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Shwet Ketu^*, Pramod Kumar Mishra
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1896-1919, 2021, DOI:10.32604/cmc.2020.012423
（This article belongs to this Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)
Abstract Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid… More >

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Ashutosh Kumar Dubey^1,*, Sushil Narang¹, Abhishek Kumar¹, Satya Murthy Sasubilli², Vicente García-Díaz³
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1921-1936, 2021, DOI:10.32604/cmc.2020.012151
（This article belongs to this Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)
Abstract Novel Coronavirus Disease (COVID-19) is a communicable disease that originated during December 2019, when China officially informed the World Health Organization (WHO) regarding the constellation of cases of the disease in the city of Wuhan. Subsequently, the disease started spreading to the rest of the world. Until this point in time, no specific vaccine or medicine is available for the prevention and cure of the disease. Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early detection of this viral disease.… More >

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Lewis Nkenyereye^1,*, S. M. Riazul Islam², Mahmud Hossain³, M. Abdullah-Al-Wadud⁴, Atif Alamri⁴
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2020.013025
（This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)
Abstract The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions’ repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the load of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers. Assuming a massive demand of PHR data within… More >

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Inzamam Mashood Nasir¹, Asima Bibi², Jamal Hussain Shah², Muhammad Attique Khan¹, Muhammad Sharif², Khalid Iqbal³, Yunyoung Nam⁴, Seifedine Kadry^5,*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945
（This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >

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Liaquat Ali Lund^1,2, Zurni Omar¹, Sumera Dero^1,3, Yuming Chu^4,5, Ilyas Khan^6,*, Kottakkaran Sooppy Nisar⁷
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1963-1975, 2021, DOI:10.32604/cmc.2020.011976
Abstract The unsteady magnetohydrodynamic (MHD) flow on a horizontal preamble surface with hybrid nanoparticles in the presence of the first order velocity and thermal slip conditions are investigated. Alumina (Al₂O₃) and copper (Cu) are considered as hybrid nanoparticles that have been dispersed in water in order to make hybrid nanofluid (Cu − Al₂O₃/water). The system of similarity equations is derived from the system of partial differential equations (PDEs) by using variables of similarity, and their solutions are gotten with shooting method in the Maple software. In certain ranges of unsteadiness and magnetic parameters, the presence of dual solutions can be found.… More >

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Salman A. Cheema¹, Tanveer Kifayat², Abdu R. Rahman², Umair Khan³, A. Zaib⁴, Ilyas Khan^5,*, Kottakkaran Sooppy Nisar⁶
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1977-1985, 2021, DOI:10.32604/cmc.2020.012096
（This article belongs to this Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)
Abstract The flow of novel coronavirus (COVID-19) has affected almost every aspect of human life around the globe. Being the emerging ground and early sufferer of the virus, Wuhan city-data remains a case of multifold significance. Further, it is of notable importance to explore the impact of unique and unprecedented public health response of Chinese authorities—the extreme lockdown of the city. In this research, we investigate the statistical nature of the viral transmission concerning social distancing, extreme quarantine, and robust lockdown interventions. We observed highly convincing and statistically significant evidences in favor of quarantine and social distancing approaches. These findings might… More >

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K. V. Praveen¹, P. M. Joe Prathap², S. Dhanasekaran³, I. S. Hephzi Punithavathi⁴, P. Duraipandy⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin^7,*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1987-2003, 2021, DOI:10.32604/cmc.2020.012398
（This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)
Abstract Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security, reliability, and efficiency. The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality. The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things (IoT) in cloud computing environment. The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques. The current research article presents… More >

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G. Mousa¹, Amr Almaddah², Ayman A. Aly^3,*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2005-2023, 2021, DOI:10.32604/cmc.2020.012580
（This article belongs to this Special Issue: Emerging Computational Intelligence Technologies for Software Engineering: Paradigms, Principles and Applications)
Abstract About 10–20% of every country’s population is disable. There are at least 650 million people with a kind of disability worldwide. Assistance and support are perquisites for many handicap people for participating in society. Electric powered wheelchairs provide efficient mobility to motor impaired persons. In this paper a smart controller of a wheel chair mobile robot using Particle Swarm Optimization Proportional controller (PSO-P) was proposed where (PSO) algorithm was utilized to tune the proportional controller’s gains for each axis. Aiming to improve wheelchair tracking trajectory, a kinematic model of a robot with linear and angular velocities parameters was developed. The… More >

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M. Bilal¹, Imran Khan¹, Taza Gul^1,*, Asifa Tassaddiq², Wajdi Alghamdi³, Safyan Mukhtar⁴, Poom Kumam⁵
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2025-2039, 2021, DOI:10.32604/cmc.2020.012677
Abstract This article aims to investigate the Darcy Forchhemier mixed convection flow of the hybrid nanofluid through an inclined extending cylinder. Two different nanoparticles such as carbon nanotubes (CNTs) and iron oxide Fe₃O₄ have been added to the base fluid in order to prepare a hybrid nanofluid. Nonlinear partial differential equations for momentum, energy and convective diffusion have been changed into dimensionless ordinary differential equations after using Von Karman approach. Homotopy analysis method (HAM), a powerful analytical approach has been used to find the solution to the given problem. The effects of the physical constraints on velocity, concentration and temperature profile… More >

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Gopi Krishna Durbhaka¹, Barani Selvaraj¹, Mamta Mittal², Tanzila Saba^3,*, Amjad Rehman³, Lalit Mohan Goyal⁴
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131
（This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

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Shazia Saqib¹, Allah Ditta², Muhammad Adnan Khan^1,*, Syed Asad Raza Kazmi³, Hani Alquhayz⁴
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2020.013905
Abstract Human activity detection and recognition is a challenging task. Video surveillance can benefit greatly by advances in Internet of Things (IoT) and cloud computing. Artificial intelligence IoT (AIoT) based devices form the basis of a smart city. The research presents Intelligent dynamic gesture recognition (IDGR) using a Convolutional neural network (CNN) empowered by edit distance for video recognition. The proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language (PSL). However, the proposed methodology can work efficiently for any type of video. The proposed research concludes that deep learning and convolutional neural… More >

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Ahmad F. Subahi^1,*, Youseef Alotaibi², Osamah Ibrahim Khalaf³, F. Ajesh⁴
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2077-2086, 2021, DOI:10.32604/cmc.2020.014094
Abstract Network security and energy consumption are deemed to be two important components of wireless and mobile ad hoc networks (WMANets). There are various routing attacks which harm Ad Hoc networks. This is because of the unsecure wireless communication, resource constrained capabilities and dynamic topology. In order to cope with these issues, Ad Hoc On-Demand Distance Vector (AODV) routing protocol can be used to remain the normal networks functionality and to adjust data transmission by defending the networks against black hole attacks. The proposed system, in this work, identifies the optimal route from sender to collector, prioritizing the number of jumps,… More >

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Jing Zhang^1,2, Keping Yu^3,*, Zheng Wen⁴, Xin Qi³, Anup Kumar Paul⁵
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2087-2104, 2021, DOI:10.32604/cmc.2020.014220
（This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)
Abstract The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter… More >

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Dah-Jing Jwo^*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2105-2126, 2021, DOI:10.32604/cmc.2020.014241
Abstract In this paper, the Global Positioning System (GPS) interferometer provides the preliminarily computed quaternions, which are then employed as the measurement of the extended Kalman filter (EKF) for the attitude determination system. The estimated quaternion elements from the EKF output with noticeably improved precision can be converted to the Euler angles for navigation applications. The aim of the study is twofold. Firstly, the GPS-based computed quaternion vector is utilized to avoid the singularity problem. Secondly, the quaternion estimator based on the EKF is adopted to improve the estimation accuracy. Determination of the unknown baseline vector between the antennas sits at… More >

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Xuting Duan^1,2, Yuanhao Zhao^1,2, Kunxian Zheng^1,2,*, Daxin Tian^1,2, Jianshan Zhou^1,2,3, Jian Gao⁴
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2127-2140, 2021, DOI:10.32604/cmc.2020.014484
Abstract Dynamic channel assignment (DCA) is significant for extending vehicular ad hoc network (VANET) capacity and mitigating congestion. However, the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario. In our preliminary field test for communication under V2X scenario, we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET. In order to improve the communication performance, we firstly demonstrate the feasibility and potential of reinforcement learning (RL) method in joint channel selection decision and access fallback adaptation design in this… More >

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1387

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Ali Saadon Al-Ogaili^1,*, Ameer Alhasan², Agileswari Ramasamy¹, Marayati Binti Marsadek¹, Tengku Juhana Tengku Hashim¹, Ammar Al-Sharaa³, Mastura Binti Aadam³, Lukman Audah²
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2141-2164, 2021, DOI:10.32604/cmc.2020.013440
（This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
Abstract The Coronavirus (COVID-19) pandemic is considered as a global public health challenge. To contain this pandemic, different measures are being taken globally. The Internet of Things (IoT) has been represented as one of the most important schemes that has been considered to fight the spread of COVID-19 in the world, practically Malaysia. In fact, there are many sectors in Malaysia would be transformed into smart services by using IoT technologies, particularly energy, transportation, healthcare sectors. This manuscript presents a comprehensive review of the IoT technologies that are being used currently in Malaysia to accelerate the measures against COVID-19. These IoT… More >

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Shaheena Khanum¹, Muhammad Adeel Ashraf², Asim Karim¹, Bilal Shoaib³, Muhammad Adnan Khan⁴, Rizwan Ali Naqvi⁵, Kamran Siddique⁶, Mohammed Alswaitti^6,*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2165-2181, 2021, DOI:10.32604/cmc.2020.013646
Abstract Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide. It is a clinically important attribute to numerous age-related, metabolic, and chronic diseases such as diabetes, Alzheimer’s, renal failure, etc. Identification of a non-enzymatic reaction are quite challenging in research. Manual identification in labs is a very costly and time-consuming process. In this research, we developed an accurate, valid, and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites. Comprehensive techniques using position relative features are used for feature extraction. An algorithm named as a random forest with some preprocessing… More >

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Waleed Albattah^1,*, Muhammad Haris Kaka Khel², Shabana Habib¹, Muhammad Islam³, Sheroz Khan^3,4, Kushsairy Abdul Kadir²
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2183-2197, 2021, DOI:10.32604/cmc.2020.014227
（This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
Abstract Hajj as the Muslim holy pilgrimage, attracts millions of humans to Mecca every year. According to statists, the pilgrimage has attracted close to 2.5 million pilgrims in 2019, and at its peak, it has attracted over 3 million pilgrims in 2012. It is considered as the world’s largest human gathering. Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided. This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj. The image classification… More >

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Nazeer Muhammad¹, Rubab², Nargis Bibi³, Oh-Young Song⁴, Muhammad Attique Khan^5,*, Sajid Ali Khan⁶
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257
（This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
Abstract Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue, this article proposes a fully… More >

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Lewis Nkenyereye¹, Bayu Adhi Tama², Sunghoon Lim^3,*
CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432
Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness… More >

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