Vol.67, No.2, 2021-Table of Contents
  • Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN
  • Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More
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  • Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation
  • Abstract Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks (VANETs) for smart transportation that results from dynamic topology, limited resources and non-centralized architecture. The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation. To design a robust clustering algorithm, careful attention must be paid to components like mobility models and performance objectives. A clustering algorithm may not perform well with every mobility pattern. Therefore, we propose a supervisory protocol (SP) that observes the mobility pattern of vehicles and identifies the… More
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  • Computational Microfluidic Channel for Separation of Escherichia coli from Blood-Cells
  • Abstract Microfluidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells, etc., in various biochemical diagnosis procedures including urinary tract infections (UTI) and bloodstream infections. This paper presents the multi physics computational model specifically designed to study the effects of design parameters of a microfluidics channel for the separation of Escherichia coli (E. coli) from various blood constituents including red blood cells (RBC) and platelets. A standard two inlet and a two outlet microchannel of length 805 m with a channel width of 40 m is simulated. The effect of electrode potentials and… More
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  • A Fractal-Fractional Model for the MHD Flow of Casson Fluid in a Channel
  • Abstract An emerging definition of the fractal-fractional operator has been used in this study for the modeling of Casson fluid flow. The magnetohydrodynamics flow of Casson fluid has cogent in a channel where the motion of the upper plate generates the flow while the lower plate is at a static position. The proposed model is non-dimensionalized using the Pi-Buckingham theorem to reduce the complexity in solving the model and computation time. The non-dimensional fractal-fractional model with the power-law kernel has been solved through the Laplace transform technique. The Mathcad software has been used for illustration of the influence of various parameters,… More
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  • Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches
  • Abstract Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization… More
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  • Prediction of Time Series Empowered with a Novel SREKRLS Algorithm
  • Abstract For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and non-stationary MackeyGlass Time Series, along… More
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  • Intrusion Detection System Using FKNN and Improved PSO
  • Abstract Intrusion detection system (IDS) techniques are used in cybersecurity to protect and safeguard sensitive assets. The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism. The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor (FKNN) algorithm. Using this method, two parameters, i.e., the neighborhood size (k) and fuzzy strength parameter (m) were characterized by implementing the particle swarm optimization (PSO). In addition to being used for FKNN parametric optimization, PSO is also used for selecting the conditional feature subsets for detection. To proficiently regulate the… More
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  • MMALE—A Methodology for Malware Analysis in Linux Environments
  • Abstract In a computer environment, an operating system is prone to malware, and even the Linux operating system is not an exception. In recent years, malware has evolved, and attackers have become more qualified compared to a few years ago. Furthermore, Linux-based systems have become more attractive to cybercriminals because of the increasing use of the Linux operating system in web servers and Internet of Things (IoT) devices. Windows is the most employed OS, so most of the research efforts have been focused on its malware protection rather than on other operating systems. As a result, hundreds of research articles, documents,… More
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  • Evaluating the Impact of Prediction Techniques: Software Reliability Perspective
  • Abstract Maintaining software reliability is the key idea for conducting quality research. This can be done by having less complex applications. While developers and other experts have made significant efforts in this context, the level of reliability is not the same as it should be. Therefore, further research into the most detailed mechanisms for evaluating and increasing software reliability is essential. A significant aspect of growing the degree of reliable applications is the quantitative assessment of reliability. There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software. However, none of these mechanisms are… More
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  • Blockchain Technology Based Information Classification Management Service
  • Abstract Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information, but also the expansion of areas and assets to be protected. In terms of information security, it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets. Also, it has caused difficulties in managing those issues due to reasons such as mutual interference, countless security events and logs’ data, etc. Within this security environment, an organization should identify and classify assets based on the value of data and their security perspective, and… More
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  • Novel Universal Windowing Multicarrier Waveform for 5G Systems
  • Abstract Fifth Generation (5G) systems aim to improve flexibility, coexistence and diverse service in several aspects to achieve the emerging applications requirements. Windowing and filtering of the traditional multicarrier waveforms are now considered common sense when designing more flexible waveforms. This paper proposed a Universal Windowing Multi-Carrier (UWMC) waveform design platform that is flexible, providing more easily coexists with different pulse shapes, and reduces the Out of Band Emissions (OOBE), which is generated by the traditional multicarrier methods that used in the previous generations of the mobile technology. The novel proposed approach is different from other approaches that have been proposed,… More
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  • M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT
  • Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture… More
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  • Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments
  • Abstract This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally weighted cubature points, the CKF… More
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  • Medical Image Compression Based on Wavelets with Particle Swarm Optimization
  • Abstract This paper presents a novel method utilizing wavelets with particle swarm optimization (PSO) for medical image compression. Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding. It transfers images into subband details and approximations using a modified Haar wavelet (MHW), and then applies a threshold. PSO is applied for selecting a particle assigned to the threshold values for the subbands. Nine positions assigned to particles values are used to represent population. Every particle updates its position depending on the global best position (gbest) (for all details subband) and local best position (pbest) (for… More
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  • Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak
  • Abstract The Coronavirus disease 2019 (COVID-19) outbreak was first discovered in Wuhan, China, and it has since spread to more than 200 countries. The World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30, 2020. Normally, a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely manner. COVID-19 is a major threat worldwide due to its ability to rapidly spread. No vaccines are yet available for COVID-19. The objective of this paper is to examine the worldwide COVID-19 pandemic, specifically studying Hubei Province, China;… More
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  • COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries
  • Abstract Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback. The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms. This phenomenon caused a state of panic among people. Different studies were conducted to stop the spread of fake news to help people cope with the situation. In this paper, a semantic analysis of three levels (negative, neutral, and positive) is used to gauge the feelings of Gulf countries… More
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  • Exploiting Deep Learning Techniques for Colon Polyp Segmentation
  • Abstract As colon cancer is among the top causes of death, there is a growing interest in developing improved techniques for the early detection of colon polyps. Given the close relation between colon polyps and colon cancer, their detection helps avoid cancer cases. The increment in the availability of colorectal screening tests and the number of colonoscopies have increased the burden on the medical personnel. In this article, the application of deep learning techniques for the detection and segmentation of colon polyps in colonoscopies is presented. Four techniques were implemented and evaluated: Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC).… More
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  • Epidemiologic Evolution Platform Using Integrated Modeling and Geographic Information System
  • Abstract At the international level, a major effort is being made to optimize the flow of data and information for health systems management. The studies show that medical and economic efficiency is strongly influenced by the level of development and complexity of implementing an integrated system of epidemiological monitoring and modeling. The solution proposed and described in this paper is addressed to all public and private institutions involved in the fight against the COVID-19 pandemic, using recognized methods and standards in this field. The Green-Epidemio is a platform adaptable to the specific features of any public institution for disease management, based… More
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  • PeachNet: Peach Diseases Detection for Automatic Harvesting
  • Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More
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  • Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19
  • Abstract The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine… More
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  • QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos
  • Abstract With the advent in services such as telemedicine and telesurgery, provision of continuous quality monitoring for these services has become a challenge for the network operators. Quality standards for provision of such services are application specific as medical imagery is quite different than general purpose images and videos. This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy (WCE) videos containing bleeding regions. Bleeding regions in gastrointestinal tract have been focused in this research, as bleeding is one of the major reasons behind several diseases within the tract. The… More
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  • Optimality of Solution with Numerical Investigation for Coronavirus Epidemic Model
  • Abstract The novel coronavirus disease, coined as COVID-19, is a murderous and infectious disease initiated from Wuhan, China. This killer disease has taken a large number of lives around the world and its dynamics could not be controlled so far. In this article, the spatio-temporal compartmental epidemic model of the novel disease with advection and diffusion process is projected and analyzed. To counteract these types of diseases or restrict their spread, mankind depends upon mathematical modeling and medicine to reduce, alleviate, and anticipate the behavior of disease dynamics. The existence and uniqueness of the solution for the proposed system are investigated.… More
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  • Weighted Gauss-Seidel Precoder for Downlink Massive MIMO Systems
  • Abstract In this paper, a novel precoding scheme based on the Gauss-Seidel (GS) method is proposed for downlink massive multiple-input multiple-output (MIMO) systems. The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process. In addition, the GS method shows a fast convergence rate to the Zero-forcing (ZF) method that requires an exact invertible matrix. However, to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels, more iterations are necessary for the GS method and increase the overall complexity. For efficient… More
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  • Multi-Gigabit CO-OFDM System over SMF and MMF Links for 5G URLLC Backhaul Network
  • Abstract The 5G cellular network aims at providing three major services: Massive machine-type communication (mMTC), ultra-reliable low-latency communications (URLLC), and enhanced-mobile-broadband (eMBB). Among these services, the URLLC and eMBB require strict end-to-end latency of 1 ms while maintaining 99.999% reliability, and availability of extremely high data rates for the users, respectively. One of the critical challenges in meeting these requirements is to upgrade the existing optical fiber backhaul network interconnecting the base stations with a multigigabit capacity, low latency and very high reliability system. To address this issue, we have numerically analyzed 100 Gbit/s coherent optical orthogonal frequency division multiplexing (CO-OFDM)… More
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  • Efficient Algorithms for Cache-Throughput Analysis in Cellular-D2D 5G Networks
  • Abstract In this paper, we propose a two-tiered segment-based Device-to-Device (S-D2D) caching approach to decrease the startup and playback delay experienced by Video-on-Demand (VoD) users in a cellular network. In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks. The first cache-block reserve for caching and delivering the beginning portion of the most popular video files and the second cache-block caches the latter portion of the requested video files ‘fully or partially’ depending on the users’ video watching behaviour and popularity of videos. In this approach before caching, video is divided and grouped in a… More
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  • Position Vectors Based Efficient Indoor Positioning System
  • Abstract With the advent and advancements in the wireless technologies, Wi-Fi fingerprinting-based Indoor Positioning System (IPS) has become one of the most promising solutions for localization in indoor environments. Unlike the outdoor environment, the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efficient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things (IoTs) and green computing. In this paper, we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors. Initially, in the database development phase, Motley Kennan propagation… More
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  • Automated Test Case Generation from Requirements: A Systematic Literature Review
  • Abstract Software testing is an important and cost intensive activity in software development. The major contribution in cost is due to test case generations. Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure. Requirement-based testing includes functional and nonfunctional requirements. The objective of this study is to explore the approaches that generate test cases from requirements. A systematic literature review based on two research questions and extensive quality assessment criteria includes studies. The study identifies 30 primary studies from 410 studies spanned from 2000 to 2018. The review’s finding shows that… More
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  • Analyzing COVID-19 Impact on the Researchers Productivity through Their Perceptions
  • Abstract Context: Since the end of 2019, the COVID-19 pandemic had a worst impact on world’s economy, healthcare, and education. There are several aspects where the impact of COVID-19 could be visualized. Among these, one aspect is the productivity of researcher, which plays a significant role in the success of an organization. Problem: There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization. Method: We perform an… More
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  • Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning
  • Abstract Quantum Machine Learning (QML) techniques have been recently attracting massive interest. However reported applications usually employ synthetic or well-known datasets. One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier (VQC), which development seems promising. Albeit being largely studied, VQC implementations for “real-world” datasets are still challenging on Noisy Intermediate Scale Quantum devices (NISQ). In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping. This pipeline enhances the prediction rates when applying VQC techniques, improving the feasibility of solving classification problems using NISQ devices. By… More
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  • High Security for De-Duplicated Big Data Using Optimal SIMON Cipher
  • Abstract Cloud computing offers internet location-based affordable, scalable, and independent services. Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events, for instance, pandemic situations. To handle massive information, clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data, with elevated velocity and modified configurations. Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server. Data deduplication also saves network bandwidth. In this paper, a new… More
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  • Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model
  • Abstract The latest advancements in highway research domain and increase inthe number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System (ITS). One of the popular research areas i.e., Vehicle License Plate Recognition (VLPR) aims at determining the characters that exist in the license plate of the vehicles. The VLPR process is a difficult one due to the differences in viewpoint, shapes, colors, patterns, and non-uniform illumination at the time of capturing images. The current study develops a robust Deep Learning (DL)-based VLPR model using Squirrel Search Algorithm (SSA)-based Convolutional Neural Network (CNN),… More
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  • Liver-Tumor Detection Using CNN ResUNet
  • Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies… More
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  • Overlapping Shadow Rendering for Outdoor Augmented Reality
  • Abstract Realism rendering methods of outdoor augmented reality (AR) is an interesting topic. Realism items in outdoor AR need advanced impacts like shadows, sunshine, and relations between unreal items. A few realistic rendering approaches were built to overcome this issue. Several of these approaches are not dealt with real-time rendering. However, the issue remains an active research topic, especially in outdoor rendering. This paper introduces a new approach to accomplish reality real-time outdoor rendering by considering the relation between items in AR regarding shadows in any place during daylight. The proposed method includes three principal stages that cover various outdoor AR… More
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  • Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication
  • Abstract With the popularity of green computing and the huge usage of networks, there is an acute need for expansion of the 5G network. 5G is used where energy efficiency is the highest priority, and it can play a pinnacle role in helping every industry to hit sustainability. While in the 5G network, conventional performance guides, such as network capacity and coverage are still major issues and need improvements. Device to Device communication (D2D) communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques. The issue of energy utilization in the IoT based… More
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  • Dynamical Comparison of Several Third-Order Iterative Methods for Nonlinear Equations
  • Abstract There are several ways that can be used to classify or compare iterative methods for nonlinear equations, for instance; order of convergence, informational efficiency, and efficiency index. In this work, we use another way, namely the basins of attraction of the method. The purpose of this study is to compare several iterative schemes for nonlinear equations. All the selected schemes are of the third-order of convergence and most of them have the same efficiency index. The comparison depends on the basins of attraction of the iterative techniques when applied on several polynomials of different degrees. As a comparison, we determine… More
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  • Image-Based Lifelogging: User Emotion Perspective
  • Abstract Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones… More
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  • Role of Fuzzy Approach towards Fault Detection for Distributed Components
  • Abstract Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment. Among other communication, teamwork, and coordination problems in global software development, the detection of faults is seen as the key challenge. Thus, there is a need to ensure the reliability of component-based applications requirements. Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations. In this study, we propose an approach for fault detection from component-based systems requirements using the fuzzy logic approach and… More
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  • A Comprehensive Review on Medical Diagnosis Using Machine Learning
  • Abstract The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine… More
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  • Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning
  • Abstract This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector… More
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  • Energy Efficient Ambience Awake Routing with OpenFlow Approach
  • Abstract A major problem in networking has always been energy consumption. Battery life is one parameter which could help improve Energy Efficiency. Existing research on wireless networking stresses on reducing signaling messages or time required for data transfer for addressing energy consumption issues. Routing or Forwarding packets in a network between the network elements like routers, switches, wireless access points, etc., is complex in conventional networks. With the advent of Software Defined Networking (SDN) for 5G network architectures, the distributed networking has embarked onto centralized networking, wherein the SDN Controller is responsible for decision making. The controller pushes its decision onto… More
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  • Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI
  • Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed… More
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  • Coverless Image Steganography Based on Jigsaw Puzzle Image Generation
  • Abstract Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels, creating the stego image. However, the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload. A coverless data hiding concept is proposed to solve this issue. Coverless does not mean that cover is not required, or the payload can be transmitted without a cover. Instead, the payload is embedded by cover generation or a secret message mapping between the cover file and the payload. In this paper, a… More
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  • Deep Learning Approach for COVID-19 Detection in Computed Tomography Images
  • Abstract With the rapid spread of the coronavirus disease 2019 (COVID-19) worldwide, the establishment of an accurate and fast process to diagnose the disease is important. The routine real-time reverse transcription-polymerase chain reaction (rRT-PCR) test that is currently used does not provide such high accuracy or speed in the screening process. Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques. In this study, a new convolutional neural network (CNN) framework for COVID-19 detection using computed tomography (CT) images is proposed. The EfficientNet architecture is applied as the backbone structure of the proposed network,… More
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  • Modeling the COVID-19 Pandemic Dynamics in Iran and China
  • Abstract The epidemic outbreak COVID-19 was first detected in the Wuhan city of China and then spread worldwide. It is of great interest to the researchers for its high rate of infection spread and its significant number of fatalities. A detailed scientific analysis of this phenomenon is yet to come. However, it is of interest of governments and other responsible institutions to have the right facts and figures to take every possible necessary action such as an arrangement of the appropriate quarantine activities, estimation of the required number of places in hospitals, assessment of the level of personal protection, and calculating… More
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  • A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems
  • Abstract Expression detection plays a vital role to determine the patient’s condition in healthcare systems. It helps the monitoring teams to respond swiftly in case of emergency. Due to the lack of suitable methods, results are often compromised in an unconstrained environment because of pose, scale, occlusion and illumination variations in the image of the face of the patient. A novel patch-based multiple local binary patterns (LBP) feature extraction technique is proposed for analyzing human behavior using facial expression recognition. It consists of three-patch [TPLBP] and four-patch LBPs [FPLBP] based feature engineering respectively. Image representation is encoded from local patch statistics… More
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  • Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic
  • Abstract The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have… More
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  • A Computational Analysis to Burgers Huxley Equation
  • Abstract The efficiency of solving computationally partial differential equations can be profoundly highlighted by the creation of precise, higher-order compact numerical scheme that results in truly outstanding accuracy at a given cost. The objective of this article is to develop a highly accurate novel algorithm for two dimensional non-linear Burgers Huxley (BH) equations. The proposed compact numerical scheme is found to be free of superiors approximate oscillations across discontinuities, and in a smooth flow region, it efficiently obtained a high-order accuracy. In particular, two classes of higher-order compact finite difference schemes are taken into account and compared based on their computational… More
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  • Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution
  • Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and 95% asymptotic confidence intervals are… More
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  • Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter
  • Abstract Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if… More
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  • A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem
  • Abstract The Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), and Whale Optimizer (WO) algorithms are being recently developed for engineering optimization problems. In this paper, the EO, GWO, and WO algorithms are applied individually for a brushless direct current (BLDC) design optimization problem. The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state. The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale, respectively. The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.… More
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  • Frequency-Agile WLAN Notch UWB Antenna for URLLC Applications
  • Abstract This paper introduces a compact dual notched UWB antenna with an independently controllable WLAN notched band integrated with fixed WiMAX band-notch. The proposed antenna utilizes a slot resonator placed in the main radiator of the antenna for fixed WiMAX band notch, while an inverted L-shaped resonator in the partial ground plane for achieving frequency agility within WLAN notched band. The inverted L-shaped resonator is also loaded with fixed and variable capacitors to control and adjust the WLAN notch. The WLAN notched band can be controlled independently with a wide range of tunability without disturbing the WiMAX band-notch performance. Step by… More
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  • Observed Impacts of Climate Variability on LULC in the Mesopotamia Region
  • Abstract Remote sensing analysis techniques have been investigated extensively, represented by a critical vision, and are used to advance our understanding of the impacts of climate change and variability on the environment. This study aims to find a means of analysis that relies on remote sensing techniques to demonstrate the effects of observed climate variability on Land Use and Land Cover (LULC) of the Mesopotamia region, defined as a historical region located in the Middle East. This study employed the combined analysis of the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and two statistical analysis methods (Pearson Correlation Analysis,… More
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  • An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters
  • Abstract Fuzzy logic control (FLC) systems have found wide utilization in several industrial applications. This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic (PV) inverters. Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere. Power converters represent the main parts for the grid integration of PV systems. However, PV power converters contain several power switches that construct their circuits. The power switches in PV systems are highly subjected to high stresses due to… More
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  • An Effective Memory Analysis for Malware Detection and Classification
  • Abstract The study of malware behaviors, over the last years, has received tremendous attention from researchers for the purpose of reducing malware risks. Most of the investigating experiments are performed using either static analysis or behavior analysis. However, recent studies have shown that both analyses are vulnerable to modern malware files that use several techniques to avoid analysis and detection. Therefore, extracted features could be meaningless and a distraction for malware analysts. However, the volatile memory can expose useful information about malware behaviors and characteristics. In addition, memory analysis is capable of detecting unconventional malware, such as in-memory and fileless malware.… More
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  • Test Case Generation from UML-Diagrams Using Genetic Algorithm
  • Abstract Software testing has been attracting a lot of attention for effective software development. In model driven approach, Unified Modelling Language (UML) is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology. Specialized tools interpret these models into other software artifacts such as code, test data and documentation. The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements. This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm (BGA). For generating the… More
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  • Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images
  • Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the… More
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  • Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19
  • Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More
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  • Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms
  • Abstract The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide. This problem is addressed by the software development industry by software product line (SPL) practices that employ feature models. However, optimal feature selection based on user requirements is a challenging task. Thus, there is a requirement to resolve the challenges of software development, to increase satisfaction and maintain high product quality, for massive customer needs within limited resources. In this work, we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and… More
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  • An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification
  • Abstract Due to the advancements in remote sensing technologies, the generation of hyperspectral imagery (HSI) gets significantly increased. Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis. The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials. Presently, deep learning (DL) models particularly, convolutional neural networks (CNNs) become useful for HSI classification owing to the effective feature representation and high performance. In this view, this paper introduces a new DL based Xception model for HSI analysis and classification, called Xcep-HSIC model. Initially, the… More
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  • COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images
  • Abstract Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited… More
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  • Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients
  • Abstract In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based… More
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  • Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities
  • Abstract Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things (IoT). The IoT is the backbone of smart city applications such as smart grids and green energy management. In smart cities, the IoT devices are used for linking power, price, energy, and demand information for smart homes and home energy management (HEM) in the smart grids. In complex smart grid-connected systems, power scheduling and secure dispatch of information are the main research challenge. These challenges can be resolved through various machine learning techniques and data analytics. In this paper, we have proposed a particle… More
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  • Optimized Predictive Framework for Healthcare Through Deep Learning
  • Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling technique. The proposed model is… More
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  • Fusion-Based Machine Learning Architecture for Heart Disease Prediction
  • Abstract The contemporary evolution in healthcare technologies plays a considerable and significant role to improve medical services and save human lives. Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes, as numerous people have been suffering from this disease globally. Heart attacks occur when the ranges of vital signs such as blood pressure, pulse rate, and body temperature exceed their normal values. The efficient diagnosis of heart diseases could play a substantial role in the field of cardiology, while diagnostic time could be reduced. It has been… More
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  • A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats
  • Abstract The number of cybersecurity incidents is on the rise despite significant investment in security measures. The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks. This is primarily due to the sophistication of the attacks and the availability of powerful tools. Interconnected devices such as the Internet of Things (IoT) are also increasing attack exposures due to the increase in vulnerabilities. Over the last few years, we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks. Edge technology brings processing power closer to the… More
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  • Rayleigh Waves Propagation in an Infinite Rotating Thermoelastic Cylinder
  • Abstract In this paper, we investigated the inuence of rotating half-space on the propagation of Rayleigh waves in a homogeneous isotropic, generalized thermo-elastic body, subject to the boundary conditions that the surface is traction free. In addition, it is subject to insulating thermal conduction. A general solution is obtained by using Lame’ potential’s and Hankel transform. The dispersion equations has been derived separately for two types of Rayleigh wave propagation properties by solving the equations of motion with appropriate boundary conditions. It is observed that the rotation, frequency and r exert some influence in the homogeneous isotropic medium due to propagation… More
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  • An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs
  • Abstract Flying ad hoc networks (FANETs) present a challenging environment due to the dynamic and highly mobile nature of the network. Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission. Several different techniques are adopted to address the issues arising in FANETs, from game theory to clustering to channel estimation and other statistical schemes. These approaches mostly employ traditional concepts for problem solutions. One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes. Several Nature-inspired schemes address cooperation and alliance which can… More
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  • Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning
  • Abstract Physical contamination of food occurs when it comes into contact with foreign objects. Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking. Therefore, a preventive method that can detect and remove foreign objects in advance is required. Several studies have attempted to detect defective products using deep learning networks. Because it is difficult to obtain foreign object-containing food data from industry, most studies on industrial anomaly detection have used unsupervised learning methods. This paper proposes a new method for real-time anomaly detection in… More
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  • Social Media and Stock Market Prediction: A Big Data Approach
  • Abstract Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns. The quantity and variety of computer data are growing exponentially for many reasons. For example, retailers are building vast databases of customer sales activity. Organizations are working on logistics financial services, and public social media are sharing a vast quantity of sentiments related to sales price and products. Challenges of big data include volume and variety in both structured and unstructured data. In this paper, we implemented several machine learning models through Spark MLlib using PySpark, which is scalable, fast, easily integrated… More
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  • Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network
  • Abstract Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using… More
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  • Statistical Medical Pattern Recognition for Body Composition Data Using Bioelectrical Impedance Analyzer
  • Abstract Identifying patterns, recognition systems, prediction methods, and detection methods is a major challenge in solving different medical issues. Few categories of devices for personal and professional assessment of body composition are available. Bioelectrical impedance analyzer is a simple, safe, affordable, mobile, non-invasive, and less expensive alternative device for body composition assessment. Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape, body mass, energy requirements, physical fitness, health status, and metabolic profile. Thus, this research aims to identify the statistical medical pattern recognition… More
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  • Coronavirus: A “Mild” Virus Turned Deadly Infection
  • Abstract Coronaviruses are a family of viruses that can be transmitted from one person to another. Earlier strains have only been mild viruses, but the current form, known as coronavirus disease 2019 (COVID-19), has become a deadly infection. The outbreak originated in Wuhan, China, and has since spread worldwide. The symptoms of COVID-19 include a dry cough, sore throat, fever, and nasal congestion. Antimicrobial drugs, pathogen–host interaction, and 2 weeks of isolation have been recommended for the treatment of the infection. Safe operating procedures, such as the use of face masks, hand sanitizer, handwashing with soap, and social distancing, are also… More
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  • A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing
  • Abstract In recent years, vehicular cloud computing (VCC) has gained vast attention for providing a variety of services by creating virtual machines (VMs). These VMs use the resources that are present in modern smart vehicles. Many studies reported that some of these VMs hosted on the vehicles are overloaded, whereas others are underloaded. As a circumstance, the energy consumption of overloaded vehicles is drastically increased. On the other hand, underloaded vehicles are also drawing considerable energy in the underutilized situation. Therefore, minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in… More
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  • Economical Requirements Elicitation Techniques During COVID-19: A Systematic Literature Review
  • Abstract Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements. This phase is cost- and time-intensive, and a project may fail if there are excessive costs and schedule overruns. COVID-19 has affected the software industry by reducing interactions between developers and customers. Such a lack of interaction is a key reason for the failure of software projects. Projects can also fail when customers do not know precisely what they want. Furthermore, selecting the unsuitable elicitation technique can also cause project failure. The present study, therefore, aimed… More
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  • Industrial Food Quality Analysis Using New k-Nearest-Neighbour methods
  • Abstract The problem of predicting continuous scalar outcomes from functional predictors has received high levels of interest in recent years in many fields, especially in the food industry. The k-nearest neighbor (k-NN) method of Near-Infrared Reflectance (NIR) analysis is practical, relatively easy to implement, and becoming one of the most popular methods for conducting food quality based on NIR data. The k-NN is often named k nearest neighbor classifier when it is used for classifying categorical variables, while it is called k-nearest neighbor regression when it is applied for predicting noncategorical variables. The objective of this paper is to use the… More
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