Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…This model innovatively introduces group convolution and multiple convolutional kernels to handle multidimensional features based on traditional attention mechanisms for visual recognition tasks, while utilizing spatial pyramid pooling (SPP) to combine and extract the most useful features. …”
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Polarization-sensitive in-sensor computing in chiral organic integrated 2D p-n heterostructures for mixed-multimodal image processing by Je-Jun Lee, Seong-Jun Han, Changsoon Choi, Chaewon Seo, Seungkwon Hwang, Jihyun Kim, Jung Pyo Hong, Jisu Jang, Jihoon Kyhm, Jung Woo Kim, Byoung-Soo Yu, Jung Ah Lim, Gunuk Wang, Joohoon Kang, Yonghun Kim, Suk-kyun Ahn, Jongtae Ahn, Do Kyung Hwang

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A Deep Learning Approach to Assist in Pottery Reconstruction from Its Sherds by Matheus Ferreira Coelho Pinho, Guilherme Lucio Abelha Mota, Gilson Alexandre Ostwald Pedro da Costa

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Intelligent Excavation Technologies for Satellite Spectrum Signals by Qiulin LYU, Xiaojin DING, Gengxin ZHANG

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Deep Learning Model of Image Classification Using Machine Learning by Qing Lv, Suzhen Zhang, Yuechun Wang

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ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao, Wenfeng Li

“…First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. …”
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Analysis and correcting pronunciation disorders based on artificial intelligence approach by Nataliia Melnykova, Bohdan Pavlyk, Oleh Basystiuk, Stepan Skopivskyi

“…The analysis of machine learning methods led to the selection of two experimental models: a Convolutional Neural Network (CNN) utilizing mel-spectrograms for image-based sound representation and a Long Short-Term Memory (LSTM) network combined with mel-frequency cepstral coefficients, aiming to explore the effectiveness of sequential data processing in the context of pronunciation disorder classification in post-traumatic military patients. …”
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A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production by Meng Lv, Yi-Xiao Xu, Yu-Hang Miao, Wen-Hao Su

“…Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). …”
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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion by Chuanjiang Wang, Junhao Ma, Guohui Wei, Xiujuan Sun

“…Post-extraction, the features from both channels are fused and classified. …”
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Research on Breast Cancer Detection Methods Based on ODMV-MulDyHead-YOLO by Yanhong Zhang, Pei Li, Yihua Lan, Xiao Jia, Yingjie Lv

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A landslide area segmentation method based on an improved UNet by Guangchen Li, Kefeng Li, Guangyuan Zhang, Ke Pan, Yuxuan Ding, Zhenfei Wang, Chen Fu, Zhenfang Zhu

“…Firstly, the feature extraction structure of the model was redesigned by integrating dilated convolution and EMA attention mechanism to enhance the model’s ability to extract image features. …”
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DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

“…The model then combines densely connected convolutional networks (DenseNet) and temporal convolutional network (TCN). …”
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Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review by Bo Sun, Bo Sun, Fei Cai, Huiman Huang, Bo Li, Bing Wei

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Addressing spatial imprecision in deep learning for satellite imagery-based socioeconomic predictions by Heather Baier, Dan Runfola

“…This paper introduces the Spatial Imprecision Adjustment (SIA) method, a neural-network-based post-processing framework designed to enhance the predictive accuracy of geospatial deep learning models trained on imprecise labels, a common challenge in socioeconomic survey data. …”
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Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion by Vijayarajan Rajangam, Dheeraj Kandikattu, Utkarsh, Mukul Kumar, Alex Noel Joseph Raj

“…The feature maps of the source images are derived from the convolution layers on which the texture analysis is done to evaluate a weight map. …”
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Advances in Neural Network assisted Tool Pressure Prediction by Göltl Florian, Harst Felix, Birkert Arndt, Stache Nicolaj C.

“…It has been demonstrated that convolutional neural networks (CNNs) can predict pressure distributions from spotting patterns. …”
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Proposing a Fuzzy Soft‐max‐based classifier in a hybrid deep learning architecture for human activity recognition by Reza Shakerian, Meisam Yadollahzadeh‐Tabari, Seyed Yaser Bozorgi Rad

“…The proposed HAR takes the advantage of staking Convolutional Neural Network and Long Short‐Term (LSTM), for extracting the high‐level features of the sensors data and for learning the time‐series behaviour of the abstracted data, respectively. …”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…Additionally, face emotion detection algorithms tuned with MnasNet-Teaching Learning Based Optimization (TLBO) and Convolution Neural Networks (CNN)-Cuckoo Search Optimization (CSO) are employed for accurate emotion recognition. …”
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An applied noise model for scintillation-based CCD detectors in transmission electron microscopy by Christian Zietlow, Jörg K. N. Lindner

“…The Poisson noise, arising from the quantized nature of the beam electrons, gets correlated by this convolution, which allows to reconstruct the detector PSF based on the Wiener–Khinchin theorem and the Pearson correlation coefficients under homogeneous illumination conditions. …”
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A Power-Efficient 0.5668 TOPS/W Digital Logic Accelerator Implemented Using 40-nm CMOS Process for Underwater Object Recognition Usage by Chua-Chin Wang, Shih-Heng Luo, Hsin-Che Wu, Ralph Gerard B. Sangalang, Chewn-Pu Jou, Harry Hsia, Lan-Chou Cho

“…DNN (deep neural network) and CNN (convolution neural network) have been widely used in real-time artificial intelligent (AI) applications, particularly image or video recognitions, because they have been proved physically in many occasions. …”
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