Vol.66, No.3, 2021-Table of Contents
  • Quintuple Band Antenna for Wireless Applications with Small Form Factor
  • Abstract A coplanar waveguide-fed quintuple band antenna with a slotted circular-shaped radiator for wireless applications with a high isolation between adjacent bands is presented in this paper. The proposed antenna resonates at multiple frequencies with corresponding center frequencies of 2.35, 4.92, 5.75, 6.52, and 8.46 GHz. The intended functionality is achieved by introducing a circular disc radiator with five slots and a U-shaped slot in the feed. The proposed antenna exhibits coverage of the maximum set of wireless applications, such as satellite communication, worldwide interoperability for microwave access, wireless local area network (WLAN), long-distance radio telecommunications, and X-band/Satcom wireless applications. The… More
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  • Two-Phase Flow of Blood with Magnetic Dusty Particles in Cylindrical Region: A Caputo Fabrizio Fractional Model
  • Abstract The present study is focused on the unsteady two-phase flow of blood in a cylindrical region. Blood is taken as a counter-example of Brinkman type fluid containing magnetic (dust) particles. The oscillating pressure gradient has been considered because for blood flow it is necessary to investigate in the form of a diastolic and systolic pressure. The transverse magnetic field has been applied externally to the cylindrical tube to study its impact on both fluids as well as particles. The system of derived governing equations based on Navier Stoke’s, Maxwell and heat equations has been generalized using the well-known Caputo–Fabrizio (C–F)… More
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  • Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management
  • Abstract COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea,… More
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  • Evaluating the Impact of Software Security Tactics: A Design Perspective
  • Abstract Design architecture is the edifice that strengthens the functionalities as well as the security of web applications. In order to facilitate architectural security from the web application’s design phase itself, practitioners are now adopting the novel mechanism of security tactics. With the intent to conduct a research from the perspective of security tactics, the present study employs a hybrid multi-criteria decision-making approach named fuzzy analytic hierarchy process-technique for order preference by similarity ideal solution (AHP-TOPSIS) method for selecting and assessing multi-criteria decisions. The adopted methodology is a blend of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order… More
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  • Predicting the Type of Crime: Intelligence Gathering and Crime Analysis
  • Abstract Crimes are expected to rise with an increase in population and the rising gap between society’s income levels. Crimes contribute to a significant portion of the socioeconomic loss to any society, not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy, social parameters, and reputation of a nation. Policing and other preventive resources are limited and have to be utilized. The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are… More
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  • Ordering Cost Depletion in Inventory Policy with Imperfect Products and Backorder Rebate
  • Abstract This study presents an inventory model for imperfect products with depletion in ordering costs and constant lead time where the price discount in the backorder is permitted. The imperfect products are refused or modified or if they reached to the customer, returned and thus some extra costs are experienced. Lately some of the researchers explicitly present on the significant association between size of lot and quality imperfection. In practical situations, the unsatisfied demands increase the period of lead time and decrease the backorders. To control customers' problems and losses, the supplier provides a price discount in backorders during shortages. Also,… More
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  • A Novel Semi-Quantum Private Comparison Scheme Using Bell Entangle States
  • Abstract

    Private comparison is the basis of many encryption technologies, and several related Quantum Private Comparison (QPC) protocols have been published in recent years. In these existing protocols, secret information is encoded by using conjugate coding or orthogonal states, and all users are quantum participants. In this paper, a novel semi-quantum private comparison scheme is proposed, which employs Bell entangled states as quantum resources. Two semi-quantum participants compare the equivalence of their private information with the help of a semi-honest third party (TP). Compared with the previous classical protocols, these two semi-quantum users can only make some particular action, such as… More

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  • Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning
  • Abstract Novel Coronavirus-19 (COVID-19) is a newer type of coronavirus that has not been formally detected in humans. It is established that this disease often affects people of different age groups, particularly those with body disorders, blood pressure, diabetes, heart problems, or weakened immune systems. The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates. Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans. It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease… More
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  • An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity
  • Abstract Text Summarization is an essential area in text mining, which has procedures for text extraction. In natural language processing, text summarization maps the documents to a representative set of descriptive words. Therefore, the objective of text extraction is to attain reduced expressive contents from the text documents. Text summarization has two main areas such as abstractive, and extractive summarization. Extractive text summarization has further two approaches, in which the first approach applies the sentence score algorithm, and the second approach follows the word embedding principles. All such text extractions have limitations in providing the basic theme of the underlying documents.… More
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  • An Advanced Analysis of Cloud Computing Concepts Based on the Computer Science Ontology
  • Abstract Our primary research hypothesis stands on a simple idea: The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics. And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago. We implemented our model based on Computer Science Ontology (CSO) and analyzed 44 years of publications. Then we derived the most important concepts related to Cloud Computing (CC) from the scientific collection offered by Clarivate Analytics. Our methodology includes data extraction using advanced web crawling… More
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  • Entanglement and Entropy Squeezing for Moving Two Two-Level Atoms Interaction with a Radiation Field
  • Abstract In this paper, we analyzed squeezing in the information entropy, quantum state fidelity, and qubit-qubit entanglement in a time-dependent system. The proposed model consists of two qubits that interact with a two-mode electromagnetic field under the dissipation effect. An analytical solution is calculated by considering the constants for the equations of motion. The effect of the general form of the time-dependent for qubit-field coupling and the dissipation term on the temporal behavior of the qubit-qubit entanglement, quantum state fidelity, entropy, and variance squeezing are examined. It is shown that the intervals of entanglement caused more squeezing for the case of… More
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  • Robust Attack Detection Approach for IIoT Using Ensemble Classifier
  • Abstract Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition.… More
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  • E-Learning during COVID-19 Outbreak: Cloud Computing Adoption in Indian Public Universities
  • Abstract In the COVID-19 pandemic situation, the need to adopt cloud computing (CC) applications by education institutions, in general, and higher education (HE) institutions, in particular, has especially increased to engage students in an online mode and remotely carrying out research. The adoption of CC across various sectors, including HE, has been picking momentum in the developing countries in the last few years. In the Indian context, the CC adaptation in the HE sector (HES) remains a less thoroughly explored sector, and no comprehensive study is reported in the literature. Therefore, the aim of the present study is to overcome this… More
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  • Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method
  • Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that… More
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  • Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in Smart Cities
  • Abstract Wireless sensor networks (WSNs) and Internet of Things (IoT) have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities. The data generated from these sensors are used by smart cities to strengthen their infrastructure, utilities, and public services. WSNs are suitable for long periods of data acquisition in smart cities. To make the networks of smart cities more reliable for sensitive information, the blockchain mechanism has been proposed. The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources; leading to extending the network lifetime of sensors.… More
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  • Motion-Based Activities Monitoring through Biometric Sensors Using Genetic Algorithm
  • Abstract Sensors and physical activity evaluation are quite limited for motion-based commercial devices. Sometimes the accelerometer of the smartwatch is utilized; walking is investigated. The combination can perform better in terms of sensors and that can be determined by sensors on both the smartwatch and phones, i.e., accelerometer and gyroscope. For biometric efficiency, some of the diverse activities of daily routine have been evaluated, also with biometric authentication. The result shows that using the different computing techniques in phones and watch for biometric can provide a suitable output based on the mentioned activities. This indicates that the high feasibility and results… More
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  • A Novel Collective User Web Behavior Simulation Method
  • Abstract

    A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range. Existing studies almost focus on individual web behavior analysis and prediction, which cannot simulate human dynamics that widely exist in large-scale users’ behaviors. To address these issues, we propose a novel collective user web behavior simulation method, in which an algorithm for constructing a connected virtual social network is proposed, and then a collective user web behavior simulation algorithm is designed on the virtual social network. In the simulation method, a new epidemic information dissemination algorithm… More

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  • Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data
  • Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using… More
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  • An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference
  • Abstract Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing. Associative rule mining, a data mining technique, has been studied and explored for a long time. However, few studies have focused on knowledge discovery in the penetration testing area. The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern. To address this problem, a Bayesian inference based penetration semantic knowledge mining algorithm is proposed. First, a directed bipartite graph model, a kind… More
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  • Deep Learning in DXA Image Segmentation
  • Abstract Many existing techniques to acquire dual-energy X-ray absorptiometry (DXA) images are unable to accurately distinguish between bone and soft tissue. For the most part, this failure stems from bone shape variability, noise and low contrast in DXA images, inconsistent X-ray beam penetration producing shadowing effects, and person-to-person variations. This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models, fully convolutional networks (FCNs), SegNet, and U-Net to distinguish femur bone from soft tissue. We investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques (i.e., a decision-tree-based method using… More
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  • Fuzzy Based Decision-Making Approach for Estimating Usable-Security of Healthcare Web Applications
  • Abstract Usability and security are often considered contradictory in nature. One has a negative impact on the other. In order to satisfy the needs of users with the security perspective, the relationship and trade-offs among security and usability must be distinguished. Security practitioners are working on developing new approaches that would help to secure healthcare web applications as well increase usability of the web applications. In the same league, the present research endeavour is premised on the usable-security of healthcare web applications. For a compatible blend of usability and security that would fulfill the users’ requirments, this research proposes an integration… More
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  • An Efficient Viewport-Dependent 360 VR System Based on Adaptive Tiled Streaming
  • Abstract Recent advances in 360 video streaming technologies have enhanced the immersive experience of video streaming services. Particularly, there is immense potential for the application of 360 video encoding formats to achieve highly immersive virtual reality (VR) systems. However, 360 video streaming requires considerable bandwidth, and its performance depends on several factors. Consequently, the optimization of 360 video bitstreams according to viewport texture is crucial. Therefore, we propose an adaptive solution for VR systems using viewport-dependent tiled 360 video streaming. To increase the degrees of freedom of users, the moving picture experts group (MPEG) recently defined three degrees plus of freedom… More
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  • Research on Electronic Document Management System Based on Cloud Computing
  • Abstract With the development of information technology, cloud computing technology has brought many conveniences to all aspects of work and life. With the continuous promotion, popularization and vigorous development of e-government and e-commerce, the number of documents in electronic form is getting larger and larger. Electronic document is an indispensable main tool and real record of e-government and business activities. How to scientifically and effectively manage electronic documents? This is an important issue faced by governments and enterprises in improving management efficiency, protecting state secrets or business secrets, and reducing management costs. This paper discusses the application of cloud computing technology… More
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  • Understanding Research Trends in Android Malware Research Using Information Modelling Techniques
  • Abstract Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never… More
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  • A Hybrid Virtual Cloud Learning Model during the COVID-19 Pandemic
  • Abstract The COVID-19 pandemic has affected the educational systems worldwide, leading to the near-total closures of schools, universities, and colleges. Universities need to adapt to changes to face this crisis without negatively affecting students’ performance. Accordingly, the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic. The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services (CCS) and Virtual Reality (VR) in a Virtual Cloud Learning Environment (VCLE) system. The VCLE system provides students with various utilities… More
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  • Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description
  • Abstract These days, imbalanced datasets, denoted throughout the paper by ID, (a dataset that contains some (usually two) classes where one contains considerably smaller number of samples than the other(s)) emerge in many real world problems (like health care systems or disease diagnosis systems, anomaly detection, fraud detection, stream based malware detection systems, and so on) and these datasets cause some problems (like under-training of minority class(es) and over-training of majority class(es), bias towards majority class(es), and so on) in classification process and application. Therefore, these datasets take the focus of many researchers in any science and there are several solutions… More
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  • Trade-Off between Efficiency and Effectiveness: A Late Fusion Multi-View Clustering Algorithm
  • Abstract Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n3) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that… More
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  • Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment
  • Abstract With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the… More
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  • A New Decision-Making Model Based on Plithogenic Set for Supplier Selection
  • Abstract Supplier selection is a common and relevant phase to initialize the supply chain processes and ensure its sustainability. The choice of supplier is a multi-criteria decision making (MCDM) to obtain the optimal decision based on a group of criteria. The health care sector faces several types of problems, and one of the most important is selecting an appropriate supplier that fits the desired performance level. The development of service/product quality in health care facilities in a country will improve the quality of the life of its population. This paper proposes an integrated multi-attribute border approximation area comparison (MABAC) based on… More
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  • Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review
  • Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading… More
  •   Views:515       Downloads:357        Download PDF
  • Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia
  • Abstract In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed… More
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  • Statistical Inference of Chen Distribution Based on Two Progressive Type-II Censoring Schemes
  • Abstract An inverse problem in practical scientific investigations is the process of computing unknown parameters from a set of observations where the observations are only recorded indirectly, such as monitoring and controlling quality in industrial process control. Linear regression can be thought of as linear inverse problems. In other words, the procedure of unknown estimation parameters can be expressed as an inverse problem. However, maximum likelihood provides an unstable solution, and the problem becomes more complicated if unknown parameters are estimated from different samples. Hence, researchers search for better estimates. We study two joint censoring schemes for lifetime products in industrial… More
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  • An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy
  • Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More
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  • Toward 6G Communication Networks: Terahertz Frequency Challenges and Open Research Issues
  • Abstract Future networks communication scenarios by the 2030s will include notable applications are three-dimensional (3D) calls, haptics communications, unmanned mobility, tele-operated driving, bio-internet of things, and the Nano-internet of things. Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications, the future networks of the 2030s will require bandwidths in several gigahertzes (GHz) (from tens of gigahertz to 1 terahertz [THz]) to perform optimally. Based on the current radio frequency allocation chart, it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz (0.09 THz). Interestingly, these… More
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  • Experimental Investigation on the Performance of Heat Pump Operating with Copper and Alumina Nanofluids
  • Abstract In the present study, an attempt is made to enhance the performance of heat pump by utilizing two types of nanofluids namely, copper and alumina nanofluids. These nanofluids were employed around the evaporator coil of the heat pump. The nanofluids were used to enhance the heat input to the system by means of providing an external jacket around the evaporator coil. Both the nanofluids were prepared in three volume fractions 1%, 2% and 5%. Water was chosen as the base fluid. The performance of the heat pump was assessed by calculating the coefficient of performance of the system when it… More
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  • A Knowledge-Based Pilot Study on Assessing the Music Influence
  • Abstract A knowledge-driven approach is proposed for assessing the music influence on university students. The proposed method of modeling and conducting the interactive pilot study can be useful to convey other surveys, interviews, and experiments created in various phases of the user interface (UI) design processes, as part of a general human-computer interaction (HCI) methodology. Benefiting from existing semantic Web and linked data standards, best practices, and tools, a microservice-oriented system is developed as a testbed platform able to generate playlists in a smart way according to users’ music preferences. This novel approach could bring also benefits for user interface adaptation… More
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  • Recognition of Offline Handwritten Arabic Words Using a Few Structural Features
  • Abstract Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages. Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts. The present paper suggests a feature extraction technique for offline Arabic handwriting recognition. A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function (RBF) neural… More
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  • A Combinatorial Optimized Knapsack Linear Space for Information Retrieval
  • Abstract Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis. Currently, the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction. The recent method disambiguates contextual sentiment using conceptual prediction with robustness, however the conceptual prediction method is not able to yield the optimal solution. Context-dependent terms are primarily evaluated by constructing linear space of context features, presuming that if the terms come together in certain consumer-related reviews, they are semantically reliant. Moreover, the more frequently they coexist, the greater the semantic dependency is.… More
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  • OTS Scheme Based Secure Architecture for Energy-Efficient IoT in Edge Infrastructure
  • Abstract For the past few decades, the Internet of Things (IoT) has been one of the main pillars wielding significant impact on various advanced industrial applications, including smart energy, smart manufacturing, and others. These applications are related to industrial plants, automation, and e-healthcare fields. IoT applications have several issues related to developing, planning, and managing the system. Therefore, IoT is transforming into G-IoT (Green Internet of Things), which realizes energy efficiency. It provides high power efficiency, enhances communication and networking. Nonetheless, this paradigm did not resolve all smart applications’ challenges in edge infrastructure, such as communication bandwidth, centralization, security, and privacy.… More
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  • Classification of Positive COVID-19 CT Scans Using Deep Learning
  • Abstract In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease’s early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method, contrast enhancement is used to… More
  •   Views:643       Downloads:416        Download PDF
  • Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques
  • Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image… More
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  • An Improved Dictionary Cracking Scheme Based on Multiple GPUs for Wi-Fi Network
  • Abstract The Internet has penetrated all aspects of human society and has promoted social progress. Cyber-crimes in many forms are commonplace and are dangerous to society and national security. Cybersecurity has become a major concern for citizens and governments. The Internet functions and software applications play a vital role in cybersecurity research and practice. Most of the cyber-attacks are based on exploits in system or application software. It is of utmost urgency to investigate software security problems. The demand for Wi-Fi applications is proliferating but the security problem is growing, requiring an optimal solution from researchers. To overcome the shortcomings of… More
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  • Product Spacing of Stress–Strength under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution
  • Abstract Maximum product spacing for stress–strength model based on progressive Type-II hybrid censored samples with different cases has been obtained. This paper deals with estimation of the stress strength reliability model R = P(Y < X) when the stress and strength are two independent exponentiated Gumbel distribution random variables with different shape parameters but having the same scale parameter. The stress–strength reliability model is estimated under progressive Type-II hybrid censoring samples. Two progressive Type-II hybrid censoring schemes were used, Case I: A sample size of stress is the equal sample size of strength, and same time of hybrid censoring, the product… More
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  • A Formal Testing Model for Operating Room Control System Using Internet of Things
  • Abstract Technological advances in recent years have significantly changed the way an operating room works. This work aims to create a platform to solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations. Using this system, a doctor can control all operation rooms, especially before an operation, and monitor their temperature and humidity to prepare for the operation. Also, in the event of a problem, an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved. The platform is tested using… More
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  • A New Class of L-Moments Based Calibration Variance Estimators
  • Abstract Variance is one of the most important measures of descriptive statistics and commonly used for statistical analysis. The traditional second-order central moment based variance estimation is a widely utilized methodology. However, traditional variance estimator is highly affected in the presence of extreme values. So this paper initially, proposes two classes of calibration estimators based on an adaptation of the estimators recently proposed by Koyuncu and then presents a new class of L-Moments based calibration variance estimators utilizing L-Moments characteristics (L-location, L-scale, L-CV) and auxiliary information. It is demonstrated that the proposed L-Moments based calibration variance estimators are more efficient than… More
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  • Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches
  • Abstract A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD… More
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  • Fuzzy Based Adaptive Deblocking Filters at Low-Bitrate HEVC Videos for Communication Networks
  • Abstract In-loop filtering significantly helps detect and remove blocking artifacts across block boundaries in low bitrate coded High Efficiency Video Coding (HEVC) frames and improves its subjective visual quality in multimedia services over communication networks. However, on faster processing of the complex videos at a low bitrate, some visible artifacts considerably degrade the picture quality. In this paper, we proposed a four-step fuzzy based adaptive deblocking filter selection technique. The proposed method removes the quantization noise, blocking artifacts and corner outliers efficiently for HEVC coded videos even at low bit-rate. We have considered Y (luma), U (chroma-blue), and V (chroma-red) components… More
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  • Space-Time Cluster Analysis of Accidental Oil Spills in Rivers State, Nigeria, 2011–2019
  • Abstract Oil spills cause environmental pollution with a serious threat to local communities and sustainable development. Accidental oil spills can be modelled as a stochastic process where each oil spill event is described by its spatial locations and incidence-time and hence allow for space-time cluster analysis. Space-time cluster analysis can detect space-time pattern distribution of oil spills which can be useful for implementing preventive measures and evidence-based decision making. This study aims to detect the space-time clusters of accidental oil spills in Rivers state, Nigeria through the Space-time Scan Statistic. The Space-time Scan Statistic was applied under the permutation model to… More
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  • Performance Analysis of DEBT Routing Protocols for Pocket Switch Networks
  • Abstract Pocket Switched Networks (PSN) represent a particular remittent network for direct communication between the handheld mobile devices. Compared to traditional networks, there is no stable topology structure for PSN where the nodes observe the mobility model of human society. It is a kind of Delay Tolerant Networks (DTNs) that gives a description to circulate information among the network nodes by the way of taking the benefit of transferring nodes from one area to another. Considering its inception, there are several schemes for message routing in the infrastructure-less environment in which human mobility is only the best manner to exchange information.… More
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  • Optimal Control Model for the Transmission of Novel COVID-19
  • Abstract As the corona virus (COVID-19) pandemic ravages socio-economic activities in addition to devastating infectious and fatal consequences, optimal control strategy is an effective measure that neutralizes the scourge to its lowest ebb. In this paper, we present a mathematical model for the dynamics of COVID-19, and then we added an optimal control function to the model in order to effectively control the outbreak. We incorporate three main control efforts (isolation, quarantine and hospitalization) into the model aimed at controlling the spread of the pandemic. These efforts are further subdivided into five functions; u1(t) (isolation of the susceptible communities), u2(t) (contact… More
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  • The Controllability of Quantum Correlation under Geometry and Entropy Discords
  • Abstract Quantum correlation plays a critical role in the maintenance of quantum information processing and nanometer device design. In the past two decades, several quantitative methods had been proposed to study the quantum correlation of certain open quantum systems, including the geometry and entropy style discord methods. However, there are differences among these quantification methods, which promote a deep understanding of the quantum correlation. In this paper, a novel time-dependent three environmental open system model is established to study the quantum correlation. This system model interacts with two independent spin-environments (two spin-environments are connected to the other spin-environment) respectively. We have… More
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  • Extension of Direct Citation Model Using In-Text Citations
  • Abstract Citations based relevant research paper recommendations can be generated primarily with the assistance of three citation models: (1) Bibliographic Coupling, (2) Co-Citation, and (3) Direct Citations. Millions of new scholarly articles are published every year. This flux of scientific information has made it a challenging task to devise techniques that could help researchers to find the most relevant research papers for the paper at hand. In this study, we have deployed an in-text citation analysis that extends the Direct Citation Model to discover the nature of the relationship degree-of-relevancy among scientific papers. For this purpose, the relationship between citing and… More
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  • Identification of Thoracic Diseases by Exploiting Deep Neural Networks
  • Abstract With the increasing demand for doctors in chest related diseases, there is a 15% performance gap every five years. If this gap is not filled with effective chest disease detection automation, the healthcare industry may face unfavorable consequences. There are only several studies that targeted X-ray images of cardiothoracic diseases. Most of the studies only targeted a single disease, which is inadequate. Although some related studies have provided an identification framework for all classes, the results are not encouraging due to a lack of data and imbalanced data issues. This research provides a significant contribution to Generative Adversarial Network (GAN)… More
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  • Improving Reconstructed Image Quality via Hybrid Compression Techniques
  • Abstract Data compression is one of the core fields of study for applications of image and video processing. The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form. In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image. These techniques are followed by entropy encoding such… More
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  • Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model
  • Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer… More
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  • Numerical Study of Computer Virus Reaction Diffusion Epidemic Model
  • Abstract Reaction–diffusion systems are mathematical models which link to several physical phenomena. The most common is the change in space and time of the meditation of one or more materials. Reaction–diffusion modeling is a substantial role in the modeling of computer propagation like infectious diseases. We investigated the transmission dynamics of the computer virus in which connected to each other through network globally. The current study devoted to the structure-preserving analysis of the computer propagation model. This manuscript is devoted to finding the numerical investigation of the reaction–diffusion computer virus epidemic model with the help of a reliable technique. The designed… More
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  • Nanofluid Flows Within Porous Enclosures Using Non-Linear Boussinesq Approximation
  • Abstract In this paper, the Galerkin finite element method (FEM) together with the characteristic-based split (CBS) scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined enclosures filled with a non-Darcy porous media and nanofluids. The enclosure has an inclination angle and its side-walls have varying sinusoidal temperature distributions. The working fluid is a nanofluid that is consisting of water as a based nanofluid and Al2O3 as nanoparticles. The porous medium is modeled using the Brinkman Forchheimer extended Darcy model. The obtained results are analyzed over wide ranges of the non-linear Boussinesq parameter 0 ≤… More
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  • Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks
  • Abstract Nowadays, healthcare applications necessitate maximum volume of medical data to be fed to help the physicians, academicians, pathologists, doctors and other healthcare professionals. Advancements in the domain of Wireless Sensor Networks (WSN) and Multimedia Wireless Sensor Networks (MWSN) are tremendous. M-WMSN is an advanced form of conventional Wireless Sensor Networks (WSN) to networks that use multimedia devices. When compared with traditional WSN, the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content. Hence, clustering techniques are deployed to achieve low amount of energy utilization. The current research work aims at introducing a new… More
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  • Natural Convection in an H-Shaped Porous Enclosure Filled with a Nanofluid
  • Abstract This study simulates natural convection flow resulting from heat partitions in an H-shaped enclosure filled with a nanofluid using an incompressible smoothed particle hydrodynamics (ISPH) method. The right area of the H-shaped enclosure is saturated with non-Darcy porous media. The center variable partitions of the H-shaped enclosure walls are kept at a high-temperature Th. The left and right walls of the H-shaped enclosure are positioned at a low temperature Tc and the other walls are adiabatic. In ISPH method, the source term in pressure Poisson equation (PPE) is modified. The influences of the controlling parameters on the temperature distributions, the… More
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  • An Adjustable Variant of Round Robin Algorithm Based on Clustering Technique
  • Abstract CPU scheduling is the basic task within any time-shared operating system. One of the main goals of the researchers interested in CPU scheduling is minimizing time cost. Comparing between CPU scheduling algorithms is subject to some scheduling criteria (e.g., turnaround time, waiting time and number of context switches (NCS)). Scheduling policy is divided into preemptive and non-preemptive. Round Robin (RR) algorithm is the most common preemptive scheduling algorithm used in the time-shared operating systems. In this paper, the authors proposed a modified version of the RR algorithm, called dynamic time slice (DTS), to combine the advantageous of the low scheduling… More
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  • Long-Term Preservation of Electronic Record Based on Digital Continuity in Smart Cities
  • Abstract Under the co-promotion of the wave of urbanization and the rise of data science, smart cities have become the new concept and new practice of urban development. Smart cities are the combination of information technology represented by the Internet of Things, cloud computing, mobile networks and big data, and urbanization. How to effectively achieve the long-term preservation of massive, heterogeneous, and multi-source digital electronic records in smart cities is a key issue that must be solved. Digital continuity can ensure the accessibility, integrity and availability of information. The quality management of electronic record, like the quality management of product, will… More
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  • A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images
  • Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More
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  • Synchronization Phenomena Investigation of a New Nonlinear Dynamical System 4D by Gardano’s and Lyapunov’s Methods
  • Abstract Synchronization is one of the most important characteristics of dynamic systems. For this paper, the authors obtained results for the nonlinear systems controller for the custom Synchronization of two 4D systems. The findings have allowed authors to develop two analytical approaches using the second Lyapunov (Lyp) method and the Gardano method. Since the Gardano method does not involve the development of special positive Lyp functions, it is very efficient and convenient to achieve excessive system SYCR phenomena. Error is overcome by using Gardano and overcoming some problems in Lyp. Thus we get a great investigation into the convergence of error… More
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  • Estimating Loss Given Default Based on Beta Regression
  • Abstract Loss given default (LGD) is a key parameter in credit risk management to calculate the required regulatory minimum capital. The internal ratings-based (IRB) approach under the Basel II allows institutions to determine the loss given default (LGD) on their own. In this study, we have estimated LGD for a credit portfolio data by using beta regression with precision parameter (∅) and mean parameter (μ). The credit portfolio data was obtained from a banking institution in Jordan; for the period of January 2010 until December 2014. In the first stage, we have used the “outstanding amount” and “amount of borrowing” to… More
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  • Street-Level IP Geolocation Algorithm Based on Landmarks Clustering
  • Abstract Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays. However, this principle is often invalid in real Internet environment, which leads to unreliable geolocation results. To improve the accuracy and reliability of locating IP in real Internet, a street-level IP geolocation algorithm based on landmarks clustering is proposed. Firstly, we use the probes to measure the known landmarks to obtain their delay vectors, and cluster landmarks using them. Secondly, the landmarks are clustered again by their latitude and longitude, and the intersection of these two clustering results is taken… More
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  • Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features
  • Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level… More
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