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  • Open Access

    ARTICLE

    GrCol-PPFL: User-Based Group Collaborative Federated Learning Privacy Protection Framework

    Jieren Cheng1, Zhenhao Liu1,*, Yiming Shi1, Ping Luo1,2, Victor S. Sheng3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1923-1939, 2023, DOI:10.32604/cmc.2023.032758

    Abstract With the increasing number of smart devices and the development of machine learning technology, the value of users’ personal data is becoming more and more important. Based on the premise of protecting users’ personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data. However, since FL can still leak the user's original data by exchanging gradient information. The existing privacy protection strategy will increase the uplink time due to encryption measures. It is a huge challenge in terms of communication. When there are a… More >

  • Open Access

    ARTICLE

    A Survey on Image Semantic Segmentation Using Deep Learning Techniques

    Jieren Cheng1,3, Hua Li2,*, Dengbo Li3, Shuai Hua2, Victor S. Sheng4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757

    Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >

  • Open Access

    ARTICLE

    A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

    Zahid Ullah1,*, Mona Jamjoom2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1457-1473, 2023, DOI:10.32604/cmc.2023.032752

    Abstract Alzheimer’s disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD’s progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such… More >

  • Open Access

    ARTICLE

    Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

    Chih-Ta Yen1,*, Tz-Yun Chen2, Un-Hung Chen3, Guo-Chang Wang3, Zong-Xian Chen3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 83-99, 2023, DOI:10.32604/cmc.2023.032739

    Abstract A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their… More >

  • Open Access

    ARTICLE

    Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

    Rashid Jahangir1,*, Muhammad Asif Nauman2, Roobaea Alroobaea3, Jasem Almotiri3, Muhammad Mohsin Malik1, Sabah M. Alzahrani3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1069-1091, 2023, DOI:10.32604/cmc.2023.032719

    Abstract Environmental sound classification (ESC) involves the process of distinguishing an audio stream associated with numerous environmental sounds. Some common aspects such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording make the ESC task much more complicated and complex. This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources. In this research, the performance of transformer and convolutional neural networks (CNN) are investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta… More >

  • Open Access

    ARTICLE

    Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

    Yun Fen Yong1,*, Chee Keong Tan1, Ian Kim Teck Tan2, Su Wei Tan1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710

    Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

  • Open Access

    ARTICLE

    Liver Ailment Prediction Using Random Forest Model

    Fazal Muhammad1,*, Bilal Khan2, Rashid Naseem3, Abdullah A Asiri4, Hassan A Alshamrani4, Khalaf A Alshamrani4, Samar M Alqhtani5, Muhammad Irfan6, Khlood M Mehdar7, Hanan Talal Halawani8

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1049-1067, 2023, DOI:10.32604/cmc.2023.032698

    Abstract Today, liver disease, or any deterioration in one’s ability to survive, is extremely common all around the world. Previous research has indicated that liver disease is more frequent in younger people than in older ones. When the liver’s capability begins to deteriorate, life can be shortened to one or two days, and early prediction of such diseases is difficult. Using several machine learning (ML) approaches, researchers analyzed a variety of models for predicting liver disorders in their early stages. As a result, this research looks at using the Random Forest (RF) classifier to diagnose the liver disease early on. The… More >

  • Open Access

    ARTICLE

    Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

    Atif Ishaq Khan*, Syed Asad Raza Kazmi, Awais Qasim

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1183-1197, 2023, DOI:10.32604/cmc.2023.032691

    Abstract A self-adaptive resource provisioning on demand is a critical factor in cloud computing. The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests. Therefore, a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload. In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy (CARSS) Framework that formally addresses these issues and is more expressive than traditional approaches. The decision making in CARSS is based on more than one factors. The MAPE-K based framework determines the state of the… More >

  • Open Access

    ARTICLE

    Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

    Amir Yasseen Mahdi1,2,*, Siti Sophiayati Yuhaniz1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1375-1392, 2023, DOI:10.32604/cmc.2023.032671

    Abstract The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text… More >

  • Open Access

    ARTICLE

    Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

    Farrukh Shahzad Ahmed, Awais Majeed, Tamim Ahmed Khan*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2211-2238, 2023, DOI:10.32604/cmc.2023.032664

    Abstract Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP… More >

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