End-to-End Multi-Scale Adaptive Remote Sensing Image Dehazing Network by Xinhua Wang, Botao Yuan, Haoran Dong, Qiankun Hao, Zhuang Li

“…In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net). In our network model, we introduce a dilated convolution adaptive module to extract global and local detail features of remote sensing images. …”
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Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions by Meshari D. Alanazi, Amna Maraoui, Imen Werda, Ahmed Ben Atitallah, Turki M. Alanazi, Mohammed Albekairi, Anis Sahbani, Amr Yousef

“…The dual difficulties of high computing needs in 3D convolutional networks and the necessity of more precise brain tumor diagnosis are addressed by this work. …”
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Research on ECG Signal Classification Based on Hybrid Residual Network by Tianyu Qi, He Zhang, Huijun Zhao, Chong Shen, Xiaochen Liu

“…This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. …”
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Single Transit Detection in Kepler with Machine Learning and Onboard Spacecraft Diagnostics by Matthew T. Hansen, Jason A. Dittmann

“…We present a novel technique using an ensemble of convolutional neural networks incorporating the onboard spacecraft diagnostics of Kepler to classify transits within a light curve. …”
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ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention by Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu

“…The model adopts a CNN-Transformer hierarchical structure, combining the strengths of Convolutional Neural Networks (CNNs) and Transformers to effectively capture and integrate both local and global features, thereby enhancing feature representation accuracy and diversity. …”
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Neuroevolutionary reinforcing learning of neural networks by Y. A. Bury, D. I. Samal

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Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders by Chang Seok Bang

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Circular Slot Antenna with Pattern Diversity by Youngje Sung

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A Method of Fatigue Driving State Detection Based on Deep Learning by XIONG Qunfang, LIN Jun, YUE Wei

“…Firstly, the face image feature points are automatic extracted using convolutional neural network and landmark algorithm. …”
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Rainfall prediction based on CNN-LSTM model under sliding window by Rui Yuan

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Image-based evaluation of single-cell mechanics using deep learning by Zhaozhao Wu, Yiting Feng, Ran Bi, Zhiqiang Liu, Yudi Niu, Yuhong Jin, Wenjing Li, Huijun Chen, Yan Shi, Yanan Du

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Applications and prospects of artificial intelligence in the auxiliary diagnosis of nail diseases by HE Mengwen, MAI Sien, MA Han

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A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data by Ningqing Pang, Hoiio Kong, Chanseng Wong, Zijun Li, Yu Du, Jeremy Cheuk-Hin Leung

“…In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. …”
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Identifying Forest Burned Area Using a Deep Learning Model Based on Post-Fire Optical and SAR Remote Sensing Images by Xiaofei Xi, Man Kang, Long Dai, Yan Jing, Peng Han, Congqiang Hou

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Automatic standard plane and diagnostic usability classification in obstetric ultrasounds by Adam Lim, Mohamed Abdalla, Farbod Abolhassani, Wyanne Law, Benjamin Fine, Dafna Sussman

“…The standard plane and diagnostic usability networks are founded on a convolutional neural network framework and employ the benefits of transfer learning. …”
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Heterogeneous long-term changes in larch forest and shrubland cover in the Kolyma lowland are not captured by coarser-scale greening trends by Heather Kropp, Michael M Loranty, Howard Epstein, Gerald V Frost, Adam Koplik, Logan T Berner

“…We pair high resolution historical (KeyHole9 1971) and current satellite imagery (WorldView-3 2020) with a convolutional neural network approach to predict forest, shrubland, and surface water cover within a region of the Kolyma lowland (171 km ^2 ) in eastern Siberia. …”
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Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

“…The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. …”
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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network by Zheng Yang, Hua Zhu, Zhao Li, Gang Wang, Meng Su

“…In recent years, convolutional neural network (CNN) has achieved great success in the field of network security protection. …”
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A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models by Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili, Mohammad Lutfi Othman, Mohammad Effendy Ya’acob, Noor Izzri Bin Abdul Wahab, Masoud Ahmadipour

“…The proposed hybrid IMGOPMFFNN model proved its superior correlation evaluations, surpassing the performance of ant lion optimizer based on random forest (ALORF) model, two stages of ANN (ALO2ANN) model, long short-term memory (LSTM), gated recurrent unit (GRU), extreme learning machine (ELM), least square support vector machine (LSSVM), and convolutional neural network (CNN) models. The MATLAB code of the IMGO is free available at: https://www.mathworks.com/matlabcentral/fileexchange/177214-improved-mgo-method.…”
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Mapping Grayscale Images to Colour Space Using Deep Learning by Anu Saini, Jyoti Tripathi

“…The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.…”
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