The old submission system has been replaced by a new version for the journal "CMC-Computers, Materials & Continua".
Please be noticed, the old submission system for this journal is now no longer accessible. The email ID registered to
the old submission system can be used to log in to the new system by using your registered email address.
If you have trouble logging in, please click on "Forgot your password" to reset your password. Please click here to access
the new submission system. If you meet any questions, please do not hesitate to contact us.

CMC-Computers, Materials & Continua

About the Journal

Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.

Indexing and Abstracting

SCI: 2019 Impact Factor 4.89; Scopus CiteScore (Impact per Publication 2019): 3.8; SNIP (Source Normalized Impact per Paper 2019): 4.801; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.

  • Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning
  • Abstract The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new… More
  •   Views:134       Downloads:48        Download PDF
  • A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network
  • Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression of the genetic programming. Stress… More
  •   Views:42       Downloads:19        Download PDF
  • An Effective Numerical Method for the Solution of a Stochastic Coronavirus (2019-nCovid) Pandemic Model
  • Abstract Nonlinear stochastic modeling plays a significant role in disciplines such as psychology, finance, physical sciences, engineering, econometrics, and biological sciences. Dynamical consistency, positivity, and boundedness are fundamental properties of stochastic modeling. A stochastic coronavirus model is studied with techniques of transition probabilities and parametric perturbation. Well-known explicit methods such as Euler Maruyama, stochastic Euler, and stochastic Runge–Kutta are investigated for the stochastic model. Regrettably, the above essential properties are not restored by existing methods. Hence, there is a need to construct essential properties preserving the computational method. The non-standard approach of finite difference is examined to maintain the above basic… More
  •   Views:51       Downloads:23        Download PDF
  • A Novel Approach to Data Encryption Based on Matrix Computations
  • Abstract In this paper, we provide a new approach to data encryption using generalized inverses. Encryption is based on the implementation of weighted Moore–Penrose inverse AMN(nxm) over the nx8 constant matrix. The square Hermitian positive definite matrix N8x8 p is the key. The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge. We have provided NIST (National Institute of Standards and Technology) quality assurance tests for a random generated Hermitian matrix (a total of 10 different tests and additional analysis with approximate entropy and random digression). In the… More
  •   Views:58       Downloads:24        Download PDF
  • Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia
  • Abstract The World Health Organization declared COVID-19 a pandemic on March 11, 2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives. COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise. The information discharged by the WHO till June 15, 2020 reports 8,063,990 cases of COVID-19. As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug, the nations are relentlessly working at the most ideal preventive systems to contain the infection. The Kingdom… More
  •   Views:44       Downloads:16        Download PDF
  • Industry 4.0: Architecture and Equipment Revolution
  • Abstract The development of science and technology has led to the era of Industry 4.0. The core concept is the combination of “material and informationization”. In the supply chain and manufacturing process, the “material” of the physical entity world is realized by data, identity, intelligence, and information. Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization. The goal is “maximizing production efficiency, minimizing production costs, and maximizing the individual needs of human beings for products and services.” Achieving this goal will surely bring about a major leap in the… More
  •   Views:66       Downloads:22        Download PDF
  • Exploiting Structural Similarities to Classify Citations
  • Abstract Citations play an important role in the scientific community by assisting in measuring multifarious policies like the impact of journals, researchers, institutions, and countries. Authors cite papers for different reasons, such as extending previous work, comparing their study with the state-of-the-art, providing background of the field, etc. In recent years, researchers have tried to conceptualize all citations into two broad categories, important and incidental. Such a categorization is very important to enhance scientific output in multiple ways, for instance, (1) Helping a researcher in identifying meaningful citations from a list of 100 to 1000 citations (2) Enhancing the impact factor… More
  •   Views:29       Downloads:13        Download PDF
  • University Learning and Anti-Plagiarism Back-End Services
  • Abstract Plagiarism refers to the use of other people’s ideas and information without acknowledging the source. In this research, anti-plagiarism software was designed especially for the university and its campuses to identify plagiarized text in students’ written assignments and laboratory reports. The proposed framework collected original documents to identify plagiarized text using natural language processing. Our research proposes a method to detect plagiarism by applying the core concept of text, which is semantic associations of words and their syntactic composition. Information on the browser was obtained through Request application programming interface by name Url.AbsoluteUri, and it is stored in a centralized… More
  •   Views:25       Downloads:12        Download PDF
  • Entanglement and Sudden Death for a Two-Mode Radiation Field Two Atoms
  • Abstract The effect of the field–field interaction on a cavity containing two qubit (TQ) interacting with a two mode of electromagnetic field as parametric amplifier type is investigated. After performing an appropriate transformation, the constants of motion are calculated. Using the Schrödinger differential equation a system of differential equations was obtained, and the general solution was obtained in the case of exact resonance. Some statistical quantities were calculated and discussed in detail to describe the features of this system. The collapses and revivals phenomena have been discussed in details. The Shannon information entropy has been applied for measuring the degree of… More
  •   Views:22       Downloads:12        Download PDF
  • Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone
  • Abstract The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis. In this study, we propose a framework referred to as smart forecasting CardioWatch (SCW) to measure the heart-rate variation (HRV) for patients with myocardial infarction (MI) who live alone or are outside their homes. In this study, HRV is used as a vital alarming sign for patients with MI. The performance of the proposed framework is measured using machine learning and deep learning techniques, namely, support vector machine, logistic regression, and decision-tree classification techniques. The results indicated that the analysis of… More
  •   Views:39       Downloads:27        Download PDF
  • Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves
  • Abstract Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations… More
  •   Views:35       Downloads:11        Download PDF
  • Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches
  • Abstract Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human gait patterns. In both the… More
  •   Views:30       Downloads:13        Download PDF
  • Survey and Analysis of VoIP Frame Aggregation Methods over A-MSDU IEEE 802.11n Wireless Networks
  • Abstract The IEEE 802.11n standard has provided prominent features that greatly contribute to ubiquitous wireless networks. Over the last ten years, voice over IP (VoIP) has become widespread around the globe owing to its low-cost or even free call rate. The combination of these technologies (VoIP and wireless) has become desirable and inevitable for organizations. However, VoIP faces a bandwidth utilization issue when working with 802.11 wireless networks. The bandwidth utilization is inefficient on the grounds that (i) 80 bytes of 802.11/RTP/UDP/IP header is appended to 10–730 bytes of VoIP payload and (ii) 765 µs waiting intervals follow each 802.11 VoIP… More
  •   Views:22       Downloads:11        Download PDF
  • A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places
  • Abstract With the emergence of the COVID19 virus in late 2019 and the declaration that the virus is a worldwide pandemic, health organizations and governments have begun to implement severe health precautions to reduce the spread of the virus and preserve human lives. The enforcement of social distancing at work environments and public areas is one of these obligatory precautions. Crowd management is one of the effective measures for social distancing. By reducing the social contacts of individuals, the spread of the disease will be immensely reduced. In this paper, a model for crowd counting in public places of high and… More
  •   Views:22       Downloads:10        Download PDF
  • IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification
  • Abstract Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion… More
  •   Views:22       Downloads:10        Download PDF
  • Urdu Ligature Recognition System: An Evolutionary Approach
  • Abstract Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics. Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks, reporting exceptional results. The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition. Overall the proposed recognition system observes the processes of pre-processing, segmentation, feature extraction, hierarchical clustering, classification rules and genetic algorithm optimization and recognition. The pre-processing stage removes noise from the sentence images, whereas, in segmentation, the sentences are segmented into ligature components. Fifteen features… More
  •   Views:58       Downloads:20        Download PDF
  • Application of Modified Extended Tanh Technique for Solving Complex Ginzburg–Landau Equation Considering Kerr Law Nonlinearity
  • Abstract The purpose of this work is to find new soliton solutions of the complex Ginzburg–Landau equation (GLE) with Kerr law non-linearity. The considered equation is an imperative nonlinear partial differential equation (PDE) in the field of physics. The applications of complex GLE can be found in optics, plasma and other related fields. The modified extended tanh technique with Riccati equation is applied to solve the Complex GLE. The results are presented under a suitable choice for the values of parameters. Figures are shown using the three and two-dimensional plots to represent the shape of the solution in real, and imaginary… More
  •   Views:22       Downloads:11        Download PDF
  • A Crowdsourcing Recommendation that Considers the Influence of Workers
  • Abstract In the context of the continuous development of the Internet, crowdsourcing has received continuous attention as a new cooperation model based on the relationship between enterprises, the public and society. Among them, a reasonably designed recommendation algorithm can recommend a batch of suitable workers for crowdsourcing tasks to improve the final task completion quality. Therefore, this paper proposes a crowdsourcing recommendation framework based on workers’ influence (CRBI). This crowdsourcing framework completes the entire process design from task distribution, worker recommendation, and result return through processes such as worker behavior analysis, task characteristics construction, and cost optimization. In this paper, a… More
  •   Views:21       Downloads:12        Download PDF
  • How Can Lean Manufacturing Lead the Manufacturing Sector during Health Pandemics Such as COVID 19: A Multi Response Optimization Framework
  • Abstract Lean manufacturing has been used for the last few decades as a process and performance improvement tool. Initially known as Toyota production system (TPS), lean is now used in almost all service and manufacturing sectors to deliver favorable results such as decreased operational cost, increased customer satisfaction, decreased cycle time, and enhanced profits. During the coronavirus disease (COVID 19) pandemic, the manufacturing sector struggled immensely and could not function well even after lockdown was eased in many countries. Many companies found out there are not ready to conform with new regulations made by authorities in many countries. This paper proposes… More
  •   Views:23       Downloads:11        Download PDF
  • An Iterative Scheme of Arbitrary Odd Order and Its Basins of Attraction for Nonlinear Systems
  • Abstract In this paper, we propose a fifth-order scheme for solving systems of nonlinear equations. The convergence analysis of the proposed technique is discussed. The proposed method is generalized and extended to be of any odd order of the form 2n − 1. The scheme is composed of three steps, of which the first two steps are based on the two-step Homeier’s method with cubic convergence, and the last is a Newton step with an appropriate approximation for the derivative. Every iteration of the presented method requires the evaluation of two functions, two Fréchet derivatives, and three matrix inversions. A comparison… More
  •   Views:23       Downloads:14        Download PDF
  • An Unsteady Oscillatory Flow of Generalized Casson Fluid with Heat and Mass Transfer: A Comparative Fractional Model
  • Abstract It is of high interest to study laminar flow with mass and heat transfer phenomena that occur in a viscoelastic fluid taken over a vertical plate due to its importance in many technological processes and its increased industrial applications. Because of its wide range of applications, this study aims at evaluating the solutions corresponding to Casson fluids’ oscillating flow using fractional-derivatives. As it has a combined mass-heat transfer effect, we considered the fluid flow upon an oscillatory infinite vertical-plate. Furthermore, we used two new fractional approaches of fractional derivatives, named AB (Atangana–Baleanu) and CF (Caputo–Fabrizio), on dimensionless governing equations and… More
  •   Views:22       Downloads:13        Download PDF
  • A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing
  • Abstract In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required… More
  •   Views:40       Downloads:12        Download PDF
  • Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN, SVM Approaches
  • Abstract The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay… More
  •   Views:42       Downloads:12        Download PDF
  • ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN
  • Abstract Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of… More
  •   Views:34       Downloads:17        Download PDF
  • Prospect Theory Based Hesitant Fuzzy Multi-Criteria Decision Making for Low Sulphur Fuel of Maritime Transportation
  • Abstract The environmental impact of maritime transport has now become a relevant issue in sustainable policy formulation and has attracted increasing interest from academia. For the sustainable development of maritime transport, International Maritime Organization stipulates that the sulfur content of ship emissions will reach 0.5 from 2020. With the approaching of the stipulated implementation date, shipowners need to adopt scientific methods to make decision on low sulfur fuel. In this study, we applied a prospect theory based hesitant fuzzy multi-criteria decision-making model to obtain the optimal decision of low Sulphur marine fuel. For this purpose, the hesitant fuzzy decision matrix is… More
  •   Views:39       Downloads:13        Download PDF
  • Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices
  • Abstract The Internet of Things (IoT) is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems. These networks of wireless sensors monitor the physical environment and report the collected data to the base station, allowing for smarter decisions. Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane. However, in some application areas, such as various surveillances, underwater monitoring systems, and various environmental monitoring applications, wireless sensors are deployed in a three-dimensional plane. Recently, localization-based applications have emerged as one of the most promising services… More
  •   Views:38       Downloads:17        Download PDF
  • A New Mixed Clustering-Based Method to Analyze the Gait of Children with Cerebral Palsy
  • Abstract Cerebral palsy is a group of persistent central movement and postural developmental disorders, and restricted activity syndromes. This syndrome is caused by non-progressive brain damage to the developing fetus or infants. Cerebral palsy assessment can determine whether the brain is behind or abnormal. If it exists, early intervention and rehabilitation can be carried out as soon as possible to restore brain function to the greatest extent. The direct external manifestation of cerebral palsy is abnormal gait. Accurately determining the muscle strength-related reasons that cause this abnormal gait is the primary problem for treatment. In this paper, clustering methods were used… More
  •   Views:24       Downloads:11        Download PDF
  • Al2O3 and γAl2O3 Nanomaterials Based Nanofluid Models with Surface Diffusion: Applications for Thermal Performance in Multiple Engineering Systems and Industries
  • Abstract Thermal transport investigation in colloidal suspensions is taking a significant research direction. The applications of these fluids are found in various industries, engineering, aerodynamics, mechanical engineering and medical sciences etc. A huge amount of thermal transport is essential in the operation of various industrial production processes. It is a fact that conventional liquids have lower thermal transport characteristics as compared to colloidal suspensions. The colloidal suspensions have high thermal performance due to the thermophysical attributes of the nanoparticles and the host liquid. Therefore, researchers focused on the analysis of the heat transport in nanofluids under diverse circumstances. As such, the… More
  •   Views:27       Downloads:13        Download PDF
  • Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-Based Distributed Systems
  • Abstract In the healthcare system, the Internet of Things (IoT) based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests. This datum is sensitive, and hence security is a must in transforming the sensational contents. In this paper, an Evolutionary Algorithm, namely the Memetic Algorithm is used for encrypting the text messages. The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels. The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the… More
  •   Views:52       Downloads:16        Download PDF
  • Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques
  • Abstract The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study… More
  •   Views:32       Downloads:24        Download PDF
  • Improving the Detection Rate of Rarely Appearing Intrusions in Network-Based Intrusion Detection Systems
  • Abstract In network-based intrusion detection practices, there are more regular instances than intrusion instances. Because there is always a statistical imbalance in the instances, it is difficult to train the intrusion detection system effectively. In this work, we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances. Our technique mitigates the statistical imbalance in these instances. We also carried out an experiment on the training model by increasing the instances, thereby increasing the attack instances step by step up to 13 levels. The experiments included not only known attacks, but also… More
  •   Views:30       Downloads:11        Download PDF
  • Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting
  • Abstract Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be done by calculating the Click Through Rate (CTR). Unfortunately, this method is not eco-friendly, since you have to gather the clicks from users then compute the CTR. This is where CTR prediction come in handy. Advertisement CTR prediction relies on the users’ log regarding click information data. Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days. CTR prediction uses… More
  •   Views:19       Downloads:14        Download PDF
  • SMConf: One-Size-Fit-Bunch, Automated Memory Capacity Configuration for In-Memory Data Analytic Platform
  • Abstract Spark is the most popular in-memory processing framework for big data analytics. Memory is the crucial resource for workloads to achieve performance acceleration on Spark. The extant memory capacity configuration approach in Spark is to statically configure the memory capacity for workloads based on user’s specifications. However, without the deep knowledge of the workload’s system-level characteristics, users in practice often conservatively overestimate the memory utilizations of their workloads and require resource manager to grant more memory share than that they actually need, which leads to the severe waste of memory resources. To address the above issue, SMConf, an automated memory… More
  •   Views:18       Downloads:13        Download PDF
  • Intelligent Decision Support System for COVID-19 Empowered with Deep Learning
  • Abstract The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would… More
  •   Views:40       Downloads:12        Download PDF
  • Efficient Routing Protection Algorithm in Large-Scale Networks
  • Abstract With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks, minimizing network disruption caused by network failure has become critical. However, a large number of relevant studies have shown that network failures occur on the Internet inevitably and frequently. The current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network failures. During the reconvergence process, the packets may be lost because of inconsistent routing information, which reduces the network’s availability greatly and affects the Internet service provider’s (ISP’s) service quality and reputation seriously. Therefore, improving network availability has become an urgent problem.… More
  •   Views:23       Downloads:13        Download PDF
  • Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients
  • Abstract Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of… More
  •   Views:65       Downloads:33        Download PDF
  • 3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks
  • Abstract Face image analysis is one among several important cues in computer vision. Over the last five decades, methods for face analysis have received immense attention due to large scale applications in various face analysis tasks. Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation. In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model. We have developed an end to end face parts segmentation framework through deep convolutional neural networks (DCNNs). For training a deep face parts parsing model, we label face images… More
  •   Views:25       Downloads:13        Download PDF
  • Hybrid Metamodel—NSGA-III—EDAS Based Optimal Design of Thin Film Coatings
  • Abstract In this work, diamond-like carbon (DLC) thin film coatings are deposited on silicon substrates by using plasma-enhanced chemical vapour deposition (PECVD) technique. By varying the hydrogen (H2) flow rate, CH4−Argon (Ar) flow rate and deposition temperature (Td) as per a Box-Behnken experimental design (BBD), 15 DLC deposition experiments are carried out. The Young’s modulus (E) and the coefficient of friction (COF) for the DLCs are measured. By using a second-order polynomial regression approach, two metamodels are built for E and COF, that establish them as functions of H2 flow rate, CH4-Ar flow rate and Td. A non-dominated sorting genetic algorithm… More
  •   Views:27       Downloads:13        Download PDF
  • A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System
  • Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection… More
  •   Views:44       Downloads:21        Download PDF
  • NURBS Modeling and Curve Interpolation Optimization of 3D Graphics
  • Abstract In order to solve the problem of complicated Non-Uniform Rational B-Splines (NURBS) modeling and improve the real-time performance of the high-order derivative of the curve interpolation process, the method of NURBS modeling based on the slicing and layering of triangular mesh is introduced. The research and design of NURBS curve interpolation are carried out from the two aspects of software algorithm and hardware structure. Based on the analysis of the characteristics of traditional computing methods with Taylor series expansion, the Adams formula and the Runge-Kutta formula are used in the NURBS curve interpolation process, and the process is then optimized… More
  •   Views:19       Downloads:10        Download PDF
  • Real Estate Management via a Decentralized Blockchain Platform
  • Abstract Blockchain technology is one of the key technological breakthroughs of the last decade. It has the ability to revolutionize numerous aspects of society, including financial systems, healthcare, e-government and many others. One such area that is able to reap the benefits of blockchain technology is the real estate industry. Like many other industries, real estate faces major administrative problems such as high transaction fees, a lack of transparency, fraud and the effects of a middleman including undue influence and commissions. Blockchain enables supporting technologies to overcome the obstacles inherent within the real estate investment market. These technologies include smart contracts,… More
  •   Views:49       Downloads:17        Download PDF
  • Analysis and Dynamics of Fractional Order Mathematical Model of COVID-19 in Nigeria Using Atangana-Baleanu Operator
  • Abstract We propose a mathematical model of the coronavirus disease 2019 (COVID-19) to investigate the transmission and control mechanism of the disease in the community of Nigeria. Using stability theory of differential equations, the qualitative behavior of model is studied. The pandemic indicator represented by basic reproductive number R0 is obtained from the largest eigenvalue of the next-generation matrix. Local as well as global asymptotic stability conditions for the disease-free and pandemic equilibrium are obtained which determines the conditions to stabilize the exponential spread of the disease. Further, we examined this model by using Atangana–Baleanu fractional derivative operator and existence criteria… More
  •   Views:131       Downloads:54        Download PDF
  • Managing Security-Risks for Improving Security-Durability of Institutional Web-Applications: Design Perspective
  • Abstract The advanced technological need, exacerbated by the flexible time constraints, leads to several more design level unexplored vulnerabilities. Security is an extremely vital component in software development; we must take charge of security and therefore analysis of software security risk assumes utmost significance. In order to handle the cyber-security risk of the web application and protect individuals, information and properties effectively, one must consider what needs to be secured, what are the perceived threats and the protection of assets. Security preparation plans, implements, tracks, updates and consistently develops safety risk management activities. Risk management must be interpreted as the major… More
  •   Views:28       Downloads:19        Download PDF
  • A Parallel Approach to Discords Discovery in Massive Time Series Data
  • Abstract A discord is a refinement of the concept of an anomalous subsequence of a time series. Being one of the topical issues of time series mining, discords discovery is applied in a wide range of real-world areas (medicine, astronomy, economics, climate modeling, predictive maintenance, energy consumption, etc.). In this article, we propose a novel parallel algorithm for discords discovery on high-performance cluster with nodes based on many-core accelerators in the case when time series cannot fit in the main memory. We assumed that the time series is partitioned across the cluster nodes and achieved parallelization among the cluster nodes as… More
  •   Views:67       Downloads:55        Download PDF
  • A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics
  • Abstract A new secured database management system architecture using intrusion detection systems (IDS) is proposed in this paper for organizations with no previous role mapping for users. A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm. A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’ profiles. Then, queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious. The IDS will stop query execution or… More
  •   Views:24       Downloads:13        Download PDF
  • Fog-Based Secure Framework for Personal Health Records Systems
  • 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
  •   Views:20       Downloads:12        Download PDF
  • Temporal Stability Analysis of Magnetized Hybrid Nanofluid Propagating through an Unsteady Shrinking Sheet: Partial Slip Conditions
  • 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
  •   Views:70       Downloads:15        Download PDF
  • Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT
  • 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
  •   Views:54       Downloads:33        Download PDF
  • Darcy-Forchheimer Hybrid Nano Fluid Flow with Mixed Convection Past an Inclined Cylinder
  • 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
  •   Views:23       Downloads:13        Download PDF
  • Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms
  • 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
  •   Views:25       Downloads:12        Download PDF
  • Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance
  • 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
  •   Views:26       Downloads:21        Download PDF
  • Packet Drop Battling Mechanism for Energy Aware Detection in Wireless Networks
  • 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
  •   Views:50       Downloads:40        Download PDF
  • 3D Reconstruction for Motion Blurred Images Using Deep Learning-Based 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
  •   Views:45       Downloads:37        Download PDF
  • Estimation of Quaternion Motion for GPS-Based Attitude Determination Using the Extended Kalman Filter
  • 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
  •   Views:55       Downloads:47        Download PDF
  • Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning
  • 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
  •   Views:53       Downloads:49        Download PDF
  • IoT Technologies for Tackling COVID-19 in Malaysia and Worldwide: Challenges, Recommendations, and Proposed Framework
  • 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
  •   Views:65       Downloads:21        Download PDF
  • Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule
  • 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
  •   Views:36       Downloads:14        Download PDF
  • Hajj Crowd Management Using CNN-Based Approach
  • 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
  •   Views:111       Downloads:76        Download PDF
  • A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
  • 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
  •   Views:45       Downloads:21        Download PDF