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Mohammed, Mohammed Ameen, Zheng Han i Yange Li. "Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models". Advances in Materials Science and Engineering 2021 (9.09.2021): 1–11. http://dx.doi.org/10.1155/2021/9923704.
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Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up-to-date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open-source CNN models (Model1, Model2, and Model3) which have been well-illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227-pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.
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Hassan, Esraa, Nora El-Rashidy i fatma M. Talaa. "Review: Mask R-CNN Models". Nile Journal of Communication and Computer Science 3, nr 1 (1.05.2022): 17–27. http://dx.doi.org/10.21608/njccs.2022.280047.
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ITOH, MAKOTO, i LEON O. CHUA. "EQUIVALENT CNN CELL MODELS AND PATTERNS". International Journal of Bifurcation and Chaos 13, nr 05 (maj 2003): 1055–161. http://dx.doi.org/10.1142/s0218127403007151.
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In this paper, canonical isolated CNN cell models are proposed by using implicit differential equations. A number of equivalent but distinct CNN cell models are derived from these canonical models. Almost every known CNN cell model can be classified into one or more groups via constrained conditions. This approach is also applied to discrete-time CNN cell models. Pattern formation mechanisms are investigated from the viewpoint of equivalent templates and genetic algorithms. A strange wave propagation phenomenon in nonuniform CNN cells is also presented in this paper. Finally, chaotic associative memories are proposed.
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Suresh, Neha, i Dr AnandiGiridharan Dr.AnandiGiridharan. "Predicting Groundnut Disease using CNN Models". Journal of University of Shanghai for Science and Technology 23, nr 06 (18.06.2021): 756–66. http://dx.doi.org/10.51201/jusst/21/05335.
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Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. Groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root, and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of convolutional neural network (CNN) because it automatically detects the important features without any human supervision. The proposed methodology can deeply detect plant disease by using a deep learning process. Ultimately, the groundnut disease classification with its overall performance of the proposed methodology provides 96% accuracy.
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Jing, Juntong. "Denoising Adversarial Examples Using CNN Models". Journal of Physics: Conference Series 2181, nr 1 (1.01.2022): 012029. http://dx.doi.org/10.1088/1742-6596/2181/1/012029.
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Abstract It has always been a complicated problem to resolve adversarial attacks because figures with adversarial attacks look similar to the original figures so that models can be fooled. With deceptive data, adversarial attacks can be a threat to neural networks. There are various ways to generate adversarial attacks. For instance, they are using one-step perturbation and using multi-step perturbation. In both methods, noise is added to the images. Therefore, a question pops up: are adversarial attacks similar to normal random noise? This paper aims to find if there is anything in common between random noise and adversarial attacks. A normal denoising CNN model is trained with random noise. Then groups of adversarial examples are collected by training on LeNet. Next, the denoising CNN model has been used to denoise those adversarial examples. Finally, after denoising the adversarial examples with the CNN model trained on normal random noise, the classification accuracy increases. Thus, it is reasonable to conclude that normal random noise and adversarial tracks have some common patterns.
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Wang, Keyi. "Static and Dynamic Hand Gesture Recognition Using CNN Models". International Journal of Bioscience, Biochemistry and Bioinformatics 11, nr 3 (2021): 65–73. http://dx.doi.org/10.17706/ijbbb.2021.11.3.65-73.
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Zhan, Zhiwei, Guoliang Liao, Xiang Ren, Guangsi Xiong, Weilin Zhou, Wenchao Jiang i Hong Xiao. "RA-CNN". International Journal of Software Science and Computational Intelligence 14, nr 1 (1.01.2022): 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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GÁL, V., J. HÁMORI, T. ROSKA, D. BÁLYA, ZS BOROSTYÁNKŐI, M. BRENDEL, K. LOTZ i in. "RECEPTIVE FIELD ATLAS AND RELATED CNN MODELS". International Journal of Bifurcation and Chaos 14, nr 02 (luty 2004): 551–84. http://dx.doi.org/10.1142/s0218127404009545.
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In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine — CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the flexibility of the CNN computing: visual, tactile and auditory modalities are concerned.
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Alofi, Najla, Wafa Alonezi i Wedad Alawad. "WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes". International Journal of Online and Biomedical Engineering (iJOE) 17, nr 13 (6.12.2021): 135–50. http://dx.doi.org/10.3991/ijoe.v17i13.27373.
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Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.
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Noh, Seol-Hyun. "Gradient Flow Analysis and Performance Comparison of CNN Models". Journal of KIISE 48, nr 1 (31.01.2021): 100–106. http://dx.doi.org/10.5626/jok.2021.48.1.100.
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Huang, Yefei, Tianlai Xu, Zexu Zhang, Hutao Cui i Yu Su. "Satellite Segmentation with Pre-trained CNN Models". Journal of Physics: Conference Series 2171, nr 1 (1.01.2022): 012003. http://dx.doi.org/10.1088/1742-6596/2171/1/012003.
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Abstract In a generic satellite relative pose estimation pipeline, finding sufficient features in objects is quite essential to build the correct matching relationship and then solve the relative movement. However, for low-earth-orbit (LEO) satellites, since the earth background contains much more texture than objects, an object segmentation process is necessary to provide a prior range for feature extraction. In this work, we address this task with the pre-trained Deeplabv3 and fully convolutional network (FCN). Unlike the fine-tuning or transfer learning processes in other researches, we obtain probabilistic maps from the high-dimensional output of the above-mentioned CNN models and achieve a rough satellite extraction. Our method makes Deeplabv3 and FCN models work in a totally unfamiliar LEO scene and still achieves 0.2927 and 0.2122 in average intersection over union (IoU) respectively.
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Kim, Gun Il, i Beakcheol Jang. "Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection". Mathematics 11, nr 3 (19.01.2023): 547. http://dx.doi.org/10.3390/math11030547.
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Crude oil plays an important role in the global economy, as it contributes one-third of the energy consumption worldwide. However, despite its importance in policymaking and economic development, forecasting its price is still challenging due to its complexity and irregular price trends. Although a significant amount of research has been conducted to improve forecasting using external factors as well as machine-learning and deep-learning models, only a few studies have used hybrid models to improve prediction accuracy. In this study, we propose a novel hybrid model that captures the finer details and interconnections between multivariate factors to improve the accuracy of petroleum oil price prediction. Our proposed hybrid model integrates a convolutional neural network and a recurrent neural network with skip connections and is trained using petroleum oil prices and external data directly accessible from the official website of South Korea’s national oil corporation and the official Yahoo Finance site. We compare the performance of our univariate and multivariate models in terms of the Pearson correlation, mean absolute error, mean squared error, root mean squared error, and R squared (R2) evaluation metrics. Our proposed models exhibited significantly better performance than the existing models based on long short-term memory and gated recurrent units, showing correlations of 0.985 and 0.988, respectively, for 10-day price predictions and obtaining better results for longer prediction periods when compared with other deep-learning models. We validated that our proposed model with skip connections outperforms the benchmark models and showed that the convolutional neural network using gated recurrent units with skip connections is superior to the compared models. The findings suggest that, to some extent, relying on a single source of data is ineffective in predicting long-term changes in oil prices, and thus, to develop a better prediction model based on time-series based data, it is necessary to take a multivariate approach and develop an efficient computational model with skip connections.
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İni̇k, Özkan, Mustafa Altıok, Erkan Ülker i Barış Koçer. "MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models". Applied Soft Computing 109 (wrzesień 2021): 107582. http://dx.doi.org/10.1016/j.asoc.2021.107582.
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Song, Hyunsun, i Hyunjun Choi. "Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models". Applied Sciences 13, nr 7 (6.04.2023): 4644. http://dx.doi.org/10.3390/app13074644.
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Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting.
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Patil, Priyadarshini, Vipul Deshpande, Vishal Malge i Abhishek Bevinmanchi. "Fake Face Detection Using CNN". International Journal for Research in Applied Science and Engineering Technology 10, nr 9 (30.09.2022): 519–22. http://dx.doi.org/10.22214/ijraset.2022.45829.
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Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and true. Regarding the models tested so far, the VGG models have the best training accuracy (86%) on VGG-16, while VGG-16 shows an excellent test set. accuracy with 10 epochs or less, which is competitively better than all other methods. The outputs of these models were examined to find out exactly
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Dhokane, Mr Rahul. "CAR DAMAGE DETECTION USING CNN". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 04 (12.04.2024): 1–5. http://dx.doi.org/10.55041/ijsrem30508.
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In today's modern society, automobiles play a crucial role, and the automatic classification of car damages holds particular significance for the auto insurance industry. Our proposed solution involves the implementation of two Convolutional Neural Network (CNN) models. Specifically, the VGG16 model is employed to identify and assess the location and severity of car damage, while the Mask R-CNN is utilized to accurately mask the damaged regions. Both models collectively provide valuable insights into the extent. The CNN models effectively filter out images without damages, allowing only those with identified damage to be passed on to the object detection model. This strategic approach enhances the overall performance of the model. The core objective of this research project is to achieve maximum accuracy through the utilization of CNN models. TensorFlow, Key Words: E-commerce, Car Damage, Detection, Classification, VGG, Mask RCNN, Severity, Location, Masking
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Elzayady, Hossam, Khaled M. Badran i Gouda I. Salama. "Arabic Opinion Mining Using Combined CNN - LSTM Models". International Journal of Intelligent Systems and Applications 12, nr 4 (8.08.2020): 25–36. http://dx.doi.org/10.5815/ijisa.2020.04.03.
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Berwo, Michael Abebe, Zhipeng Wang, Yong Fang, Jabar Mahmood i Nan Yang. "Off-road Quad-Bike Detection Using CNN Models". Journal of Physics: Conference Series 2356, nr 1 (1.10.2022): 012026. http://dx.doi.org/10.1088/1742-6596/2356/1/012026.
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Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and assess these designs. In this paper, we show that the Yolov4-tiny architecture outperforms the Yolov4, and Yolov5s in terms of mAP@50 and computing time per image.
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Chen, Nuo, Boyu Han, Zhixin Li i Haotian Wang. "Breast Cancer Prediction Based on the CNN Models". Highlights in Science, Engineering and Technology 34 (28.02.2023): 103–9. http://dx.doi.org/10.54097/hset.v34i.5388.
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In modern society, the natural lifespan of an individual increased dramatically benefitting from advanced yet accurate methods of medical treatment. Though many diseases could be treated with a cure, the treatment of cancer has yet to be overcome. Related medical research has proven that the combination of accurate breast cancer diagnoses and treatments at an early stage could prevent the spread of cancer cells as it could increase a person's potential lifespan by a large margin. This research has conducted a comprehensive study on improving the efficiency of autonomous image recognition of breast cancer diagnosis using deep learning models. We use the most advanced CNN baseline models for image recognition, including VGG, ResNet, Efficient, etc. We also select two typical breast cancer datasets and tested the models on them to make our result more convincing. The final enhanced model of ResNet 101 can achieve a recognition rate of 89.98% for the benign and malignant samples.
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D, Ms Suseela, Varsha S, Bharaneedharan C i Lekshana Shivani C. "CLASSIFICATION OF FRESH AND ROTTEN FRUITS USING DIFFERENT CNN MODELS". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, nr 10 (1.10.2023): 1–11. http://dx.doi.org/10.55041/ijsrem26057.
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Fruit freshness automated classification is crucial to the agricultural sector. In the traditional procedure, a human being grades the fruit. Additionally, this process is labor-intensive, time-consuming, and ineffective. Additionally, it raises production costs. Therefore, a quick, precise, and automated system that may lessen human effort, enhance production, and decrease manufacturing time and cost is needed for industrial applications. The deep learning- based model for classifying fruit freshness is used in the current work. Various Convolution Neural Network (CNN) models are proposed, and they are implemented using the publicly available "fruit fresh and rotten for classification" kaggle dataset. Three fresh fruit varieties (Apple, Banana, and Oranges) and their rotting category are employed in an experiment using the dataset. From the given fruit photos, traits or attributes are extracted using a CNN model based on deep learning. The input photos are then divided into fresh and rotting categories by a softmax method. The classification of fresh and rotten fruits uses a variety of CNN models, including Resnet50 (50 Layers), InceptionV3 (48 Layers), and VGG16 (16 Layers). The proposed various CNN models accurately and efficiently evaluate the dataset. Later, the accuracy of the proposed CNN models is compared and the highest accuracy among the three CNN models is identified. In this way, the best accuracy CNN models will be identified for classifying the fresh and rotten fruits. KEYWORDS: Deep learning, CNN model, Inception V3, Resnet50, VGG16.
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Gao, Xue-Yao, Bo-Yu Yang i Chun-Xiang Zhang. "Combine EfficientNet and CNN for 3D model classification". Mathematical Biosciences and Engineering 20, nr 5 (2023): 9062–79. http://dx.doi.org/10.3934/mbe.2023398.
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<abstract> <p>With the development of multimedia technology, the number of 3D models on the web or in databases is becoming increasingly larger and larger. It becomes more and more important to classify and retrieve 3D models. 3D model classification plays important roles in the mechanical design field, education field, medicine field and so on. Due to the 3D model's complexity and irregularity, it is difficult to classify 3D model correctly. Many methods of 3D model classification pay attention to local features from 2D views and neglect the 3D model's contour information, which cannot express it better. So, accuracy the of 3D model classification is poor. In order to improve the accuracy of 3D model classification, this paper proposes a method based on EfficientNet and Convolutional Neural Network (CNN) to classify 3D models, in which view feature and shape feature are used. The 3D model is projected into 2D views from different angles. EfficientNet is used to extract view feature from 2D views. Shape descriptors D1, D2, D3, Zernike moment and Fourier descriptors of 2D views are adopted to describe the 3D model and CNN is applied to extract shape feature. The view feature and shape feature are combined as discriminative features. Then, the softmax function is used to determine the 3D model's category. Experiments are conducted on ModelNet 10 dataset. Experimental results show that the proposed method achieves better than other methods.</p> </abstract>
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Jain, Tanmay, Harshada Mhaske, Sanjay Chilveri, Aniket Chaudhar i Chinmay Doshi. "Cloudy Weather Prediction Using CNN Models and Satellite Images". International Journal for Research in Applied Science and Engineering Technology 12, nr 4 (30.04.2024): 1168–75. http://dx.doi.org/10.22214/ijraset.2024.59342.
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Abstract: Cloudy weather classification is a vital task in meteorology and remote sensing, facilitating various applications such as weather forecasting, climate monitoring, and environmental analysis. In this study, we explore the application of convolutional neural network (CNN) techniques for classifying cloudy weather conditions using the Cloudy Weather Dataset sourced from Kaggle. The primary CNN architectures investigated include AlexNet, LeNet, and ResNet. The dataset undergoes preprocessing steps, including resizing and normalization to floating-point representation. Additionally, for calculating cloud cover percentage, the images are processed through grayscaling followed by thresholding. The performance of each CNN model is evaluated based on metrics of accuracy, that is providing insights into their effectiveness for cloudy weather classification
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Gunasekaran, Hemalatha, K. Ramalakshmi, A. Rex Macedo Arokiaraj, S. Deepa Kanmani, Chandran Venkatesan i C. Suresh Gnana Dhas. "Analysis of DNA Sequence Classification Using CNN and Hybrid Models". Computational and Mathematical Methods in Medicine 2021 (15.07.2021): 1–12. http://dx.doi.org/10.1155/2021/1835056.
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In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K -mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K -mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.
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Alshirbaji, Tamer Abdulbaki, Nour Aldeen Jalal, Paul D. Docherty, Thomas Neumuth i Knut Moeller. "Assessing Generalisation Capabilities of CNN Models for Surgical Tool Classification". Current Directions in Biomedical Engineering 7, nr 2 (1.10.2021): 476–79. http://dx.doi.org/10.1515/cdbme-2021-2121.
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Abstract Accurate recognition of surgical tools is a crucial component in the development of robust, context-aware systems. Recently, deep learning methods have been increasingly adopted to analyse laparoscopic videos. Existing work mainly leverages the ability of convolutional neural networks (CNNs) to model visual information of laparoscopic images. However, the performance was evaluated only on data belonging to the same dataset used for training. A more comprehensive evaluation of CNN performance on data from other datasets can provide a more rigorous assessment of the approaches. In this work, we investigate the generalisation capability of different CNN architectures to classify surgical tools in laparoscopic images recorded at different institutions. This research highlights the need to determine the effect of using data from different surgical sites on CNN generalisability. Experimental results imply that training a CNN model using data from multiple sites improves generalisability to new surgical locations.
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Kim, Junyoung, Jongho Jeon, Minkwan Kee i Gi-Ho Park. "The Method Using Reduced Classification Models for Distributed Processing of CNN Models in Multiple Edge Devices". Journal of KIISE 47, nr 8 (31.08.2020): 787–92. http://dx.doi.org/10.5626/jok.2020.47.8.787.
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Ha, Yeongseo, Jihee Park i Jaechang Shim. "Comparison of Face Recognition Performance Using CNN Models and Siamese Networks". Journal of Korea Multimedia Society 26, nr 2 (28.02.2023): 413–19. http://dx.doi.org/10.9717/kmms.2023.26.2.413.
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GILLI, M., T. ROSKA, L. O. CHUA i P. P. CIVALLERI. "CNN DYNAMICS REPRESENTS A BROADER CLASS THAN PDEs". International Journal of Bifurcation and Chaos 12, nr 10 (październik 2002): 2051–68. http://dx.doi.org/10.1142/s0218127402005868.
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The relationship between Cellular Nonlinear Networks (CNNs) and Partial Differential Equations (PDEs) is investigated. The equivalence between discrete-space CNN models and continuous-space PDE models is rigorously defined. The key role of space discretization is explained. The problem of the equivalence is split into two subproblems: approximation and topological equivalence, that can be explicitly studied for any CNN model. It is known that each PDE can be approximated by a space difference scheme, i.e. a CNN model, that presents a similar dynamic behavior. It is shown, through several examples, that there exist CNN models that are not equivalent to any PDEs, either because they do not approximate any PDE models, or because they have a qualitatively different dynamic behavior (i.e. they are not topologically equivalent to the PDE that they approximate). This proves that the spatio-temporal CNN dynamics is broader than that described by PDEs.
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Sahel, S., M. Alsahafi, M. Alghamdi i T. Alsubait. "Logo Detection Using Deep Learning with Pretrained CNN Models". Engineering, Technology & Applied Science Research 11, nr 1 (6.02.2021): 6724–29. http://dx.doi.org/10.48084/etasr.3919.
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Logo detection in images and videos is considered a key task for various applications, such as vehicle logo detection for traffic-monitoring systems, copyright infringement detection, and contextual content placement. The main contribution of this work is the application of emerging deep learning techniques to perform brand and logo recognition tasks through the use of multiple modern convolutional neural network models. In this work, pre-trained object detection models are utilized in order to enhance the performance of logo detection tasks when only a portion of labeled training images taken in truthful context is obtainable, evading wide manual classification costs. Superior logo detection results were obtained. In this study, the FlickrLogos-32 dataset was used, which is a common public dataset for logo detection and brand recognition from real-world product images. For model evaluation, the efficiency of creating the model and of its accuracy was considered.
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Nour, Nahla, Mohammed Elhebir i Serestina Viriri. "Face Expression Recognition using Convolution Neural Network (CNN) Models". International Journal of Grid Computing & Applications 11, nr 4 (30.12.2020): 1–11. http://dx.doi.org/10.5121/ijgca.2020.11401.
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This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that AlexNet model achieved the best accuracy (88.2%) compared to other models.
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Wibawa, Febrianti, Ferhat Ozgur Catak, Salih Sarp i Murat Kuzlu. "BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models". Cryptography 6, nr 3 (1.07.2022): 34. http://dx.doi.org/10.3390/cryptography6030034.
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Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers to this challenge. Using homomorphic encryption, this research presents a privacy-preserving federated learning system for medical data. The proposed technique employs a secure multi-party computation protocol to safeguard the deep learning model from adversaries. The proposed approach is tested in terms of model performance using a real-world medical dataset in this paper.
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Chen, Zhaohe, Ming Tang i Jinghai Li. "Inversion Attacks against CNN Models Based on Timing Attack". Security and Communication Networks 2022 (26.02.2022): 1–11. http://dx.doi.org/10.1155/2022/6285909.
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Model confidentiality attacks on convolutional neural networks (CNN) are becoming more and more common. At present, model reverse attack is an important means of model confidentiality attacks, but all of these attacks require strong attack ability, meanwhile, the success rates of these attacks are low. We study the time leakage of CNN running on the SoC (system on-chip) system and propose a reverse method based on side-channel attack. It uses the SDK tool-profiler to collect the time leakage of different networks of various CNNs. According to the linear relationship between time leakage, calculation, and memory usage parameters, we take the profiling attack to establish a mapping library of time and the different networks. After that, the smallest difference between the measured time of unknown models and the theoretical time in the mapping library is considered to be the real parameters of the unknown models. Finally, we can reverse other layers even the entire model. Based on the experiments, the reverse success rate of common convolutional layers is above 78.5%, and the reverse success rates of different CNNs (such as AlexNet, ConvNet, LeNet, etc.) are all above 67.67%. Moreover, the results show that the success rate of our method is 10% higher than the traditional methods on average. In the adversarial sample attack, the success rate reached 97%.
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Dai, Rong. "Text Data Mining Algorithm Combining CNN and DBM Models". Mobile Information Systems 2021 (20.11.2021): 1–7. http://dx.doi.org/10.1155/2021/2150488.
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The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining.
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Noh, Seol-Hyun. "Performance Comparison of CNN Models Using Gradient Flow Analysis". Informatics 8, nr 3 (13.08.2021): 53. http://dx.doi.org/10.3390/informatics8030053.
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Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the locality and correlation of input data using structures in which convolutional layers are successively applied to the input data. In general, the performance of neural networks has improved as the depth of CNNs has increased. However, an increase in the depth of a CNN is not always accompanied by an increase in the accuracy of the neural network. This is because the gradient vanishing problem may arise, causing the weights of the weighted layers to fail to converge. Accordingly, the gradient flows of the VGGNet, ResNet, SENet, and DenseNet models were analyzed and compared in this study, and the reasons for the differences in the error rate performances of the models were derived.
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Mounika, Siripurapu. "Crypto-Currency Price Prediction using CNN and LSTM Models". International Journal for Research in Applied Science and Engineering Technology 9, nr 3 (31.03.2021): 107–14. http://dx.doi.org/10.22214/ijraset.2021.33191.
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Saravagi, Deepika, Shweta Agrawal, Manisha Saravagi, Jyotir Moy Chatterjee i Mohit Agarwal. "Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models". Computational Intelligence and Neuroscience 2022 (13.04.2022): 1–12. http://dx.doi.org/10.1155/2022/7459260.
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Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3’s 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy’s (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model’s performance on other publicly available datasets, we have generalised our approach on the public platform.
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Hong, Min, Beanbonyka Rim, Hongchang Lee, Hyeonung Jang, Joonho Oh i Seongjun Choi. "Multi-Class Classification of Lung Diseases Using CNN Models". Applied Sciences 11, nr 19 (6.10.2021): 9289. http://dx.doi.org/10.3390/app11199289.
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In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.
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Zarandy, A., L. Orzo, E. Grawes i F. Werblin. "CNN-based models for color vision and visual illusions". IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 46, nr 2 (1999): 229–38. http://dx.doi.org/10.1109/81.747190.
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Kamarudin, MH, i Zool H. Ismail. "Lightweight deep CNN models for identifying drought stressed plant". IOP Conference Series: Earth and Environmental Science 1091, nr 1 (1.11.2022): 012043. http://dx.doi.org/10.1088/1755-1315/1091/1/012043.
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Drought is one of the most severe climatological disasters that has negative impact on agricultural production around the world. Over the years, computer vision technology has been used in conjunction with machine learning applications to replace traditional destructive and time-consuming methods for real-time monitoring of drought-affected plant. Deep learning (DL) techniques have gained a stellar reputation in image classification recently, with convolutional neural network (CNN) emerging as the industry standard. However, the size of deep CNN models is frequently large due to massive number of parameters and field application is often not feasible due to limited storage and computational resources. Several lightweight CNN models have been selected based on the number of network parameters of less than 6M and were trained and tested. The EfficientNet model has achieved a classification accuracy of 88.12 and 88.97 percent for identifying severe drought, mild drought, and no drought plants on visible and near-infrared images respectively. The findings of this study can be used to assist in the development of automated early detection of drought stressed plant with model sizes suitable for real-time plant diagnosis on mobile or embedded devices.
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Corinto, F., M. Biey i M. Gilli. "Non-linear coupled CNN models for multiscale image analysis". International Journal of Circuit Theory and Applications 34, nr 1 (styczeń 2006): 77–88. http://dx.doi.org/10.1002/cta.343.
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P P, Aswathi Mohan, i Uma V. "Fetal Hypoxia Detection using CTG Signals and CNN Models". International Research Journal on Advanced Science Hub 5, Issue 05S (28.05.2023): 434–41. http://dx.doi.org/10.47392/irjash.2023.s059.
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Davuluri, Ragavamsi, Vyshnavi Mallapragada, Uma Maheswara Rao Mamillapalli, Manikanta M i Sireesha Peeka. "Alzheimer’s Disease Diagnosis Using CNN Based Pre-trained Models". International Journal on Recent and Innovation Trends in Computing and Communication 11, nr 4 (4.05.2023): 315–23. http://dx.doi.org/10.17762/ijritcc.v11i4.6456.
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Memory loss and impairment are signs of Alzheimer's disease (AD), which may also cause other issues. It has a significant impact on patients' lives and is incurable, but rapid recognition of Alzheimer's disease can be useful to initiate appropriate therapy to avoid further deterioration to the brain. Previously, Machine Learning methodswere used to detect Alzheimer's disease. In recent times, Deep Learning algorithms have become more popular for pattern recognition. This workconcentrates on the recognition of Alzheimer's disease at a preliminary phase using advanced convolutional neural network models. As the disease advances, they steadily forget everything. It is critical to detect the disease as quickly as possible. The proposed model usespre-trained models that uses magnetic resonance imaging of the brain to determine if a person has very mild, mild, moderate, or non-dementia. The models used for classification are VGG16, VGG19, and ResNet50 architectures and provide performance comparison.
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Ajay M. Pol, Et al. "Enhancing Sign Language Recognition through Fusion of CNN Models". International Journal on Recent and Innovation Trends in Computing and Communication 11, nr 10 (2.11.2023): 902–10. http://dx.doi.org/10.17762/ijritcc.v11i10.8608.
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This study introduces a pioneering hybrid model designed for the recognition of sign language, with a specific focus on American Sign Language (ASL) and Indian Sign Language (ISL). Departing from traditional machine learning methods, the model ingeniously blends hand-crafted techniques with deep learning approaches to surmount inherent limitations. Notably, the hybrid model achieves an exceptional accuracy rate of 96% for ASL and 97% for ISL, surpassing the typical 90-93% accuracy rates of previous models. This breakthrough underscores the efficacy of combining predefined features and rules with neural networks. What sets this hybrid model apart is its versatility in recognizing both ASL and ISL signs, addressing the global variations in sign languages. The elevated accuracy levels make it a practical and accessible tool for the hearing-impaired community. This has significant implications for real-world applications, particularly in education, healthcare, and various contexts where improved communication between hearing-impaired individuals and others is paramount. The study represents a noteworthy stride in sign language recognition, presenting a hybrid model that excels in accurately identifying ASL and ISL signs, thereby contributing to the advancement of communication and inclusivity.
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Rawal, Purvi. "Skin Cancer Diagnosis: Integrating CNN and Machine Learning Models". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 03 (18.03.2024): 1–5. http://dx.doi.org/10.55041/ijsrem29407.
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Skin cancer is serious health concern that arises from abnormal growth of skin cells. It encompasses of three types, melanoma being the most aggressive form of cancer. The skin cancer starts to arise globally so accordingly there is being necessary public awareness, prevention for this, and strategies for early detection. Accurate diagnosis treatment and Early detection are better for effective management which will improve accuracy and efficiency for diagnostic process. Also, the Machine learning and deep learning algorithms we research and analyse for better outcomes and for innovation in fields of skin cancer detection. And for better treatment for patients, to reduce healthcare costs and overall management of skin cancer. Better constituency and through ongoing research we can try to overcome with help of future advancements in technology and the learning algorithm model. According to our reports and research we did through the dataset of skin images ML and deep learning we got 73% through Naïve Bayes 90% accuracy through random forest algorithm and 89% through CNN model algorithm. We do not conclude that Machine learning algorithm can be better since there are many other factors use in different algorithms so results are not based on high accuracy reports. Keywords: Deep learning, CNN model, Machine learning, Skin cancer
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Makhir, Abdelmalek, My Hachem El Yousfi Alaoui i Larbi Belarbi. "Comprehensive Cardiac Ischemia Classification Using Hybrid CNN-Based Models". International Journal of Online and Biomedical Engineering (iJOE) 20, nr 03 (27.02.2024): 154–65. http://dx.doi.org/10.3991/ijoe.v20i03.45769.
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This study addresses the critical issue of classifying cardiac ischemia, a disease with significant global health implications that contributes to the global mortality rate. In our study, we tackle the classification of ischemia using six diverse electrocardiogram (ECG) datasets and a convolutional neural network (CNN) as the primary methodology. We combined six separate datasets to gain a more comprehensive understanding of cardiac electrical activity, utilizing 12 leads to obtain a broader perspective. A discrete wavelet transform (DWT) preprocessing was used to eliminate irrelevant information from the signals, aiming to improve classification results. Focusing on accuracy and minimizing false negatives (FN) in ischemia detection, we enhance our study by incorporating various machine learning models into our base model. These models include multilayer perceptron (MLP), support vector machines (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), allowing us to leverage the strengths of each algorithm. The CNN-BiLSTM model achieved the highest accuracy of 99.23% and demonstrated good sensitivity of 98.53%, effectively reducing false negative cases in the overall tests. The CNN-BiLSTM model demonstrated the ability to effectively identify abnormalities, misclassifying only 25 out of 1,673 ischemic cases in the test set as normal. This is due to the BiLSTM’s efficiency in capturing long-range dependencies and sequential patterns, making it suitable for tasks involving time-series data such as ECG signals. In addition, CNNs are well-suited for hierarchical feature learning and complex pattern recognition in ECG data.
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Chai, Chee Chiet, Wee How Khoh, Ying Han Pang i Hui Yen Yap. "A Lung Cancer Detection with Pre-Trained CNN Models". Journal of Informatics and Web Engineering 3, nr 1 (14.02.2024): 41–54. http://dx.doi.org/10.33093/jiwe.2024.3.1.3.
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Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%.
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Jeon, Ho-Kun, Seungryong Kim, Jonathan Edwin i Chan-Su Yang. "Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models". Electronics 9, nr 2 (11.02.2020): 311. http://dx.doi.org/10.3390/electronics9020311.
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This study proposes an approaching method of identifying sea fog by using Geostationary Ocean Color Imager (GOCI) data through applying a Convolution Neural Network Transfer Learning (CNN-TL) model. In this study, VGG19 and ResNet50, pre-trained CNN models, are used for their high identification performance. The training and testing datasets were extracted from GOCI images for the area of coastal regions of the Korean Peninsula for six days in March 2015. With varying band combinations and changing whether Transfer Learning (TL) is applied, identification experiments were executed. TL enhanced the performance of the two models. Training data of CNN-TL showed up to 96.3% accuracy in matching, both with VGG19 and ResNet50, identically. Thus, it is revealed that CNN-TL is effective for the detection of sea fog from GOCI imagery.
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Yue, Wang, i Li Lei. "Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification". Information 26, nr 3 (15.09.2023): 117–62. http://dx.doi.org/10.47880/inf2603-02.
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With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in the field of natural language processing and data mining in recent ten years. The paper starts with the topic of "Sentiment Analysis using a CNN-BiLSTM deep model based on attention mechanism classification". First, it conducts an in-depth investigation on the current research status and commonly used algorithms at home and abroad, and briefly introduces and analyzes the current mainstream sentiment analysis methods. As a direction of machine learning, deep learning has become a hot research topic in emotion classification in the field of natural language processing. This paper uses deep learning models to study the sentiment classification problem of short and long text sentiment classification tasks. The main research contents are as follows. Firstly, Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. The feature dimension is too high, and the feature information of the pool layer is lost, which leads to the loss of the details of the emotion vocabulary. To solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined in Quora dataset. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long- term dependencies between words hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. Secondly, we propose an attention based CNN-BiLSTM hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism in IMDB movie reviews dataset. In the experiment, under the control of single variable of Data volume and Epoch, the proposed hybrid model was compared with the results of various indicators including recall, precision, F1 score and accuracy of CNN, LSTM and CNN-LSTM in long text. When the data size was 13 k, the proposed model had the highest accuracy at 0.908, and the F1 score also showed the highest performance at 0.883. When the epoch value for obtaining the optimal accuracy of each model was 10 for CNN, 14 for LSTM, 5 for MLP and 15 epochs for CNN-LSTM, which took the longest learning time. The F1 score also showed the best performance of the proposed model at 0.906, and accuracy of the proposed model was the highest at 0.929. Finally, the experimental results show that the bidirectional long- and short-term memory convolutional neural network (BiLSTM-CNN) model based on attention mechanism can effectively improve the performance of sentiment classification of data sets when processing long-text sentiment classification tasks. Keywords: sentiment analysis, CNN, BiLSTM, attention mechanism, text classification
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Rhanoui, Maryem, Mounia Mikram, Siham Yousfi i Soukaina Barzali. "A CNN-BiLSTM Model for Document-Level Sentiment Analysis". Machine Learning and Knowledge Extraction 1, nr 3 (25.07.2019): 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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Haviluddin, Haviluddin, i Rayner Alfred. "Multi-step CNN forecasting for COVID-19 multivariate time-series". International Journal of Advances in Intelligent Informatics 9, nr 2 (1.07.2023): 176. http://dx.doi.org/10.26555/ijain.v9i2.1080.
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The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.
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Zheng, Tong, Jin Li, Hao Tian i Qing Wu. "The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models". Sensors 23, nr 14 (17.07.2023): 6461. http://dx.doi.org/10.3390/s23146461.
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Recently, attention has been paid to the convolutional neural network (CNN) based synthetic aperture radar (SAR) target recognition method. Because of its advantages of automatic feature extraction and the preservation of translation invariance, the recognition accuracies are stronger than traditional methods. However, similar to other deep learning models, CNN is a “black-box” model, whose working process is vague. It is difficult to locate the decision reasons. Because of this, we focus on the process analysis of a pre-trained CNN model. The role of the processing to feature extraction and final recognition decision is discussed. The discussed components of CNN models are convolution, activation function, and full connection. Here, the convolution processing can be deemed as image filtering. The activation function provides a nonlinear element of processing. Moreover, the fully connected layers can also further extract features. In the experiment, four classical CNN models, i.e., AlexNet, VGG16, GoogLeNet, and ResNet-50, are trained by public MSTAR data, which can realize ten-category SAR target recognition. These pre-trained CNN models are processing objects of the proposed process analysis method. After the analysis, the content of the SAR image target features concerned by these pre-trained CNN models is further clarified. In summary, we provide a paradigm to process the analysis of pre-trained CNN models used for SAR target recognition in this paper. To some degree, the adaptability of these models to SAR images is verified.