Kashaninia

“…In this research, we propose a novel method to classify arousal disorders from EEG data and extract post-classification diagnostic features. To our knowledge, this is the first instance of such categorization achieved using a deformable convergence network. …”
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Defect detection in textiles using back propagation neural classifier by Subrata Das, Amitabh Wahi, Suresh Jayaram

“…After successful training of the neural network on train dataset, the performance of the trained neural network was evaluated on the test dataset. …”
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