Evaluation Method of Music Teaching Effect Based on Fusion of Deep Neural Network under the Background of Big Data by Yifan Fan

“…Hybrid CNN-LSTM with LSTM neural network has higher accuracy in predicting music teaching effect than single neural network technique. …”
Get full text

Uji Parameter dan Arsitektur Convolutional Neural Network untuk Mendeteksi Citra Wajah Bermasker by Dewi Novita Sari, Muh. Arif Rahman, Randy Cahya Wihandika

“…Therefore, this study aims to know the relationship between parameters in the CNN model architecture so that the best performance can be produced in detecting masked face images. …”
Get full text

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum by Guangliang Pan, Jun Li, Fei Lin

“…Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. …”
Get full text

Deep convolutional neural networks for double compressed AMR audio detection by Aykut Büker, Cemal Hanilçi

“…Here, the authors propose to use convolutional neural networks (CNN) for DC AMR audio detection. The CNN is used as (i) an end‐to‐end DC AMR audio detection system and (ii) a feature extractor. …”
Get full text

Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie by Kaiwei Liang, Na Qin, Deqing Huang, Yuanzhe Fu

“…In this paper, by combining CNN and RNN, a so-called convolutional recurrent neural network (CRNN) is proposed to diagnose various faults of the HST bogie, where the capabilities of CNN and RNN are inherited simultaneously. …”
Get full text

Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints by Jun Fukae, Yoshiharu Amasaki, Yuichiro Fujieda, Yuki Sone, Ken Katagishi, Tatsunori Horie, Tamotsu Kamishima, Tatsuya Atsumi

“…Objective To study the classification performance of a pre-trained convolutional neural network (CNN) with transfer learning by artificial joint ultrasonography images in rheumatoid arthritis (RA). …”
Get full text

Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning by Lin Zhou

“…Mixup is used to generate virtual data, the original data and virtual data are used to train the network together, the number of training samples is expanded, the overfitting phenomenon of 3D-CNN is alleviated, and 3D-CNN is used for feature extraction and classification. …”
Get full text

Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images by Soichiro Kuwayama, Yuji Ayatsuka, Daisuke Yanagisono, Takaki Uta, Hideaki Usui, Aki Kato, Noriaki Takase, Yuichiro Ogura, Tsutomu Yasukawa

“…Some of rare diseases such as Vogt–Koyanagi–Harada disease were correctly detected by image augmentation in the CNN training. Conclusion. Automated detection of macular diseases from OCT images might be feasible using the CNN model. …”
Get full text

Optimizing Image Feature Extraction with Convolutional Neural Networks for Chicken Meat Detection Applications by Azeddine Mjahad, Antonio Polo-Aguado, Luis Llorens-Serrano, Alfredo Rosado-Muñoz

“…In the first phase, the original images were used without applying traditional filters or color modifications, processing them solely with a CNN. In the second phase, color filters were applied to help separate the images based on their chromatic characteristics, while still using a CNN for processing. …”
Get full text

A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection by Wasswa Shafik, Ali Tufail, Chandratilak Liyanage De Silva, Rosyzie Anna Awg Haji Mohd Apong

“…In contrast to ordinary CNN architectures, it extends the network for better feature extraction, improving PDDC performance that demands diverse feature competencies. …”
Get full text

Impact of lens autofluorescence and opacification on retinal imaging by Frank G Holz, Maximilian Pfau, Monika Fleckenstein, Raffael Liegl, Geena C Rennen, Marc Vaisband, Jan Hasenauer

“…CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). …”
Get full text

Deep patch‐wise supervision for presentation attack detection by Alperen Kantarcı, Hasan Dertli, Hazım Kemal Ekenel

“…In order to detect these attacks, convolutional neural networks (CNN)‐based systems have gained significant popularity recently. …”
Get full text

Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. Linear and non-linear correlations demonstrated that the CNN model correlates well with experimental data on dynamic flux (R2–1.00). …”
Get full text

Early Warning and Management Method of Abnormal Performance of Tourist Scenic Spots Assisted by Image Recognition Technology by Zhaozhen Song, Jing Lu

“…CNN also has good accuracy in predicting related features such as the flow of people in scenic spots. …”
Get full text

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy by Qi Wan, Clifford Lindsay, Chenxi Zhang, Jisoo Kim, Xin Chen, Jing Li, Raymond Y. Huang, David A. Reardon, Geoffrey S. Young, Lei Qin

“…Tumor segmentation was performed by expert radiologists and a convolutional neural network (CNN). From the segmented tumors, 2553 radiomic features were extracted for each case. …”
Get full text

Correlation-guided decoding strategy for low-resource Uyghur scene text recognition by Miaomiao Xu, Jiang Zhang, Lianghui Xu, Wushour Silamu, Yanbing Li

“…Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. …”
Get full text

Sistem Kontrol Perangkat Inframerah Menggunakan Speech Recognition dengan Spectrogram dan Convolutional Neural Network Berbasis Mikrokontroler by Irfan Muzakky Nurrizqy, Barlian Henryranu Prasetio, Rekyan Regasari Mardi Putri

“…Hasil pengujian menunjukkan bahwa akurasi model CNN sebesar 93% dan akurasi percobaan terhadap pengguna sebesar 74,25%. …”
Get full text

Detection of Mild Moldy-Core Disease in Apples by Fusing Acoustic-Vibration Signals and Visible-Near-Infrared Transmission Spectroscopy by GU Jiahui, LAI Lisi, WANG Kai, ZHANG Hui

“…Convolutional neural networks (CNN), long short-term memory (LSTM), and CNN-LSTM were employed to construct discrimination models based on single and fused features, separately. …”
Get full text

Targeted Advertising in Social Media Platforms Using Hybrid Convolutional Learning Method besides Efficient Feature Weights by Seyed Mohsen Ebadi Jokandan, Peyman Bayat, Mehdi Farrokhbakht Foumani

“…To predict the user engagement rate, we extract the significant attributes of posts and introduce an adaptive hybrid convolutional model based on FW-CNN-LSTM. We cluster the selected data based on the weight and significance of their attributes using the FCM and XGBoost algorithms and then apply CNN- and LSTM-based methods to select similar features. …”
Get full text

Predicting and synthesizing terahertz spoof surface plasmon polariton devices with a convolutional neural network model by Vahid Najafy, Bijan Abbasi-Arand, Maryam Hesari-Shermeh

“…Three examples are provided for inversely designing several sensor devices and absorbers in the terahertz band using the proposed CNN and the genetic optimization algorithm.…”
Get full text