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  • Open Access

    ARTICLE

    Simulation of Non-Isothermal Turbulent Flows Through Circular Rings of Steel

    Abid. A. Memon1, M. Asif Memon1, Kaleemullah Bhatti1, Kamsing Nonlaopon2,*, Ilyas Khan3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4341-4355, 2022, DOI:10.32604/cmc.2022.019407

    Abstract This article is intended to examine the fluid flow patterns and heat transfer in a rectangular channel embedded with three semi-circular cylinders comprised of steel at the boundaries. Such an organization is used to generate the heat exchangers with tube and shell because of the production of more turbulence due to zigzag path which is in favor of rapid heat transformation. Because of little maintenance, the heat exchanger of such type is extensively used. Here, we generate simulation of flow and heat transfer using non-isothermal flow interface in the Comsol multiphysics 5.4 which executes the Reynolds averaged Navier stokes equation… More >

  • Open Access

    ARTICLE

    Efficient Deep CNN Model for COVID-19 Classification

    Walid El-Shafai1,2,*, Amira A. Mahmoud1, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Mohammed Abd-Elnaby3, Fathi E. Abd El-Samie1,4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4373-4391, 2022, DOI:10.32604/cmc.2022.019354

    Abstract Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate… More >

  • Open Access

    ARTICLE

    Ontology Driven Testing Strategies for IoT Applications

    Muhammad Raza Naqvi1, Muhammad Waseem Iqbal2, Muhammad Usman Ashraf3, Shafiq Ahmad4, Ahmed T. Soliman4, Shahzada Khurram5, Muhammad Shafiq6,*, Jin-Ghoo Choi6

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5855-5869, 2022, DOI:10.32604/cmc.2022.019188

    Abstract Internet-of-Things (IoT) has attained a major share in embedded software development. The new era of specialized intelligent systems requires adaptation of customized software engineering approaches. Currently, software engineering has merged the development phases with the technologies provided by industrial automation. The improvements are still required in testing phase for the software developed to IoT solutions. This research aims to assist in developing the testing strategies for IoT applications, therein ontology has been adopted as a knowledge representation technique to different software engineering processes. The proposed ontological model renders 101 methodology by using Protégé. After completion, the ontology was evaluated in… More >

  • Open Access

    ARTICLE

    A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

    Jhonn Pablo Rodríguez1,*, David Camilo Corrales1,2, David Griol3, Zoraida Callejas3, Juan Carlos Corrales1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4725-4743, 2022, DOI:10.32604/cmc.2022.019135

    Abstract Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In this paper, we propose a… More >

  • Open Access

    ARTICLE

    Malaria Parasite Detection Using a Quantum-Convolutional Network

    Javaria Amin1 , Muhammad Almas Anjum2 , Abida Sharif3 , Mudassar Raza4 , Seifedine Kadry5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6023-6039, 2022, DOI:10.32604/cmc.2022.019115

    Abstract

    Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the… More >

  • Open Access

    ARTICLE

    Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

    Talha Saeed, Chu Kiong Loo*, Muhammad Shahreeza Safiruz Kassim

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076

    Abstract

    Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

  • Open Access

    ARTICLE

    Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

    Nithya Rekha Sivakumar1,*, Ahmed Zohair Ibrahim2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4467-4483, 2022, DOI:10.32604/cmc.2022.019041

    Abstract

    Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-and-forward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there… More >

  • Open Access

    ARTICLE

    Fuzzy Based Latent Dirichlet Allocation for Intrusion Detection in Cloud Using ML

    S. Ranjithkumar1,*, S. Chenthur Pandian2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4261-4277, 2022, DOI:10.32604/cmc.2022.019031

    Abstract The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system (IDS). IDS are considered as an essential factor to fulfill security requirements. Recently, there are diverse Machine Learning (ML) approaches that are used for modeling effectual IDS. Most IDS are based on ML techniques and categorized as supervised and unsupervised. However, IDS with supervised learning is based on labeled data. This is considered as a common drawback and it fails to identify the attack patterns. Similarly, unsupervised learning fails to… More >

  • Open Access

    ARTICLE

    An Improved Convolutional Neural Network Model for DNA Classification

    Naglaa. F. Soliman1,*, Samia M. Abd-Alhalem2 , Walid El-Shafai2 , Salah Eldin S. E. Abdulrahman3, N. Ismaiel3 , El-Sayed M. El-Rabaie2 , Abeer D. Algarni1, Fathi E. Abd El-Samie1,2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5907-5927, 2022, DOI:10.32604/cmc.2022.018860

    Abstract

    Recently, deep learning (DL) became one of the essential tools in bioinformatics. A modified convolutional neural network (CNN) is employed in this paper for building an integrated model for deoxyribonucleic acid (DNA) classification. In any CNN model, convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features. A novel method based on downsampling and CNNs is introduced for feature reduction. The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy. The two-dimensional discrete transform (2D DT) and two-dimensional random projection (2D RP) methods are applied for downsampling.… More >

  • Open Access

    ARTICLE

    Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

    Imran Ashraf, Sadia Din, Soojung Hur, Yongwan Park*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5213-5232, 2022, DOI:10.32604/cmc.2022.018707

    Abstract Precise information on indoor positioning provides a foundation for position-related customer services. Despite the emergence of several indoor positioning technologies such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, Wi-Fi is one of the most widely used technologies. Predominantly, Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades. Wi-Fi positioning faces three core problems: device heterogeneity, robustness to signal changes caused by human mobility, and device attitude, i.e., varying orientations. The existing methods do not cover these aspects owing to the unavailability of publicly available datasets. This… More >

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