Vol.66, No.1, 2021-Table of Contents
  • Design of Authoring Tool for Static and Dynamic Projection Mapping
  • Abstract This study introduces the design details of a tool to create interactive projection-mapping content in a convenient manner. For the proposed tool design, a homography-based camera–projector calibration method was applied with the use of red–green–blue-depth images from a Kinect V2 sensor that did not require accurate camera calibration prerequisites. In addition, the proposed tool simultaneously achieved static projection mapping that projected the image content onto a fixed object, and dynamic projection mapping that projected the image content onto a user’s body, by tracing the moving user. To verify the effectiveness of the proposed content-creation tool, users with no programming capabilities… More
  •   Views:1149       Downloads:660        Download PDF
  • Detecting Lumbar Implant and Diagnosing Scoliosis from Vietnamese X-Ray Imaging Using the Pre-Trained API Models and Transfer Learning
  • Abstract With the rapid growth of the autonomous system, deep learning has become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the human body. There are numerous kinds of conditions such as scoliosis, vertebra degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the… More
  •   Views:871       Downloads:398        Download PDF
  • Design of a Compact Monopole Antenna for UWB Applications
  • Abstract In this paper, a low cost, highly efficient and low profile monopole antenna for ultra-wideband (UWB) applications is presented. A new inverted triangular-shape structure possessing meander lines is designed to achieve a wideband response and high efficiency. To design the proposed structure, three steps are utilized to achieve an UWB response. The bandwidth of the proposed antenna is improved with changing meander lines parameters, miniaturization of the ground width and optimization of the feeding line. The measured and simulated frequency band ranges from 3.2 to 12 GHz, while the radiation patterns are measured at 4, 5.3, 6 and 8 GHz… More
  •   Views:758       Downloads:351        Download PDF
  • A Smart Wellness Service Platform and Its Practical Implementation
  • Abstract Advances in the field of medical sciences and medical technology, and present-day challenges, such as an aging population, rising medical expenses, and lifestyle-related diseases, have collectively catalyzed a research ecosystem termed “smart wellness.” This article describes the establishment of a smart wellness service platform designed to empower individuals to create a sense of balance in their lives. Step-by-step details include service model, design, and architectural considerations. As a proof of concept, implementation details of a Health Improvement and Management Systems (HIMS) Hub, a Smart Wellness Service Platform deployed in six cities in South Korea, are presented. An on-site survey conducted… More
  •   Views:904       Downloads:437        Download PDF
  • Fingerprint-Based Millimeter-Wave Beam Selection for Interference Mitigation in Beamspace Multi-User MIMO Communications
  • Abstract Millimeter-wave communications are suitable for application to massive multiple-input multiple-output systems in order to satisfy the ever-growing data traffic demands of the next-generation wireless communication. However, their practical deployment is hindered by the high cost of complex hardware, such as radio frequency (RF) chains. To this end, operation in the beamspace domain, through beam selection, is a viable solution. Generally, the conventional beam selection schemes focus on the feedback and exhaustive search techniques. In addition, since the same beam in the beamspace may be assigned to a different user, conventional beam selection schemes suffer serious multi-user interference. In addition, some… More
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  • Multilayer Self-Defense System to Protect Enterprise Cloud
  • Abstract A data breach can seriously impact organizational intellectual property, resources, time, and product value. The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols. As TCP relies on IP addresses, an attacker may easily trace the IP address of the organization. Given that many organizations run the risk of data breach and cyber-attacks at a certain point, a repeatable and well-developed incident response framework is critical to shield them. Enterprise cloud possesses the challenges of security, lack of transparency, trust and loss of controls. Technology eases quickens the processing of information but… More
  •   Views:581       Downloads:216        Download PDF
  • On Modeling the Medical Care Insurance Data via a New Statistical Model
  • Abstract Proposing new statistical distributions which are more flexible than the existing distributions have become a recent trend in the practice of distribution theory. Actuaries often search for new and appropriate statistical models to address data related to financial and risk management problems. In the present study, an extension of the Lomax distribution is proposed via using the approach of the weighted T-X family of distributions. The mathematical properties along with the characterization of the new model via truncated moments are derived. The model parameters are estimated via a prominent approach called the maximum likelihood estimation method. A brief Monte Carlo… More
  •   Views:876       Downloads:230        Download PDF
  • Dual Branches of MHD Three-Dimensional Rotating Flow of Hybrid Nanofluid on Nonlinear Shrinking Sheet
  • Abstract In this study, magnetohydrodynamic (MHD) three-dimensional (3D) flow of alumina (Al2O3) and copper (Cu) nanoparticles of an electrically conducting incompressible fluid in a rotating frame has been investigated. The shrinking surface generates the flow that also has been examined. The single-phase (i.e., Tiwari and Das) model is implemented for the hybrid nanofluid transport phenomena. Results for alumina and copper nanomaterials in the water base fluid are achieved. Boundary layer approximations are used to reduce governing partial differential (PDEs) system into the system of the ordinary differential equations (ODEs). The three-stage Lobatto IIIa method in bvp4c solver is applied for solutions… More
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  • Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine
  • Abstract The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try… More
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  • Fault Tolerant Suffix Trees
  • Abstract Classical algorithms and data structures assume that the underlying memory is reliable, and the data remain safe during or after processing. However, the assumption is perilous as several studies have shown that large and inexpensive memories are vulnerable to bit flips. Thus, the correctness of output of a classical algorithm can be threatened by a few memory faults. Fault tolerant data structures and resilient algorithms are developed to tolerate a limited number of faults and provide a correct output based on the uncorrupted part of the data. Suf- fix tree is one of the important data structures that has widespread… More
  •   Views:548       Downloads:277        Download PDF
  • Three-Dimensional Meshfree Analysis of Interlocking Concrete Blocks for Step Seawall Structure
  • Abstract This study adapts the flexible characteristic of meshfree method in analyzing three-dimensional (3D) complex geometry structures, which are the interlocking concrete blocks of step seawall. The elastostatic behavior of the block is analysed by solving the Galerkin weak form formulation over local support domain. The 3D moving least square (MLS) approximation is applied to build the interpolation functions of unknowns. The pre-defined number of nodes in an integration domain ranging from 10 to 60 nodes is also investigated for their effect on the studied results. The accuracy and efficiency of the studied method on 3D elastostatic responses are validated through… More
  •   Views:554       Downloads:195        Download PDF
  • ACLSTM: A Novel Method for CQA Answer Quality Prediction Based on Question-Answer Joint Learning
  • Abstract Given the limitations of the community question answering (CQA) answer quality prediction method in measuring the semantic information of the answer text, this paper proposes an answer quality prediction model based on the question-answer joint learning (ACLSTM). The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer (Q&A) pairs. Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate their matching degree. Besides, answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer. Compared with other quality… More
  •   Views:457       Downloads:216        Download PDF
  • A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet
  • Abstract In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents. The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA). Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach. The SAMMZWA approach embeds and detects the watermark logically without altering the original text document. The extracted… More
  •   Views:461       Downloads:194        Download PDF
  • A 360-Degree Panoramic Image Inpainting Network Using a Cube Map
  • Abstract Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it… More
  •   Views:452       Downloads:232        Download PDF
  • Numerical Treatment of MHD Flow of Casson Nanofluid via Convectively Heated Non-Linear Extending Surface with Viscous Dissipation and Suction/Injection Effects
  • Abstract This paper introduces the effect of heat absorption (generation) and suction (injection) on magnetohydrodynamic (MHD) boundary-layer flow of Casson nanofluid (CNF) via a non-linear stretching surface with the viscous dissipation in two dimensions. By utilizing the similarity transformations, the leading PDEs are transformed into a set of ODEs with adequate boundary conditions and then resolved numerically by (4–5)th-order Runge-Kutta Fehlberg procedure based on the shooting technique. Numerical computations are carried out by Maple 15 software. With the support of graphs, the impact of dimensionless control parameters on the nanoparticle concentration profiles, the temperature, and the flow velocity are studied. Other… More
  •   Views:511       Downloads:239        Download PDF
  • Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment
  • Abstract Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual structure, and the whole network… More
  •   Views:503       Downloads:1074        Download PDF
  • MIMO-Terahertz in 6G Nano-Communications: Channel Modeling and Analysis
  • Abstract With the development of wireless mobile communication technology, the demand for wireless communication rate and frequency increases year by year. Existing wireless mobile communication frequency tends to be saturated, which demands for new solutions. Terahertz (THz) communication has great potential for the future mobile communications (Beyond 5G), and is also an important technique for the high data rate transmission in spatial information network. THz communication has great application prospects in military-civilian integration and coordinated development. In China, important breakthroughs have been achieved for the key techniques of THz high data rate communications, which is practically keeping up with the most… More
  •   Views:694       Downloads:346        Download PDF
  • Computer Methodologies for the Comparison of Some Efficient Derivative Free Simultaneous Iterative Methods for Finding Roots of Non-Linear Equations
  • Abstract In this article, we construct the most powerful family of simultaneous iterative method with global convergence behavior among all the existing methods in literature for finding all roots of non-linear equations. Convergence analysis proved that the order of convergence of the family of derivative free simultaneous iterative method is nine. Our main aim is to check out the most regularly used simultaneous iterative methods for finding all roots of non-linear equations by studying their dynamical planes, numerical experiments and CPU time-methodology. Dynamical planes of iterative methods are drawn by using MATLAB for the comparison of global convergence properties of simultaneous… More
  •   Views:445       Downloads:193        Download PDF
  • Analysis of Factors that Affect Government Digitization: A Pilot Case Study of Pakistan
  • Abstract One of the greatest factors that affects the economic condition of a country is its institutions. In the model of good governance, the primary elements for stronger institution include efficiency, transparency, and accountability; and technology plays a major role in improving these elements. However, there are myriad of challenges when it comes to practical integration of technology in these institutions for efficiency. It is more challenging when a country is developing and one that is already weak economically. It is also important to mention that the challenges of digitization in public sector is not limited to developing countries only. It… More
  •   Views:420       Downloads:180        Download PDF
  • Confocal 3D Optical Intraoral Scanners and Comparison of Image Capturing Accuracy
  • Abstract Several capture techniques are used in intraoral optical scanners in the dental market, such as Triangulation (Cerec Omnicam, Dentsply Sirona), Activewave front sampling (3M ESPE) and confocal technology (iTero, Align). The accuracy of intraoral scanners is the most significant focal point for developers to research. This in-vitro study studied the accuracy of confocal scanners launched from 2015-2020 (Trios 3, Trios 4, iTero Element; 3Shape Trios A/S, Copenhagen, Denmark, and iTero Element2, and iTero Element5D; Align Technologies, San Jose, CA, USA). A 3D printing model modified from the American National Standard No. 132 was scanned five times each scanner. Both Trios3… More
  •   Views:652       Downloads:376        Download PDF
  • Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method
  • Abstract The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and… More
  •   Views:572       Downloads:264        Download PDF
  • Forecast the Influenza Pandemic Using Machine Learning
  • Abstract Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through pigs. In 1918, influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population. Afterwards, that influenza became a pandemic several times on a local and global levels. In 2009, influenza ‘A’ subtype H1N1 again took many human lives. The disease spread like in a pandemic quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward… More
  •   Views:612       Downloads:303        Download PDF
  • Prediction Model of Abutment Pressure Affected by Far-Field Hard Stratum Based on Elastic Foundation Theory
  • Abstract In view of the three-dimensional dynamic abutment pressure, the influence of the far-field hard stratum (FHS) in deep, thick coal seams is indeterminant. Based on elastic foundation theory, a three-dimensional dynamic prediction model of the abutment pressure was established. Using this model, the dynamic change in the coal seam abutment pressure caused by the movement of the FHS was studied, and a method for determining the dynamic change range of the abutment pressure was developed. The results of the new prediction model of the abutment pressure are slightly higher than the measured values, with an error of 0.51%, which avoids… More
  •   Views:362       Downloads:181        Download PDF
  • Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach
  • Abstract Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four… More
  •   Views:617       Downloads:216        Download PDF
  • Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification
  • Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then… More
  •   Views:577       Downloads:314        Download PDF
  • Design and Analysis of a Water Quality Monitoring Data Service Platform
  • Abstract Water is one of the basic resources for human survival. Water pollution monitoring and protection have been becoming a major problem for many countries all over the world. Most traditional water quality monitoring systems, however, generally focus only on water quality data collection, ignoring data analysis and data mining. In addition, some dirty data and data loss may occur due to power failures or transmission failures, further affecting data analysis and its application. In order to meet these needs, by using Internet of things, cloud computing, and big data technologies, we designed and implemented a water quality monitoring data intelligent… More
  •   Views:472       Downloads:307        Download PDF
  • Adversarial Active Learning for Named Entity Recognition in Cybersecurity
  • Abstract Owing to the continuous barrage of cyber threats, there is a massive amount of cyber threat intelligence. However, a great deal of cyber threat intelligence come from textual sources. For analysis of cyber threat intelligence, many security analysts rely on cumbersome and time-consuming manual efforts. Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence. As the foundation for constructing cybersecurity knowledge graph, named entity recognition (NER) is required for identifying critical threat-related elements from textual cyber threat intelligence. Recently, deep neural network-based models have attained very good results in NER. However, the performance of these… More
  •   Views:451       Downloads:214        Download PDF
  • Mixed Convection of Non-Newtonian Erying Powell Fluid with TemperatureDependent Viscosity over a Vertically Stretched Surface
  • Abstract The viscosity of a substance or material is intensely influenced by the temperature, especially in the field of lubricant engineering where the changeable temperature is well executed. In this paper, the problem of temperature-dependent viscosity on mixed convection flow of Eyring Powell fluid was studied together with Newtonian heating thermal boundary condition. The flow was assumed to move over a vertical stretching sheet. The model of the problem, which is in partial differential equations, was first transformed to ordinary differential equations using appropriate transformations. This approach was considered to reduce the complexity of the equations. Then, the transformed equations were… More
  •   Views:397       Downloads:160        Download PDF
  • Financing Strategy of Low-Carbon Supply Chain with Capital Constraint under Cap-and-Trade Regulation
  • Abstract Cap-and-trade regulation provides incentives for manufacturers to reduce carbon emissions, but manufacturers’ insufficient capital can disrupt the implementation of low-carbon emission reduction technologies. To alleviate capital constraints, manufacturers can adopt external financing for low-carbon emission reduction investments. This paper studies the independent financing and financing cooperation behavior in a supply chain in which the manufacturer and retailer first implement low-carbon emission reduction technologies and then organize production and sales in accordance with wholesale price contracts. Through comparing the optimal profits and low-carbon emission reduction levels under the independent financing and financing cooperation mode, we come to the following conclusions: (1)… More
  •   Views:603       Downloads:295        Download PDF
  • Image Recognition of Citrus Diseases Based on Deep Learning
  • Abstract In recent years, with the development of machine learning and deep learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition method of citrus diseases based on deep learning is proposed. We built a citrus image dataset including six common citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,… More
  •   Views:585       Downloads:423        Download PDF
  • Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine
  • Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular… More
  •   Views:624       Downloads:618        Download PDF
  • Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events
  • Abstract Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness… More
  •   Views:423       Downloads:169        Download PDF
  • Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain
  • Abstract In the digital era, electronic medical record (EMR) has been a major way for hospitals to store patients’ medical data. The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing. The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput. To address these issues, we propose a secure and efficient medical data storage and sharing scheme based on double blockchain. In our scheme, we encrypt the original EMR and store it in the cloud. The storage blockchain stores the index of the… More
  •   Views:589       Downloads:273        Download PDF
  • A Dynamically Reconfigurable Accelerator Design Using a Sparse-Winograd Decomposition Algorithm for CNNs
  • Abstract Convolutional Neural Networks (CNNs) are widely used in many fields. Due to their high throughput and high level of computing characteristics, however, an increasing number of researchers are focusing on how to improve the computational efficiency, hardware utilization, or flexibility of CNN hardware accelerators. Accordingly, this paper proposes a dynamically reconfigurable accelerator architecture that implements a Sparse-Winograd F(2 2.3 3)-based high-parallelism hardware architecture. This approach not only eliminates the pre-calculation complexity associated with the Winograd algorithm, thereby reducing the difficulty of hardware implementation, but also greatly improves the flexibility of the hardware; as a result, the accelerator can realize the… More
  •   Views:396       Downloads:167        Download PDF
  • Modelling Insurance Losses with a New Family of Heavy-Tailed Distributions
  • Abstract The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues. In this article, we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences. A specific sub-model form of our suggested family, named as a new extended heavy-tailed Weibull distribution is examined in detail. Some basic characterizations, including quantile function and raw moments have been derived. The estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation method. To judge the performance of the maximum likelihood estimators, a simulation analysis is performed in… More
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  • A Rasterized Lightning Disaster Risk Method for Imbalanced Sets Using Neural Network
  • Abstract Over the past 10 years, lightning disaster has caused a large number of casualties and considerable economic loss worldwide. Lightning poses a huge threat to various industries. In an attempt to reduce the risk of lightning-caused disaster, many scholars have carried out in-depth research on lightning. However, these studies focus primarily on the lightning itself and other meteorological elements are ignored. In addition, the methods for assessing the risk of lightning disaster fail to give detailed attention to regional features (lightning disaster risk). This paper proposes a grid-based risk assessment method based on data from multiple sources. First, this paper… More
  •   Views:458       Downloads:201        Download PDF
  • Effects of Combined Heat and Mass Transfer on Entropy Generation due to MHD Nanofluid Flow over a Rotating Frame
  • Abstract The current investigation aims to explore the combined effects of heat and mass transfer on free convection of Sodium alginate-Fe3O4 based Brinkmann type nanofluid flow over a vertical rotating frame. The Tiwari and Das nanofluid model is employed to examine the effects of dimensionless numbers, including Grashof, Eckert, and Schmidt numbers and governing parameters like solid volume fraction of nanoparticles, Hall current, magnetic field, viscous dissipation, and the chemical reaction on the physical quantities. The dimensionless nonlinear partial differential equations are solved using a finite difference method known as Runge-Kutta Fehlberg (RKF-45) method. The variation of dimensionless velocity, temperature, concentration,… More
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  • FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams
  • Abstract Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This… More
  •   Views:449       Downloads:188        Download PDF
  • Recommender Systems Based on Tensor Decomposition
  • Abstract Recommender system is an effective tool to solve the problems of information overload. The traditional recommender systems, especially the collaborative filtering ones, only consider the two factors of users and items. While social networks contain abundant social information, such as tags, places and times. Researches show that the social information has a great impact on recommendation results. Tags not only describe the characteristics of items, but also reflect the interests and characteristics of users. Since the traditional recommender systems cannot parse multi-dimensional information, in this paper, a tensor decomposition model based on tag regularization is proposed which incorporates social information… More
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  • A Framework for Systematic Classification of Assets for Security Testing
  • Abstract Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper,… More
  •   Views:518       Downloads:193        Download PDF
  • Resampling Factor Estimation via Dual-Stream Convolutional Neural Network
  • Abstract The estimation of image resampling factors is an important problem in image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest. However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information. In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image. Firstly, the resampling process will introduce correlations between neighboring pixels. In this case, a set of periodic pixels that are correlated to their neighbors… More
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  • The Effect of Surface Pit Treatment on Fretting Fatigue Crack Initiation
  • Abstract This paper analyses the effect of surface treatment on fretting fatigue specimen by numerical simulations using Finite Element Analysis. The processed specimen refers to artificially adding a cylindrical pit to its contact surface. Then, the contact radius between the pad and the specimen is controlled by adjusting the radius of the pit. The stress distribution and slip amplitude of the contact surface under different contact geometries are compared. The critical plane approach is used to predict the crack initiation life and to evaluate the effect of processed specimen on its fretting fatigue performance. Both crack initiation life and angle can… More
  •   Views:414       Downloads:173        Download PDF
  • Analysis of Convective Transport of Temperature-Dependent Viscosity for Non-Newtonian Erying Powell Fluid: A Numerical Approach
  • Abstract Non-Newtonian is a type of fluid that does not comply with the viscosity under the Law of Newton and is being widely used in industrial applications. These include those related to chemical industries, cosmetics manufacturing, pharmaceutical field, food processing, as well as oil and gas activities. The inability of the conventional equations of Navier–Stokes to accurately depict rheological behavior for certain fluids led to an emergence study for non-Newtonian fluids’ models. In line with this, a mathematical model of forced convective flow on non-Newtonian Eyring Powell fluid under temperature-dependent viscosity (TDV) circumstance is formulated. The fluid model is embedded with… More
  •   Views:384       Downloads:162        Download PDF
  • Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learning
  • Abstract The innovation and development in data science have an impact in all trades of life. The commercialization of sport has encouraged players, coaches, and other concerns to use technology to be in better position than r their opponents. In the past, the focus was on improved training techniques for better physical performance. These days, sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players. Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past. The… More
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  • Potential Inhibitory Effect of Vitamins Against COVID-19
  • Abstract Coronavirus disease 2019 (COVID-19) is a current pandemic that has affected more than 195 countries worldwide. In this severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, when treatment strategies are not yet clear and vaccines are not available, vitamins are an excellent choice to protect against this viral infection. The rationale behind this study was to examine the inhibitory effect of vitamins B, C, and D against the main protease of SARSCoV-2 and angiotensin-converting enzyme 2 (ACE2), which have critical rolesin the immune system. Molecular docking, performed by using MOE-Dock of the Chemical Computing Group, was used to understand the… More
  •   Views:753       Downloads:454        Download PDF
  • Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications
  • Abstract Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer… More
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  • Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things
  • Abstract Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with… More
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  • Towards Interference-Aware ZigBee Transmissions in Heterogeneous Wireless Networks
  • Abstract Cross-technology interference (CTI) from diverse wireless networks such as ZigBee, Bluetooth, and Wi-Fi has become a severe problem in the 2.4 GHz Industrial Scientific and Medical (ISM) band. Especially, low power and lossy networks are vulnerable to the signal interferences from other aggressive wireless networks when they perform low power operations to conserve the energy consumption. This paper presents CoSense, which accurately detects ZigBee signals with a reliable signal correlation scheme in the presence of the CTI. The key concept of CoSense is to reduce false wake-ups of low power listening (LPL) by identifying the pre-defined ZigBee signatures. Our scheme… More
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  • Anomaly Classification Using Genetic Algorithm-Based Random Forest Model for Network Attack Detection
  • Abstract Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using machine learning (ML) methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks. Among several ML methods, random forest (RF) is a robust method that can be used in ML-based network intrusion detection solutions. However, the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy. Therefore, optimal parameter selection is a real problem in… More
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  • Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing
  • Abstract The application field of the Internet of Things (IoT) involves all aspects, and its application in the fields of industry, agriculture, environment, transportation, logistics, security and other infrastructure has effectively promoted the intelligent development of these aspects. Although the IoT has gradually grown in recent years, there are still many problems that need to be overcome in terms of technology, management, cost, policy, and security. We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data. To avoid the leakage and loss of various user data, this paper developed a hybrid algorithm of… More
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  • Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic
  • Abstract During the current epidemic, it is necessary to ensure the rehabilitation treatment of children with serious illness. At the same time, however, it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting. To resolve this contradiction, the rehabilitation department of Nanjing Children’s Hospital adjusted its bed allocation based on the queuing model, with reference to the regional source and classification of the children’s conditions in the rehabilitation department ward. The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from other places. A M/G/2 queuing… More
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  • Nonlinear Time Series Analysis of Pathogenesis of COVID-19 Pandemic Spread in Saudi Arabia
  • Abstract This article discusses short–term forecasting of the novel Corona Virus (COVID-19) data for infected and recovered cases using the ARIMA method for Saudi Arabia. The COVID-19 data was obtained from the Worldometer and MOH (Ministry of Health, Saudi Arabia). The data was analyzed for the period from March 2, 2020 (the first case reported) to June 15, 2020. Using ARIMA (2, 1, 0), we obtained the short forecast up to July 02, 2020. Several statistical parameters were tested for the goodness of fit to evaluate the forecasting methods. The results show that ARIMA (2, 1, 0) gave a better forecast… More
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  • A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning
  • Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital… More
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  • Blockchain Data Privacy Access Control Based on Searchable Attribute Encryption
  • Abstract Data privacy is important to the security of our society, and enabling authorized users to query this data efficiently is facing more challenge. Recently, blockchain has gained extensive attention with its prominent characteristics as public, distributed, decentration and chronological characteristics. However, the transaction information on the blockchain is open to all nodes, the transaction information update operation is even more transparent. And the leakage of transaction information will cause huge losses to the transaction party. In response to these problems, this paper combines hierarchical attribute encryption with linear secret sharing, and proposes a blockchain data privacy protection control scheme based… More
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  • Self-Management of Low Back Pain Using Neural Network
  • Abstract Low back pain (LBP) is a morbid condition that has afflicted several citizens in Europe. It has negatively impacted the European economy due to several man-days lost, with bed rest and forced inactivity being the usual LBP care and management steps. Direct models, which incorporate various regression analyses, have been executed for the investigation of this premise due to the simplicity of translation. However, such straight models fail to completely consider the impact of association brought about by a mix of nonlinear connections and autonomous factors.In this paper, we discuss a system that aids decision-making regarding the best-suited support system… More
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  • Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks
  • Abstract Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced… More
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  • A New Logarithmic Family of Distributions: Properties and Applications
  • Abstract In recent years, there has been an increased interest among the researchers to propose new families of distributions to provide the best fit to lifetime data with monotonic (increasing, decreasing, constant) and non-monotonic (unimodal, modified unimodal, bathtub) hazard functions. We further carry this area of research and propose a new family of lifetime distributions called a new logarithmic family via the T-X family approach. For the proposed family, explicit expressions for some mathematical properties along with the estimation of parameters through Maximum likelihood method are discussed. A sub-model, called a new logarithmic Weibull distribution is taken up. The proposed model… More
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  • A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System
  • Abstract In RFID (Radio Frequency IDentification) system, when multiple tags are in the operating range of one reader and send their information to the reader simultaneously, the signals of these tags are superimposed in the air, which results in a collision and leads to the degrading of tags identifying efficiency. To improve the multiple tags’ identifying efficiency due to collision, a physical layer network coding based binary search tree algorithm (PNBA) is proposed in this paper. PNBA pushes the conflicting signal information of multiple tags into a stack, which is discarded by the traditional anti-collision algorithm. In addition, physical layer network… More
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  • Comparative Thermal Performance in SiO2–H2O and (MoS2–SiO2)–H2O Over a Curved Stretching Semi-Infinite Region: A Numerical Investigation
  • Abstract The investigation of Thermal performance in nanofluids and hybrid nanofluids over a curved stretching infinite region strengthens its roots in engineering and industry. Therefore, the comparative thermal analysis in SiO2–H2O and (MoS2–SiO2)–H2O is conducted over curved stretching surface. The model is reduced in the dimensional version via similarity transformation and then treated numerically. The velocity and thermal behavior for both the fluids is decorated against the preeminent parameters. From the analysis, it is examined that the motion of under consideration fluids declines against Fr and λ. The thermal performance enhances for higher volumetric fraction and λ. Further, it is noticed… More
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  • Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home
  • Abstract The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which… More
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  • Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks
  • Abstract The diagnosis of multiple sclerosis (MS) is based on accurate detection of lesions on magnetic resonance imaging (MRI) which also provides ongoing essential information about the progression and status of the disease. Manual detection of lesions is very time consuming and lacks accuracy. Most of the lesions are difficult to detect manually, especially within the grey matter. This paper proposes a novel and fully automated convolution neural network (CNN) approach to segment lesions. The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly. The first CNN network is implemented to segment lesions accurately,… More
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  • Peristaltic Flow of Dusty Nanofluids in Curved Channels
  • Abstract In this paper, numerical investigations for peristaltic motion of dusty nanofluids in a curved channel are performed. Two systems of partial differential equations are presented for the nanofluid and dusty phases and then the approximations of the long wave length and low Reynolds number are applied. The physical domain is transformed to a rectangular computational model using suitable grid transformations. The resulting systems are solved numerically using shooting method and mathematical forms for the pressure distributions are introduced. The controlling parameters in this study are the thermal buoyancy parameter Gr, the concentration buoyancy parameter Gc, the amplitude ratio ϵ, the… More
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  • MEIM: A Multi-Source Software Knowledge Entity Extraction Integration Model
  • Abstract Entity recognition and extraction are the foundations of knowledge graph construction. Entity data in the field of software engineering come from different platforms and communities, and have different formats. This paper divides multi-source software knowledge entities into unstructured data, semi-structured data and code data. For these different types of data, Bi-directional Long ShortTerm Memory (Bi-LSTM) with Conditional Random Field (CRF), template matching, and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model (MEIM) to extract software entities. The model can be updated continuously based on user’s feedbacks to improve the accuracy. To deal… More
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  • An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots
  • Abstract As the agricultural internet of things (IoT) technology has evolved, smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments. In this paper, we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots, which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters. First, the speeded-up robust feature (SURF) extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular… More
  •   Views:598       Downloads:318        Download PDF
  • A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China
  • Abstract The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam, and an accurate prediction of valley deformation (VD) remains a challenging part of risk mitigation. In order to enhance the accuracy of VD prediction, a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising (EEMD-ITD), Differential evolutions—Shuffled frog leaping algorithm (DE-SFLA) and Least squares support vector machine (LSSVM) is proposed. The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD; then, ITD is applied for redundant information denoising on special sub-series, and the denoised deformation is… More
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