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Gibrael Abosamra^*, Hadi Oqaibi
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1-28, 2021, DOI:10.32604/cmc.2021.015318 - 22 March 2021
（This article belongs to the 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… More >

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Kamran Sadiq Awan¹, Tahir Mahmood¹, Mohammad Shorfuzzaman², Rashid Ali³, Raja Majid Mehmood^4,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 29-43, 2021, DOI:10.32604/cmc.2021.014824 - 22 March 2021
（This article belongs to the 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 More >

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Rana Zeeshan Ahamad¹, Abdul Rehman Javed^2,*, 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 - 22 March 2021
（This article belongs to the 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 More >

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Mohamed Abdelsabour Fahmy^1,2,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 59-76, 2021, DOI:10.32604/cmc.2021.015115 - 22 March 2021
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 More >

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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 - 22 March 2021
（This article belongs to the 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);… More >

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Mazhar Hussain Malik^1,*, Amjed Sid Ahmed¹, Sulaiman Al Hasani²
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 103-115, 2021, DOI:10.32604/cmc.2021.014562 - 22 March 2021
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… More >

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Sameh Abd ElGhany^1,2, Mai Ramadan Ibraheem³, Madallah Alruwaili⁴, Mohammed Elmogy^5,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102 - 22 March 2021
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 More >

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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 - 22 March 2021
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… More >

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1458

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Shamsah Majed Alotaibi¹, Atta-ur-Rahman¹, Mohammed Imran Basheer¹, Muhammad Adnan Khan^2,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 149-165, 2021, DOI:10.32604/cmc.2021.015976 - 22 March 2021
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… More >

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Surajo Muhammad^1,*, 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 - 22 March 2021
（This article belongs to the 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 0.13λ_g × 0.1λ_g. The… More >

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2168

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Hegazy Rezk^1,2,*, Mohammed Mazen Alhato³, Mohemmed Alhaider¹, Soufiene Bouallègue^3,4
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 185-199, 2021, DOI:10.32604/cmc.2021.016175 - 22 March 2021
（This article belongs to the 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) More >

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Nauman Ahmed^1,2, Umbreen Fatima¹, Shahzaib Iqbal¹, Ali Raza³, Muhammad Rafiq^4,*, 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 - 22 March 2021
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 More >

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1335

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Adnan¹, Umar Khan², Naveed Ahmed³, Syed Tauseef Mohyud-Din⁴, Dumitru Baleanu^5,6,7, Kottakkaran Sooppy Nisar⁸, Ilyas Khan^9,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 213-228, 2021, DOI:10.32604/cmc.2021.014383 - 22 March 2021
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… More >

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A. F. M. Suaib Akhter^1,*, 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 - 22 March 2021
（This article belongs to the 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).… More >

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Amjed Sid Ahmed^1,*, Rosilah Hassan², Faizan Qamar³, Mazhar Malik¹
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 247-265, 2021, DOI:10.32604/cmc.2021.014233 - 22 March 2021
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… More >

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Jialin Wan¹, Muhammad Waqas^1,2, Shanshan Tu^1,*, 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 - 22 March 2021
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 More >

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Jinseok Kim¹, Babar Shah², Ki-Il Kim^3,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042 - 22 March 2021
（This article belongs to the 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… More >

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1802

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Abdullah Alharbi^1,*, Wael Alosaimi¹, M. Irfan Uddin²
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 303-317, 2021, DOI:10.32604/cmc.2021.016230 - 22 March 2021
（This article belongs to the 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… More >

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H. F. Wong^1,2, Muhammad Sohail³, Z. Siri¹, N. F. M. Noor^1,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 319-336, 2021, DOI:10.32604/cmc.2021.015710 - 22 March 2021
（This article belongs to the 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… More >

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Amal S. Hassan¹, Ehab M. Almetwally^2,*, Gamal M. Ibrahim³
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 337-358, 2021, DOI:10.32604/cmc.2021.013971 - 22 March 2021
（This article belongs to the 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… More >

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Alaa Omran Almagrabi¹, Rashid Ali², Daniyal Alghazzawi¹, Abdullah AlBarakati¹, Tahir Khurshaid^3,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 359-376, 2021, DOI:10.32604/cmc.2021.014753 - 22 March 2021
（This article belongs to the 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.… More >

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Kun Ding¹, Shanshan Liu¹, Yuhao Zhang², Hui Zhang¹, Xiaoxiong Zhang^1,*, Tongtong Wu^2,3, Xiaolei Zhou¹
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 377-389, 2021, DOI:10.32604/cmc.2021.016301 - 22 March 2021
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. More >

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Aqib Ali^1,2, Wali Khan Mashwani³, Samreen Naeem², Muhammad Irfan Uddin⁴, Wiyada Kumam⁵, Poom Kumam^6,7,*, Hussam Alrabaiah^8,9, Farrukh Jamal¹⁰, Christophe Chesneau¹¹
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 391-407, 2021, DOI:10.32604/cmc.2021.016037 - 22 March 2021
（This article belongs to the 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… More >

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Omar Almomani^*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 409-429, 2021, DOI:10.32604/cmc.2021.016113 - 22 March 2021
（This article belongs to the 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… More >

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Ahmed S. Salama^1,2,3, Mohamed Amr Mokhtar³, Mazhar B. Tayel³, Esraa Eldesouky^4,6, Ahmed Ali^5,6,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 431-446, 2021, DOI:10.32604/cmc.2021.016165 - 22 March 2021
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… More >

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Muhammad Adil¹, Jehad Ali², Muhammad Sajjad Khan³, Junsu Kim³, Ryan Alturki⁴, Mohammad Zakarya⁴, Mukhtaj Khan⁴, Rahim Khan⁴, Su Min Kim^3,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 447-470, 2021, DOI:10.32604/cmc.2021.014967 - 22 March 2021
（This article belongs to the 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 More >

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1498

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Norah Alsaaran^*, Maha Alrabiah
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 471-485, 2021, DOI:10.32604/cmc.2021.016054 - 22 March 2021
（This article belongs to the 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… More >

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Thavavel Vaiyapuri^*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998 - 22 March 2021
（This article belongs to the 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… More >

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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 - 22 March 2021
（This article belongs to the 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 More >

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1809

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Minh Thanh Vo¹, Anh H. Vo², Trang Nguyen³, Rohit Sharma⁴, Tuong Le^2,5,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 521-535, 2021, DOI:10.32604/cmc.2021.015645 - 22 March 2021
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… More >

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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 - 22 March 2021
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… More >

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Pandia Rajan Jeyaraj¹, Aravind Chellachi Kathiresan¹, Siva Prakash Asokan¹, Edward Rajan Samuel Nadar¹, Hegazy Rezk^2,3,*, Thanikanti Sudhakar Babu⁴
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 553-567, 2021, DOI:10.32604/cmc.2021.015128 - 22 March 2021
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… More >

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Amir Parnianifard¹, Muhammad Saadi², Manus Pengnoo¹, Muhammad Ali Imran³, Sattam Al Otaibi⁴, Pruk Sasithong¹, Pisit Vanichchanunt⁵, Tuchsanai Polysuwan⁶, Lunchakorn Wuttisittikulkij^1,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 569-587, 2021, DOI:10.32604/cmc.2021.015730 - 22 March 2021
（This article belongs to the 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… More >

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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 - 22 March 2021
（This article belongs to the 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… More >

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Shadi Nashwan^*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 607-642, 2021, DOI:10.32604/cmc.2021.015597 - 22 March 2021
（This article belongs to the 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… More >

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Belal Al-Khateeb^1,*, Kawther Ahmed², Maha Mahmood¹, Dac-Nhuong Le^3,4
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 643-654, 2021, DOI:10.32604/cmc.2021.013648 - 22 March 2021
（This article belongs to the 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… More >

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Yingya Guo^1,2,3,7, Kai Huang¹, Cheng Hu^4,*, Jiangyuan Yao⁵, Siyu Zhou⁶
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 655-670, 2021, DOI:10.32604/cmc.2021.016364 - 22 March 2021
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… More >

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Muhammad Ashfaq Khan^1,2, Yangwoo Kim^1,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 671-687, 2021, DOI:10.32604/cmc.2021.015647 - 22 March 2021
（This article belongs to the 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… More >

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Khalid A. AlShalfan¹, Mohammed Zakariah^2,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 689-704, 2021, DOI:10.32604/cmc.2021.015989 - 22 March 2021
（This article belongs to the 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 More >

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Emad Felemban¹, Sultan Daud Khan², Atif Naseer³, Faizan Ur Rehman^4,*, Saleh Basalamah¹
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 705-725, 2021, DOI:10.32604/cmc.2021.015085 - 22 March 2021
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 More >

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Yao Zhao¹, Jian Dong^1,*, Hongwei Liu¹, Jin Wu², Yanxin Liu¹
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 727-741, 2021, DOI:10.32604/cmc.2021.016462 - 22 March 2021
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,… More >

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Miaocao Wang¹, Yuhua Huang¹, Jinming Li¹, Ling Xu², Fulong Zhu^1,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 743-759, 2021, DOI:10.32604/cmc.2021.016372 - 22 March 2021
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.… More >

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Kang-Cheng Chen¹, Chia-Mu Yu^2,*, Tooska Dargahi³
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 761-787, 2021, DOI:10.32604/cmc.2021.014984 - 22 March 2021
（This article belongs to the 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… More >

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Yi-Ming Huang¹, Xiao-Yu Li^1,*, 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 - 22 March 2021
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… More >

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Fawaz Waselallah Alsaade¹, Theyazn H. H. Aldhyani^2,*, Mosleh Hmoud Al-Adhaileh³
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 805-819, 2021, DOI:10.32604/cmc.2021.016264 - 22 March 2021
（This article belongs to the 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… More >

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Muhammad Adeel Azam¹, Khan Bahadar Khan^2,*, Muhammad Ahmad³, Manuel Mazzara⁴
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 821-840, 2021, DOI:10.32604/cmc.2021.016131 - 22 March 2021
（This article belongs to the 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… More >

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Xiaoli Qiang¹, Muhammad Aamir^2,*, 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 - 22 March 2021
（This article belongs to the 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… More >

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Dah-Jing Jwo^*, Jui-Tao Lee
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 857-876, 2021, DOI:10.32604/cmc.2021.016894 - 22 March 2021
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)… More >

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Abdullah Alharbi¹, Mohd Faizan², Wael Alosaimi¹, Hashem Alyami³, Mohd Nadeem², Suhel Ahmad Khan⁴, Alka Agrawal², Raees Ahmad Khan^2,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 877-886, 2021, DOI:10.32604/cmc.2021.016887 - 22 March 2021
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 More >

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Jeonghoon Park¹, Jinsu Kim¹, B. B. Gupta², Namje Park^1,*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 887-901, 2021, DOI:10.32604/cmc.2021.015172 - 22 March 2021
（This article belongs to the 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… More >

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