scholarly journals Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4267
Author(s):  
Bethany Gosala ◽  
Sripriya Roy Chowdhuri ◽  
Jyoti Singh ◽  
Manjari Gupta ◽  
Alok Mishra
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unified Modeling Language ◽  
Image Features ◽  
Machine Learning Techniques ◽  
Modeling Language ◽  
Uml Diagrams ◽  
Class Diagrams ◽  
The Impact

Unified Modeling Language (UML) includes various types of diagrams that help to study, analyze, document, design, or develop any software efficiently. Therefore, UML diagrams are of great advantage for researchers, software developers, and academicians. Class diagrams are the most widely used UML diagrams for this purpose. Despite its recognition as a standard modeling language for Object-Oriented software, it is difficult to learn. Although there exist repositories that aids the users with the collection of UML diagrams, there is still much more to explore and develop in this domain. The objective of our research was to develop a tool that can automatically classify the images as UML class diagrams and non-UML class diagrams. Earlier research used Machine Learning techniques for classifying class diagrams. Thus, they are required to identify image features and investigate the impact of these features on the UML class diagrams classification problem. We developed a new approach for automatically classifying class diagrams using the approach of Convolutional Neural Network under the domain of Deep Learning. We have applied the code on Convolutional Neural Networks with and without the Regularization technique. Our tool receives JPEG/PNG/GIF/TIFF images as input and predicts whether it is a UML class diagram image or not. There is no need to tag images of class diagrams as UML class diagrams in our dataset.

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals Super Resolution pada Citra Udara menggunakan Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
MUHAMMAD EFAN ABDULFATTAH ◽  
LEDYA NOVAMIZANTI ◽  
SYAMSUL RIZAL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Super Resolution ◽  
Image Features ◽  
Scale Factor ◽  
Aerial Imagery ◽  
Aerial Vehicle

ABSTRAKBencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.Kata kunci: citra udara, deep learning, super resolution ABSTRACTDisasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.Keywords: aerial imagery, deep learning, super resolution

scholarly journals Classification of Traffic Vehicle Density Using Deep Learning

2020 ◽  
Vol 14 (1) ◽  
pp. 69
Author(s):  
Abdul Kholik ◽  
Agus Harjoko ◽  
Wahyono Wahyono
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Classification Model ◽  
Density Level ◽  
Vehicle Density ◽  
The Impact ◽  
Fold Cross Validation

The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data.

Urban management image classification approach based on deep learning

2021 ◽  
Vol 13 (5) ◽  
pp. 347-360
Author(s):  
Qinqing Kang ◽  
Xiong Ding
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Smart City ◽  
Management System ◽  
Road Traffic ◽  
Image Features ◽  
Fine Tuning ◽  
Support Vector ◽  
City Management

Based on the case images in the smart city management system, the advantage of deep learning is used to learn image features on its own, an improved deep convolutional neural network algorithm is proposed in this paper, and the algorithm is used to improve the smart city management system (hereinafter referred to as “Smart City Management”). These case images are quickly and accurately classified, the automatic classification of cases is completed in the city management system. ZCA (Zero-phase Component Analysis)-whitening is used to reduce the correlation between image data features, an eight-layer convolutional neural network model is built to classify the whitened images, and rectified linear unit (ReLU) is used in the convolutional layer to accelerate the training process, the dropout technology is used in the pooling layer, the algorithm is prevented from overfitting. Back Propagation (BP) algorithm is used for optimization in the network fine-tuning stage, the robustness of the algorithm is improved. Based on the above method, the two types of case images of road traffic and city appearance environment were subjected to two classification experiments. The accuracy has reached 97.5%, and the F1-Score has reached 0.98. The performance exceeded LSVM (Langrangian Support Vector Machine), SAE (Sparse autoencoder), and traditional CNN (Convolution Neural Network). At the same time, this method conducts four-classification experiments on four types of cases: electric vehicles, littering, illegal parking of motor vehicles, and mess around garbage bins. The accuracy is 90.5%, and the F1-Score is 0.91. The performance still exceeds LSVM, SAE and traditional CNN and other methods.

scholarly journals Evaluation of Task fMRI Decoding With Deep Learning on a Small Sample Dataset

2021 ◽  
Vol 15 ◽  
Author(s):  
Sunao Yotsutsuji ◽  
Miaomei Lei ◽  
Hiroyuki Akama
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Statistical Power ◽  
Small Sample Size ◽  
Information Leakage ◽  
Small Sample ◽  
Accuracy Evaluation ◽  
Learning Models ◽  
The Impact

Recently, several deep learning methods have been applied to decoding in task-related fMRI, and their advantages have been exploited in a variety of ways. However, this paradigm is sometimes problematic, due to the difficulty of applying deep learning to high-dimensional data and small sample size conditions. The difficulties in gathering a large amount of data to develop predictive machine learning models with multiple layers from fMRI experiments with complicated designs and tasks are well-recognized. Group-level, multi-voxel pattern analysis with small sample sizes results in low statistical power and large accuracy evaluation errors; failure in such instances is ascribed to the individual variability that risks information leakage, a particular issue when dealing with a limited number of subjects. In this study, using a small-size fMRI dataset evaluating bilingual language switch in a property generation task, we evaluated the relative fit of different deep learning models, incorporating moderate split methods to control the amount of information leakage. Our results indicated that using the session shuffle split as the data folding method, along with the multichannel 2D convolutional neural network (M2DCNN) classifier, recorded the best authentic classification accuracy, which outperformed the efficiency of 3D convolutional neural network (3DCNN). In this manuscript, we discuss the tolerability of within-subject or within-session information leakage, of which the impact is generally considered small but complex and essentially unknown; this requires clarification in future studies.

scholarly journals Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety

2021 ◽  
Vol 25 (1) ◽  
pp. 40-49
Author(s):  
Guobing Yan ◽  
◽  
Qiang Sun ◽  
Jianying Huang ◽  
Yonghong Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Recognition Algorithm ◽  
Image Features ◽  
Image Feature ◽  
Feature Extraction Method ◽  
Power Construction

Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.

scholarly journals Deep learning convolutional neural network in rainfall–runoff modelling

2020 ◽  
Vol 22 (3) ◽  
pp. 541-561 ◽  
Author(s):  
Song Pham Van ◽  
Hoang Minh Le ◽  
Dat Vi Thanh ◽  
Thanh Duc Dang ◽  
Ho Huu Loc ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Water Cycle ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Rainfall Runoff ◽  
Convolutional Network

Abstract Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

scholarly journals Deep Learning-based Pipeline to Recognize Alzheimer′s Disease using fMRI Data

2016 ◽  
Author(s):  
Saman Sarraf ◽  
Ghassem Tofighi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Biomedical Sciences ◽  
Scale Invariant

Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low- to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimer′s brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimer′s disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimer′s disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer′s subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.

scholarly journals A Novel Deep Convolutional Neural Network Based on ResNet-18 and Transfer Learning for Detection of Wood Knot Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mingyu Gao ◽  
Peng Song ◽  
Fei Wang ◽  
Junyan Liu ◽  
Andreas Mandelis ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Defect Detection ◽  
Image Features ◽  
Deep Convolutional Neural Network ◽  
Model Parameters ◽  
Basic Block ◽  
Training Time

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves wood utilization. Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so-called ReSENet-18) for wood knot defect detection that combined deep learning and transfer learning is proposed. The “squeeze-and-excitation” (SE) module is firstly embedded into the “residual basic block” structure for a “SE-Basic-Block” module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet-18 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

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