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  • Open Access


    Classification and Research of Skin Lesions Based on Machine Learning

    Jian Liu1, Wantao Wang1, Jie Chen2, *, Guozhong Sun3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1187-1200, 2020, DOI:10.32604/cmc.2020.05883

    Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of More >

  • Open Access


    Scalable Skin Lesion Multi-Classification Recognition System

    Fan Liu1, Jianwei Yan2, Wantao Wang2, Jian Liu2, *, Junying Li3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 801-816, 2020, DOI:10.32604/cmc.2020.07039

    Abstract Skin lesion recognition is an important challenge in the medical field. In this paper, we have implemented an intelligent classification system based on convolutional neural network. First of all, this system can classify whether the input image is a dermascopic image with an accuracy of 99%. And then diagnose the dermoscopic image and the non-skin mirror image separately. Due to the limitation of the data, we can only realize the recognition of vitiligo by non-skin mirror. We propose a vitiligo recognition based on the probability average of three structurally identical CNN models. The method is More >

  • Open Access


    A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

    Sasan Saqaeeyan1, Hamid Haj Seyyed Javadi1,2,*, Hossein Amirkhani1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 815-834, 2019, DOI:10.32604/cmes.2019.07848

    Abstract Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone. Compared to the previous studies done on this topic, less attention has been given to hybrid methods. This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home. First, it employs various algorithms with different characteristics to detect anomalies from sensory data. Then, it aggregates their results using a Bayesian network. In this Bayesian network, abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods. Experimental evaluation More >

  • Open Access


    Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

    Yanzhen Wang1, Xuefeng Yan1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.1, pp. 123-144, 2019, DOI:10.32604/cmes.2019.07052

    Abstract A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between variables. By contrast, ensemble models can effectively solve this problem. Three key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel, the diversity between subsample sets and the optimal ensemble method. This study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the model. Our proposed method first uses a bagging algorithm to generate multiple subsample sets. Second, an indicator vector is defined to describe these subsample sets. More >

  • Open Access


    RETRACTED: A Hybrid Nonlinear Active Noise Control Method Using Chebyshev Nonlinear Filter

    Bin Chen1, *, Shuyue Yu1, Yan Gao2

    Sound & Vibration, Vol.52, No.4, pp. 21-27, 2018, DOI:10.32604/sv.2018.03974

    Abstract Investigations into active noise control (ANC) technique have been conducted with the aim of effective control of the low-frequency noise. In practice, however, the performance of currently available ANC systems degrades due to the effects of nonlinearity in the primary and secondary paths, primary noise and louder speaker. This paper proposes a hybrid control structure of nonlinear ANC system to control the non-stationary noise produced by the rotating machinery on the nonlinear primary path. A fast version of ensemble empirical mode decomposition is used to decompose the non-stationary primary noise into intrinsic mode functions, which More >

  • Open Access


    Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

    Jun Wu1,*, Kui Hu1, Yiwei Cheng2, Ji Wang1, Chao Deng2,*, Yuanhan Wang3

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

    Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds More >

  • Open Access


    Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method

    Wentao Zhao1, Pan Li1,*, Chengzhang Zhu1,2, Dan Liu1, Xiao Liu1

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 817-832, 2019, DOI:10.32604/cmc.2019.05957

    Abstract The defense techniques for machine learning are critical yet challenging due to the number and type of attacks for widely applied machine learning algorithms are significantly increasing. Among these attacks, the poisoning attack, which disturbs machine learning algorithms by injecting poisoning samples, is an attack with the greatest threat. In this paper, we focus on analyzing the characteristics of positioning samples and propose a novel sample evaluation method to defend against the poisoning attack catering for the characteristics of poisoning samples. To capture the intrinsic data characteristics from heterogeneous aspects, we first evaluate training data More >

  • Open Access


    An Ensemble Based Hand Vein Pattern Authentication System

    M. Rajalakshmi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 209-220, 2018, DOI:10.3970/cmes.2018.114.209

    Abstract Amongst several biometric traits, Vein pattern biometric has drawn much attention among researchers and diverse users. It gains its importance due to its difficulty in reproduction and inherent security advantages. Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics. However, most implementations have been based on small datasets due to the difficulties in obtaining samples. In this paper, a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks (CNN) using a larger dataset. Also, modifications are suggested for implementation using More >

  • Open Access


    Differing Mapping using Ensemble of Metamodels for Global Variable-fidelity Metamodeling

    Qi Zhou1, Xinyu Shao1, Ping Jiang1,2, Longchao Cao1, Hui Zhou1, Leshi Shu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.106, No.5, pp. 323-355, 2015, DOI:10.3970/cmes.2015.106.323

    Abstract Computational simulation models with different fidelity have been widely used in complex systems design. However, running the most accurate simulation models tends to be very time-consuming and can therefore only be used sporadically, while incorporating less accurate, inexpensive models into the design process may result in inaccurate design alternatives. To make a trade-off between high accuracy and low expense, variable fidelity (VF) metamodeling approaches that aim to integrate information from both low fidelity (LF) and high-fidelity (HF) models have gained increasing popularity. In this paper, a Difference Mapping Framework using Ensemble of Metamodels (DMF-EM) for… More >

  • Open Access


    A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

    Changting Shi1, *

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 635-652, 2018, DOI:10.32604/cmc.2018.03754

    Abstract In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. More >

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