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

“…However, not enough attention has been paid to the domain shift issue, which affects classification performance when pre-trained models are used in areas with different forest covers and deforestation practices. …”
Get full text

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. …”
Get full text

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

“…We solve the problem of different sensitivities by sequentially lowering the parameters of the convolutional layers in the four convolutional groups, thus obtaining high accuracy in audio classification. …”
Get full text

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. …”
Get full text

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

“…Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

Improved Complex Convolutional Neural Network Based on SPIRiT and Dense Connection for Parallel MRI Reconstruction by Jizhong Duan, Xinmin Ren

“…The experimental results on two clinical knee datasets as well as the fastMRI brain dataset under different undersampling patterns show that the SPIRiT-Net model achieves better reconstruction performance in terms of visual effect, peak signal-to-noise ratio, and structural similarity over SPIRiT, Deepcomplex, and DONet. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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.…”
Get full text

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.…”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. by Mehran Ghafari, Justin Clark, Hao-Bo Guo, Ruofan Yu, Yu Sun, Weiwei Dang, Hong Qin

“…Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. …”
Get full text

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks by Skowron Maciej

“…The technique used was based on the use of a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.…”
Get full text

Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 by Liping Bai, Chenglei Xia, Fei Liu, Xing Yang, Tai Zhang

“…The proposed model demonstrated superior accuracy in identifying strawberries of different ripeness levels. The improvements in the proposed model indicate its effectiveness in strawberry recognition tasks, providing more accurate results in varying environmental conditions. …”
Get full text

Combining Convolutional Neural Network (CNN) and Grad-CAM for Parkinson’s Disease Prediction and Visual Explanation by Reyhaneh Dehghan, Marjan Naderan, Seyed Enayatallah Alavi

“…To evaluate the proposed method, different measures such as accuracy, sensitivity (recall) and f1-score are used. …”
Get full text