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  • Open Access

    ARTICLE

    SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

    Yudong Zhang1, Muhammad Attique Khan2, Ziquan Zhu1, Shuihua Wang1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172 - 20 January 2023

    Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due… More >

  • Open Access

    ARTICLE

    ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

    M. Jamuna Rani1,*, C. Vasanthanayaki2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1953-1970, 2023, DOI:10.32604/csse.2023.028713 - 03 November 2022

    Abstract Underwater imagery and transmission possess numerous challenges like lower signal bandwidth, slower data transmission bit rates, Noise, underwater blue/green light haze etc. These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques. Due to the presence of blue/green light in underwater imagery, shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region. This method is proposed to efficiently deploy an Extreme Learning Machine (ELM) model-based shape adaptive Discrete Cosine Transformation (DCT) for underwater images. Underwater More >

  • Open Access

    ARTICLE

    Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

    Kisaezehra1, Muhammad Umer Farooq1,*, Muhammad Aslam Bhutto2, Abdul Karim Kazi1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 911-927, 2023, DOI:10.32604/iasc.2023.031359 - 29 September 2022

    Abstract The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain… More >

  • Open Access

    ARTICLE

    Fine Characterization and Analysis of Drying Strain of the ELM Board via DIC Technology

    Yuanchu Liu, Xiaodong Zhu, Zhengmin Jin, Yingying Liu, Qingjian Wei, Bonan Liang, Yingchun Cai*, Jingyao Zhao*

    Journal of Renewable Materials, Vol.11, No.2, pp. 567-580, 2023, DOI:10.32604/jrm.2022.023037 - 22 September 2022

    Abstract In this paper, the occurrence and development mechanism of strain on the cross-section during the wood drying is explored. Therefore, strain regularity on the cross-section of 50 mm thickness elm (Ulmus rubra) board at the temperature of 40°C and 80°C is detected via digital image correlation technology. Hence, the difference between tangential and radial strain at surface and core layers was denoted. The results showed that strain distribution in the width direction of the board is uneven. Moreover, a large drying shrinkage strain occurs at the near-core layer, while the maximum strain difference reaches 4.08%. Hence, More > Graphic Abstract

    Fine Characterization and Analysis of Drying Strain of the ELM Board via DIC Technology

  • Open Access

    ARTICLE

    Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

    Atif Ikram1,2,*, Masita Abdul Jalil1, Amir Bin Ngah1, Saqib Raza6, Ahmad Salman Khan3, Yasir Mahmood3,4, Nazri Kama4, Azri Azmi4, Assad Alzayed5

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1871-1886, 2023, DOI:10.32604/cmc.2023.030818 - 22 September 2022

    Abstract Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to… More >

  • Open Access

    ARTICLE

    RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

    Ziquan Zhu1, Muhammad Attique Khan2, Shui-Hua Wang1, Yu-Dong Zhang1,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 101-111, 2023, DOI:10.32604/cmc.2023.030790 - 22 September 2022

    Abstract Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to… More >

  • Open Access

    ARTICLE

    Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

    U. Muthaiah1,*, S. Chitra2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3447-3458, 2023, DOI:10.32604/iasc.2023.028869 - 17 August 2022

    Abstract Image processing, agricultural production, and field monitoring are essential studies in the research field. Plant diseases have an impact on agricultural production and quality. Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops. Manually identifying the agricultural pests is usually evident in plants; also, it takes more time and is an expensive technique. A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations. An atmosphere generates vast amounts of data as it is monitored closely; the evaluation of this big… More >

  • Open Access

    ARTICLE

    Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

    Ashit Kumar Dutta1,*, Nazik M. A. Zakari2, Yasser Albagory3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2195-2207, 2023, DOI:10.32604/csse.2023.026461 - 01 August 2022

    Abstract Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past… More >

  • Open Access

    ARTICLE

    PREDICTING THE WAX DEPOSITION RATE BASED ON EXTREME LEARNING MACHINE

    Qi Zhuanga,* , Zhuo Chenb, Dong Liuc, Yangyang Tiand

    Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-8, 2022, DOI:10.5098/hmt.19.19

    Abstract In order to improve the accuracy and efficiency of wax deposition rate prediction of waxy crude oil in pipeline transportation, A GRA-IPSO-ELM model was established to predict wax deposition rate. Using Grey Relational Analysis (GRA) to calculate the correlation degree between various factors and wax deposition rate, determine the input variables of the prediction model, and establish the Extreme Learning Machine (ELM) prediction model, improved particle swarm optimization (IPSO) is used to optimize the parameters of ELM model. Taking the experimental data of wax deposition in Huachi operation area as an example, the prediction performance More >

  • Open Access

    ARTICLE

    Dynamic Active Noise Control of Broadband Noise in Fighter Aircraft Pilot Helmet

    Y. K. Bharath1,*, S. Veena2

    Sound & Vibration, Vol.56, No.4, pp. 319-331, 2022, DOI:10.32604/sv.2022.015634 - 03 March 2023

    Abstract This paper presents the development of a dynamic Active Noise Control (ANC) algorithm aimed towards reducing the broadband noise inside the helmet earcups of a fighter aircraft pilot helmet. The dynamic ANC involves a Variable Step-Size Griffiths (VSSG) FxLMS algorithm to attenuate noise entering directly through helmet, a LMS based adaptive noise canceller to attenuate noise entering through the pilot microphone, and energy detectors for failure protection and optimized battery power usage. The algorithms are implemented on Texas Instruments’ TMS320C6748 processor and are tested in a helmet ANC experimental setup. More >

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