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  • Open Access


    Context Awareness by Noise-Pattern Analysis of a Smart Factory

    So-Yeon Lee1, Jihoon Park1, Dae-Young Kim2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1497-1514, 2023, DOI:10.32604/cmc.2023.034914

    Abstract Recently, to build a smart factory, research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology, a field of artificial intelligence. Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset. However, compared to numerical raw data, learning based on image data has the disadvantage that creating a training dataset is very time-consuming. Therefore, we devised a two-step data preprocessing… More >

  • Open Access


    Radon CLF: A Novel Approach for Skew Detection Using Radon Transform

    Yuhang Chen1, Mahdi Bahaghighat2,*, Aghil Esmaeili Kelishomi3, Jingyi Du1,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 675-697, 2023, DOI:10.32604/csse.2023.038234

    Abstract In the digital world, a wide range of handwritten and printed documents should be converted to digital format using a variety of tools, including mobile phones and scanners. Unfortunately, this is not an optimal procedure, and the entire document image might be degraded. Imperfect conversion effects due to noise, motion blur, and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition (OCR) systems. In Document Image Analysis Systems (DIAS), skew estimation of images is a crucial step. In this paper, a novel, fast, and reliable skew detection… More >

  • Open Access


    Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

    Bareen Shamsaldeen Tahir1, Zainab Salih Ageed2, Sheren Sadiq Hasan3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4009-4024, 2023, DOI:10.32604/cmc.2023.037433

    Abstract Traditional indoor human activity recognition (HAR) is a time-series data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problem-solving technique in the HAR system have recently been deep learning (DL). The study develops… More >

  • Open Access


    A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

    Waleed Akbar, Afaq Muhammad, Wang-Cheol Song*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3743-3759, 2023, DOI:10.32604/iasc.2023.036440

    Abstract Data is growing quickly due to a significant increase in social media applications. Today, billions of people use an enormous amount of data to access the Internet. The backbone network experiences a substantial load as a result of an increase in users. Users in the same region or company frequently ask for similar material, especially on social media platforms. The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user. Applications that require relatively low latency can use Content Delivery Network (CDN) technology to meet their requirements. An edge and the… More >

  • Open Access


    Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

    Kainat Ibrar1, Abdul Muiz Fayyaz1, Muhammad Attique Khan2, Majed Alhaisoni3, Usman Tariq4, Seob Jeon5, Yunyoung Nam6,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2351-2368, 2023, DOI:10.32604/csse.2023.036185

    Abstract Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains. Gait is a person’s identity that can reflect reliable information about his mood, emotions, and substantial personality traits under scrutiny. This research focuses on recognizing key personality traits, including neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, in line with the big-five model of personality. We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors. For experimentation, we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks… More >

  • Open Access


    Numerical Comparison of Shapeless Radial Basis Function Networks in Pattern Recognition

    Sunisa Tavaen, Sayan Kaennakham*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4081-4098, 2023, DOI:10.32604/cmc.2023.032329

    Abstract This work focuses on radial basis functions containing no parameters with the main objective being to comparatively explore more of their effectiveness. For this, a total of sixteen forms of shapeless radial basis functions are gathered and investigated under the context of the pattern recognition problem through the structure of radial basis function neural networks, with the use of the Representational Capability (RC) algorithm. Different sizes of datasets are disturbed with noise before being imported into the algorithm as ‘training/testing’ datasets. Each shapeless radial basis function is monitored carefully with effectiveness criteria including accuracy, condition number (of the interpolation matrix),… More >

  • Open Access


    A Prototype for Diagnosis of Psoriasis in Traditional Chinese Medicine

    Hai Long1, Zhe Wang1, Yidi Cui2,3, Junhui Wang4, Bo Gao5, Chao Chen5, Yan Zhu5,*, Heinrich Herre1

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5197-5217, 2022, DOI:10.32604/cmc.2022.029365

    Abstract Psoriasis is a chronic, non-communicable, painful, disfiguring and disabling disease for which there is no cure, with great negative impact on patients’ quality of life (QoL). Diagnosis and treatment with traditional Chinese medical technique based on syndrome differentiation has been used in practice for a long time and proven effective, though, up to now, there are only a few available studies about the use of semantic technologies and the knowledge systems that use Traditional Chinese Medicine (TCM)-syndrome differentiation for information retrieval and automated reasoning. In this paper we use semantic techniques based on ontologies to develop a prototypical system for… More >

  • Open Access


    Deep Learning Based Power Transformer Monitoring Using Partial Discharge Patterns

    D. Karthik Prabhu1,*, R. V. Maheswari2, B. Vigneshwaran2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1441-1454, 2022, DOI:10.32604/iasc.2022.024128

    Abstract Measurement and recognition of Partial Discharge (PD) in power apparatus is considered a protuberant tool for condition monitoring and assessing the state of a dielectric system. During operating conditions, PD may occur either in the form of single and multiple patterns in nature. Currently, for PD pattern recognition, deep learning approaches are used. To evaluate spatial order less features from the large-scale patterns, a pre-trained network is used. The major drawback of traditional approaches is that they generate high dimensional data or requires additional steps like dictionary learning and dimensionality reduction. However, in real-time applications, interference incorporated in the measured… More >

  • Open Access


    On-line Recognition of Abnormal Patterns in Bivariate Autocorrelated Process Using Random Forest

    Miao Xu1, Bo Zhu1,*, Chunmei Chen1, Yuwei Wan2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1707-1722, 2022, DOI:10.32604/cmc.2022.027708

    Abstract It is not uncommon that two or more related process quality characteristics are needed to be monitored simultaneously in production process for most of time. Meanwhile, the observations obtained online are often serially autocorrelated due to high sampling frequency and process dynamics. This goes against the statistical I.I.D assumption in using the multivariate control charts, which may lead to the performance of multivariate control charts collapse soon. Meanwhile, the process control method based on pattern recognition as a non-statistical approach is not confined by this limitation, and further provide more useful information for quality practitioners to locate the assignable causes… More >

  • Open Access


    Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

    D. Venugopal1, V. Mohan2, S. Ramesh3, S. Janupriya4, Sangsoon Lim5,*, Seifedine Kadry6

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 545-558, 2022, DOI:10.32604/csse.2022.024239

    Abstract In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc. It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform. Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on. In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification. In this view, this paper designs pattern recognition of communication signal modulation using fractal features… More >

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