FilletCam AI: A handheld tool for precise fillet color profiling of Atlantic salmon and rainbow trout by Rakesh Ranjan, Harsh Shroff, Kata Sharrer, Scott Tsukuda, Christopher Good

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Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study by Lan Mou, Yuqi Shen, Boyuan Wu, Chengyu Zhang, Jiayun Zhu, Qian Tan, Xiaomei Zhang, Zefeng Wang, Zhongxia Shen

“…Additionally, it evaluated the reliability of fNIRS as a diagnostic tool for cognitive assessments through a deep learning approach using one-dimensional convolutional networks. The study included 73 patients with UD, 59 patients with BD, and 40 healthy controls (HC). …”
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Spatiotemporal hybrid deep learning for estimating and analyzing carbon stocks: a case study in Jiangsu province, China by Lizhi Miao, Jvmin Wang, Kaiwen Wu, Heng Xu, Xiying Sun, Gang Lu, Mei-Po Kwan

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FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images by Bochao Chen, Yapeng Wang, Xu Yang, Xiaochen Yuan, Sio Kei Im

“…FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. …”
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Forensic of video object removal tamper based on 3D dual-stream network by Lizhi XIONG, Mengqi CAO, Zhangjie FU

“…In order to solve the problems of inaccurate temporal detection and location of the object removal tampered video， a video tamper forensics method based on 3D dual-stream network was proposed.Firstly， the spatial rich model （SRM） layer was used to extract the high-frequency information from video frames.Secondly， the improved 3D convolution （C3D） network was used as the feature extractor of the dual-stream network to extract the high-frequency information and low-frequency information from the high-frequency frame and the original video frame respectively.Finally， through compact bilinear pooling （CBP） layer， two sets of different feature vectors were fused into one set of feature vectors for classification prediction.The experimental results demonstrate that the classification accuracy of the proposed method in all video frames has an advantage in SYSU-OBJFORG dataset， which makes the temporal detection and location of object removal tampered video more accurate.…”
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Quadrature Solution for Fractional Benjamin–Bona–Mahony–Burger Equations by Waleed Mohammed Abdelfattah, Ola Ragb, Mokhtar Mohamed, Mohamed Salah, Abdelfattah Mustafa

“…The novelty of these methods is based on the generalized Caputo sense, classical differential quadrature method, and discrete singular convolution methods based on two different kernels. …”
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DTCNet: finger flexion decoding with three-dimensional ECoG data by Fufeng Wang, Zihe Luo, Wei Lv, XiaoLin Zhu

“…The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. …”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…In this paper, we propose LSEVGG, a novel and efficient CNN architecture that enhances the classic VGG structure through the integration of lightweight convolution techniques and channel attention mechanisms. …”
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A Small-Sample Target Detection Method for Transmission Line Hill Fires Based on Meta-Learning YOLOv11 by Yaoran Huo, Yang Zhang, Jian Xu, Xu Dai, Luocheng Shen, Conghong Liu, Xia Fang

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Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals by Venkatesh Bhandage, Tejeswar Pokuri, Devansh Desai, Andrew Jeyabose

“…We examined the use of three different pretrained CNN architectures, namely, EfficientNetB0, MobileNetV2, and ResNet18. …”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Experimental results show that compared to the YOLOv8n baseline model, the proposed method improves detection accuracy for eight defect types in different tree species, with the mean average precision (mAP) increased by 5.9%. …”
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Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study by Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

“…This task poses significant challenges due to the complexity and variability of musical signals.MethodsIn this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations—STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz—to the classification of ten different musical instruments. …”
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Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM by Vladyslav Yavtukhovskyi, Violeta Tretynyk

“…Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. …”
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SFRADNet: Object Detection Network with Angle Fine-Tuning Under Feature Matching by Keliang Liu, Yantao Xi, Donglin Jing, Xue Zhang, Mingfei Xu

“…Existing detectors often utilize feature pyramid networks (FPN) and deformable (or rotated) convolutions to adapt to variations in object scale and orientation. …”
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Vegetation classification in a subtropical region with Sentinel-2 time series data and deep learning by Ming Zhang, Dengqiu Li, Guiying Li, Dengsheng Lu

“…Conv1D model based on one-dimensional convolution, GoogLeNet model based on two-dimensional convolution, and CGNet model which fused Conv1D and GoogLeNet) for vegetation classification, respectively. …”
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Consistency regularization based semi-supervised plant disease recognition by Murat Ilsever, Ipek Baz

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A Multi-Architecture Approach for Offensive Language Identification Combining Classical Natural Language Processing and BERT-Variant Models by Ashok Yadav, Farrukh Aslam Khan, Vrijendra Singh

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Comparative Study of Deep Learning-Based Sentiment Classification by Seungwan Seo, Czangyeob Kim, Haedong Kim, Kyounghyun Mo, Pilsung Kang

“…Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives.…”
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Application of big data and artificial intelligence in visual communication art design by Ailing Zhang

“…This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. …”
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Dual-stream dynamic graph structure network for document-level relation extraction by Yu Zhong, Bo Shen

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