Статті в журналах: "CNN MODEL"

1

Prasad, G. Shyam Chandra, and K. Adi Narayana Reddy. "Sentiment Analysis Using Multi-Channel CNN-LSTM Model." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 489–94. http://dx.doi.org/10.5373/jardcs/v11sp12/20193243.

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2

Hasan, Moh Arie, Yan Riyanto, and Dwiza Riana. "Grape leaf image disease classification using CNN-VGG16 model." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (July 5, 2021): 218–23. http://dx.doi.org/10.14710/jtsiskom.2021.14013.

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This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and the classification uses CNN. The dataset is from Kaggle of 4000 grape leaf images for four classes: leaves with black measles, leaf spot, healthy leaf, and blight. Google images of 100 pieces were also used as test data outside the dataset. The accuracy of the CNN model training is 99.50 %. The classification yields an accuracy of 97.25 % using the test data, while using test image data outside the dataset obtains an accuracy of 95 %. The designed image processing method can be applied to identify and classify disease images on grape leaves.

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3

Choi, Jiwoo, Sangil Choi, and Taewon Kang. "Personal Identification CNN Model using Gait Cycle." Journal of Korean Institute of Information Technology 20, no. 11 (November 30, 2022): 127–36. http://dx.doi.org/10.14801/jkiit.2022.20.11.127.

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4

Sen, Amit Prakash, Nirmal Kumar Rout, Tuhinansu Pradhan, and Amrit Mukherjee. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal Of Science And Technology 15, no. 41 (November 5, 2022): 2121–28. http://dx.doi.org/10.17485/ijst/v15i41.1421.

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5

Vyshnavi, Ramineni, and Goo-Rak Kwon. "A Comparative Study of the CNN Model for AD Diagnosis." Korean Institute of Smart Media 12, no. 7 (August 31, 2023): 52–58. http://dx.doi.org/10.30693/smj.2023.12.7.52.

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Alzheimer’s disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

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6

Tajalsir, Mohammed, Susana Mu˜noz Hern´andez, and Fatima Abdalbagi Mohammed. "ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Model." Signal & Image Processing : An International Journal 13, no. 1 (February 28, 2022): 45–53. http://dx.doi.org/10.5121/sipij.2022.13104.

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When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy.

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7

Et. al., Ms K. N. Rode,. "Unsupervised CNN model for Sclerosis Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2577–83. http://dx.doi.org/10.17762/turcomat.v12i2.2223.

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Sclerosis detection using brain magnetic resonant imaging (MRI) im-ages is challenging task. With the promising results for variety of ap-plications in terms of classification accuracy using of deep neural net-work models, one can use such models for sclerosis detection. The fea-tures associated with sclerosis is important factor which is highlighted with contrast lesion in brain MRI images. The sclerosis classification initially can be considered as binary task in which the sclerosis seg-mentation can be avoided for reduced complexity of the model. The sclerosis lesion show considerable impact on the features extracted us-ing convolution process in convolution neural network models. The images are used to train the convolutional neural network composed of 35 layers for the classification of sclerosis and normal images of brain MRI. The 35 layers are composed of combination of convolutional lay-ers, Maxpooling layers and Upscaling layers. The results are com-pared with VGG16 model and results are found satisfactory and about 92% accuracy is seen for validation set.

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8

Kamundala, Espoir K., and Chang Hoon Kim. "CNN Model to Classify Malware Using Image Feature." KIISE Transactions on Computing Practices 24, no. 5 (May 31, 2018): 256–61. http://dx.doi.org/10.5626/ktcp.2018.24.5.256.

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9

Lee, Seonggu, and Jitae Shin. "Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter." International Journal of Information and Electronics Engineering 9, no. 1 (March 2019): 34–38. http://dx.doi.org/10.18178/ijiee.2019.9.1.701.

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10

Srinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.

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11

Wang, Jinnan, Weiqin Tong, and Xiaoli Zhi. "Model Parallelism Optimization for CNN FPGA Accelerator." Algorithms 16, no. 2 (February 14, 2023): 110. http://dx.doi.org/10.3390/a16020110.

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Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. Model parallelism is an effective way to reduce resource usage by distributing CNN inference among several devices. However, parallelizing a CNN model is not easy, because CNN models have an essentially tightly-coupled structure. In this work, we propose a novel model parallelism method to decouple the CNN structure with group convolution and a new channel shuffle procedure. Our method could eliminate inter-device synchronization while reducing the memory footprint of each device. Using the proposed model parallelism method, we designed a parallel FPGA accelerator for the classic CNN model ShuffleNet. This accelerator was further optimized with features such as aggregate read and kernel vectorization to fully exploit the hardware-level parallelism of the FPGA. We conducted experiments with ShuffleNet on two FPGA boards, each of which had an Intel Arria 10 GX1150 and 16GB DDR3 memory. The experimental results showed that when using two devices, ShuffleNet achieved a 1.42× speed increase and reduced its memory footprint by 34%, as compared to its non-parallel counterpart, while maintaining accuracy.

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12

Dal Cortivo, Davide, Sara Mandelli, Paolo Bestagini, and Stefano Tubaro. "CNN-Based Multi-Modal Camera Model Identification on Video Sequences." Journal of Imaging 7, no. 8 (August 5, 2021): 135. http://dx.doi.org/10.3390/jimaging7080135.

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Identifying the source camera of images and videos has gained significant importance in multimedia forensics. It allows tracing back data to their creator, thus enabling to solve copyright infringement cases and expose the authors of hideous crimes. In this paper, we focus on the problem of camera model identification for video sequences, that is, given a video under analysis, detecting the camera model used for its acquisition. To this purpose, we develop two different CNN-based camera model identification methods, working in a novel multi-modal scenario. Differently from mono-modal methods, which use only the visual or audio information from the investigated video to tackle the identification task, the proposed multi-modal methods jointly exploit audio and visual information. We test our proposed methodologies on the well-known Vision dataset, which collects almost 2000 video sequences belonging to different devices. Experiments are performed, considering native videos directly acquired by their acquisition devices and videos uploaded on social media platforms, such as YouTube and WhatsApp. The achieved results show that the proposed multi-modal approaches significantly outperform their mono-modal counterparts, representing a valuable strategy for the tackled problem and opening future research to even more challenging scenarios.

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13

Mukkapati, Naveen, and M. S. Anbarasi. "Brain Tumor Classification Based on Enhanced CNN Model." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 125–30. http://dx.doi.org/10.18280/ria.360114.

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Brain tumor classification is important process for doctors to plan the treatment for patients based on the stages. Various CNN based architecture is applied for the brain tumor classification to improve the classification performance. Existing methods in brain tumor segmentation have the limitations of overfitting and lower efficiency in handling large dataset. In this research, for brain tumor segmentation purpose the enhanced CNN architecture based on U-Net, for pattern analysis purpose RefineNet and for classifying brain tumor purpose SegNet architecture is proposed. The brain tumor benchmark dataset was used to analysis the efficiency of the enhanced CNN model. The U-Net provides good segmentation based on the local and context information of MRI image. The SegNet selects the important features for classification and also reduces the trainable parameters. When compared with the existing methods of brain tumor classification, the enhanced CNN method has the higher performance. The enhanced CNN model has the accuracy of 96.85% and existing CNN with transfer learning has 94.82% accuracy.

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14

Zhan, Zhiwei, Guoliang Liao, Xiang Ren, Guangsi Xiong, Weilin Zhou, Wenchao Jiang, and Hong Xiao. "RA-CNN." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–14. http://dx.doi.org/10.4018/ijssci.311446.

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Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environment. It integrates CNN for local feature extraction, RNN for global feature extraction, and attention mechanism for feature scaling. As a result, it can acquire the correct meaning of sentences. After experimenting with the hotel review dataset, it has an improvement in positive feeling classification compared with the baseline model (3%~13%), and it showed a competitive performance compared with ordinary deep learning models (~1%). On negative feeling classification, it also performed well close to other models.

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15

Zhang, Jilin, Lishi Ye, and Yongzeng Lai. "Stock Price Prediction Using CNN-BiLSTM-Attention Model." Mathematics 11, no. 9 (April 23, 2023): 1985. http://dx.doi.org/10.3390/math11091985.

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Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a CNN-BiLSTM-Attention-based model is proposed to boost the accuracy of predicting stock prices and indices. First, the temporal features of sequence data are extracted using a convolutional neural network (CNN) and bi-directional long and short-term memory (BiLSTM) network. Then, an attention mechanism is introduced to fit weight assignments to the information features automatically; and finally, the final prediction results are output through the dense layer. The proposed method was first used to predict the price of the Chinese stock index—the CSI300 index and was found to be more accurate than any of the other three methods—LSTM, CNN-LSTM, CNN-LSTM-Attention. In order to investigate whether the proposed model is robustly effective in predicting stock indices, three other stock indices in China and eight international stock indices were selected to test, and the robust effectiveness of the CNN-BiLSTM-Attention model in predicting stock prices was confirmed. Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases.

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16

Slavova, Angela, and Ronald Tetzlaff. "Edge of chaos in reaction diffusion CNN model." Open Mathematics 15, no. 1 (February 2, 2017): 21–29. http://dx.doi.org/10.1515/math-2017-0002.

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Abstract In this paper, we study the dynamics of a reaction-diffusion Cellular Nonlinear Network (RD-CNN) nodel in which the reaction term is represented by Brusselator cell. We investigate the RD-CNN dynamics by means of describing function method. Comparison with classical results for Brusselator equation is provided. Then we introduce a new RD-CNN model with memristor coupling, for which the edge of chaos regime in the parameter space is determined. Numerical simulations are presented for obtaining dynamic patterns in the RD-CNN model with memristor coupling.

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17

Zhao, Xinzhuo, Shouliang Qi, Baihua Zhang, He Ma, Wei Qian, Yudong Yao, and Jianjun Sun. "Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning." Journal of X-Ray Science and Technology 27, no. 4 (September 4, 2019): 615–29. http://dx.doi.org/10.3233/xst-180490.

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18

Yin, Qiwei, Ruixun Zhang, and XiuLi Shao. "CNN and RNN mixed model for image classification." MATEC Web of Conferences 277 (2019): 02001. http://dx.doi.org/10.1051/matecconf/201927702001.

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In this paper, we propose a CNN(Convolutional neural networks) and RNN(recurrent neural networks) mixed model for image classification, the proposed network, called CNN-RNN model. Image data can be viewed as two-dimensional wave data, and convolution calculation is a filtering process. It can filter non-critical band information in an image, leaving behind important features of image information. The CNN-RNN model can use the RNN to Calculate the Dependency and Continuity Features of the Intermediate Layer Output of the CNN Model, connect the characteristics of these middle tiers to the final full-connection network for classification prediction, which will result in better classification accuracy. At the same time, in order to satisfy the restriction of the length of the input sequence by the RNN model and prevent the gradient explosion or gradient disappearing in the network, this paper combines the wavelet transform (WT) method in the Fourier transform to filter the input data. We will test the proposed CNN-RNN model on a widely-used datasets CIFAR-10. The results prove the proposed method has a better classification effect than the original CNN network, and that further investigation is needed.

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19

Jeong, Jaemin, Ji-Ho Cho, and Jeong-Gun Lee. "Filter combination learning for CNN model compression." ICT Express 7, no. 1 (March 2021): 5–9. http://dx.doi.org/10.1016/j.icte.2021.01.001.

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20

Athavale, Vijay Anant, Suresh Chand Gupta, Deepak Kumar, and Savita. "Human Action Recognition Using CNN-SVM Model." Advances in Science and Technology 105 (April 2021): 282–90. http://dx.doi.org/10.4028/www.scientific.net/ast.105.282.

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In this paper, a pre-trained CNN model VGG16 with the SVM classifier is presented for the HAR task. The deep features are learned via the VGG16 pre-trained CNN model. The VGG 16 network is previously used for the image classification task. We used VGG16 for the signal classification of human activity, which is recorded by the accelerometer sensor of the mobile phone. The UniMiB dataset contains the 11771 samples of the daily life activity of humans. A Smartphone records these samples through the accelerometer sensor. The features are learned via the fifth max-pooling layer of the VGG16 CNN model and feed to the SVM classifier. The SVM classifier replaced the fully connected layer of the VGG16 model. The proposed VGG16-SVM model achieves effective and efficient results. The proposed method of VGG16-SVM is compared with the previously used schemes. The classification accuracy and F-Score are the evaluation parameters, and the proposed method provided 79.55% accuracy and 71.63% F-Score.

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21

Imane, Kadi, Messaoud Abbas, Amara Miloudi, and Mohammed Charaf Eddine Meftah. "A CNN Model for Early Leukemia Diagnosis." International Journal of Organizational and Collective Intelligence 12, no. 1 (January 1, 2022): 1–20. http://dx.doi.org/10.4018/ijoci.304889.

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Blood cancer (leukemia) is one of the most serious diseases that affect blood-forming tissues. It usually involves white blood cells. The early detection of this severe disease helps doctors to provide efficient treatment. However, the discovery of this sickness at its first stages is often not easy due to similar morphological characteristics of malignant and healthy blood cells. Flow cytometry was the only used technique for early detection of leukemia, but it is very expensive and usually unavailable in hospitals. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans learn by examples. In the last few years, deep learning has achieved great successes to solve concrete problems. In particular, it has proven successful in medical imaging classification. In this work, we propose a Convolutional Neural Network (CNN) experiment for the classification of malignant white blood cells from normal ones using a dataset of microscopic images. The proposed approach leads to a balanced model that reaches a high level accuracy.

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22

Arun Kumar, A., and Radha Krishna Karne. "IIoT-IDS Network using Inception CNN Model." Journal of Trends in Computer Science and Smart Technology 4, no. 3 (August 18, 2022): 126–38. http://dx.doi.org/10.36548/jtcsst.2022.3.002.

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Modern network and Industrial Internet of Things (IIoT) technologies are quite advanced. Networks experience data breaches annually. As a result, an Intrusion Detection System is designed for enhancing the IIoT security protection under privacy laws. The Internet of Things' structural system and security performance criteria must meet high standards in an adversarial network. The network system must use a system that is very stable and has a low rate of data loss. The basic deep learning network technology is picked after analysing it with a huge number of other network configurations. Further, the network is upgraded and optimised by the Convolutional Neural Network technique. Additionally, an IIoT anti-intrusion detection system is built by combining three network technologies. The system's performance is evaluated and confirmed. The proposed model gives a better detection rate with a minimum false positive rate, and good data correctness. As a result, the proposed method can be used for securing an IIoT data privacy under the law.

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23

Wang, Zhong, and Tong Li. "A Lightweight CNN Model Based on GhostNet." Computational Intelligence and Neuroscience 2022 (July 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/8396550.

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The existing deep learning models have problems such as large weight parameters and slow inference speed of equipment. In practical applications such as fire detection, they often cannot be deployed on equipment with limited resources due to the huge amount of parameters and low efficiency. In response to this problem, this paper proposes a lightweight smoke detection model based on the convolutional attention mechanism module. The model is based on the YOLOv5 lightweight framework. The backbone network draws on the GhostNet design idea, replaces the CSP structure of the FPN and head layers with the GhostBottleNeck module, adds a convolutional attention mechanism module to the backbone network layer, and uses the CIoU loss function to improve the regression accuracy. Using YOLOv5s as the benchmark model, the parameter amount of the proposed lightweight neural network model is 2.75 M, and the floating-point calculation amount is 2.56 G, which is much lower than the parameter amount and calculation amount of the benchmark model. Tested on the public fire dataset, compared with the traditional deep learning algorithm, the model proposed in the paper has better detection performance and the detection speed is significantly better than the benchmark model. Tested under the unquantized simulator, the speed of the proposed model to detect a single picture is 60 ms, which can meet the requirements of real-time engineering applications.

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V, Akshatha, and K. R. Sumana. "Analysis of Wildfire Detection using CNN Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 241–45. http://dx.doi.org/10.22214/ijraset.2022.46157.

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Abstract: Forest is viewed as one of the most significant and essential asset. It is an enormous surface of region loaded up with trees, bunches of dried leaves, woods, etc. These components support the fire when it begins. The fire can be lighted through many reasons, for example, high temperature in summer seasons, smoking, or firecrackers. When fire begins, it will stay until it recognized totally. The harm and the expense for recognize firein view of wild fire can be decreased when the fire distinguished right on time as could be expected. Thus, the fire discovery is significant in this situation. There are various kinds of fire location techniques utilized by the Government specialists, for example, satellite observing, tower checking, utilizing sensors, optical cameras, etc. In any case, these strategies actually have a few disadvantages in distinguishing the beginning phase of the fire. In our project, we propose an original framework for distinguishing wildfire utilizing Convolutional Neural Networks (CNN). CNN and a classification network, named ResNet50 is used as a feature extraction network to achieve rapid and accurate extraction of image feature information.

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25

Singh, Ajay Kumar, Ihtiram Raza Khan, Shakir Khan, Kumud Pant, Sandip Debnath, and Shahajan Miah. "Multichannel CNN Model for Biomedical Entity Reorganization." BioMed Research International 2022 (March 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/5765629.

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Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

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Kaur, Gagandeep, Ritesh Sinha, Puneet Kumar Tiwari, Srijan Kumar Yadav, Prabhash Pandey, Rohit Raj, Anshu Vashisth, and Manik Rakhra. "Face mask recognition system using CNN model." Neuroscience Informatics 2, no. 3 (September 2022): 100035. http://dx.doi.org/10.1016/j.neuri.2021.100035.

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27

Yang, Yu-Xin, Chang Wen, Kai Xie, Fang-Qing Wen, Guan-Qun Sheng, and Xin-Gong Tang. "Face Recognition Using the SR-CNN Model." Sensors 18, no. 12 (December 3, 2018): 4237. http://dx.doi.org/10.3390/s18124237.

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In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

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28

Al-Hammadi, Muneer, Ghulam Muhammad, Wadood Abdul, Mansour Alsulaiman, and M. Shamim Hossain. "Hand Gesture Recognition Using 3D-CNN Model." IEEE Consumer Electronics Magazine 9, no. 1 (January 1, 2020): 95–101. http://dx.doi.org/10.1109/mce.2019.2941464.

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29

Hemmat, Maedeh, and Azadeh Davoodi. "Power-efficient ReRAM-aware CNN model generation." Integration 69 (November 2019): 369–80. http://dx.doi.org/10.1016/j.vlsi.2019.08.003.

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30

Cozzolino, Davide, and Luisa Verdoliva. "Noiseprint: A CNN-Based Camera Model Fingerprint." IEEE Transactions on Information Forensics and Security 15 (2020): 144–59. http://dx.doi.org/10.1109/tifs.2019.2916364.

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Abdulnabi, Abrar H., Gang Wang, Jiwen Lu, and Kui Jia. "Multi-Task CNN Model for Attribute Prediction." IEEE Transactions on Multimedia 17, no. 11 (November 2015): 1949–59. http://dx.doi.org/10.1109/tmm.2015.2477680.

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32

Lei, Xinyu, Hongguang Pan, and Xiangdong Huang. "A Dilated CNN Model for Image Classification." IEEE Access 7 (2019): 124087–95. http://dx.doi.org/10.1109/access.2019.2927169.

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33

Espejo, S., R. Carmona, R. Domínguez-Castro, and A. Rodríguez-Vázquez. "A VLSI-oriented continuous-time CNN model." International Journal of Circuit Theory and Applications 24, no. 3 (May 1996): 341–56. http://dx.doi.org/10.1002/(sici)1097-007x(199605/06)24:3<341::aid-cta920>3.0.co;2-l.

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34

Guo, Kejun, Shizhe Song, and Qijia Yang. "CNN-based Model for Face Expression Recognition." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 269–74. http://dx.doi.org/10.54097/hset.v34i.5483.

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Face recognition is a biometric technique that uses data on facial features to identify individuals. It is also a key area of study for computer vision researchers. CNN is a subclass of feedforward neural networks with convolutional processing and depth structure and one of the illustrative deep learning techniques. Since the deep learning theory was put forth and computational power increased, CNN has rapidly advanced and is now utilized in computer vision, natural language processing, and other fields. Our research is focused on face recognition, and because the mini-Xception model has a condensed volume and few parameters, it is used in this study. The dataset we used is fer2013, which is a classical dataset among CNN algorithms and is used in many studies. We also used data augmentation methods, and Keras’ ImageDataGenerator image generator was the optimal data augmentation method we came up with after reading the paper. Finally, we came up with a final model with 61% accuracy, which we are satisfied with and within the error results of the papers we reviewed.

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Hong, Yun-Pyo, Hee-Tak Kim, Seok-Hun Jeon, and Tae-Ho Hwang. "Implementation of SNN/CNN Accumulator H/W using LIF/IF Model." Journal of the Institute of Electronics and Information Engineers 59, no. 1 (January 31, 2022): 112–17. http://dx.doi.org/10.5573/ieie.2022.59.1.112.

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Park, Jin Hyeok, Byeong Dae Lee, and Myung Hoon Sunwoo. "MaskSLIC based CNN Classification Model for Mammogram Feature Extraction." Journal of the Institute of Electronics and Information Engineers 58, no. 10 (October 31, 2021): 59–67. http://dx.doi.org/10.5573/ieie.2021.58.10.59.

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S, Hemnath, and Geetha Ramalingam. "Comparing the Performance of Accuracy Using 3D CNN Model with the Fixed Spatial Transform With 3D CNN Model for the Detection of Pulmonary Nodules." E3S Web of Conferences 399 (2023): 09003. http://dx.doi.org/10.1051/e3sconf/202339909003.

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Анотація:

Aim: The research study aims to detect the accuracy level of the pulmonary nodule using a convolutional neural network (CNN). The comparison between the Novel 3D CNN-fixed spatial transform algorithm and Novel 3D CNN Model algorithm for accurate detection. Materials and Methods: The information for this study was gained from the Kaggle website. The samples were taken into consideration as (N=20) for 3D CNN-fixed spatial transform and (N=20) 3D CNN Model according to the clinical. com, total sample size calculation was performed. Python software is used for accurate detection. Threshold Alpha is 0.05 %, G power is 80% and the enrollment ratio is set to 1. Result: This research study found that the 3D CNN with 89.29% of accuracy is preferred over 3D CNN with fixed spatial transform which gives 78.5% accuracy with a significance value (p=0.001), (p<0.05) with a 95% confidence interval. There is statistical significance between the two groups. Conclusion: The mean value of 3D CNN -fixed spatial transform is 78.5% and Novel 3D CNN is 89.29%.Novel 3D CNN appears to give better accuracy than 3D CNN-fixed spatial transform.

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Chen, Mei-Hsin, Yao-Chung Chen, Tien-Yin Chou, and Fang-Shii Ning. "PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework." International Journal of Environmental Research and Public Health 20, no. 5 (February 24, 2023): 4077. http://dx.doi.org/10.3390/ijerph20054077.

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Анотація:

Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN–RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN–RF hybrid model has fewer excess residuals at thresholds of 10 μg/m3, 20 μg/m3, and 30 μg/m3. The results revealed that the proposed CNN–RF ensemble framework is a stable, reliable, and accurate method that can generate superior results compared with the single CNN and RF methods. The proposed method could be a valuable reference for readers and may inspire researchers to develop even more effective methods for air pollution modeling. This research has important implications for air pollution research, data analysis, model estimation, and machine learning.

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Rhanoui, Maryem, Mounia Mikram, Siham Yousfi, and Soukaina Barzali. "A CNN-BiLSTM Model for Document-Level Sentiment Analysis." Machine Learning and Knowledge Extraction 1, no. 3 (July 25, 2019): 832–47. http://dx.doi.org/10.3390/make1030048.

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Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press articles and blog posts about a particular product or company, and it requires a high concentration, especially when the topic being discussed is sensitive. Nevertheless, most existing models and techniques are designed to process short text from social networks and collaborative platforms. In this paper, we propose a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models, with Doc2vec embedding, suitable for opinion analysis in long texts. The CNN-BiLSTM model is compared with CNN, LSTM, BiLSTM and CNN-LSTM models with Word2vec/Doc2vec embeddings. The Doc2vec with CNN-BiLSTM model was applied on French newspapers articles and outperformed the other models with 90.66% accuracy.

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Ben Ismail, Mohamed Maher. "Insult detection using a partitional CNN-LSTM model." Computer Science and Information Technologies 1, no. 2 (July 1, 2020): 84–92. http://dx.doi.org/10.11591/csit.v1i2.p84-92.

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Recently, deep learning has been coupled with notice- able advances in Natural Language Processing related research. In this work, we propose a general framework to detect verbal offense in social networks comments. We introduce a partitional CNN-LSTM architecture in order to automatically recognize ver- bal offense patterns in social network comments. Specifically, we use a partitional CNN along with a LSTM model to map the social network comments into two predefined classes. In particular, rather than considering a whole document/comments as input as performed using typical CNN, we partition the comments into parts in order to capture and weight the locally relevant information in each partition. The resulting local information is then sequentially exploited across partitions using LSTM for verbal offense detection. The combination of the partitional CNN and LSTM yields the integration of the local within comments information and the long distance correlation across comments. The proposed approach was assessed using real dataset, and the obtained results proved that our solution outperforms existing relevant solutions.

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包, 娜萍. "Bitcoin Price Prediction Based on CNN-LSTM Model." Advances in Applied Mathematics 11, no. 05 (2022): 2956–66. http://dx.doi.org/10.12677/aam.2022.115315.

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42

Cho, Young-Bok. "Keras based CNN Model for Disease Extraction in Ultrasound Image." Journal of Digital Contents Society 19, no. 10 (October 31, 2018): 1975–80. http://dx.doi.org/10.9728/dcs.2018.19.10.1975.

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43

Nguyen, Thieu, Giang Nguyen, and Binh Minh Nguyen. "EO-CNN: An Enhanced CNN Model Trained by Equilibrium Optimization for Traffic Transportation Prediction." Procedia Computer Science 176 (2020): 800–809. http://dx.doi.org/10.1016/j.procs.2020.09.075.

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44

Wang, Zhaolian, Hong Huang, Rui Du, Xing Li, and Guotao Yuan. "IoT Intrusion Detection Model based on CNN-GRU." Frontiers in Computing and Intelligent Systems 4, no. 2 (June 26, 2023): 90–95. http://dx.doi.org/10.54097/fcis.v4i2.10302.

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With the rapid development of IoT technology, security concerns surrounding IoT devices have gained attention. An intrusion detection system for IoT can quickly and accurately identify highly redundant data features in IoT traffic categories. To reduce data, feature redundancy during the identification process, this study proposes the use of Extreme Gradient Boosting (XGBoost) for feature selection to obtain an optimal feature subset. Additionally, to improve the accuracy of identifying malicious traffic in IoT devices, a fusion model combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for IoT intrusion detection is proposed. Finally, a comparative analysis experiment is conducted between CNN-GRU and CNN-LSTM, demonstrating that the proposed model achieves lower processing time while ensuring accuracy. Furthermore, the proposed method outperforms classical IoT intrusion detection algorithms in terms of precision and recall.

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45

Wang, Haiyao, Jianxuan Wang, Lihui Cao, Yifan Li, Qiuhong Sun, and Jingyang Wang. "A Stock Closing Price Prediction Model Based on CNN-BiSLSTM." Complexity 2021 (September 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/5360828.

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As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error (MAE), root-mean-squared error (RMSE), and R-square (R2) evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.

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46

Pan, Cong, Minyan Lu, Biao Xu, and Houleng Gao. "An Improved CNN Model for Within-Project Software Defect Prediction." Applied Sciences 9, no. 10 (May 24, 2019): 2138. http://dx.doi.org/10.3390/app9102138.

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Анотація:

To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convolutional neural network (CNN), and used automatically generated features extracted from abstract syntax trees (ASTs) and deep learning models to improve defect prediction performance. However, the research on the CNN model failed to reveal clear conclusions due to its limited dataset size, insufficiently repeated experiments, and outdated baseline selection. To solve these problems, we built the PROMISE Source Code (PSC) dataset to enlarge the original dataset in the CNN research, which we named the Simplified PROMISE Source Code (SPSC) dataset. Then, we proposed an improved CNN model for within-project defect prediction (WPDP) and compared our results to existing CNN results and an empirical study. Our experiment was based on a 30-repetition holdout validation and a 10 * 10 cross-validation. Experimental results showed that our improved CNN model was comparable to the existing CNN model, and it outperformed the state-of-the-art machine learning models significantly for WPDP. Furthermore, we defined hyperparameter instability and examined the threat and opportunity it presents for deep learning models on defect prediction.

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Livieris, Ioannis E., Niki Kiriakidou, Stavros Stavroyiannis, and Panagiotis Pintelas. "An Advanced CNN-LSTM Model for Cryptocurrency Forecasting." Electronics 10, no. 3 (January 26, 2021): 287. http://dx.doi.org/10.3390/electronics10030287.

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Анотація:

Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.

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Gunawan, M. Zarlis, P. Sihombing, and Sutarman. "Optimization of the CNN model for smart agriculture." IOP Conference Series: Materials Science and Engineering 1088, no. 1 (February 1, 2021): 012029. http://dx.doi.org/10.1088/1757-899x/1088/1/012029.

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Irmak, Emrah. "COVID‐19 disease severity assessment using CNN model." IET Image Processing 15, no. 8 (March 7, 2021): 1814–24. http://dx.doi.org/10.1049/ipr2.12153.

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Łach, Błażej, and Edyta Łukasik. "Faster R-CNN model learning on synthetic images." Journal of Computer Sciences Institute 17 (December 30, 2020): 401–4. http://dx.doi.org/10.35784/jcsi.2285.

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Machine learning requires a human description of the data. The manual dataset description is very time consuming. In this article was examined how the model learns from artificially created images, with the least human participation in describing the data. It was checked how the model learned on artificially produced images with augmentations and progressive image size. The model has achieve up to 3.35 higher mean average precision on syntetic dataset in the training with increasing images resolution. Augmentations improved the quality of detection on real photos. The production of artificially generated training data has a great impact on the acceleration of prepare training, because it does not require as much human resources as normal learning process.

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