Reliability Analysis Using Deep Learning

2018 ◽  
Author(s):  
Chong Chen ◽  
Ying Liu ◽  
Xianfang Sun ◽  
Shixuan Wang ◽  
Carla Di Cairano-Gilfedder ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Reliability Analysis ◽  
General Principle ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Empirical Results ◽  
Time Between Failures ◽  
Learning Techniques ◽  
Deep Learning Model

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and nonlinearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

scholarly journals Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoting Yin ◽  
Xiaosha Tao
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Process ◽  
Deep Learning Algorithm ◽  
Sales Prediction ◽  
Different Types ◽  
Online Business ◽  
Product Sales ◽  
Deep Learning Model

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.

scholarly journals Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model

2020 ◽  
Vol 10 (21) ◽  
pp. 7751
Author(s):  
Seong-Jae Hong ◽  
Won-Kyung Baek ◽  
Hyung-Sup Jung
Keyword(s):  
Deep Learning ◽  
Learning Process ◽  
Intensity Difference ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Model ◽  
Learning Techniques ◽  
Vessel Detection ◽  
Intensity Images ◽  
Deep Learning Model

Synthetic aperture radar (SAR) images have been used in many studies for ship detection because they can be captured without being affected by time and weather. In recent years, the development of deep learning techniques has facilitated studies on ship detection in SAR images using deep learning techniques. However, because the noise from SAR images can negatively affect the learning of the deep learning model, it is necessary to reduce the noise through preprocessing. In this study, deep learning vessel detection was performed using preprocessed SAR images, and the effects of the preprocessing of the images on deep learning vessel detection were compared and analyzed. Through the preprocessing of SAR images, (1) intensity images, (2) decibel images, and (3) intensity difference and texture images were generated. The M2Det object detection model was used for the deep learning process and preprocessed SAR images. After the object detection model was trained, ship detection was performed using test images. The test results are presented in terms of precision, recall, and average precision (AP), which were 93.18%, 91.11%, and 89.78% for the intensity images, respectively, 94.16%, 94.16%, and 92.34% for the decibel images, respectively, and 97.40%, 94.94%, and 95.55% for the intensity difference and texture images, respectively. From the results, it can be found that the preprocessing of the SAR images can facilitate the deep learning process and improve the ship detection performance. The results of this study are expected to contribute to the development of deep learning-based ship detection techniques in SAR images in the future.

Deep learning model for preicting multistage cyber attacks

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
Ayei E. Ibor ◽  
Florence A. Oladeji ◽  
Olusoji B. Okunoye ◽  
Charles O. Uwadia
Keyword(s):  
Deep Learning ◽  
Cyber Attacks ◽  
Alert Correlation ◽  
Unauthorized Access ◽  
System Parameters ◽  
Disruptive Technologies ◽  
Learning Techniques ◽  
Class Labels ◽  
New Applications ◽  
Deep Learning Model

The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel – with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.Keywords: Alert correlation, Cyberattack prediction, Cybersecurity, Deep learning, Cyberattacks, Supervised and Unsupervised LearningVol. 26 No 1, June 2019

scholarly journals Data mining in web personalization using the blended deep learning model

2020 ◽  
Vol 20 (3) ◽  
pp. 1507
Author(s):  
Qusay Abdullah Abed ◽  
Osamah Mohammed Fadhil ◽  
Wathiq Laftah Al-Yaseen
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Web Application ◽  
Large Data ◽  
Learning Model ◽  
Multidimensional Data ◽  
Data Sets ◽  
Web Personalization ◽  
Deep Learning Model ◽  
The Web

In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model.

scholarly journals Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN

2022 ◽  
Vol 3 (4) ◽  
pp. 322-335
Author(s):  
C. R. Nagarathna ◽  
M. Kusuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Hybrid Model ◽  
Magnetic Resonance Images ◽  
Medical Field ◽  
The Past ◽  
Learning Techniques ◽  
Learning Technique ◽  
Deep Learning Model

Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.

scholarly journals Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Noha E. El-Attar ◽  
Mohamed K. Hassan ◽  
Othman A. Alghamdi ◽  
Wael A. Awad
Keyword(s):  
Deep Learning ◽  
Essential Oil ◽  
Biological Activities ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Chemical Compositions ◽  
Linear Processes ◽  
Learning Techniques ◽  
Important Trend ◽  
Deep Learning Model

AbstractReliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

scholarly journals A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Antonio Hernández-Blanco ◽  
Boris Herrera-Flores ◽  
David Tomás ◽  
Borja Navarro-Colorado
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Learning Approaches ◽  
Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

scholarly journals MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content

2021 ◽  
Vol 11 (18) ◽  
pp. 8701
Author(s):  
Pranav Kompally ◽  
Sibi Chakkaravarthy Sethuraman ◽  
Steven Walczak ◽  
Samuel Johnson ◽  
Meenalosini Vimal Cruz
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
System Level ◽  
Learning Approach ◽  
Unique Identifier ◽  
Detection Systems ◽  
Learning Techniques ◽  
Cyberbullying Detection ◽  
Textual Content ◽  
Deep Learning Model

Cyberbullying is a growing and significant problem in today’s workplace. Existing automated cyberbullying detection solutions rely on machine learning and deep learning techniques. It is proven that the deep learning-based approaches produce better accuracy for text-based classification than other existing approaches. A novel decentralized deep learning approach called MaLang is developed to detect abusive textual content. MaLang is deployed at two levels in a network: (1) the System Level and (2) the Cloud Level, to tackle the usage of toxic or abusive content on any messaging application within a company’s networks. The system-level module consists of a simple deep learning model called CASE that reads the user’s messaging data and classifies them into abusive and non-abusive categories, without sending any raw or readable data to the cloud. Identified abusive messages are sent to the cloud module with a unique identifier to keep user profiles hidden. The cloud module, called KIPP, utilizes deep learning to determine the probability of a message containing different categories of toxic content, such as: ‘Toxic’, ‘Insult’, ‘Threat’, or ‘Hate Speech’. MaLang achieves a 98.2% classification accuracy that outperforms other current cyberbullying detection systems.

scholarly journals Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3940-3943
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Maintenance Cost ◽  
Support Vector ◽  
Machine Condition Monitoring ◽  
Learning Techniques ◽  
Catastrophic Failures ◽  
Better Than

Gearbox is an important component used for automobiles, machine tools, industries etc. Failure of any component in gearbox will cause huge maintenance cost and production loss. Failure should be detected as early as possible in order to avoid sudden breakdown which even cause catastrophic failures. Vibration signals are used for machine condition monitoring for predictive maintenance and efficiently predicts fault in the gearbox. In this paper signals from vibration is used for diagnosis of gearbox fault. The experiment uses four different conditions of gearbox in four different load conditions. Then statistical feature extraction is done and obtained result is given to Decision Tree, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for fault diagnosis. The efficiency of these four techniques is compared and shows that machine learning is better than deep learning in gearbox fault diagnosis.

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