Journals / CMC / Vol.68, No.1
Table of Content


  • ARTICLE

    An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification

    Gibrael Abosamra*, Hadi Oqaibi
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1-28, 2021, DOI:10.32604/cmc.2021.015318
    (This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
    Abstract Even though much advancements have been achieved with regards to the recognition of handwritten characters, researchers still face difficulties with the handwritten character recognition problem, especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset (EMNIST). The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability. Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset. The presence of intra-class variability is mainly due to different shapes written by different writers for the same character. In this… More >

  • ARTICLE

    A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

    Kamran Sadiq Awan1, Tahir Mahmood1, Mohammad Shorfuzzaman2, Rashid Ali3, Raja Majid Mehmood4,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 29-43, 2021, DOI:10.32604/cmc.2021.014824
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions,… More >

  • ARTICLE

    Interference Mitigation in D2D Communication Underlying Cellular Networks: Towards Green Energy

    Rana Zeeshan Ahamad1, Abdul Rehman Javed2,*, Shakir Mehmood3, Mohammad Zubair Khan4, Abdulfattah Noorwali5, Muhammad Rizwan6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 45-58, 2021, DOI:10.32604/cmc.2021.016082
    (This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract Device to Device (D2D) communication is emerging as a new participant promising technology in 5G cellular networks to promote green energy networks. D2D communication can improve communication delays, spectral efficiency, system capacity, data off-loading, and many other fruitful scenarios where D2D can be implemented. Nevertheless, induction of D2D communication in reuse mode with the conventional cellular network can cause severe interference issues, which can significantly degrade network performance. To reap all the benefits of induction of D2D communication with conventional cellular communication, it is imperative to minimize interference’s detrimental effects. Efficient power control can minimize the negative effects of interference… More >

  • ARTICLE

    A New BEM for Fractional Nonlinear Generalized Porothermoelastic Wave Propagation Problems

    Mohamed Abdelsabour Fahmy1,2,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 59-76, 2021, DOI:10.32604/cmc.2021.015115
    Abstract The main purpose of the current article is to develop a novel boundary element model for solving fractional-order nonlinear generalized porothermoelastic wave propagation problems in the context of temperature-dependent functionally graded anisotropic (FGA) structures. The system of governing equations of the considered problem is extremely very difficult or impossible to solve analytically due to nonlinearity, fractional order diffusion and strongly anisotropic mechanical and physical properties of considered porous structures. Therefore, an efficient boundary element method (BEM) has been proposed to overcome this difficulty, where, the nonlinear terms were treated using the Kirchhoff transformation and the domain integrals were treated using… More >

  • ARTICLE

    Tamper Detection and Localization for Quranic Text Watermarking Scheme Based on Hybrid Technique

    Ali A. R. Alkhafaji*, Nilam Nur Amir Sjarif, M. A. Shahidan, Nurulhuda Firdaus Mohd Azmi, Haslina Md Sarkan, Suriayati Chuprat
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 77-102, 2021, DOI:10.32604/cmc.2021.015770
    (This article belongs to this Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)
    Abstract The text of the Quran is principally dependent on the Arabic language. Therefore, improving the security and reliability of the Quran’s text when it is exchanged via internet networks has become one of the most difficult challenges that researchers face today. Consequently, the diacritical marks in the Holy Quran which represent Arabic vowels () known as the kashida (or “extended letters”) must be protected from changes. The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR), and Normalized Cross-Correlation (NCC); thus, the location for tamper… More >

  • ARTICLE

    COVID-19 and Learning Styles: GCET as Case Study

    Mazhar Hussain Malik1,*, Amjed Sid Ahmed1, Sulaiman Al Hasani2
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 103-115, 2021, DOI:10.32604/cmc.2021.014562
    Abstract The COVID-19 pandemic has caused higher educational institutions around the world to close campus-based activities and move to online delivery. The aim of this paper is to present the case of Global College of Engineering and Technology (GCET) and how its practices including teaching, students/staff support, assessments, and exam policies were affected. The paper investigates the mediating role of no detriment policy impact on students’ result along with the challenges faced by the higher educational institution, recommendations and suggestions. The investigation concludes that the strategies adopted for online delivery, student support, assessments and exam policies have helped students to effectively… More >

  • ARTICLE

    Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

    Sameh Abd ElGhany1,2, Mai Ramadan Ibraheem3, Madallah Alruwaili4, Mohammed Elmogy5,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102
    Abstract With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions… More >

  • ARTICLE

    An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

    Park Chan-Yeob, Hyun-Ro Jae, Jun-Yong Jang, Song Hyoung-Kyu*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 137-148, 2021, DOI:10.32604/cmc.2021.016108
    Abstract Linear precoding methods such as zero-forcing (ZF) are near optimal for downlink massive multi-user multiple input multiple output (MIMO) systems due to their asymptotic channel property. However, as the number of users increases, the computational complexity of obtaining the inverse matrix of the gram matrix increases. For solving the computational complexity problem, this paper proposes an improved Jacobi (JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems. The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation. However, the conventional JC… More >

  • ARTICLE

    Ensemble Machine Learning Based Identification of Pediatric Epilepsy

    Shamsah Majed Alotaibi1, Atta-ur-Rahman1, Mohammed Imran Basheer1, Muhammad Adnan Khan2,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 149-165, 2021, DOI:10.32604/cmc.2021.015976
    Abstract Epilepsy is a type of brain disorder that causes recurrent seizures. It is the second most common neurological disease after Alzheimer’s. The effects of epilepsy in children are serious, since it causes a slower growth rate and a failure to develop certain skills. In the medical field, specialists record brain activity using an Electroencephalogram (EEG) to observe the epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate due to human errors; therefore, automated detection of epileptic pediatric seizures might be the optimal solution. This paper investigates the detection of epileptic seizures… More >

  • ARTICLE

    A Compact Dual-Port Multi-Band Rectifier Circuit for RF Energy Harvesting

    Surajo Muhammad1,*, Jun Jiat Tiang1, Sew Kin Wong1, Amjad Iqbal1, Amor Smida2, Mohamed Karim Azizi3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 167-184, 2021, DOI:10.32604/cmc.2021.016133
    (This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract This paper presents a compact multi-band rectifier with an improved impedance matching bandwidth. It uses a combination of п–matching network (MN) at Port-1, with a parallel connection of three cell branch MN at Port-2. The proposed impedance matching network (IMN) is adopted to reduce circuit complexity, to improve circuit performance, and power conversion efficiency (PCE) of the rectifier at low input power. The fabricated rectifier prototype operates at 0.92, 1.82, 2.1, 2.46 and 2.65 GHz covering GSM/900, GSM/1800, UMTS2100, and Wi-Fi/2.45–LTE2600. The size of the compact rectifier on the PCB board is 0.13λg × 0.1λg. The fabricated rectifier achieved an… More >

  • ARTICLE

    Fractional-Order Control of a Wind Turbine Using Manta Ray Foraging Optimization

    Hegazy Rezk1,2,*, Mohammed Mazen Alhato3, Mohemmed Alhaider1, Soufiene Bouallègue3,4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 185-199, 2021, DOI:10.32604/cmc.2021.016175
    (This article belongs to this Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)
    Abstract In this research paper, an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator (DFIG) based wind energy system has been proposed. The proposed strategy used the robust Fractional-Order (FO) Proportional-Integral (PI) control technique. The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits. It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness. The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization (MRFO) algorithm. During the optimization process,… More >

  • ARTICLE

    Spatio-Temporal Dynamics and Structure Preserving Algorithm for Computer Virus Model

    Nauman Ahmed1,2, Umbreen Fatima1, Shahzaib Iqbal1, Ali Raza3, Muhammad Rafiq4,*, Muhammad Aziz-ur-Rehman2, Shehla Saeed1, Ilyas Khan5, Kottakkaran Sooppy Nisar6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 201-212, 2021, DOI:10.32604/cmc.2021.014171
    Abstract The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered (SLBR) epidemic model. It describes the computer virus dynamics with vertical transmission via the internet. In these types of dynamics models, the absolute values of the state variables are the fundamental requirement that must be fulfilled by the numerical design. By taking into account this key property, the positivity preserving algorithm is designed to solve the underlying SLBR system. Since, the state variables associated with the phenomenon, represent the computer nodes, so they must take in absolute. Moreover, the continuous system (SLBR) acquires two steady states… More >

  • ARTICLE

    Second Law Analysis of Magneto Radiative GO-MoS2/H2O–(CH2OH)2 Hybrid Nanofluid

    Adnan1, Umar Khan2, Naveed Ahmed3, Syed Tauseef Mohyud-Din4, Dumitru Baleanu5,6,7, Kottakkaran Sooppy Nisar8, Ilyas Khan9,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 213-228, 2021, DOI:10.32604/cmc.2021.014383
    Abstract Entropy Generation Optimization (EGO) attained huge interest of scientists and researchers due to its numerous applications comprised in mechanical engineering, air conditioners, heat engines, thermal machines, heat exchange, refrigerators, heat pumps and substance mixing etc. Therefore, the study of radiative hybrid nanofluid (GO-MoS2/C2H6O2–H2O) and the conventional nanofluid (MoS2/C2H6O2–H2O) is conducted in the presence of Lorentz forces. The flow configuration is modeled between the parallel rotating plates in which the lower plate is permeable. The models which govern the flow in rotating system are solved numerically over the domain of interest and furnished the results for the temperature, entropy generation and… More >

  • ARTICLE

    A Secured Message Transmission Protocol for Vehicular Ad Hoc Networks

    A. F. M. Suaib Akhter1,*, A. F. M. Shahen Shah2, Mohiuddin Ahmed3, Nour Moustafa4, Unal Çavuşoğlu1, Ahmet Zengin1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 229-246, 2021, DOI:10.32604/cmc.2021.015447
    (This article belongs to this Special Issue: Security and Computing in Internet of Things)
    Abstract Vehicular Ad hoc Networks (VANETs) become a very crucial addition in the Intelligent Transportation System (ITS). It is challenging for a VANET system to provide security services and parallelly maintain high throughput by utilizing limited resources. To overcome these challenges, we propose a blockchain-based Secured Cluster-based MAC (SCB-MAC) protocol. The nearby vehicles heading towards the same direction will form a cluster and each of the clusters has its blockchain to store and distribute the safety messages. The message which contains emergency information and requires Strict Delay Requirement (SDR) for transmission are called safety messages (SM). Cluster Members (CMs) sign SMs… More >

  • ARTICLE

    IPv6 Cryptographically Generated Address: Analysis, Optimization and Protection

    Amjed Sid Ahmed1,*, Rosilah Hassan2, Faizan Qamar3, Mazhar Malik1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 247-265, 2021, DOI:10.32604/cmc.2021.014233
    Abstract In networking, one major difficulty that nodes suffer from is the need for their addresses to be generated and verified without relying on a third party or public authorized servers. To resolve this issue, the use of self-certifying addresses have become a highly popular and standardized method, of which Cryptographically Generated Addresses (CGA) is a prime example. CGA was primarily designed to deter the theft of IPv6 addresses by binding the generated address to a public key to prove address ownership. Even though the CGA technique is highly effective, this method is still subject to several vulnerabilities with respect to… More >

  • ARTICLE

    An Efficient Impersonation Attack Detection Method in Fog Computing

    Jialin Wan1, Muhammad Waqas1,2, Shanshan Tu1,*, Syed Mudassir Hussain3, Ahsan Shah2, Sadaqat Ur Rehman4, Muhammad Hanif2
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 267-281, 2021, DOI:10.32604/cmc.2021.016260
    Abstract Fog computing paradigm extends computing, communication, storage, and network resources to the network’s edge. As the fog layer is located between cloud and end-users, it can provide more convenience and timely services to end-users. However, in fog computing (FC), attackers can behave as real fog nodes or end-users to provide malicious services in the network. The attacker acts as an impersonator to impersonate other legitimate users. Therefore, in this work, we present a detection technique to secure the FC environment. First, we model a physical layer key generation based on wireless channel characteristics. To generate the secret keys between the… More >

  • ARTICLE

    Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

    Jinseok Kim1, Babar Shah2, Ki-Il Kim3,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.… More >

  • ARTICLE

    Automatic Surveillance of Pandemics Using Big Data and Text Mining

    Abdullah Alharbi1,*, Wael Alosaimi1, M. Irfan Uddin2
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 303-317, 2021, DOI:10.32604/cmc.2021.016230
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans. Different countries have tried different solutions to control the spread of the disease, including lockdowns of countries or cities, quarantines, isolation, sanitization, and masks. Patients with symptoms of COVID-19 are tested using medical testing kits; these tests must be conducted by healthcare professionals. However, the testing process is expensive and time-consuming. There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken. This paper introduces a… More >

  • ARTICLE

    Numerical Solutions for Heat Transfer of An Unsteady Cavity with Viscous Heating

    H. F. Wong1,2, Muhammad Sohail3, Z. Siri1, N. F. M. Noor1,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 319-336, 2021, DOI:10.32604/cmc.2021.015710
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract The mechanism of viscous heating of a Newtonian fluid filled inside a cavity under the effect of an external applied force on the top lid is evaluated numerically in this exploration. The investigation is carried out by assuming a two-dimensional laminar in-compressible fluid flow subject to Neumann boundary conditions throughout the numerical iterations in a transient analysis. All the walls of the square cavity are perfectly insulated and the top moving lid produces a constant finite heat flux even though the fluid flow attains the steady-state condition. The objective is to examine the effects of viscous heating in the fully… More >

  • ARTICLE

    Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data

    Amal S. Hassan1, Ehab M. Almetwally2,*, Gamal M. Ibrahim3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 337-358, 2021, DOI:10.32604/cmc.2021.013971
    (This article belongs to this Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)
    Abstract In this paper, an attempt is made to discover the distribution of COVID-19 spread in different countries such as; Saudi Arabia, Italy, Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recently inverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leone distribution, is proposed by combining the Kumaraswamy-G family and the inverted Topp–Leone distribution. We initially provide a linear representation of its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferroni curves, entropies measures and stress-strength reliability. Then,… More >

  • ARTICLE

    Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

    Alaa Omran Almagrabi1, Rashid Ali2, Daniyal Alghazzawi1, Abdullah AlBarakati1, Tahir Khurshaid3,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 359-376, 2021, DOI:10.32604/cmc.2021.014753
    (This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)
    Abstract The scope of the Internet of Things (IoT) applications varies from strategic applications, such as smart grids, smart transportation, smart security, and smart healthcare, to industrial applications such as smart manufacturing, smart logistics, smart banking, and smart insurance. In the advancement of the IoT, connected devices become smart and intelligent with the help of sensors and actuators. However, issues and challenges need to be addressed regarding the data reliability and protection for significant next-generation IoT applications like smart healthcare. For these next-generation applications, there is a requirement for far-reaching privacy and security in the IoT. Recently, blockchain systems have emerged… More >

  • ARTICLE

    A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

    Kun Ding1, Shanshan Liu1, Yuhao Zhang2, Hui Zhang1, Xiaoxiong Zhang1,*, Tongtong Wu2,3, Xiaolei Zhou1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 377-389, 2021, DOI:10.32604/cmc.2021.016301
    Abstract The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes. For entity and relation extraction in a specific domain, we propose a hybrid neural framework consisting of two parts: a span-based model and a graph-based model. The span-based model can tackle overlapping problems compared with BILOU methods, whereas the graph-based model treats relation prediction as graph classification. Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain, such as domain dictionaries and dependency structures from texts, into end-to-end neural models. We conducted extensive experiments on… More >

  • ARTICLE

    COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

    Aqib Ali1,2, Wali Khan Mashwani3, Samreen Naeem2, Muhammad Irfan Uddin4, Wiyada Kumam5, Poom Kumam6,7,*, Hussam Alrabaiah8,9, Farrukh Jamal10, Christophe Chesneau11
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 391-407, 2021, DOI:10.32604/cmc.2021.016037
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an… More >

  • ARTICLE

    A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

    Omar Almomani*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 409-429, 2021, DOI:10.32604/cmc.2021.016113
    (This article belongs to this Special Issue: Security and Computing in Internet of Things)
    Abstract Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one… More >

  • ARTICLE

    A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

    Ahmed S. Salama1,2,3, Mohamed Amr Mokhtar3, Mazhar B. Tayel3, Esraa Eldesouky4,6, Ahmed Ali5,6,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 431-446, 2021, DOI:10.32604/cmc.2021.016165
    Abstract Assuring medical images protection and robustness is a compulsory necessity nowadays. In this paper, a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform (DWT) with the energy compaction of the Discrete Wavelet Transform (DCT). The multi-level Encryption-based Hybrid Fusion Technique (EbhFT) aims to achieve great advances in terms of imperceptibility and security of medical images. A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform. Afterwards, a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to… More >

  • ARTICLE

    An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks

    Muhammad Adil1, Jehad Ali2, Muhammad Sajjad Khan3, Junsu Kim3, Ryan Alturki4, Mohammad Zakarya4, Mukhtaj Khan4, Rahim Khan4, Su Min Kim3,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 447-470, 2021, DOI:10.32604/cmc.2021.014967
    (This article belongs to this Special Issue: Security Issues in Industrial Internet of Things)
    Abstract Internet of Things (IoT) network used for industrial management is vulnerable to different security threats due to its unstructured deployment, and dynamic communication behavior. In literature various mechanisms addressed the security issue of Industrial IoT networks, but proper maintenance of the performance reliability is among the common challenges. In this paper, we proposed an intelligent mutual authentication scheme leveraging authentication aware node (AAN) and base station (BS) to identify routing attacks in Industrial IoT networks. The AAN and BS uses the communication parameter such as a route request (RREQ), node-ID, received signal strength (RSS), and round-trip time (RTT) information to… More >

  • ARTICLE

    Arabic Named Entity Recognition: A BERT-BGRU Approach

    Norah Alsaaran*, Maha Alrabiah
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 471-485, 2021, DOI:10.32604/cmc.2021.016054
    (This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
    Abstract Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models… More >

  • ARTICLE

    Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

    Thavavel Vaiyapuri*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998
    (This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
    Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing… More >

  • ARTICLE

    A Fault-Handling Method for the Hamiltonian Cycle in the Hypercube Topology

    Adnan A. Hnaif*, Abdelfatah A. Tamimi, Ayman M. Abdalla, Iqbal Jebril
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 505-519, 2021, DOI:10.32604/cmc.2021.016123
    (This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)
    Abstract Many routing protocols, such as distance vector and link-state protocols are used for finding the best paths in a network. To find the path between the source and destination nodes where every node is visited once with no repeats, Hamiltonian and Hypercube routing protocols are often used. Nonetheless, these algorithms are not designed to solve the problem of a node failure, where one or more nodes become faulty. This paper proposes an efficient modified Fault-free Hamiltonian Cycle based on the Hypercube Topology (FHCHT) to perform a connection between nodes when one or more nodes become faulty. FHCHT can be applied… More >

  • ARTICLE

    Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions

    Minh Thanh Vo1, Anh H. Vo2, Trang Nguyen3, Rohit Sharma4, Tuong Le2,5,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 521-535, 2021, DOI:10.32604/cmc.2021.015645
    Abstract In recent years, the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age. Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting. However, the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs. This causes a reduction in the predictability and performance of traditional machine learning models. We therefore present an efficient framework that uses an oversampling technique called FJD-OT… More >

  • ARTICLE

    Language Model Using Differentiable Neural Computer Based on Forget Gate-Based Memory Deallocation

    Donghyun Lee, Hosung Park, Soonshin Seo, Changmin Kim, Hyunsoo Son, Gyujin Kim, Ji-Hwan Kim*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 537-551, 2021, DOI:10.32604/cmc.2021.015430
    Abstract A differentiable neural computer (DNC) is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism. Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems. In this study, we apply a DNC to a language model (LM) task. The LM task is one of the reasoning problems, because it can predict the next word using the previous word sequence. However, memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains… More >

  • ARTICLE

    Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

    Pandia Rajan Jeyaraj1, Aravind Chellachi Kathiresan1, Siva Prakash Asokan1, Edward Rajan Samuel Nadar1, Hegazy Rezk2,3,*, Thanikanti Sudhakar Babu4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 553-567, 2021, DOI:10.32604/cmc.2021.015128
    Abstract The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on… More >

  • ARTICLE

    Hybrid Metamodeling/Metaheuristic Assisted Multi-Transmitters Placement Planning

    Amir Parnianifard1, Muhammad Saadi2, Manus Pengnoo1, Muhammad Ali Imran3, Sattam Al Otaibi4, Pruk Sasithong1, Pisit Vanichchanunt5, Tuchsanai Polysuwan6, Lunchakorn Wuttisittikulkij1,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 569-587, 2021, DOI:10.32604/cmc.2021.015730
    (This article belongs to this Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)
    Abstract With every passing day, the demand for data traffic is increasing, and this urges the research community not only to look for an alternating spectrum for communication but also urges radio frequency planners to use the existing spectrum efficiently. Cell sizes are shrinking with every upcoming communication generation, which makes base station placement planning even more complex and cumbersome. In order to make the next-generation cost-effective, it is important to design a network in such a way that it utilizes the minimum number of base stations while ensuring seamless coverage and quality of service. This paper aims at the development… More >

  • ARTICLE

    Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classification

    Nany Katamesh, Osama Abu-Elnasr*, Samir Elmougy
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 589-606, 2021, DOI:10.32604/cmc.2021.015761
    (This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
    Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by any of the above mentioned… More >

  • ARTICLE

    An End-to-End Authentication Scheme for Healthcare IoT Systems Using WMSN

    Shadi Nashwan*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 607-642, 2021, DOI:10.32604/cmc.2021.015597
    (This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
    Abstract The healthcare internet of things (IoT) system has dramatically reshaped this important industry sector. This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers. The goal is the remote monitoring of a patient’s physiological data by physicians. Moreover, this system can reduce the number and expenses of healthcare centers, make up for the shortage of healthcare centers in remote areas, enable consultation with expert physicians around the world, and increase the health awareness of communities. The major challenges that affect the rapid deployment and widespread acceptance of… More >

  • ARTICLE

    Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

    Belal Al-Khateeb1,*, Kawther Ahmed2, Maha Mahmood1, Dac-Nhuong Le3,4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 643-654, 2021, DOI:10.32604/cmc.2021.013648
    (This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
    Abstract This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food. Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group. Forty-eight (22 unimodal and 26 multimodal) test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm. A comparative efficiency analysis also checks RHSO… More >

  • ARTICLE

    Traffic Engineering in Dynamic Hybrid Segment Routing Networks

    Yingya Guo1,2,3,7, Kai Huang1, Cheng Hu4,*, Jiangyuan Yao5, Siyu Zhou6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 655-670, 2021, DOI:10.32604/cmc.2021.016364
    Abstract The emergence of Segment Routing (SR) provides a novel routing paradigm that uses a routing technique called source packet routing. In SR architecture, the paths that the packets choose to route on are indicated at the ingress router. Compared with shortest-path-based routing in traditional distributed routing protocols, SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router. Despite the advantages of SR, it may be difficult to update the existing IP network to a full SR deployed network, for economical and technical reasons. Updating partial of the traditional IP network to the SR network,… More >

  • ARTICLE

    Deep Learning-Based Hybrid Intelligent Intrusion Detection System

    Muhammad Ashfaq Khan1,2, Yangwoo Kim1,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 671-687, 2021, DOI:10.32604/cmc.2021.015647
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop… More >

  • ARTICLE

    Detecting Driver Distraction Using Deep-Learning Approach

    Khalid A. AlShalfan1, Mohammed Zakariah2,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 689-704, 2021, DOI:10.32604/cmc.2021.015989
    (This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
    Abstract Currently, distracted driving is among the most important causes of traffic accidents. Consequently, intelligent vehicle driving systems have become increasingly important. Recently, interest in driver-assistance systems that detect driver actions and help them drive safely has increased. In these studies, although some distinct data types, such as the physical conditions of the driver, audio and visual features, and vehicle information, are used, the primary data source is images of the driver that include the face, arms, and hands taken with a camera inside the car. In this study, an architecture based on a convolution neural network (CNN) is proposed to… More >

  • ARTICLE

    Deep Trajectory Classification Model for Congestion Detection in Human Crowds

    Emad Felemban1, Sultan Daud Khan2, Atif Naseer3, Faizan Ur Rehman4,*, Saleh Basalamah1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 705-725, 2021, DOI:10.32604/cmc.2021.015085
    Abstract In high-density gatherings, crowd disasters frequently occur despite all the safety measures. Timely detection of congestion in human crowds using automated analysis of video footage can prevent crowd disasters. Recent work on the prevention of crowd disasters has been based on manual analysis of video footage. Some methods also measure crowd congestion by estimating crowd density. However, crowd density alone cannot provide reliable information about congestion. This paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian trajectories. The proposed framework divided the input video into several temporal segments. We then extracted dense trajectories from each… More >

  • ARTICLE

    Improving Cache Management with Redundant RDDs Eviction in Spark

    Yao Zhao1, Jian Dong1,*, Hongwei Liu1, Jin Wu2, Yanxin Liu1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 727-741, 2021, DOI:10.32604/cmc.2021.016462
    Abstract Efficient cache management plays a vital role in in-memory data-parallel systems, such as Spark, Tez, Storm and HANA. Recent research, notably research on the Least Reference Count (LRC) and Most Reference Distance (MRD) policies, has shown that dependency-aware caching management practices that consider the application’s directed acyclic graph (DAG) perform well in Spark. However, these practices ignore the further relationship between RDDs and cached some redundant RDDs with the same child RDDs, which degrades the memory performance. Hence, in memory-constrained situations, systems may encounter a performance bottleneck due to frequent data block replacement. In addition, the prefetch mechanisms in some… More >

  • ARTICLE

    Local Stress Field in Wafer Thinning Simulations with Phase Space Averaging

    Miaocao Wang1, Yuhua Huang1, Jinming Li1, Ling Xu2, Fulong Zhu1,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 743-759, 2021, DOI:10.32604/cmc.2021.016372
    Abstract From an ingot to a wafer then to a die, wafer thinning plays an important role in the semiconductor industry. To reveal the material removal mechanism of semiconductor at nanoscale, molecular dynamics has been widely used to investigate the grinding process. However, most simulation analyses were conducted with a single phase space trajectory, which is stochastic and subjective. In this paper, the stress field in wafer thinning simulations of 4H-SiC was obtained from 50 trajectories with spatial averaging and phase space averaging. The spatial averaging was conducted on a uniform spatial grid for each trajectory. A variable named mask was… More >

  • ARTICLE

    Evaluating the Risk of Disclosure and Utility in a Synthetic Dataset

    Kang-Cheng Chen1, Chia-Mu Yu2,*, Tooska Dargahi3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 761-787, 2021, DOI:10.32604/cmc.2021.014984
    (This article belongs to this Special Issue: Trust, Security and Privacy for FinTech)
    Abstract The advancement of information technology has improved the delivery of financial services by the introduction of Financial Technology (FinTech). To enhance their customer satisfaction, Fintech companies leverage artificial intelligence (AI) to collect fine-grained data about individuals, which enables them to provide more intelligent and customized services. However, although visions thereof promise to make customers’ lives easier, they also raise major security and privacy concerns for their users. Differential privacy (DP) is a common privacy-preserving data publishing technique that is proved to ensure a high level of privacy preservation. However, an important concern arises from the trade-off between the data utility… More >

  • ARTICLE

    Learning Unitary Transformation by Quantum Machine Learning Model

    Yi-Ming Huang1, Xiao-Yu Li1,*, Yi-Xuan Zhu1, Hang Lei1, Qing-Sheng Zhu2, Shan Yang3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 789-803, 2021, DOI:10.32604/cmc.2021.016663
    Abstract Quantum machine learning (QML) is a rapidly rising research field that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientific research and improving data processing. How to efficiently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing. It can be described as learning or approximating a unitary operator. Since the success of the hybrid-based quantum machine learning model proposed in recent years, we investigate to apply the techniques from QML to tackle this problem. Based on the Choi–Jamiołkowski isomorphism in quantum computing, we transfer the original problem of learning… More >

  • ARTICLE

    Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

    Fawaz Waselallah Alsaade1, Theyazn H. H. Aldhyani2,*, Mosleh Hmoud Al-Adhaileh3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 805-819, 2021, DOI:10.32604/cmc.2021.016264
    (This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)
    Abstract The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to develop a system for predicting… More >

  • ARTICLE

    Multimodal Medical Image Registration and Fusion for Quality Enhancement

    Muhammad Adeel Azam1, Khan Bahadar Khan2,*, Muhammad Ahmad3, Manuel Mazzara4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 821-840, 2021, DOI:10.32604/cmc.2021.016131
    (This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet… More >

  • ARTICLE

    Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model

    Xiaoli Qiang1, Muhammad Aamir2,*, Muhammad Naeem2, Shaukat Ali3, Adnan Aslam4, Zehui Shao1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 841-856, 2021, DOI:10.32604/cmc.2021.012540
    (This article belongs to this Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)
    Abstract COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world. Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases. In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July. For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied. EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs). For individual IMFs modelling, we use the Autoregressive Integrated Moving… More >

  • ARTICLE

    Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

    Dah-Jing Jwo*, Jui-Tao Lee
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 857-876, 2021, DOI:10.32604/cmc.2021.016894
    Abstract This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under… More >

  • ARTICLE

    A Link Analysis Algorithm for Identification of Key Hidden Services

    Abdullah Alharbi1, Mohd Faizan2, Wael Alosaimi1, Hashem Alyami3, Mohd Nadeem2, Suhel Ahmad Khan4, Alka Agrawal2, Raees Ahmad Khan2,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 877-886, 2021, DOI:10.32604/cmc.2021.016887
    Abstract The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities. The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking, violence and terrorist activities among others. The government and law enforcement agencies are working relentlessly to control the misuse of Tor network. This is a study in the similar league, with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web.… More >

  • ARTICLE

    Network Log-Based SSH Brute-Force Attack Detection Model

    Jeonghoon Park1, Jinsu Kim1, B. B. Gupta2, Namje Park1,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 887-901, 2021, DOI:10.32604/cmc.2021.015172
    (This article belongs to this Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)
    Abstract The rapid advancement of IT technology has enabled the quick discovery, sharing and collection of quality information, but has also increased cyberattacks at a fast pace at the same time. There exists no means to block these cyberattacks completely, and all security policies need to consider the possibility of external attacks. Therefore, it is crucial to reduce external attacks through preventative measures. In general, since routers located in the upper part of a firewall can hardly be protected by security systems, they are exposed to numerous unblocked cyberattacks. Routers block unnecessary services and accept necessary ones while taking appropriate measures… More >

  • ARTICLE

    Imperative Dynamic Routing Between Capsules Network for Malaria Classification

    G. Madhu1,*, A. Govardhan2, B. Sunil Srinivas3, Kshira Sagar Sahoo4, N. Z. Jhanjhi5, K. S. Vardhan1, B. Rohit6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 903-919, 2021, DOI:10.32604/cmc.2021.016114
    Abstract Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully trained capsule networks for… More >

  • ARTICLE

    Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection

    Bader Rasheed1, Adil Khan1, S. M. Ahsan Kazmi2, Rasheed Hussain2, Md. Jalil Piran3,*, Doug Young Suh4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 921-939, 2021, DOI:10.32604/cmc.2021.015452
    (This article belongs to this Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract Detecting malicious Uniform Resource Locators (URLs) is crucially important to prevent attackers from committing cybercrimes. Recent researches have investigated the role of machine learning (ML) models to detect malicious URLs. By using ML algorithms, first, the features of URLs are extracted, and then different ML models are trained. The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL. Therefore, deep learning (DL) models are used to solve these issues since they are able to perform featureless detection. Furthermore, DL models give better accuracy and generalization to newly… More >

  • ARTICLE

    Genetic Algorithm Routing Protocol for Mobile Ad Hoc Network

    Raed Alsaqour1, Saif Kamal2, Maha Abdelhaq3,*, Yazan Al Jeroudi4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 941-960, 2021, DOI:10.32604/cmc.2021.015921
    Abstract Mobile ad hoc network (MANET) is a dynamically reconfigurable wireless network with time-variable infrastructure. Given that nodes are highly mobile, MANET’s topology often changes. These changes increase the difficulty in finding the routes that the packets use when they are routed. This study proposes an algorithm called genetic algorithm-based location-aided routing (GALAR) to enhance the MANET routing protocol efficiency. The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination. The GALAR was constructed based on… More >

  • ARTICLE

    Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

    Oday Ali Hassen1, Sarmad Omar Abter2, Ansam A. Abdulhussein3, Saad M. Darwish4,*, Yasmine M. Ibrahim4, Walaa Sheta5
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 961-981, 2021, DOI:10.32604/cmc.2021.014404
    (This article belongs to this Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)
    Abstract Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper’s key contribution focuses on evaluating machine learning… More >

  • ARTICLE

    Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

    Ajay Arunachalam1,*, Vinayakumar Ravi2, Moez Krichen3, Roobaea Alroobaea4, Jehad Saad Alqurni5
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 983-1001, 2021, DOI:10.32604/cmc.2021.015371
    (This article belongs to this Special Issue: Intelligent Communication Systems: Smart Wireless Digital Devices and IoT)
    Abstract One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each… More >

  • ARTICLE

    Classification of COVID-19 CT Scans via Extreme Learning Machine

    Muhammad Attique Khan1, Abdul Majid1, Tallha Akram2, Nazar Hussain1, Yunyoung Nam3,*, Seifedine Kadry4, Shui-Hua Wang5, Majed Alhaisoni6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.015541
    (This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
    Abstract Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors; this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an Extreme Learning Machine (ELM) served… More >

  • ARTICLE

    AI-Based Culture Independent Pervasive M-Learning Prototype Using UI Plasticity Design

    Mahdi H. Miraz1,2,*, Maaruf Ali3, Peter S. Excell2, Sajid Khan4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1021-1039, 2021, DOI:10.32604/cmc.2021.015405
    Abstract This paper explains the development of a culturally inclusive ubiquitous M-Learning platform (“Mobile Academy”) with an AI-based adaptive user interface. The rationale and need for this research and development are justified by the continuing widespread adoption of the Internet and Internet enabled devices, especially smartphones. The M-learning platform was designed from the onset for the global traveller. The characteristics and limitations of the application are also discussed. The Mobile Academy, proof of concept prototype, was created to facilitate teaching and learning on the move or in environments where the use of a desktop computer is inconvenient or simply impossible. The… More >

  • ARTICLE

    Computer Decision Support System for Skin Cancer Localization and Classification

    Muhammad Attique Khan1, Tallha Akram2, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307
    (This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)
    Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More >

  • ARTICLE

    Computer Vision-Control-Based CNN-PID for Mobile Robot

    Rihem Farkh1,5,*, Mohammad Tabrez Quasim2, Khaled Al jaloud1, Saad Alhuwaimel3, Shams Tabrez Siddiqui4
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1065-1079, 2021, DOI:10.32604/cmc.2021.016600
    (This article belongs to this Special Issue: Machine Learning for Data Analytics)
    Abstract With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed… More >

  • ARTICLE

    A Storage and Transmission Joint Planning Method for Centralized Wind Power Transmission

    Xiuyu Yang1,*, Qi Guo1, Jianzhong Gui2, Renyong Chai3, Xueyuan Liu1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1081-1097, 2021, DOI:10.32604/cmc.2021.016375
    Abstract Centralized delivery has become the main operation mode under the scaled development of wind power. Transmission channels are usually the guarantee of out-delivered wind power for large-scale wind base. The configuration of transmission capacity, which has the features of low utilization and poor economy, is hardly matching correctly due to the volatility and low energy density of wind. The usage of energy storage can mitigate wind power fluctuations and reduce the requirement of out-delivery transmission capacity, but facing the issue of energy storage cost recovery. Therefore, it is necessary to optimize the allocation of energy storage while considering the problem… More >

  • ARTICLE

    Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification

    Ayesha Bin T. Tahir1, Muhamamd Attique Khan1, Majed Alhaisoni2, Junaid Ali Khan1, Yunyoung Nam3,*, Shui-Hua Wang4, Kashif Javed5
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154
    (This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
    Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More >

  • ARTICLE

    Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

    Shankarnarayanan Nalini1,*, Nagappan Krishnaraj2, Thangaiyan Jayasankar3, Kalimuthu Vinothkumar4, Antony Sagai Francis Britto5, Kamalraj Subramaniam6, Chokkalingam Bharatiraja7
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1117-1128, 2021, DOI:10.32604/cmc.2021.012431
    Abstract Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop. Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. A novel deep neural network (DNN) classification model is proposed for the identification of paddy leaf disease using plant image data. Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search… More >

  • ARTICLE

    Thermodynamic Simulation on the Change in Phase for Carburizing Process

    Anh Tuan Hoang1, Xuan Phuong Nguyen2, Osamah Ibrahim Khalaf3, Thi Xuan Tran4, Minh Quang Chau5, Thi Minh Hao Dong2, Duong Nam Nguyen6,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1129-1145, 2021, DOI:10.32604/cmc.2021.015349
    (This article belongs to this Special Issue: Application of Big Data Analytics in the Management of Business)
    Abstract The type of technology used to strengthen the surface structure of machine parts, typically by carbon-permeation, has made a great contribution to the mechanical engineering industry because of its outstanding advantages in corrosion resistance and enhanced mechanical and physical properties. Furthermore, carbon permeation is considered as an optimal method of heat treatment through the diffusion of carbon atoms into the surface of alloy steel. This study presented research results on the thermodynamic calculation and simulation of the carbon permeability process. Applying Fick’s law, the paper calculated the distribution of carbon concentration in the alloy steel after it is absorbed from… More >

  • ARTICLE

    Skin Melanoma Classification System Using Deep Learning

    R. Thamizhamuthu*, D. Manjula
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1147-1160, 2021, DOI:10.32604/cmc.2021.015503
    Abstract The deadliest type of skin cancer is malignant melanoma. The diagnosis requires at the earliest to reduce the mortality rate. In this study, an efficient Skin Melanoma Classification (SMC) system is presented using dermoscopic images as a non-invasive procedure. The SMC system consists of four modules; segmentation, feature extraction, feature reduction and finally classification. In the first module, k-means clustering is applied to cluster the colour information of dermoscopic images. The second module extracts meaningful and useful descriptors based on the statistics of local property, parameters of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model of wavelet and spatial patterns by Dominant… More >

  • ARTICLE

    Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

    Said Ali Hassan1, Khalid Alnowibet2,3, Prachi Agrawal4, Ali Wagdy Mohamed5,6,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1161-1181, 2021, DOI:10.32604/cmc.2021.015494
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics. A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specific time period. A nonlinear binary mathematical programming model for the problem is formulated. The decision variables are binary ones that represent whether to choose a specific consumer, and design constraints are formulated to keep track of the chosen route. To better illustrate the problem,… More >

  • ARTICLE

    Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

    Said Ali Hassan1, Khalid Alnowibet2, Prachi Agrawal3, Ali Wagdy Mohamed4,5,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1183-1202, 2021, DOI:10.32604/cmc.2021.015514
    (This article belongs to this Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract Determining the optimum location of facilities is critical in many fields, particularly in healthcare. This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019 (COVID-19) pandemic. The used model is the most appropriate among the three most common location models utilized to solve healthcare problems (the set covering model, the maximal covering model, and the P-median model). The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints. The model is used to determine the optimum location of field hospitals for COVID-19… More >

  • ARTICLE

    Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective

    Yoosef B. Abushark1, Asif Irshad Khan1,*, Fawaz Jaber Alsolami1, Abdulmohsen Almalawi1, Md Mottahir Alam2, Alka Agrawal3, Rajeev Kumar3,4, Raees Ahmad Khan3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1203-1218, 2021, DOI:10.32604/cmc.2021.016610
    Abstract Most of the security strategies today are primarily designed to provide security protection, rather than to solve one of the basic security issues related to adequate software product architecture. Several models, frameworks and methodologies have been introduced by the researchers for a secure and sustainable software development life cycle. Therefore it is important to assess the usability of the popular security requirements engineering (SRE) approaches. A significant factor in the management and handling of successful security requirements is the assessment of security requirements engineering method performance. This assessment will allow changes to the engineering process of security requirements. The consistency… More >

  • ARTICLE

    Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

    Rupkumar Mahapatra1, Sovan Samanta2, Madhumangal Pal1, Jeong-Gon Lee3,*, Shah Khalid Khan4, Usman Naseem5, Robin Singh Bhadoria6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1219-1233, 2021, DOI:10.32604/cmc.2021.015590
    (This article belongs to this Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)
    Abstract Graph colouring is the system of assigning a colour to each vertex of a graph. It is done in such a way that adjacent vertices do not have equal colour. It is fundamental in graph theory. It is often used to solve real-world problems like traffic light signalling, map colouring, scheduling, etc. Nowadays, social networks are prevalent systems in our life. Here, the users are considered as vertices, and their connections/interactions are taken as edges. Some users follow other popular users’ profiles in these networks, and some don’t, but those non-followers are connected directly to the popular profiles. That means,… More >

  • ARTICLE

    Rasch Model Assessment for Bloom Digital Taxonomy Applications

    Mohd Effendi Ewan Mohd Matore*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1235-1253, 2021, DOI:10.32604/cmc.2021.016143
    (This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
    Abstract Assessment using Bloom’s taxonomy levels has evolved in a variety of contexts and uses. In the era of the COVID-19 pandemic, which necessitates use of online assessment, the need for teachers to use digital-based taxonomy skills or Bloom’s Digital Taxonomy (BDT) has increased even more. However, the existing studies on validity and reliability of BDT items are limited. To overcome this limitation, this study aims to test whether BDT has good psychometric characteristics as a teacher’s self-assessment tool using the Rasch model analysis and to investigate the pattern of BDT usage in teaching and learning. By using a quantitative online… More >

  • ARTICLE

    A New Enhanced Arabic Light Stemmer for IR in Medical Documents

    Ra’ed M. Al-Khatib1,*, Taha Zerrouki2, Mohammed M. Abu Shquier3, Amar Balla4, Asef Al-Khateeb5
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1255-1269, 2021, DOI:10.32604/cmc.2021.016155
    (This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
    Abstract This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem, especially in medical documents. Our proposed algorithm is a light stemming algorithm for extracting stems and roots from the input data. One of the main challenges facing the light stemming algorithm is cutting off the input word, to extract the initial segments. When initiating the light stemmer with strong initial segments, the final extracting stems and roots will be more accurate. Therefore, a new enhanced segmentation based on deploying the Direct Acyclic Graph (DAG) model is utilized. In addition to extracting the powerful initial segments,… More >

  • ARTICLE

    Down to Zero Size of VoIP Packet Payload

    Mosleh M. Abualhaj*, Qusai Y. Shambour, Abdelrahman H. Hussein, Qasem M. Kharma
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1271-1283, 2021, DOI:10.32604/cmc.2021.014928
    Abstract Voice over Internet Protocol (VoIP) is widely used by companies, schools, universities, and other institutions. However, VoIP faces many issues that slow down its propagation. An important issue is poor utilization of the VoIP service network bandwidth, which results from the large header of the VoIP packet. The objective of this study is to handle this poor utilization of the network bandwidth. Therefore, this study proposes a novel method to address this large header overhead problem. The proposed method is called zero size payload (ZSP), which aims to reemploy and use the header information (fields) of the VoIP packet that… More >

  • ARTICLE

    Deep Reinforcement Learning for Multi-Phase Microstructure Design

    Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1285-1302, 2021, DOI:10.32604/cmc.2021.016829
    Abstract This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms… More >

  • ARTICLE

    Dynamic Multi-Attribute Decision-Making Method with Double Reference Points and Its Application

    Haoran Huang1, Qinyong Lin2, Weitong Chen3, Kai Fang4, Huazhou Chen5, Ken Cai2,*
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1303-1320, 2021, DOI:10.32604/cmc.2021.016163
    Abstract To better reflect the psychological behavior characteristics of loss aversion, this paper builds a double reference point decision making method for dynamic multi-attribute decision-making (DMADM) problem, taking bottom-line and target as reference pints. First, the gain/loss function is given, and the state is divided according to the relationship between the gain/loss value and the reference point. Second, the attitude function is constructed based on the results of state division to establish the utility function. Third, the comprehensive utility value is calculated as the basis for alternatives classification and ranking. Finally, the new method is used to evaluate the development level… More >

  • ARTICLE

    Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks

    Mohammed H. Alsharif1, Abu Jahid2, Mahmoud A. Albreem3, Peerapong Uthansakul4,*, Jamel Nebhen5, Khalid Yahya6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1321-1339, 2021, DOI:10.32604/cmc.2021.016738
    (This article belongs to this Special Issue: Advanced 5G Communication System for Transforming Health Care)
    Abstract The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective… More >

  • ARTICLE

    Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

    Shabib Aftab1,2, Saad Alanazi3, Munir Ahmad1, Muhammad Adnan Khan4,*, Areej Fatima5, Nouh Sabri Elmitwally3,6
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1341-1357, 2021, DOI:10.32604/cmc.2021.016814
    Abstract Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin deficiency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above “normal,” defined as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis and comprehensive analysis of this… More >

  • ARTICLE

    Evolutionary GAN–Based Data Augmentation for Cardiac Magnetic Resonance Image

    Ying Fu1,2,*, Minxue Gong1, Guang Yang1, Hong Wei3, Jiliu Zhou1,2
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1359-1374, 2021, DOI:10.32604/cmc.2021.016536
    Abstract Generative adversarial networks (GANs) have considerable potential to alleviate challenges linked to data scarcity. Recent research has demonstrated the good performance of this method for data augmentation because GANs synthesize semantically meaningful data from standard signal distribution. The goal of this study was to solve the overfitting problem that is caused by the training process of convolution networks with a small dataset. In this context, we propose a data augmentation method based on an evolutionary generative adversarial network for cardiac magnetic resonance images to extend the training data. In our structure of the evolutionary GAN, the most optimal generator is… More >

  • ARTICLE

    DeepFake Videos Detection Based on Texture Features

    Bozhi Xu1, Jiarui Liu1, Jifan Liang1, Wei Lu1,*, Yue Zhang2
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760
    Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >

  • ARTICLE

    Classification of Emergency Responses to Fatal Traffic Accidents in Chinese Urban Areas

    Pengfei Gong1,2, Qun Wang2,*, Junjun Zhu3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1389-1408, 2021, DOI:10.32604/cmc.2021.016483
    Abstract Fatal traffic accidents in urban areas can adversely affect the urban road traffic system and pose many challenges for urban traffic management. Therefore, it is necessary to first classify emergency responses to such accidents and then handle them quickly and correctly. The aim of this paper is to develop an evaluation index system and to use appropriate methods to investigate emergency-response classifications to fatal traffic accidents in Chinese urban areas. This study used a multilevel hierarchical structural model to determine emergency-response classification. In the model, accident attributes, urban road network vulnerability, and institutional resilience were used as classification criteria. Each… More >

  • ARTICLE

    Systematic Analysis of Safety and Security Risks in Smart Homes

    Habib Ullah Khan1,*, Mohammad Kamel Alomari1, Sulaiman Khan2, Shah Nazir2, Asif Qumer Gill3, Alanoud Ali Al-Maadid4, Zaki Khalid Abu-Shawish1, Mostafa Kamal Hassan1
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1409-1428, 2021, DOI:10.32604/cmc.2021.016058
    (This article belongs to this Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)
    Abstract The revolution in Internet of Things (IoT)-based devices and applications has provided smart applications for humans. These applications range from healthcare to traffic-flow management, to communication devices, to smart security devices, and many others. In particular, government and private organizations are showing significant interest in IoT-enabled applications for smart homes. Despite the perceived benefits and interest, human safety is also a key concern. This research is aimed at systematically analyzing the available literature on smart homes and identifying areas of concern or risk with a view to supporting the design of safe and secure smart homes. For this systematic review… More >

  • ARTICLE

    Blockchain-Based Flexible Double-Chain Architecture and Performance Optimization for Better Sustainability in Agriculture

    Luona Song1, Xiaojuan Wang1,*, Peng Wei1, Zikui Lu1, Xiaojun Wang2, Nicolas Merveille3
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1429-1446, 2021, DOI:10.32604/cmc.2021.016954
    Abstract Blockchain is an emerging decentralized distributed technology that can cross the boundaries and guarantee safe and trustworthy value transfers between participants. Combining the blockchain technology with the Internet of Things (IoT) technology to enhance the transparency and sustainability of agricultural supply chains, has attracted researchers from both academia and industry. This paper reviews the latest applications of the blockchain and IoT technologies in the sustainable agricultural supply chain management and explores the design and implementation of a blockchain-based sustainable solution. By placing the sustainable agricultural supply chain management at its core, a blockchain-based framework is designed. Considering the heterogeneity of… More >

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