PolSAR image classification using shallow to deep feature fusion network with complex valued attention by Mohammed Q. Alkhatib, M. Sami Zitouni, Mina Al-Saad, Nour Aburaed, Hussain Al-Ahmad

“…The results indicate that the proposed approach achieves notable improvements in Overall Accuracy (OA), with enhancements of 1.30% and 0.80% for the AIRSAR datasets, and 0.50% for the ESAR dataset. However, the most remarkable performance of the CV-ASDF2Net model is observed with the Flevoland dataset; the model achieves an impressive OA of 96.01% with only a 1% sampling ratio. …”
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Application of deep learning in cloud cover prediction using geostationary satellite images by Yeonjin Lee, Seyun Min, Jihyun Yoon, Jongsung Ha, Seungtaek Jeong, Seonghyun Ryu, Myoung-Hwan Ahn

“…We explore the effectiveness of advanced deep learning techniques – specifically 3D Convolutional Neural Networks, Long Short-Term Memory networks, and Convolutional Long Short-Term Memory (ConvLSTM) – using GK2A cloud detection data, which provides updates every 10 minutes at 2 km spatial resolution. …”
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Models, systems, networks in economics, engineering, nature and society by D.V. Mirosh

“…The technical diagnostic tools used today in the repair and maintenance of asynchronous motors are an important aspect of the functioning of the most important devices in enterprises. Asynchronous drive is used in many areas of human activity, in industry as well as in everyday life. …”

Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression by Heba M. Afify, Kamel K. Mohammed, Aboul Ella Hassanien

“…The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. …”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…This model exhibited more accurate prediction results compared to the commonly used prediction models like backpropagation neural network (BPNN), random forest (RF), and Transformer while avoiding excessive errors produced by most of these models on the validation and test sets. …”
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HIDS-RPL: A Hybrid Deep Learning-Based Intrusion Detection System for RPL in Internet of Medical Things Network by Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi

“…We evaluated our novel methodology against five DDoS attacks: DNS, UDP, UDP-Lag, NTP, and SYN. In comparison to the most recent methods, our suggested model achieves an accuracy of 99.87%, a precision of 98.5%, a recall rate of 98.64%, and an F1-score of 98.54%.…”
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Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA by Claudine Gravel-Miguel, Grant Snitker, Jayde N. Hirniak, Katherine Peck, Alex Fetterhoff

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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…The suggested method analyzes high-resolution retinal pictures using deep learning methods, most especially Convolutional Neural Networks (CNNs). …”
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Gradient-Controlled Gaussian Kernel for Image Inpainting by Hossein Noori

“…Image inpainting is the process of filling in damaged or missing regions in an image by using information from known regions or known pixels of the image. One of the most important techniques for inpainting is convolution-based methods, in which a kernel is convolved with the damaged image iteratively. …”
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A Study on Energy Consumption in AI-Driven Medical Image Segmentation by R. Prajwal, S. J. Pawan, Shahin Nazarian, Nicholas Heller, Christopher J. Weight, Vinay Duddalwar, C.-C. Jay Kuo

“…While training is energy-intensive, the recurring nature of inference often results in significantly higher cumulative energy consumption over a model’s life cycle. Depthwise Convolution with Mixed Precision achieves the lowest energy consumption during training while maintaining strong performance, making it the most energy-efficient configuration among those tested. …”
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Two-stage Detection Method for Abnormal Cluster Cervical Cells by LIANG Yi-qin, ZHAO Si-qi, WANG Hai-tao, HE Yong-jun

“…In the first stage, we use YOLO-v5 target detection network. The standard convolution in the network is replaced by deformable convolution. …”
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IDNet: An inception-like deformable non-local network for projection compensation over non-flat textured surfaces. by Yuqiang Zhang, Huamin Yang, Cheng Han, Chao Zhang, Chao Xu, Shiyu Lu

“…Our experimental validation demonstrates that the proposed method achieves comparable compensation performance to existing approaches, particularly in the most challenging and geometrically complex edge regions of projected images.…”
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Airport Clearance Detection Based on Vision Transformer and Multi-Scale Feature Fusion by Yutong Chen, Yufen Liu, Zhixiong Guo, Qiang Gao

“…Secondly, to improve the feature extraction ability, partial convolution is replaced with dynamic convolution, and attention is introduced to the convolution kernel from four dimensions. …”
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Collective cell migration drives morphogenesis of the kidney nephron. by Aleksandr Vasilyev, Yan Liu, Sudha Mudumana, Steve Mangos, Pui-Ying Lam, Arindam Majumdar, Jinhua Zhao, Kar-Lai Poon, Igor Kondrychyn, Vladimir Korzh, Iain A Drummond

“…Complete blockade of pronephric fluid flow prevented cell migration and proximal nephron convolution. Selective blockade of proximal, filtration-driven fluid flow shifted the position of tubule convolution distally and revealed a role for cilia-driven fluid flow in persistent migration of distal nephron cells. …”
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Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning by Guangning Qin

“…It introduces multi-scale convolutional attention (MSCA) to maximize the ability of mining multi-scale convolutional features through element multiplication, and enhance the attention to spatial details. …”
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HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation by Soumya Sara Koshy, L. Jani Anbarasi

“…IntroductionBreast cancer is a severe illness predominantly affecting women, and in most cases, it leads to loss of life if left undetected. …”
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A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG by Guanghui Song, Jiajian Zhang, Dandan Mao, Genlang Chen, Chaoyi Pang

“…Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. …”
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An automatic modulation recognition network based on multi-channel lightweighting by WAN Jinhua, SHANG Junna, ZHANG Huadi

“…Existing automatic modulation recognition models perform well in recognition accuracy, but most methods have difficulty in achieving an ideal balance between the number of parameters and model performance. …”
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BCDnet: Parallel heterogeneous eight-class classification model of breast pathology. by Qingfang He, Guang Cheng, Huimin Ju

“…The model uses the VGG16 convolution base and Resnet50 convolution base as the parallel convolution base of the model. …”
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Noise-Robust Local Ternary Pattern Center for Noisy Texture Classification by Farhan A. Alenizi, Mokhtar Mohammadi, Mohammad Hossein Shakoor

“…Local Binary Pattern (LBP) has been widely used for texture analysis in machine vision and image processing. However, most of these descriptors provide a high number of features that are sensitive to noise and rotation. …”
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