Improvement of Classification Results of Convolutional Neural Networks Using Various Gan-Based Augmentation Techniques by Kališková Lenka, Butka Peter

“…In the presented work, we focus on image augmentation with the use of several variations of GAN to improve the classification of convolutional neural network. Accordingly, to prove the advantage of GAN-based image augmentation in comparison with methods of classical augmentation, we used specifically three different degrees of image rotation and compared classification results of convolutional neural network that use images from these augmentation methods. …”
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A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging by Mahir Kaya, Alper Özatılgan

“…Successful results in the detection of diseases from medical images with Convolutional Neural Networks (CNN) depend on the optimum creation of the number of layers and other hyper-parameters. …”
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Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network by Nandita Goyal, Munesh Chandra Trivedi

“…But the truth is that everything has a price and cloud computing is no different. With Cloud computing there comes a number of security concerns which need to be addressed. …”
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Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. …”
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Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes by P. J. Soto Vega, D. Lobo Torres, G. X. Andrade-Miranda, G. A. O. P. da Costa, R. Q. Feitosa

“…Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have become popular for change detection tasks, including the deforestation mapping application. …”
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Recognition of multi-symptomatic rice leaf blast in dual scenarios by using convolutional neural networks by Huiru Zhou, Dingzhou Cai, Lijie Lin, Dong Huang, Bo-Ming Wu

“…Firstly, the impact of different training methods on imbalanced datasets was compared. …”
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Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion by Canghong Shi, Xin Qiu, Min Wu, Xianhua Niu, Xiaojie Li, Sani M. Abdullahi

“…The DCKNN model consists of a combination of four convolutional groups, each with different sensitivities to the two audio categories. …”
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Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation by Matteo Piccardo, Emanuele Massaro, Luca Caporaso, Alessandro Cescatti, Grégory Duveiller

“…In response, we introduce the Convolutional Linear-Regression Model (CLRM), a minimal complexity approach that focuses on two key assumptions: (i) correlations between LST at different times and spatial resolutions are considered without additional variables, and (ii) these correlations are modelled using linear relationships. …”
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Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). …”
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Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs by Zhenghao Han, Li Li, Weiqi Jin, Xia Wang, Gangcheng Jiao, Hailin Wang

“…The color correction matrix model widely used in current commercial color digital cameras cannot handle the complicated mapping function between biased color and ground truth color. Convolutional neural networks (CNNs) are good at fitting such complicated relationships, but they require a large quantity of training image pairs of different scenes. …”
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Medical Image Retrieval Based on Ensemble Learning using Convolutional Neural Networks and Vision Transformers by Ahmed Yahya, Dalya Khaled, Waleed Al-Azzawi, Tawfeeq Alghazali, H. Sabah Jabr, R. Madhat Abdulla, M. Kadhim Abbas Al-Maeeni, N. Hussin Alwan, S. Saad Najeeb, Kh. T. Falih

“…Our proposed framework can be very effective in retrieving multimodal medical images with the images of different organs in the body.…”
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Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology by Zinon Zinonos, Socratis Gkelios, Ala F. Khalifeh, Diofantos G. Hadjimitsis, Yiannis S. Boutalis, Savvas A. Chatzichristofis

“…To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model fine-tuning. …”
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Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction by Jizhong Duan, Xinmin Ren

“…To accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT-Net), which combines the iterative self-consistent parallel imaging reconstruction model (SPIRiT) with the cascaded complex convolutional neural networks (CCNNs). More specifically, this model adopts the SPIRiT model for reconstruction in the k-space domain and the cascaded CCNNs with dense connection for reconstruction in the image domain. …”
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Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal by Zeliang AN, Tianqi ZHANG, Baoze MA, Pan DENG, Yuqing XU

“…To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.…”
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Design and implementation of piano audio automatic music transcription algorithm based on convolutional neural network by Mengshan Li

“…In this study, we adopt the cepstral coefficient derived from cochlear filters, a method commonly used in speech signal processing, for extracting features from transformed musical audio. Conventional convolutional neural networks often rely on a universally shared convolutional kernel when processing piano audio, but this approach fails to account for the variations in information across different frequency bands. …”
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A network traffic classification method based on random forest and improved convolutional neural network by Bensheng YUN, Xiaoya GAN, Yaguan QIAN

“…In order to improve the efficiency and reduce the complexity of network traffic classification model, a classification method based on random forest and improved convolutional neural network was proposed.Firstly, the random forest was used to evaluate the importance of each feature of network traffic, and the feature was selected according to the importance ranking.Secondly, AdamW optimizer and triangular cyclic learning rate were adopted to optimize the convolutional neural network classification model.Then, the model was built on Spark cluster to realize the parallelization of model training.Adopting triangular cyclic learning rate with constant cycle amplitude, the experimental results of selecting 1 024, 400, 256 and 100 most important features as input show that the model accuracy is improved to 97.68%, 95.84%, 95.03% and 94.22%, respectively.The 256 most important features were selected and the experimental results based on adopting different learning rates show that the learning rate with half the cycle amplitude works best, the accuracy of the model is improved to 95.25%, and training time of the model is reduced by nearly half.…”
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Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal by Zeliang AN, Tianqi ZHANG, Baoze MA, Pan DENG, Yuqing XU

“…To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed.With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals.Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones.Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy.Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than-2 dB.…”
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Adaptive convolutional neural network-based principal component analysis algorithm for the detection of manufacturing data by Tsun-Kuo Lin

“…Herein, an adaptive convolutional neural network (CNN)-based principal component analysis (PCA) algorithm for the detection of manufacturing data is proposed. …”
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Multi-modal physiological signal emotion recognition based on 3D hierarchical convolution fusion by Wenfen LING, Sihan CHEN, Yong PENG, Wanzeng KONG

“…In recent years, physiological signals such as electroencephalograhpy (EEG) have gradually become popular objects of emotion recognition research because they can objectively reflect true emotions.However, the single-modal EEG signal has the problem of incomplete emotional information representation, and the multi-modal physiological signal has the problem of insufficient emotional information interaction.Therefore, a 3D hierarchical convolutional fusion model was proposed, which aimed to fully explore multi-modal interaction relationships and more accurately describe emotional information.The method first extracted the primary emotional representation information of EEG , electro-oculogram (EOG) and electromyography (EMG) by depthwise separable convolution network, and then performed 3D convolution fusion operation on the obtained multi-modal primary emotional representation information to realize the pairwise mode local interactions between states and global interactions among all modalities, so as to obtain multi-modal fusion representations containing emotional characteristics of different physiological signals.The results show that the accuracy in the valence and arousal of the two-class and four-class tasks on DEAP dataset are both 98% by the proposed model.…”
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Movie Genre Classification Based on Poster and Subtitles Using Hybrid Combination of Convolutional Neural Networks by Yuxiang Zhang

“…Automatic genre detection in movies is an important and catchy topic that can be used in many applications and contexts by different industries, such as personal development systems, database management, content analysis systems, and marketing and advertising systems. …”
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