Comparative analysis of transfer learning performance on generalised EEG data for use in a depression diagnosis task by Shusharina, Natalia Nikolaevna

“…Therefore, EEGNet was replaced by a 1D CNN architecture with a larger number of parameters, which led to an increase in the quality performance of the models. …”
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Multiscale Time-Frequency Sparse Transformer Based on Partly Interpretable Method for Bearing Fault Diagnosis by Shouquan Che, Jianfeng Lu, Congwang Bao, Caihong Zhang, Yongzhi Liu

“…Transformer model is being gradually studied and applied in bearing fault diagnosis tasks, which can overcome the feature extraction defects caused by long-term dependencies in convolution neural network (CNN) and recurrent neural network (RNN). To optimize the structure of existing transformer-like methods and improve the diagnostic accuracy, we proposed a novel method based on the multiscale time-frequency sparse transformer (MTFST) in this paper. …”
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Research on Safe Driving Evaluation Method Based on Machine Vision and Long Short-Term Memory Network by Dongmei Shi, Hongyu Tang

“…The spatial stream channel uses the CNN method to extract the spatial characteristic value of video image and uses pyramid pooling instead of traditional pooling, normalizing the scale transformation. …”
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Automated Zebrafish Spine Scoring System Based on Instance Segmentation by Wen-Hsin Chen, Tien-Ying Kuo, Yu-Jen Wei, Cheng-Jung Ho, Ming-der Lin, Huan Chen, Wen-Ying Lin

“…Our method was designed using Mask R-CNN as the instance segmentation backbone, enhanced with a mask enhancement module and a small object preprocessing approach to strengthen its detection abilities. …”
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Enhanced Emotion-Aware Conversational Agent: Analyzing User Behavioral Status for Tailored Reponses in Chatbot Interactions by S. Abinaya, K. S. Ashwin, A. Sherly Alphonse

“…To address this, this study proposes a new hybrid chatbot model called “TEBC-Net” (Text Emotion Bert CNN Network), which combines text and video analysis to interpret user emotions and generate more empathetic responses. …”
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Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN by Ali Erbey, Necaattin Barışçı

“…The dataset has divided into two subsets: the face region and the lip region. CNN, LSTM, and 3DCNN-based models, including C3D, I3D, and 3DCNN+BiLSTM, were used. …”
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HandSegNet: Hand segmentation using convolutional neural network for contactless palmprint recognition by Koichi Ito, Yusei Suzuki, Hiroya Kawai, Takafumi Aoki, Masakazu Fujio, Yosuke Kaga, Kenta Takahashi

“…HandSegNet employs a new CNN architecture consisting of an encoder–decoder model with a pyramid pooling module. …”
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Comparison and verification of detection accuracy for late deceleration with and without uterine contractions signals using convolutional neural networks by Ikumi Sato, Ikumi Sato, Yuta Hirono, Yuta Hirono, Eiri Shima, Hiroto Yamamoto, Kousuke Yoshihara, Chiharu Kai, Chiharu Kai, Akifumi Yoshida, Fumikage Uchida, Naoki Kodama, Satoshi Kasai

“…Therefore, this study compares models using FHR only (FHR-only model) and FHR with UC (FHR + UC model) constructed using a Convolutional Neural Network (CNN) to examine whether LD could be detected using only the FHR signal.MethodsThe data used to construct the CNN model were obtained from the publicly available CTU-UHB database. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…The CNN model achieved an overall accuracy of 93.1% for forest or nonforest, 82.6% for hardwood, softwood, and nonforest, and 74.7% for three dominant forest types including paper birch, black spruce, and white spruce along with nonforest. …”
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Intrusion detection using synaptic intelligent convolutional neural networks for dynamic Internet of Things environments by Hui Chen, Zhendong Wang, Shuxin Yang, Xiao Luo, Daojing He, Sammy Chan

“…The SICNN model leverages the synaptic intelligence (SI) algorithm to optimize the synaptic structure of the convolutional neural network (CNN), significantly mitigating the forgetfulness of the CNN for past detection tasks and simplifying model training. …”
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Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring by Ahmad Qurthobi, Robertas Damasevicius, Vytautas Barzdaitis, Rytis Maskeliunas

“…The results indicate that the proposed hybrid CNN-BiLSTM model significantly improved classification loss and accuracy scores compared to the standalone pre-trained models. …”
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Digital Twin Framework Using Real-Time Asset Tracking for Smart Flexible Manufacturing System by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

“…It achieved a mean coordinate error (MCE) between 1.2 and 2.8 m and a 100% building rate. Furthermore, the CNN combined with the ADAM optimizer produced the best results, with a mean squared error of 0.83. …”
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MambaShadowDet: A High-Speed and High-Accuracy Moving Target Shadow Detection Network for Video SAR by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke, Tianjiao Zeng

“…Existing convolution neural network (CNN)-based video synthetic aperture radar (SAR) moving target shadow detectors are difficult to model long-range dependencies, while transformer-based ones often suffer from greater complexity. …”
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Brittleness evaluation of main coal seams in Permian Taiyuan-Shanxi formations, Baode block, Ordos Basin: based on a convolutional neural network method by Qingfeng ZHANG, Ziling LI, Jikun ZHANG, Shuai HAO, Xiaoguang SUN, Yanjie SHANG, Yun ZUO

“…The results indicated that CNN-based predictions of rock mechanical parameters had good applicability for coal-bearing layers. …”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

“…A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.…”
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Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds by Joarder Jafor Sadique, Imtiaz Nasim, Ahmed S. Ibrahim

“…Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. …”
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Bilingual hate speech detection on social media: Amharic and Afaan Oromo by Teshome Mulugeta Ababu, Michael Melese Woldeyohannis, Emuye Bawoke Getaneh

“…In this work, a Bilingual hate speech detection for Amharic and Afaan Oromo languages were conducted using four different deep learning classifiers (CNN, BiLSTM, CNN-BiLSTM, and BiGRU) and three feature extraction (Keras word embedding, word2vec, and FastText) techniques. …”
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A Metaheuristic Approach to Detecting and Mitigating DDoS Attacks in Blockchain-Integrated Deep Learning Models for IoT Applications by Manal Alkhammash

“…Besides, the MHADMA-BCIDL technique employs an attention-based convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM-Attention) method for the detection and classification of attacks. …”
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Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset by Xuhui Song, Linyuan Fan

“…After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. …”
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Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning by Manish Sharma, Jamison Heard, Eli Saber, Panagiotis Markopoulos

“…To address this, we propose an efficient training method for CNN compression via dynamic parameter rank pruning. …”
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