Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition by Xi Cheng

“…To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. This method uses a selective search algorithm to obtain the candidate images of the sample images. …”
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Corrigendum: Abnormal respiratory sounds classification using deep CNN through artificial noise addition by Rizwana Zulfiqar, Fiaz Majeed, Rizwana Irfan, Hafiz Tayyab Rauf, Elhadj Benkhelifa, Abdelkader Nasreddine Belkacem

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STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN by P. Kravchenko, I. Burlachenko

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Corrigendum to “1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA” by Wenguang Hou, Shaojie Mei, Qiuling Gui, Yingcheng Zou, Yifan Wang, Xianbo Deng, Qimin Cheng

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Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

“…In addition, a Convolutional Neural Network (CNN) regression model was constructed and optimized with the Northern Goshawk Optimization (NGO) algorithm, resulting in a more precise CNN-NGO prediction model. …”
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Plant Leaf Identification Using Feature Fusion of Wavelet Scattering Network and CNN With PCA Classifier by S. Gowthaman, Abhishek Das

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Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study by Li‐peng Xing, Gang Liu, Hao‐chen Zhang, Lei Wang, Shan Zhu, Man Du La Hua Bao, Yan‐ni Wang, Chao Chen, Zhi Wang, Xin‐yu Liu, Shuai Zhang, Qiang Yang

“…This study developed and investigated the performance of convolutional neural network (CNN) in detecting and grading MCs based on their maximum vertical extent. …”
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How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting by Zinnet Duygu Aksehir, Erdal Kilic

Subjects: “…CNN model…”
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A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system by R. Shelishiyah, Deepa Beeta Thiyam, M. Jehosheba Margaret, N. M. Masoodhu Banu

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Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection by Istiak Mahmud, Md. Mohsin Kabir, Jungpil Shin, Chayan Mistry, Yoichi Tomioka, M. F. Mridha

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A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution. by Sheng Miao, Guoqing Ni, Guangze Kong, Xiuhe Yuan, Chao Liu, Xiang Shen, Weijun Gao

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A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification by Nagarjun Malagol, Tanuj Rao, Anna Werner, Reinhard Töpfer, Ludger Hausmann

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Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model by Weilong Yu, Shuaibing Li, Hao Zhang, Yongqiang Kang, Hongwei Li, Haiying Dong

“…To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term memory, and an attention mechanism network. …”
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Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings by Biplov Paneru, Bipul Thapa, Bishwash Paneru

Subjects: “…Convolutional neural network (CNN)…”
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WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM by JING DongXing, CHEN YangHui, QUAN Zhe

“…Aiming at the subtle fault features of the wind turbines rolling bearing, the fault signal is nonlinear, non-stationary and contains noise interference, and the fault signal has the characteristics of space and time feature information, a space-time fusion convolutional shared weight long short-term memory network (CSwLSTM) model based on one-dimensional convolutional neural network (1D-CNN) and the shared weight long short-term memory network (SWLSTM) was proposed for wind turbine rolling bearing fault diagnosis. …”
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Comparative Analysis of Federated and Centralized Learning Systems in Predicting Cellular Downlink Throughput Using CNN by Kukuh Nugroho, Hendrawan, Iskandar

“…The experimental results indicate that the CNN model implemented in FL outperforms both CL and the other models. …”
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Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data by Zhu Zhimeng

“…A systematic comparative analysis of VGG19-BMT and traditional CNN models was conducted using the Kaggle dataset “Brain MRI Images for Brain Tumor Detection.” …”
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Computing native network (CNN) technology and application driven by joint communication ＆ computing (JC＆C) by Jie SUN, Leiming MA, Aidong YANG, Xiaozhou YE, Peng LYU, Zhigang WANG, Ye OUYANG

“…The global telecom industry is carrying out research and exploration of potential 6G technical architectures and key techs, of which it has become a consensus in the industry to further realize the vision of 6G smart inclusion by better enabling all new 6G network technologies and services through joint communication ＆amp; computing (JC＆amp;C).Therefore, how to realize the deep integration and real-time collaboration of communication and computing to meet the needs of ultra-low latency and real-time response of new services in dynamic and complex network environments has become the challenge and key of JC＆amp;C.A computing native network (CNN) driven by JC＆amp;C was proposed, which realized the decoupling of communication and computing of base station or multiple base stations, computing pooling and intelligent JC＆amp;C scheduling through computing time division multiplexing and intelligent JC＆amp;C scheduling, thus realizing the base station to support communication and computation services simultaneously.The technology has been technically verified and piloted, and the experimental and pilot results demonstrate that CNN can effectively improve the computing utilization rate of wireless networks and realize the base station to support communication and computing services simultaneously, facilitating the evolution of 6G JC＆amp;C.…”
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A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection by Xiaoshuai Cao, Shaojie Zheng, Jincan Zhang, Wenna Chen, Ganqin Du

“…Finally, seizure states are classified using Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM). Results The method was rigorously validated on the Bonn and New Delhi datasets. …”
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A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism by Qingbo Hua, Zengliang Fan, Wei Mu, Jiqiang Cui, Rongxin Xing, Huabo Liu, Junwei Gao

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