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  • Open Access

    ARTICLE

    A GDPR Compliant Approach to Assign Risk Levels to Privacy Policies

    Abdullah R. Alshamsan1, Shafique A. Chaudhry1,2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4631-4647, 2023, DOI:10.32604/cmc.2023.034039

    Abstract Data privacy laws require service providers to inform their customers on how user data is gathered, used, protected, and shared. The General Data Protection Regulation (GDPR) is a legal framework that provides guidelines for collecting and processing personal information from individuals. Service providers use privacy policies to outline the ways an organization captures, retains, analyzes, and shares customers’ data with other parties. These policies are complex and written using legal jargon; therefore, users rarely read them before accepting them. There exist a number of approaches to automating the task of summarizing privacy policies and assigning risk levels. Most of the… More >

  • Open Access

    ARTICLE

    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

    Noureen Talpur1,*, Said Jadid Abdulkadir1, Mohd Hilmi Hasan1, Hitham Alhussian1, Ayed Alwadain2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5799-5820, 2023, DOI:10.32604/cmc.2023.034025

    Abstract Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science. Since medical science is a sensitive field, the pre-processing of medical data requires careful handling to make quality clinical decisions. Generally, medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features. These factors indirectly upset the disease prediction and classification accuracy of any ML model. To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. However, the majority of such techniques frequently suffer from local minima issues… More >

  • Open Access

    ARTICLE

    Impact of Artificial Compressibility on the Numerical Solution of Incompressible Nanofluid Flow

    Tohid Adibi1, Shams Forruque Ahmed2,*, Seyed Esmail Razavi3, Omid Adibi4, Irfan Anjum Badruddin5, Syed Javed5

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5123-5139, 2023, DOI:10.32604/cmc.2023.034008

    Abstract The numerical solution of compressible flows has become more prevalent than that of incompressible flows. With the help of the artificial compressibility approach, incompressible flows can be solved numerically using the same methods as compressible ones. The artificial compressibility scheme is thus widely used to numerically solve incompressible Navier-Stokes equations. Any numerical method highly depends on its accuracy and speed of convergence. Although the artificial compressibility approach is utilized in several numerical simulations, the effect of the compressibility factor on the accuracy of results and convergence speed has not been investigated for nanofluid flows in previous studies. Therefore, this paper… More >

  • Open Access

    ARTICLE

    An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

    Ala Saleh Alluhaidan1,*, Pushparaj2, Anitha Subbappa3, Ved Prakash Mishra4, P. V. Chandrika5, Anurika Vaish6, Sarthak Sengupta6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5821-5836, 2023, DOI:10.32604/cmc.2023.033995

    Abstract ESystems based on EHRs (Electronic health records) have been in use for many years and their amplified realizations have been felt recently. They still have been pioneering collections of massive volumes of health data. Duplicate detections involve discovering records referring to the same practical components, indicating tasks, which are generally dependent on several input parameters that experts yield. Record linkage specifies the issue of finding identical records across various data sources. The similarity existing between two records is characterized based on domain-based similarity functions over different features. De-duplication of one dataset or the linkage of multiple data sets has become… More >

  • Open Access

    ARTICLE

    CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

    Abishek Mahesh1, Deeptimaan Banerjee1, Ahona Saha1, Manas Ranjan Prusty2,*, A. Balasundaram2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5967-5982, 2023, DOI:10.32604/cmc.2023.033920

    Abstract A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

    Jakkrit Suttanuruk1, Sajjakaj Jomnonkwao1,*, Vatanavong Ratanavaraha1, Sarunya Kanjanawattana2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5573-5588, 2023, DOI:10.32604/cmc.2023.033900

    Abstract Thailand has been on the World Health Organization (WHO)’s notorious deadliest road list for several years, currently ranking eighth on the list. Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts. Passenger overloading is illegal, but it is often overlooked. The country often uses police checkpoints to enforce traffic laws. However, there are few or no highway patrols to apprehend offending drivers. Therefore, in this study, we propose the use of existing closed-circuit television… More >

  • Open Access

    ARTICLE

    Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

    Muhammad Umair1,*, Zafar Saeed1, Faisal Saeed2, Hiba Ishtiaq1, Muhammad Zubair1, Hala Abdel Hameed3,4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5431-5446, 2023, DOI:10.32604/cmc.2023.033884

    Abstract As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and… More >

  • Open Access

    ARTICLE

    Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

    Ibrar Amin1, Saima Hassan1, Samir Brahim Belhaouari2,*, Muhammad Hamza Azam3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860

    Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results.… More >

  • Open Access

    ARTICLE

    A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

    Farah Batool1, Abdul Rehman2, Dongsun Kim2,*, Assad Abbas1, Raheel Nawaz3, Tahir Mustafa Madni1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6535-6553, 2023, DOI:10.32604/cmc.2023.033832

    Abstract The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with the higher value of α… More >

  • Open Access

    ARTICLE

    A Transaction Frequency Based Trust for E-Commerce

    Dong Huang1,*, Sean Xu2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5319-5329, 2023, DOI:10.32604/cmc.2023.033798

    Abstract Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration, which makes it easy for one party of the transaction to obtain a high trust value in a short time, and brings many disadvantages, uncertainties and even attacks. To solve this problem, a transaction frequency based trust is proposed in this study. The proposed method is composed of two parts. The first part is built on the classic Bayes analysis based trust models which are ease of computing for the E-commerce system. The second part is the transaction frequency module which can… More >

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