scholarly journals A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 17804-17818 ◽  
Author(s):  
Halefom Tekle Weldegebriel ◽  
Han Liu ◽  
Anwar Ul Haq ◽  
Emmanuel Bugingo ◽  
Defu Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting

scholarly journals Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy

2020 ◽  
Vol 9 (11) ◽  
pp. 3415
Author(s):  
HyunBum Kim ◽  
Juhyeong Jeon ◽  
Yeon Jae Han ◽  
YoungHoon Joo ◽  
Jonghwan Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity And Specificity ◽  
Laryngeal Cancer ◽  
Healthy Subjects ◽  
Gradient Boosting ◽  
Support Vector ◽  
Vowel Sound ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Voice changes may be the earliest signs in laryngeal cancer. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. We extracted features using the software package for speech analysis in phonetics (PRAAT) and calculated the Mel-frequency cepstral coefficients (MFCCs) from voice samples of a vowel sound of /a:/. The proposed method was tested with six algorithms: support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosted machine (LGBM), artificial neural network (ANN), one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN). Their performances were evaluated in terms of accuracy, sensitivity, and specificity. The result was compared with human performance. A total of four volunteers, two of whom were trained laryngologists, rated the same files. The 1D-CNN showed the highest accuracy of 85% and sensitivity and sensitivity and specificity levels of 78% and 93%. The two laryngologists achieved accuracy of 69.9% but sensitivity levels of 44%. Automated analysis of voice signals could differentiate subjects with laryngeal cancer from those of healthy subjects with higher diagnostic properties than those performed by the four volunteers.

scholarly journals Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network

2018 ◽  
Vol 8 (12) ◽  
pp. 2610 ◽  
Author(s):  
L. Dang ◽  
Syed Hassan ◽  
Suhyeon Im ◽  
Jaecheol Lee ◽  
Sujin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Layer Structure ◽  
Imbalanced Data ◽  
Generative Adversarial Networks ◽  
Gradient Boosting ◽  
Face Identification ◽  
Large Computer ◽  
Face Images ◽  
Extreme Gradient Boosting

Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research purposes because the number of realistic computer-generated images is still limited and scattered on the internet. Thus, a development of a novel decision support system for analyzing and detecting computer-generated face images generated by the GAN network is crucial. In this paper, we propose a customized convolutional neural network, namely CGFace, which is specifically designed for the computer-generated face detection task by customizing the number of convolutional layers, so it performs well in detecting computer-generated face images. After that, an imbalanced framework (IF-CGFace) is created by altering CGFace’s layer structure to adjust to the imbalanced data issue by extracting features from CGFace layers and use them to train AdaBoost and eXtreme Gradient Boosting (XGB). Next, we explain the process of generating a large computer-generated dataset based on the state-of-the-art PCGAN and BEGAN model. Then, various experiments are carried out to show that the proposed model with augmented input yields the highest accuracy at 98%. Finally, we provided comparative results by applying the proposed CNN architecture on images generated by other GAN researches.

scholarly journals A Hierarchical Approach for Android Malware Detection Using Authorization-Sensitive Features

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 432
Author(s):  
Hui Chen ◽  
Zhengqiang Li ◽  
Qingshan Jiang ◽  
Abdur Rasool ◽  
Lifei Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Malware Detection ◽  
Classification Model ◽  
Gradient Boosting ◽  
Basic Block ◽  
Hierarchical Approach ◽  
Android Malware ◽  
Android Malware Detection ◽  
Extreme Gradient Boosting

Android’s openness has made it a favorite for consumers and developers alike, driving strong app consumption growth. Meanwhile, its popularity also attracts attackers’ attention. Android malware is continually raising issues for the user’s privacy and security. Hence, it is of great practical value to develop a scientific and versatile system for Android malware detection. This paper presents a hierarchical approach to design a malware detection system for Android. It extracts four authorization-sensitive features: basic blocks, permissions, Application Programming Interfaces (APIs), and key functions, and layer-by-layer detects malware based on the similar module and the proposed deep learning model Convolutional Neural Network and eXtreme Gradient Boosting (CNNXGB). This detection approach focuses not only on classification but also on the details of the similarities between malware software. We serialize the key function in light of the sequence of API calls and pick up a similar module that captures the global semantics of malware. We propose a new method to convert the basic block into a multichannel picture and use Convolutional Neural Network (CNN) to learn features. We extract permissions and API calls based on their called frequency and train the classification model by XGBoost. A dynamic similar module feature library is created based on the extracted features to assess the sample’s behavior. The model is trained by utilizing 11,327 Android samples collected from Github, Google Play, Fdroid, and VirusShare. Promising experimental results demonstrate a higher accuracy of the proposed approach and its potential to detect Android malware attacks and reduce Android users’ security risks.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

scholarly journals A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

2022 ◽  
Vol 12 (1) ◽  
pp. 721
Author(s):  
Ahmed Nasser ◽  
Huthaifa AL-Khazraji
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Predictive Maintenance ◽  
Fault Prediction ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

scholarly journals ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure

2021 ◽  
Author(s):  
Oguz Akbilgic ◽  
Liam Butler ◽  
Ibrahim Karabayir ◽  
Patricia P Chang ◽  
Dalane W Kitzman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Heart Failure ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Model Output ◽  
Light Gradient

Abstract Aims Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction. Methods and results Data from the baseline visits (1987–89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age ± standard deviation of 54 ± 5) participants were eligible. A total of 803 (5.5%) participants developed HF within 10 years from baseline. Convolutional neural network utilizing solely ECG achieved an AUC of 0.756 (0.717–0.795) on the hold-out test data. ARIC and Framingham Heart Study (FHS) HF risk calculators yielded AUC of 0.802 (0.750–0.850) and 0.780 (0.740–0.830). The highest AUC of 0.818 (0.778–0.859) was obtained when ECG-AI model output, age, gender, race, body mass index, smoking status, prevalent coronary heart disease, diabetes mellitus, systolic blood pressure, and heart rate were used as predictors of HF within LGBM. The ECG-AI model output was the most important predictor of HF. Conclusions ECG-AI model based solely on information extracted from ECG independently predicts HF with accuracy comparable to existing FHS and ARIC risk calculators.

scholarly journals Prediction of Electropulse-Induced Nonlinear Temperature Variation of Mg Alloy Based on Machine Learning

2020 ◽  
Vol 58 (6) ◽  
pp. 413-422
Author(s):  
Jinyeong Yu ◽  
Myoungjae Lee ◽  
Young Hoon Moon ◽  
Yoojeong Noh ◽  
Taekyung Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Temperature Variation ◽  
High Energy ◽  
Mg Alloy ◽  
Model Complexity ◽  
Gradient Boosting ◽  
Learning Technology ◽  
Extreme Gradient Boosting ◽  
Nonlinear Temperature

Electropulse-induced heating has attracted attention due to its high energy efficiency. However, the process gives rise to a nonlinear temperature variation, which is difficult to predict using a traditional physics model. As an alternative, this study employed machine-learning technology to predict such temperature variation for the first time. Mg alloy was exposed to a single electropulse with a variety of pulse magnitudes and durations for this purpose. Nine machine-learning models were established from algorithms from artificial neural network (ANN), deep neural network (DNN), and extreme gradient boosting (XGBoost). The ANN models showed an insufficient predicting capability with respect to the region of peak temperature, where temperature varied most significantly. The DNN models were built by increasing model complexity, enhancing architectures, and tuning hyperparameters. They exhibited a remarkable improvement in predicting capability at the heating-cooling boundary as well as overall estimation. As a result, the DNN-2 model in this group showed the best prediction of nonlinear temperature variation among the machinelearning models built in this study. The XGBoost model exhibited poor predicting performance when default hyperparameters were applied. However, hyperparameter tuning of learning rates and maximum depths resulted in a decent predicting capability with this algorithm. Furthermore, XGBoost models exhibited an extreme reduction in learning time compared with the ANN and DNN models. This advantage is expected to be useful for predicting more complicated cases including various materials, multi-step electropulses, and electrically-assisted forming.

scholarly journals Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 256 ◽  
Author(s):  
Jiangyong An ◽  
Wanyi Li ◽  
Maosong Li ◽  
Sanrong Cui ◽  
Huanran Yue
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Drought Stress ◽  
Convolutional Neural Network ◽  
Extraction Process ◽  
Deep Convolutional Neural Network ◽  
Gradient Boosting ◽  
Moderate Drought ◽  
Detection And Diagnosis

Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.

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