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Jiyoung Yun, Mainak Basak, Myung-Mook Han^*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2827-2843, 2021, DOI:10.32604/cmc.2021.017266 - 24 August 2021
（This article belongs to the Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
Abstract Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients… More >

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Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2845-2861, 2021, DOI:10.32604/cmc.2021.014361 - 24 August 2021
Abstract With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents, which must be done without losing important features and information. This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure. The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the More >

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2056

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1638

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Jagannath Paramguru¹, Subrat Kumar Barik¹, Ajit Kumar Barisal², Gaurav Dhiman³, Rutvij H. Jhaveri⁴, Mohammed Alkahtani^5,6, Mustufa Haider Abidi^5,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2863-2882, 2021, DOI:10.32604/cmc.2021.016002 - 24 August 2021
（This article belongs to the Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
Abstract Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue. Various non-linearity are added to make the fossil fuel-based power systems more practical. In order to achieve an accurate economical schedule, valve point loading effect, ramp rate constraints, and prohibited operating zones are being considered for realistic scenarios. In this paper, an improved, and modified version of conventional particle swarm optimization (PSO), called Oscillatory PSO (OPSO), is devised to provide a cheaper schedule with optimum cost. The conventional PSO is More >

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Mohamed Abdel-Basset¹, Reda Mohamed¹, Ripon K. Chakrabortty², Michael J. Ryan², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2883-2901, 2021, DOI:10.32604/cmc.2021.017854 - 24 August 2021
Abstract This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance, redundancy, or less information; this pre-processing process is often known as feature selection. This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization (GNDO) supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values. Further, a novel… More >

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Jia-Shun Zhang¹, Gang Xu^2,*, Xiu-Bo Chen¹, Haseeb Ahmad³, Xin Liu⁴, Wen Liu^5,6,7
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2903-2916, 2021, DOI:10.32604/cmc.2021.017227 - 24 August 2021
Abstract With the rapid development of cloud computing technology, cloud services have now become a new business model for information services. The cloud server provides the IT resources required by customers in a self-service manner through the network, realizing business expansion and rapid innovation. However, due to the insufficient protection of data privacy, the problem of data privacy leakage in cloud storage is threatening cloud computing. To address the problem, we propose BC-PECK, a data protection scheme based on blockchain and public key searchable encryption. Firstly, all the data is protected by the encryption algorithm. The… More >

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1194

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Muhammad Rizwan Latif¹, Muhamamd Attique Khan¹, Muhammad Younus Javed¹, Haris Masood², Usman Tariq³, Yunyoung Nam^4,*, Seifedine Kadry⁵
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364 - 24 August 2021
（This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these… More >

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Sawroop Kaur¹, Aman Singh^1,*, G. Geetha², Mehedi Masud³, Mohammed A. Alzain⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2933-2948, 2021, DOI:10.32604/cmc.2021.019030 - 24 August 2021
Abstract Web crawlers have evolved from performing a meagre task of collecting statistics, security testing, web indexing and numerous other examples. The size and dynamism of the web are making crawling an interesting and challenging task. Researchers have tackled various issues and challenges related to web crawling. One such issue is efficiently discovering hidden web data. Web crawler’s inability to work with form-based data, lack of benchmarks and standards for both performance measures and datasets for evaluation of the web crawlers make it still an immature research domain. The applications like vertical portals and data integration… More >

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1354

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Asadullah Tariq¹, Irfan ud din¹, Rana Asif Rehman², Byung-Seo Kim^3,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2949-2966, 2021, DOI:10.32604/cmc.2021.017658 - 24 August 2021
（This article belongs to the Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)
Abstract Internet of Vehicles (IoV), a rapidly growing technology for efficient vehicular communication and it is shifting the trend of traditional Vehicular Ad Hoc Networking (VANET) towards itself. The centralized management of IoV endorses its uniqueness and suitability for the Intelligent Transportation System (ITS) safety applications. Named Data Networking (NDN) is an emerging internet paradigm that fulfills most of the expectations of IoV. Limitations of the current IP internet architecture are the main motivation behind NDN. Software-Defined Networking (SDN) is another emerging networking paradigm of technology that is highly capable of efficient management of overall networks… More >

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1394

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Arun Kumar^1,*, Sivabalan Ambigapathy², Mehedi Masud³, Emad Sami Jaha⁴, Sumit Chakravarty⁵, Kanchan Sengar¹
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2967-2981, 2021, DOI:10.32604/cmc.2021.019092 - 24 August 2021
Abstract The article introduces Non-Orthogonal Multiple Access (NOMA) and Filter Bank Multicarrier (FBMC), known as hybrid waveform (NOMA-FBMC), as two of the most deserving contenders for fifth-generation (5G) network. High spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid NOMA-FBMC. We compare the spectral efficiency of Orthogonal Frequency Division Multiplexing (OFDM), FBMC, NOMA, and NOMA-FBMC. It is seen that the hybrid waveform outperforms the existing waveforms. Peak to Average Power Ratio (PAPR) is regarded as a significant issue in multicarrier waveforms. The combination of Selective Mapping-Partial Transmit Sequence (SLM-PTS) is an effective way More >

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2006

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1105

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El-Sayed M. El-kenawy¹, Hattan F. Abutarboush², Ali Wagdy Mohamed^3,4, Abdelhameed Ibrahim^5,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2983-2995, 2021, DOI:10.32604/cmc.2021.019114 - 24 August 2021
（This article belongs to the Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
Abstract Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer More >

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2073

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Hussain Askari, Niamat Hussain, Domin Choi, Md. Abu Sufian, Anees Abbas, Nam Kim^*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2997-3013, 2021, DOI:10.32604/cmc.2021.018855 - 24 August 2021
（This article belongs to the Special Issue: Advances in 5G Antenna Designs and Systems)
Abstract This paper presents an AMC (artificial magnetic conductor)-based wideband circularly polarized printed monopole antenna for unidirectional radiation. The antenna includes an AMC reflector, a coplanar waveguide (CPW) feed structure to excite the antenna, a ground plane with a rectangular slot on the left side of feedline, and an asymmetrical ground plane on its right side. The induced surface currents on CWP feedline, rectangularly slotted, and asymmetrical ground planes cause circularly polarized radiations. The AMC reflector consisting periodic metallic square patches is used instead of the conventional PEC (perfect electric conductor) reflector, the distance between the More >

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Chintakindi Balaram Murthy¹, Mohammad Farukh Hashmi¹, Ghulam Muhammad^2,3,*, Salman A. AlQahtani^2,3
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781 - 24 August 2021
（This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature… More >

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2194

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2001

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Orawit Thinnukool¹, Thammarat Panityakul², Mahwish Bano^3,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3033-3046, 2021, DOI:10.32604/cmc.2021.019153 - 24 August 2021
Abstract In the most recent decades, a major number of image encryption plans have been proposed. The vast majority of these plans reached a high-security level; however, their moderate speeds because of their complicated processes made them of no use in real-time applications. Inspired by this, we propose another efficient and rapid image encryption plan dependent on the Trigonometric chaotic guide. In contrast to the most of current plans, we utilize this basic map to create just a couple of arbitrary rows and columns. Moreover, to additionally speed up, we raise the processing unit from the More >

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1944

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1180

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Manar A. Alqudah¹, Thabet Abdeljawad^2,3,4,*, Anwar Zeb⁵, Izaz Ullah Khan⁵, Fatma Bozkurt⁶
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3047-3063, 2021, DOI:10.32604/cmc.2021.017752 - 24 August 2021
（This article belongs to the Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
Abstract Since the end of 2019, the world has suffered from a pandemic of the disease called COVID-19. WHO reports show approximately 113 M confirmed cases of infection and 2.5 M deaths. All nations are affected by this nightmare that continues to spread. Widespread fear of this pandemic arose not only from the speed of its transmission: a rapidly changing “normal life” became a fear for everyone. Studies have mainly focused on the spread of the virus, which showed a relative decrease in high temperature, low humidity, and other environmental conditions. Therefore, this study targets the… More >

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1185

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3

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ARTICLE

Nataliya Shakhovska^1,*, Mykola Medykovskyy², Roman Melnyk², Nataliya Kryvinska³
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3065-3078, 2021, DOI:10.32604/cmc.2021.018761 - 24 August 2021
Abstract The article presents the results of research on the possibilities of using genetic algorithms for solving the multicriteria optimization problem of determining the active components of a wind farm. Optimization is carried out on two parameters: efficiency factor of wind farm use (integrated parameter calculated on the basis of 6 parameters of each of the wind farm), average power deviation level (average difference between the load power and energy generation capabilities of the active wind farm). That was done an analysis of publications on the use of genetic algorithms to solve multicriteria optimization problems. Computer… More >

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1024

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N. R. Rejin Paul^1,*, D. Paul Raj²
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3079-3093, 2021, DOI:10.32604/cmc.2021.018993 - 24 August 2021
Abstract Security is an essential part of the cloud environment. For ensuring the security of the data being communicated to and from the cloud server, a significant parameter called trust was introduced. Trust-based security played a vital role in ensuring that the communication between cloud users and service providers remained unadulterated and authentic. In most cloud-based data distribution environments, emphasis is placed on accepting trusted client users’ requests, but the cloud servers’ integrity is seldom verified. This paper designs a trust-based access control model based on user and server characteristics in a multi-cloud environment to address… More >

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Lin Tie¹, Bin Huang¹, Bin Pan¹, Guang Sun^1,2,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3095-3107, 2021, DOI:10.32604/cmc.2021.019363 - 24 August 2021
Abstract COVID-19 was first reported in China and quickly spread throughout the world. Weak investor confidence in government efforts to control the pandemic seriously affected global financial markets. This study investigated chaos in China’s futures market during COVID-19, focusing on the degree of chaos at different periods during the pandemic. We constructed a phase diagram to observe the attractor trajectory of index futures (IFs). During the COVID-19 outbreak, overall chaos in China’s futures market was increasing, and there was a clear correlation between market volatility and the macroenvironment (mainly government regulation). The Hurst index, calculated by More >

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1997

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A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612 - 24 August 2021
Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

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Sujeet More¹, Jimmy Singla¹, Oh-Young Song^2,*, Usman Tariq³, Sharaf Malebary⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3127-3143, 2021, DOI:10.32604/cmc.2021.018230 - 24 August 2021
（This article belongs to the Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)
Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for More >

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1264

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Yu-Dong Zhang¹, Muhammad Attique Khan², Ziquan Zhu³, Shui-Hua Wang^4,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3145-3162, 2021, DOI:10.32604/cmc.2021.018040 - 24 August 2021
（This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way… More >

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1413

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9

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M. Iyapparaja¹, M. Sathish Kumar¹, S. Siva Rama Krishnan¹, Chiranji Lal Chowdhary¹, Byungun Yoon², Saurabh Singh², Gi Hwan Cho^3,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3163-3178, 2021, DOI:10.32604/cmc.2021.017302 - 24 August 2021
（This article belongs to the Special Issue: Emerging Trends in Software-Defined Networking for Industry 4.0)
Abstract Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the… More >

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Yousef Sanjalawe^1,2, Mohammed Anbar^1,*, Salam Al-E’mari¹, Rosni Abdullah¹, Iznan Hasbullah¹, Mohammed Aladaileh¹
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3179-3204, 2021, DOI:10.32604/cmc.2021.018546 - 24 August 2021
Abstract The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-increasing users’ requests and the number of data centers required to execute these requests. Cloud service broker policy defines cloud data center’s selection, which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution. Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness, and it is well suited for selecting an More >

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Ziqing Yan^1,2, Pengpeng Yang^1,2, Rongrong Ni^1,2,*, Yao Zhao^1,2, Hairong Qi³
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3205-3216, 2021, DOI:10.32604/cmc.2021.020324 - 24 August 2021
Abstract As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of tampering when verifying the authenticity of digital images. Contrast enhancement forensics techniques have always drawn significant attention for image forensics community, although most approaches have obtained effective detection results, existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format. The detection of forgery on contrast adjustments in the presence of JPEG post processing is… More >

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1128

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S. Lalitha¹, Deepa Gupta^2,*, Mohammed Zakariah³, Yousef Ajami Alotaibi³
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3217-3238, 2021, DOI:10.32604/cmc.2021.018406 - 24 August 2021
（This article belongs to the Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)
Abstract Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion. Limited work with standalone speech emotion recognition (SER) systems proposed for continuous speech only has been reported. In the recent decade, various effective SER systems have been proposed for discrete speech, i.e., short speech phrases. It would be more helpful if these systems could also recognize emotions from continuous speech. However, if these systems are applied directly to test emotions from continuous speech, emotion recognition performance would not be… More >

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Muhammad Ramzan¹, Mudassar Raza¹, Muhammad Sharif¹, Muhammad Attique Khan², Yunyoung Nam^3,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920 - 24 August 2021
（This article belongs to the Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the… More >

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Habib Dhahri^1,2,*, Besma Rabhi³, Slaheddine Chelbi⁴, Omar Almutiry¹, Awais Mahmood¹, Adel M. Alimi³
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3259-3274, 2021, DOI:10.32604/cmc.2021.018449 - 24 August 2021
（This article belongs to the Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
Abstract The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, More >

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Chuanrong Wu^1,*, Haotian Cui¹, Zhi Lu², Xiaoming Yang³, Mark E. McMurtrey⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3275-3287, 2021, DOI:10.32604/cmc.2021.019969 - 24 August 2021
Abstract Big data knowledge, such as customer demands and consumer preferences, is among the crucial external knowledge that firms need for new product development in the big data environment. Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients. This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients: subscription pricing and pay-per-use pricing. We find that: (1) the subscription price of big data knowledge has no effect on… More >

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Ahmed Y. Hamed^1,*, Monagi H. Alkinani²
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3289-3301, 2021, DOI:10.32604/cmc.2021.018658 - 24 August 2021
Abstract Task scheduling is the main problem in cloud computing that reduces system performance; it is an important way to arrange user needs and perform multiple goals. Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation, task scheduling, security, privacy, etc. To improve system performance, an efficient task-scheduling algorithm is required. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time, and execution cost. In this paper, a task scheduling algorithm based on a Genetic Algorithm (GA) has been presented for assigning and executing different… More >

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P. Naga Srinivasu¹, Shakeel Ahmed^2,*, Abdulaziz Alhumam², Akash Bhoi Kumar³, Muhammad Fazal Ijaz⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3303-3319, 2021, DOI:10.32604/cmc.2021.018472 - 24 August 2021
Abstract In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training,… More >

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1282

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Shabana Habib¹, Noreen Fayyaz Khan^2,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3321-3335, 2021, DOI:10.32604/cmc.2021.015504 - 24 August 2021
Abstract Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used… More >

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1030

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Fatmah Abdulrahman Baothman^*, Osama Ahmed Abulnaja, Fatima Jafar Muhdher
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3337-3363, 2021, DOI:10.32604/cmc.2021.017637 - 24 August 2021
（This article belongs to the Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)
Abstract Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the… More >

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Imran Ahmed^1,*, Humaira Sardar¹, Hanan Aljuaid², Fakhri Alam Khan¹, Muhammad Nawaz¹, Adnan Awais¹
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3365-3381, 2021, DOI:10.32604/cmc.2021.018486 - 24 August 2021
（This article belongs to the Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)
Abstract Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can… More >

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Chao Li¹, Fan Li^1,2, Wei Du³, Lihua Yin^1,*, Bin Wang⁴, Chonghua Wang⁵, Tianjie Luo¹
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3383-3397, 2021, DOI:10.32604/cmc.2021.020765 - 24 August 2021
Abstract Material identification is a technology that can help to identify the type of target material. Existing approaches depend on expensive instruments, complicated pre-treatments and professional users. It is difficult to find a substantial yet effective material identification method to meet the daily use demands. In this paper, we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier, which can significantly reduce the cost and guarantee a high level accuracy. In practical measurement of Wi-Fi based material identification, these two features are… More >

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Suliman Aladhadh¹, Hidayat Ur Rehman², Ali Mustafa Qamar^3,4,*, Rehan Ullah Khan¹
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724 - 24 August 2021
（This article belongs to the Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep… More >

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Mahmood ul Hassan^1,*, Amin Al-Awady¹, Khalid Mahmood², Shahzad Ali³, Ibrahim Algamdi¹, Muhammad Kashif Saeed⁴, Safdar Zaman⁵
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3413-3427, 2021, DOI:10.32604/cmc.2021.018544 - 24 August 2021
Abstract Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving… More >

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Ngo Tung Son^1,2,*, Jafreezal Jaafar¹, Izzatdin Abdul Aziz¹, Bui Ngoc Anh², Hoang Duc Binh², Muhammad Umar Aftab³
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3429-3444, 2021, DOI:10.32604/cmc.2021.017710 - 24 August 2021
（This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
Abstract The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional… More >

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Haining Yu¹, Lailai Yin^1,*, Hongli Zhang¹, Dongyang Zhan^1,2, Jiaxing Qu³, Guangyao Zhang⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3445-3458, 2021, DOI:10.32604/cmc.2021.019462 - 24 August 2021
Abstract Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services. The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation. To alleviate privacy concerns about location privacy leaks during road distance computation, it is desirable to have a secure and efficient road distance computation approach. In this paper, we propose two secure road distance computation approaches, which can compute road distance over encrypted data efficiently. An approximate road distance computation approach is More >

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Harshit Kaushik¹, Dilbag Singh², Shailendra Tiwari³, Manjit Kaur², Chang-Won Jeong⁴, Yunyoung Nam^5,*, Muhammad Attique Khan⁶
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3459-3475, 2021, DOI:10.32604/cmc.2021.017337 - 24 August 2021
（This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
Abstract In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we… More >

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ARTICLE

Sk Sarif Hassan¹, Ranjeet Kumar Rout², Kshira Sagar Sahoo³, Nz Jhanjhi⁴, Saiyed Umer⁵, Thamer A. Tabbakh^6,*, Zahrah A. Almusaylim⁷
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3477-3493, 2021, DOI:10.32604/cmc.2021.017206 - 24 August 2021
Abstract Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their… More >

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Seyed Amin Hosseini Seno¹, Mahdi Nikooghadam¹, Rahmat Budiarto^2,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3495-3512, 2021, DOI:10.32604/cmc.2021.019051 - 24 August 2021
（This article belongs to the Special Issue: Advances of AI and Blockchain technologies for Future Smart City)
Abstract Tele-medical information system provides an efficient and convenient way to connect patients at home with medical personnel in clinical centers. In this system, service providers consider user authentication as a critical requirement. To address this crucial requirement, various types of validation and key agreement protocols have been employed. The main problem with the two-way authentication of patients and medical servers is not built with thorough and comprehensive analysis that makes the protocol design yet has flaws. This paper analyzes carefully all aspects of security requirements including the perfect forward secrecy in order to develop an… More >

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Rajendra Prasad Nayak¹, Srinivas Sethi², Sourav Kumar Bhoi³, Kshira Sagar Sahoo⁴, Nz Jhanjhi⁵, Thamer A. Tabbakh⁶, Zahrah A. Almusaylim^7,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.018930 - 24 August 2021
Abstract Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in… More >

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Nora Shoaip¹, Amira Rezk¹, Shaker EL-Sappagh^2,3, Tamer Abuhmed^4,*, Sherif Barakat¹, Mohammed Elmogy⁵
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3531-3548, 2021, DOI:10.32604/cmc.2021.019069 - 24 August 2021
Abstract Alzheimer’s disease (AD) is a very complex disease that causes brain failure, then eventually, dementia ensues. It is a global health problem. 99% of clinical trials have failed to limit the progression of this disease. The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms. Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction. In this regard, the need becomes more urgent for biomarker-based detection. A key issue in understanding AD is the need… More >

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ARTICLE

Wei Sun^1,2,*, Xuan Chen³, Xiaorui Zhang^1,3, Guangzhao Dai², Pengshuai Chang², Xiaozheng He⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3549-3561, 2021, DOI:10.32604/cmc.2021.021627 - 24 August 2021
Abstract Vehicle re-identification (ReID) aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario. It has gradually become a core technology of intelligent transportation system. Most existing vehicle re-identification models adopt the joint learning of global and local features. However, they directly use the extracted global features, resulting in insufficient feature expression. Moreover, local features are primarily obtained through advanced annotation and complex attention mechanisms, which require additional costs. To solve this issue, a multi-feature learning model with enhanced local attention for vehicle re-identification (MFELA)… More >

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ARTICLE

Yonghun Jang, Chang-Hyeon Park, Dong-Gun Lee, Yeong-Seok Seo^*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3563-3579, 2021, DOI:10.32604/cmc.2021.018901 - 24 August 2021
（This article belongs to the Special Issue: Advances of AI and Blockchain technologies for Future Smart City)
Abstract Following the development of communication techniques and smart devices, the era of Artificial Intelligence (AI) and big data has arrived. The increased connectivity, referred to as hyper-connectivity, has led to the development of smart cities. People in these smart cities can access numerous online contents and are always connected. These developments, however, also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels. Information cannot often be verified, which can confuse the users. The increasing influence of social media has… More >

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Rabia Saleem¹, Jamal Hussain Shah^1,*, Muhammad Sharif¹, Ghulam Jillani Ansari²
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3581-3601, 2021, DOI:10.32604/cmc.2021.017700 - 24 August 2021
（This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
Abstract Due to the high demand for mango and being the king of all fruits, it is the need of the hour to curb its diseases to fetch high returns. Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms. Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases, i.e., Anthracnose, apical-necrosis, etc., of a mango plant leaf. To solve this issue, we proposed a CNN based Fully-convolutional-network (FrCNnet) model for the segmentation of the diseased part of the mango leaf. The proposed… More >

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Abid Sohail¹, Ammar Haseeb¹, Mobashar Rehman^2,*, Dhanapal Durai Dominic³, Muhammad Arif Butt⁴
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3603-3618, 2021, DOI:10.32604/cmc.2021.017795 - 24 August 2021
（This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
Abstract There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between More >

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Yuan Li^1,7, Jiaxuan Xie¹, Mu Xia², Qianqian Li³, Meng Li⁴, Lei Guo^5,*, Zhen Zhang⁶
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3619-3628, 2021, DOI:10.32604/cmc.2021.016187 - 24 August 2021
Abstract The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed More >

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1088

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Kalaipriyan Thirugnansambandam¹, Debnath Bhattacharyya², Jaroslav Frnda³, Dinesh Kumar Anguraj², Jan Nedoma^4,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3629-3644, 2021, DOI:10.32604/cmc.2021.018939 - 24 August 2021
Abstract In Wireless Sensor Network (WSN), coverage and connectivity are the vital challenges in the target-based region. The linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement position. In this paper, a multiobjective problem on target-based WSN (t-WSN) is derived, which minimizes the number of deployed nodes, and maximizes the cost of coverage and sensing range. An Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is incorporated to tackle this multiobjective problem… More >

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Din Prathumwan¹, Inthira Chaiya², Kamonchat Trachoo^2,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3645-3657, 2021, DOI:10.32604/cmc.2021.018241 - 24 August 2021
Abstract Lumbar spine stenosis (LSS) is a narrowing of the spinal canal that results in pressure on the spinal nerves. This orthopedic disorder can cause severe pain and dysfunction. LSS is a common disabling problem amongst elderly people. In this paper, we developed a finite element model (FEM) to study the forces and the von Mises stress acting on the spine when people bend down. An artificial lumbar spine (L3) was generated from CT data by using the FEM, which is a powerful tool to study biomechanics. The proposed model is able to predict the effect… More >

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1280

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Sangdo Lee¹, Jun-Ho Huh^2,*
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3659-3684, 2021, DOI:10.32604/cmc.2021.015632 - 24 August 2021
（This article belongs to the Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)
Abstract An analysis of the recent major security incidents related to industrial control systems, revealed that most had been caused by company employees. Therefore, enterprise security management systems have been developed to focus on companies’ personnel. Nonetheless, several hacking incidents, involving major companies and public/financial institutions, were actually attempted by the cooperative firms or the outsourced manpower undertaking maintenance work. Specifically, institutions that operate industrial control systems (ICSs) associated with critical national infrastructures, such as traffic or energy, have contracted several cooperative firms. Nonetheless, ICT's importance is gradually increasing, due to outsourcing, and is the most… More >

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