Retracted: Articulatory-to-Acoustic Conversion Using BiLSTM-CNN Word-Attention-Based Method by null Complexity

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Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP. by Sajid Mehmood, Rashid Amin, Jamal Mustafa, Mudassar Hussain, Faisal S Alsubaei, Muhammad D Zakaria

“…We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. …”
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Positioning of the Moving and Static Contacts of the Switch Machine Based on Double-Layer Mask R-CNN by Jiacheng Yin, Zhaomin Lv, Xingjie Chen, Kun Yang

“…Therefore, a positioning method for moving and static contact based on double-layer Mask R-CNN (DLM) is proposed in this paper: first, the moving contact is roughly positioned by Mask R-CNN to obtain the predicted target area; second, the subgraph of the target area is preprocessed; finally, the precise positioning is used to determine the precise position of the moving and static contact. …”
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An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification by Aijaz Ahmad Reshi, Furqan Rustam, Arif Mehmood, Abdulaziz Alhossan, Ziyad Alrabiah, Ajaz Ahmad, Hessa Alsuwailem, Gyu Sang Choi

“…The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. …”
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Evaluating CNN Architectures and Hyperparameter Tuning for Enhanced Lung Cancer Detection Using Transfer Learning by Mohd Munazzer Ansari, Shailendra Kumar, Umair Tariq, Md Belal Bin Heyat, Faijan Akhtar, Mohd Ammar Bin Hayat, Eram Sayeed, Saba Parveen, Dustin Pomary

“…This study evaluates the performance of six convolutional neural network (CNN) architectures, ResNet-50, VGG-16, ResNet-101, VGG-19, DenseNet-201, and EfficientNet-B4, using the LIDC-IDRI dataset. …”
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Faster R-CNN and 3D reconstruction for handling tasks implementing a Scara robot by Julian Herrera-Benavidez, Cesar Pachón-Suescún, Robinson Jimenez-Moreno

Subjects: “…faster r-cnn…”
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Dense Nursery Stock Detecting and Counting Based on UAV Aerial Images and Improved LSC-CNN by PENG Xiaodan, CHEN Fengjun, ZHU Xueyan, CAI Jiawei, GU Mengmeng

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Automated delamination detection in concrete bridge decks using 1D-CNN and GPR data by Ahmed Elseicy, Mercedes Solla, Henrique Lorenzo

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Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance by Yankun WANG, Dengke GUO, Dongtang MA, Jun XIONG, Xiaoying ZHANG

“…To address the problems of poor robustness and high complexity of current physical layer user authentication algorithms, a lightweight convolutional neural network (CNN) channel feature extraction algorithm was proposed to reduce the channel state response required for training by changing the form of network input, and a multi-user physical layer channel feature-assisted authentication mechanism was established based on this algorithm to design a detailed process from user registration to authentication, and multi-user authentication and network parameter update online were completed.Simulation results show that the proposed algorithm can complete multi-user authentication, obtain good detection performance with smaller training rounds, and require fewer training samples than existing multi-user authentication algorithms.…”
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Combining Convolutional Neural Network (CNN) and Grad-CAM for Parkinson’s Disease Prediction and Visual Explanation by Reyhaneh Dehghan, Marjan Naderan, Seyed Enayatallah Alavi

Subjects: “…convolutional neural network (cnn)…”
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An overview of skin cancer classification based on deep learning by Toqa A. Sadoon

Subjects: “…cnn…”
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Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection by Eca Indah Anggraini, Fachdy Nurdin, Mohammad Obie Restianto, Sudarti Dahsan, Andini Aprilia Ardhana, Asep Adang Supriyadi, Yahya Darmawan, Syachrul Arief, Agus Haryanto Ikhsanudin

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A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification by Arafat Rahman, Nazmun Nahid, Björn Schuller, Md Atiqur Rahman Ahad

“…After intermediate and final feature extraction, we use an ensemble of a random forest classifier and a stacked convolutional neural network (S-CNN) model to detect activities and users. Unlike traditional CNN, the S-CNN takes the input feature channels in separate pathways with equal importance, which makes it robust to intra-class variation and produces accurate results. …”
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Employing the concept of stacking ensemble learning to generate deep dream images using multiple CNN variants by Lafta Alkhazraji, Ayad R. Abbas, Abeer S. Jamil, Zahraa Saddi Kadhim, Wissam Alkhazraji, Sabah Abdulazeez Jebur, Bassam Noori Shaker, Mohammed Abdallazez Mohammed, Mohanad A. Mohammed, Basim Mohammed Al-Araji, Abdulkareem Z. Mohmmed, Wasiq Khan, Bilal Khan, Abir Jaafar Hussain

“…For model development, a series of five pre-trained Convolutional Neural Network (CNN) architectures—VGG-19, Inception v3, VGG-16, Inception-ResNet-V2, and Xception were stacked in an ensemble learning approach to create Deep Dream images whereby the upper hidden layers of the architectures were activated, and the models were trained via the Adam optimizer. …”
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Plant Disease Classifier: Detection of Dual-Crop Diseases Using Lightweight 2D CNN Architecture by Hasibul Islam Peyal, Md. Nahiduzzaman, Md. Abu Hanif Pramanik, Md. Khalid Syfullah, Saleh Mohammed Shahriar, Abida Sultana, Mominul Ahsan, Julfikar Haider, Amith Khandakar, Muhammad E. H. Chowdhury

Subjects: “…Convolution neural network (CNN)…”
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Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT by Lal Khan, Atika Qazi, Hsien-Tsung Chang, Mousa Alhajlah, Awais Mahmood

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Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration by Salman Saeidlou, Nikdokht Ghadiminia, Kwadwo Oti-Sarpong

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Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging by Jagamohan Padhi, Kunal Mishra, Ashoka Kumar Ratha, Santi Kumari Behera, Prabira Kumar Sethy, Aziz Nanthaamornphong

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Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection by Sofia Biju Francis, Sofia Biju Francis, Jai Prakash Verma

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Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example by Xie Renjun, Yuan Junliang, Wu Yi, Shu Mengcheng

“…ResNet-50 mainly solves the problem of network degradation and overfitting caused by deepening of the network layer when extracting the deep features of faults. Faster R-CNN realizes end-to-end training, combines the advantages of ResNet-50 and Faster R-CNN, and has a precise positioning efficiency. …”
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