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  • Open Access

    ARTICLE

    SRI-XDFM: A Service Reliability Inference Method Based on Deep Neural Network

    Yang Yang1,*, Jianxin Wang1, Zhipeng Gao1, Yonghua Huo2, Xuesong Qiu1

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1459-1475, 2020, DOI:10.32604/iasc.2020.011688

    Abstract With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with the help of character encoding… More >

  • Open Access

    ARTICLE

    Automatic Channel Detection Using DNN on 2D Seismic Data

    Fahd A. Alhaidari1, Saleh A. Al-Dossary2, Ilyas A. Salih1,*, Abdlrhman M. Salem1, Ahmed S. Bokir1, Mahmoud O. Fares1, Mohammed I. Ahmed1, Mohammed S. Ahmed1

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 57-67, 2021, DOI:10.32604/csse.2021.013843

    Abstract Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image… More >

  • Open Access

    ARTICLE

    A Deep Learning Based Approach for Response Prediction of Beam-like Structures

    Tianyu Wang1, Wael A. Altabey1,2, Mohammad Noori3,*, Ramin Ghiasi1

    Structural Durability & Health Monitoring, Vol.14, No.4, pp. 315-338, 2020, DOI:10.32604/sdhm.2020.011083

    Abstract Beam-like structures are a class of common but important structures in engineering. Over the past few centuries, extensive research has been carried out to obtain the static and dynamic response of beam-like structures. Although building the finite element model to predict the response of these structures has proven to be effective, it is not always suitable in all the application cases because of high computational time or lack of accuracy. This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural network and the governing differential equation of Euler-Bernoulli beam. The Prandtl-Ishlinskii… More >

  • Open Access

    ARTICLE

    Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

    Gopi Krishna Durbhaka1, Barani Selvaraj1, Mamta Mittal2, Tanzila Saba3,*, Amjad Rehman3, Lalit Mohan Goyal4

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131

    Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

  • Open Access

    ARTICLE

    Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning

    Fouzia Nawaz1, Muhammad Ramzan1, Khalid Mehmood1, Hikmat Ullah Khan2, Saleem Hayat Khan3,4, Muhammad Raheel Bhutta5,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1631-1645, 2021, DOI:10.32604/cmc.2020.012887

    Abstract Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning.… More >

  • Open Access

    ARTICLE

    A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

    Lewis Nkenyereye1, Bayu Adhi Tama2, Sunghoon Lim3,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432

    Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness… More >

  • Open Access

    ARTICLE

    Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

    Kun Zhu1, Nana Zhang1, Qing Zhang2, Shi Ying1, *, Xu Wang3

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1467-1486, 2020, DOI:10.32604/cmc.2020.011415

    Abstract Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features. In addition, software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques. To address these two issues, we propose the following two solutions in this paper: (1) We leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the… More >

  • Open Access

    ARTICLE

    Adversarial Attacks on License Plate Recognition Systems

    Zhaoquan Gu1, Yu Su1, Chenwei Liu1, Yinyu Lyu1, Yunxiang Jian1, Hao Li2, Zhen Cao3, Le Wang1, *

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1437-1452, 2020, DOI:10.32604/cmc.2020.011834

    Abstract The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on LPRS directly. In this paper,… More >

  • Open Access

    ARTICLE

    DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

    Abdu Gumaei1, 2, *, Mabrook Al-Rakhami1, 2, Hussain AlSalman2, Sk. Md. Mizanur Rahman3, Atif Alamri1, 2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1033-1057, 2020, DOI:10.32604/cmc.2020.011740

    Abstract Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful… More >

  • Open Access

    ARTICLE

    Predicting Concentration of PM10 Using Optimal Parameters of Deep Neural Network

    Byoung-Doo Oha,b, Hye-Jeong Songa,b, Jong-Dae Kima,b, Chan-Young Parka,b, Yu-Seop Kima,b

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 343-350, 2019, DOI:10.31209/2019.100000095

    Abstract Accurate prediction of fine dust (PM10) concentration is currently recognized as an important problem in East Asia. In this paper, we try to predict the concentration of PM10 using Deep Neural Network (DNN). Meteorological factors, yellow dust (sand), fog, and PM10 are used as input data. We test two cases. The first case predicts the concentration of PM10 on the next day using the day’s weather forecast data. The second case predicts the concentration of PM10 on the next day using the previous day’s data. Based on this, we compare the various performance results from the DNN model. In the… More >

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