Vol.67, No.1, 2021-Table of Contents
  • Non-Associative Algebra Redesigning Block Cipher with Color Image Encryption
  • Abstract The substitution box (S-box) is a fundamentally important component of symmetric key cryptosystem. An S-box is a primary source of non-linearity in modern block ciphers, and it resists the linear attack. Various approaches have been adopted to construct S-boxes. S-boxes are commonly constructed over commutative and associative algebraic structures including Galois fields, unitary commutative rings and cyclic and non-cyclic finite groups. In this paper, first a non-associative ring of order 512 is obtained by using computational techniques, and then by this ring a triplet of 8 × 8 S-boxes is designed. The motivation behind the designing of these S-boxes is… More
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  • SAPEM: Secure Attestation of Program Execution and Program Memory for IoT Applications
  • Abstract Security is one of the major challenges that devices connected to the Internet of Things (IoT) face today. Remote attestation is used to measure these devices’ trustworthiness on the network by measuring the device platform’s integrity. Several software-based attestation mechanisms have been proposed, but none of them can detect runtime attacks. Although some researchers have attempted to tackle these attacks, the proposed techniques require additional secured hardware parts to be integrated with the attested devices to achieve their aim. These solutions are expensive and not suitable in many cases. This paper proposes a dual attestation process, SAPEM, with two phases:… More
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  • A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis
  • Abstract Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy… More
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  • Application of Metaheuristic Algorithms for Optimizing Longitudinal Square Porous Fins
  • Abstract The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms. A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 method. The temperature of the base surface is higher than the fin surface, and the fin tip is kept adiabatic or cooled by convection heat transfer. The other pertinent parameters include Rayleigh number (100 ≤ Ra ≤ 104), Darcy number, (10−4 ≤ Da ≤ 10−2), relative thermal conductivity ratio of solid phase to fluid (1000 ≤ kr ≤ 8000), Nusselt number (10 ≤ Nu ≤ 100), porosity (0.1 ≤ φ ≤ 0.9).… More
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  • Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review
  • Abstract Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles… More
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  • Medical Diagnosis Using Machine Learning: A Statistical Review
  • Abstract Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry… More
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  • A Blockchain Based Framework for Stomach Abnormalities Recognition
  • Abstract Wireless Capsule Endoscopy (WCE) is an imaging technology, widely used in medical imaging for stomach infection recognition. However, a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured. The privacy of patients is very important and manual inspection is time consuming and costly. Therefore, an automated system for recognition of stomach infections from WCE frames is always needed. An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding. Initially, images are normalized in fixed dimension and… More
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  • Technology Provides Better Document Search Results on Slovak Legislation Webpage as Result of a Simulation of Webpage Performance Parameters
  • Abstract This article acquaints the public with the insights gained from conducting document searches in the Slovak public administration information system, when supported by knowledge of its management. Additionally, it discusses the advantages of simulating performance parameters and comparing the obtained results with the real parameters of the eZbierka (eCollection) legislation webpage. This comparison was based upon simulated results, obtained through the Gatling simulation tool, versus those obtained from measuring the properties of the public administration legislation webpage. Both sets of data (simulated and real), were generated via the the document search technologies in place on the eZbierka legislation webpage. The… More
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  • A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation
  • Abstract Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and… More
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  • Estimating Security Risk of Healthcare Web Applications: A Design Perspective
  • Abstract In the recent years, the booming web-based applications have attracted the hackers’ community. The security risk of the web-based hospital management system (WBHMS) has been increasing rapidly. In the given context, the main goal of all security professionals and website developers is to maintain security divisions and improve on the user’s confidence and satisfaction. At this point, the different WBHMS tackle different types of security risks. In WBHMS, the security of the patients’ medical information is of utmost importance. All in all, there is an inherent security risk of data and assets in the field of the medical industry as… More
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  • Blockchain-Enabled EHR Framework for Internet of Medical Things
  • Abstract The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for healthcare services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services. The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses. However, unless the underlying commination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients. Patients also lose full control of their EHR since most healthcare services within… More
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  • A Secure NDN Framework for Internet of Things Enabled Healthcare
  • Abstract Healthcare is a binding domain for the Internet of Things (IoT) to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet. The current IP-based Internet architecture suffers from latency, mobility, location dependency, and security. The Named Data Networking (NDN) has been projected as a future internet architecture to cope with the limitations of IP-based Internet. However, the NDN infrastructure does not have a secure framework for IoT healthcare information. In this paper, we proposed a secure NDN framework for IoT-enabled Healthcare (IoTEH). In the proposed work, we adopt the services of Identity-Based Signcryption… More
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  • A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing
  • Abstract Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor networks. Quantum computing, theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or communication. In this paper, we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model, which is able to be accelerated by quantum algorithms. A novel multi-party phase estimation algorithm… More
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  • Optimal Resource Allocation and Quality of Service Prediction in Cloud
  • Abstract In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients. Resources are in self-administration; consequently, clients can adjust their usage according to their requirements. Resource usage is estimated and clients can pay according to their utilization. In literature, the existing method describes the usage of various hardware assets. Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources. Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS.… More
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  • A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data
  • Abstract Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the… More
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  • A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything
  • Abstract Internet of Everything (IoE) indicates a fantastic vision of the future, where everything is connected to the internet, providing intelligent services and facilitating decision making. IoE is the collection of static and moving objects able to coordinate and communicate with each other. The moving objects may consist of ground segments and flying segments. The speed of flying segment e.g., Unmanned Ariel Vehicles (UAVs) may high as compared to ground segment objects. The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE (Ue-IoE). The routing maintenance overhead may increase when scaling the Ue-IoE (number of… More
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  • Detection Technique of Software-Induced Rowhammer Attacks
  • Abstract Side-channel attacks have recently progressed into software-induced attacks. In particular, a rowhammer attack, which exploits the characteristics of dynamic random access memory (DRAM), can quickly and continuously access the cells as the cell density of DRAM increases, thereby generating a disturbance error affecting the neighboring cells, resulting in bit flips. Although a rowhammer attack is a highly sophisticated attack in which disturbance errors are deliberately generated into data bits, it has been reported that it can be exploited on various platforms such as mobile devices, web browsers, and virtual machines. Furthermore, there have been studies on bypassing the defense measures… More
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  • Information Theoretic Weighted Fuzzy Clustering Ensemble
  • Abstract In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means (RBFCM),… More
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  • Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach
  • Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a… More
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  • Computation Analysis of Brand Experience Dimensions: Indian Online Food Delivery Platforms
  • Abstract Online Food Delivery Platforms (OFDPs) has witnessed phenomenal growth in the past few years, especially this year due to the COVID-19 pandemic. This Pandemic has forced many governments across the world to give momentum to OFD services and make their presence among the customers. The Presence of several multinational and national companies in this sector has enhanced the competition and companies are trying to adapt various marketing strategies and exploring the brand experience (BEX) dimension that helps in enhancing the brand equity (BE) of OFDPs. BEXs are critical for building brand loyalty (BL) and making companies profitable. Customers can experience… More
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  • Efficient UAV Communications: Recent Trends and Challenges
  • Abstract Unmanned Ariel Vehicles (UAVs) are flying objects whose trajectory can be remotely controlled. UAVs have lot of potential applications in the areas of wireless communications, internet of things, security, traffic management, monitoring, and smart surveying. By enabling reliable communication between UAVs and ground nodes, emergency notifications can be efficiently and quickly disseminated to a wider area. UAVs can gather data from remote areas, industrial units, and emergency scenarios without human involvement. UAVs can support ubiquitous connectivity, green communications, and intelligent wireless resource management. To efficiently use UAVs for all these applications, important challenges need to be investigated. In this paper,… More
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  • Novel Analytical Thermal Performance Rate Analysis in ZnO-SAE50 Nanolubricant: Nonlinear Mathematical Model
  • Abstract The investigation of local thermal transport rate in the nanolubricants is significant. These lubricants are broadly used in environmental pollution, mechanical engineering and in the paint industry due to high thermal performance rate. Therefore, thermal transport in ZnO-SAE50 nanolubricant under the impacts of heat generation/absorption is conducted. The colloidal suspension is flowing between parallel stretching disks in which the lower disk is positioned at z = 0 and upper disk apart from distance d. The problem is transformed in dimensionless version via described similarity transforms. In the next stage, an analytical technique (VPM) is implemented for the solution purpose. The… More
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  • Accurate Fault Location Modeling for Parallel Transmission Lines Considering Mutual Effect
  • Abstract A new accurate algorithms based on mathematical modeling of two parallel transmissions lines system (TPTLS) as influenced by the mutual effect to determine the fault location is discussed in this work. The distance relay measures the impedance to the fault location which is the positive-sequence. The principle of summation the positive-, negative-, and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS. Also, the impedance of the transmission line to the fault location is determined. These algorithms are applied to single-line-to-ground (SLG) and double-line-to-ground (DLG) faults. To detect the fault location along the transmission… More
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  • Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic
  • Abstract Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach… More
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  • Suitability of VVC and HEVC for Video Telehealth Systems
  • Abstract Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the impact of compression artefacts on… More
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  • Tele-COVID: A Telemedicine SOA-Based Architectural Design for COVID-19 Patients
  • Abstract In Wuhan, China, a novel Corona Virus (COVID-19) was detected in December 2019; it has changed the entire world and to date, the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died. This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients. One of the precautionary measures for COVID-19 patients is isolation. To support this, there is an urgent need for a platform that makes treatment possible from a distance. Telemedicine systems have been drastically increasing in number and size over recent years. This increasing number intensifies the… More
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  • The Interaction between Microcapsules with Different Sizes and Propagating Cracks
  • Abstract The microcapsule-contained self-healing materials are appealing since they can heal the cracks automatically and be effective for a long time. Although many experiments have been carried out, the influence of the size of microcapsules on the self-healing effect is still not well investigated. This study uses the two-dimensional discrete element method (DEM) to investigate the interaction between one microcapsule and one microcrack. The influence of the size of microcapsules is considered. The potential healing time and the influence of the initial damage are studied. The results indicate that the coalescence crack is affected by the size of holes. The elastic… More
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  • Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning
  • Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More
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  • Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network
  • Abstract Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse coupled neural network (IPCNN) algorithm,… More
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  • Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City
  • Abstract The world is rapidly changing with the advance of information technology. The expansion of the Internet of Things (IoT) is a huge step in the development of the smart city. The IoT consists of connected devices that transfer information. The IoT architecture permits on-demand services to a public pool of resources. Cloud computing plays a vital role in developing IoT-enabled smart applications. The integration of cloud computing enhances the offering of distributed resources in the smart city. Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability, security, performance, confidentiality, and privacy. The key reason… More
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  • Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms
  • Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More
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  • Hacking Anti-Shoplifting System to Hide Data within Clothes
  • Abstract Steganography has been used to prevent unauthorized access to private information during transmission. It is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, or text. Current steganography methods are working by assigning a cover file then embed the payload within it by making some modifications, creating the stego-file. However, the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload. Aiming to solve this issue, a novel, highly robust steganography method based on hacking anti-shoplifting systems has proposed to hide data within clothes. The… More
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  • Dynamical Behaviors of Nonlinear Coronavirus (COVID-19) Model with Numerical Studies
  • Abstract The development of mathematical modeling of infectious diseases is a key research area in various fields including ecology and epidemiology. One aim of these models is to understand the dynamics of behavior in infectious diseases. For the new strain of coronavirus (COVID-19), there is no vaccine to protect people and to prevent its spread so far. Instead, control strategies associated with health care, such as social distancing, quarantine, travel restrictions, can be adopted to control the pandemic of COVID-19. This article sheds light on the dynamical behaviors of nonlinear COVID-19 models based on two methods: the homotopy perturbation method (HPM)… More
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  • Survey of Robotics in Education, Taxonomy, Applications, and Platforms during COVID-19
  • Abstract The coronavirus disease 2019 (COVID-19) is characterized as a disease caused by a novel coronavirus known as severe acute respiratory coronavirus syndrome 2 (SARS-CoV-2; formerly known as 2019-nCoV). In December 2019, COVID-19 began to appear in a few countries. By the beginning of 2020, it had spread to most countries across the world. This is when education challenges began to arise. The COVID-19 crisis led to the closure of thousands of schools and universities all over the world. Such a situation requires reliance on e-learning and robotics education for students to continue their studies to avoid the mingling between people… More
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  • Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature
  • Abstract Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from… More
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  • An Efficient Sound and Data Steganography Based Secure Authentication System
  • Abstract The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks. Further, the pin-based security system is an inadequate mechanism for handling such a scenario. The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data. This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound, thereby disregarding the pins’ manual verification. Further, the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches. Firstly, a random signal is encrypted, and then it is transformed into… More
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  • On the Genesis of the Marshall-Olkin Family of Distributions via the T-X Family Approach: Statistical Modeling
  • Abstract In the last couple of years, there Has been an increased interest among the statisticians to define new families of distributions by adding one or more additional parameter(s) to the baseline distribution. In this regard, a number of families have been introduced and studied. One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions. Whenever, we see a new method, the natural questions come in to mind are (i) what are the genesis of the newly proposed method and (ii) how did the proposed method is obtained.… More
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  • Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection
  • Abstract Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial… More
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  • Secure Cloud Data Storage System Using Hybrid Paillier–Blowfish Algorithm
  • Abstract Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically reconfigured in order to deliver optimum resource utilization by exploiting the pay-per-use model. However, concerns around security have been an impediment in the extensive adoption of the cloud computing model. In this regard, advancements in cryptography, accelerated by the wide usage of the internet worldwide, has emerged as a key area in addressing some of these security concerns. In this document, a hybrid cryptographic protocol deploying Blowfish and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption… More
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  • Flower Pollination Heuristics for Nonlinear Active Noise Control Systems
  • Abstract Abstract In this paper, a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems. The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal, random and complex random signals as noise interferences. The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series. The comparative study on statistical observations in terms of accuracy, convergence and complexity measures… More
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  • Geospatial Analytics for COVID-19 Active Case Detection
  • Abstract Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with… More
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  • Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering
  • Abstract The controller is indispensable in software-defined networking (SDN). With several features, controllers monitor the network and respond promptly to dynamic changes. Their performance affects the quality-of-service (QoS) in SDN. Every controller supports a set of features. However, the support of the features may be more prominent in one controller. Moreover, a single controller leads to performance, single-point-of-failure (SPOF), and scalability problems. To overcome this, a controller with an optimum feature set must be available for SDN. Furthermore, a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN. Herein, leveraging an analytical network process… More
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  • SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data
  • Abstract The growing collection of scientific data in various web repositories is referred to as Scientific Big Data, as it fulfills the four “V’s” of Big Data–-volume, variety, velocity, and veracity. This phenomenon has created new opportunities for startups; for instance, the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs. Traditionally, the content of the papers are compared to list the relevant papers from a repository. The conventional method results in a long list of papers that is often impossible to interpret productively. Therefore, the need for… More
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  • Smart Object Detection and Home Appliances Control System in Smart Cities
  • Abstract During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.… More
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  • Generic Attribute Scoring for Information Decay in Threat Information Sharing Platform
  • Abstract Cyber Threat Intelligence (CTI) has gained massive attention to collect hidden knowledge for a better understanding of the various cyber-attacks and eventually paving the way for predicting the future of such attacks. The information exchange and collaborative sharing through different platforms have a significant contribution towards a global solution. While CTI and the information exchange can help a lot in focusing and prioritizing on the use of the large volume of complex information among different organizations, there exists a great challenge ineffective processing of large count of different Indicators of Threat (IoT) which appear regularly, and that can be solved… More
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  • AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories
  • Abstract The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of… More
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  • An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network
  • Abstract Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can predict diabetes more and more… More
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  • Modeling COVID-19 Pandemic Dynamics in Two Asian Countries
  • Abstract The current epidemic outbreak COVID-19 first took place in the Wuhan city of China and then spread worldwide. This deadly disease affected millions of people and compelled the governments and other concerned institutions to take serious actions. Around 0.28 million people have died from the COVID-19 outbreak as of May 11, 2020, 05:41 GMT, and the number is still increasing exponentially. The results of any scientific investigation of this phenomenon are still to come. However, now it is urgently needed to evaluate and compare the disease dynamics to improve the quarantine activities and the level of individual protection, to at… More
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  • Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms
  • Abstract After the digital revolution, large quantities of data have been generated with time through various networks. The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques. While Intrusion Detection Systems (IDSs) secure resources against threats, they still face challenges in improving detection accuracy, reducing false alarm rates, and detecting the unknown ones. This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection. Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset. Our study focuses… More
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  • Power Inverted Topp–Leone Distribution in Acceptance Sampling Plans
  • Abstract We introduce a new two-parameter model related to the inverted Topp–Leone distribution called the power inverted Topp–Leone (PITL) distribution. Major properties of the PITL distribution are stated; including; quantile measures, moments, moment generating function, probability weighted moments, Bonferroni and Lorenz curve, stochastic ordering, incomplete moments, residual life function, and entropy measure. Acceptance sampling plans are developed for the PITL distribution, when the life test is truncated at a pre-specified time. The truncation time is assumed to be the median lifetime of the PITL distribution with pre-specified factors. The minimum sample size necessary to ensure the specified life test is obtained… More
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  • Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning
  • Abstract Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis (CAD) systems. This study explores… More
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  • An Ontology Based Test Case Prioritization Approach in Regression Testing
  • Abstract Regression testing is a widely studied research area, with the aim of meeting the quality challenges of software systems. To achieve a software system of good quality, we face high consumption of resources during testing. To overcome this challenge, test case prioritization (TCP) as a sub-type of regression testing is continuously investigated to achieve the testing objectives. This study provides an insight into proposing the ontology-based TCP (OTCP) approach, aimed at reducing the consumption of resources for the quality improvement and maintenance of software systems. The proposed approach uses software metrics to examine the behavior of classes of software systems.… More
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  • Analysis of Magnetic Resistive Flow of Generalized Brinkman Type Nanofluid Containing Carbon Nanotubes with Ramped Heating
  • Abstract In recent times, scientists and engineers have been most attracted to electrically conducted nanofluids due to their numerous applications in various fields of science and engineering. For example, they are used in cancer treatment (hyperthermia), magnetic resonance imaging (MRI), drug-delivery, and magnetic refrigeration (MR). Bearing in mind the significance and importance of electrically conducted nanofluids, this article aims to study an electrically conducted water-based nanofluid containing carbon nanotubes (CNTs). CNTs are of two types, single-wall carbon nanotubes (SWCNTs) and multiple-wall carbon nanotubes (MWCNTs). The CNTs (SWCNTs and MWCNTs) have been dispersed in regular water as base fluid to form water-CNTs… More
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  • Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns
  • Abstract Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation… More
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  • Authenblue: A New Authentication Protocol for the Industrial Internet of Things
  • Abstract The Internet of Things (IoT) is where almost anything can be controlled and managed remotely by means of sensors. Although the IoT evolution led to quality of life enhancement, many of its devices are insecure. The lack of robust key management systems, efficient identity authentication, low fault tolerance, and many other issues lead to IoT devices being easily targeted by attackers. In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network (CPANs) in an Industrial IoT (IIoT) environment. This study proposed Authenblue protocol as a… More
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  • A Novel Broadband Antenna Design for 5G Applications
  • Abstract Wireless communication is one of the rapidly-growing fields of the communication industry. This continuous growth motivates the antenna community to design new radiating structures to meet the needs of the market. The 5G wireless communication has received a lot of attention from both academia and industry and significant efforts have been made to improve different aspects, such as data rate, latency, mobility, reliability and QoS. Antenna design has received renewed attention in the last decade due to its potential applications in 5G, IoT, mmWave, and massive MIMO. This paper proposes a novel design of broadband antenna for 5G mmWave and… More
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  • Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs
  • Abstract Routing protocols in Mobile Ad Hoc Networks (MANETs) operate with Expanding Ring Search (ERS) mechanism to avoid flooding in the network while tracing step. ERS mechanism searches the network with discerning Time to Live (TTL) values described by respective routing protocol that save both energy and time. This work exploits the relation between the TTL value of a packet, traffic on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior (AERSPHB) rendition of requests handling. Each search request is classified based on ERS attributes and then processed for routing while monitoring… More
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  • COVID-19 and Unemployment: A Novel Bi-Level Optimal Control Model
  • Abstract Since COVID-19 was declared as a pandemic in March 2020, the world’s major preoccupation has been to curb it while preserving the economy and reducing unemployment. This paper uses a novel Bi-Level Dynamic Optimal Control model (BLDOC) to coordinate control between COVID-19 and unemployment. The COVID-19 model is the upper level while the unemployment model is the lower level of the bi-level dynamic optimal control model. The BLDOC model’s main objectives are to minimize the number of individuals infected with COVID-19 and to minimize the unemployed individuals, and at the same time minimizing the cost of the containment strategies. We… More
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  • Toward the Optimization of the Region-Based P300 Speller
  • Abstract Technology has tremendously contributed to improving communication and facilitating daily activities. Brain-Computer Interface (BCI) study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis (ALS). However, with the advancements in cost-effective electronics and computer interface equipment, the BCI study is flourishing, and the exploration of BCI applications for people without disabilities, to enhance normal functioning, is increasing. Particularly, the P300-based spellers are among the most promising applications of the BCI technology. In this context, the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem… More
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  • An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment
  • Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of… More
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  • Optimal Reordering Trace Files for Improving Software Testing Suitcase
  • Abstract An invariant can be described as an essential relationship between program variables. The invariants are very useful in software checking and verification. The tools that are used to detect invariants are invariant detectors. There are two types of invariant detectors: dynamic invariant detectors and static invariant detectors. Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm. Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program; then, it gathers the values of its variables, and finally, it detects relationships between the variables based on a simple… More
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  • Identifying Driver Genes Mutations with Clinical Significance in Thyroid Cancer
  • Abstract Advances in technology are enabling gene mutations in papillary thyroid carcinoma (PTC) to be analyzed and clinical outcomes, such as recurrence, to be predicted. To date, the most common genetic mutation in PTC is in BRAF kinase (BRAF). However, whether mutations in other genes coincide with those in BRAF remains to be clarified. The aim of this study was to find mutations in other genes that co-exist with mutated BRAF, and to analyze their frequency and clinical relevance in PTC. Clinical and genetic data were collected from 213 PTC patients with a total of 36,572 mutation sites in 735 genes.… More
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  • High Order Block Method for Third Order ODEs
  • Abstract Many initial value problems are difficult to be solved using ordinary, explicit step-by-step methods because most of these problems are considered stiff. Certain implicit methods, however, are capable of solving stiff ordinary differential equations (ODEs) usually found in most applied problems. This study aims to develop a new numerical method, namely the high order variable step variable order block backward differentiation formula (VSVO-HOBBDF) for the main purpose of approximating the solutions of third order ODEs. The computational work of the VSVO-HOBBDF method was carried out using the strategy of varying the step size and order in a single code. The… More
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  • Intelligent Software-Defined Network for Cognitive Routing Optimization Using Deep Extreme Learning Machine Approach
  • Abstract In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN’s characteristics (e.g., logically centralized control, global network view, software-based site visitor analysis, and dynamic updating of… More
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  • Predicting the Electronic and Structural Properties of Two-Dimensional Materials Using Machine Learning
  • Abstract Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known… More
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  • Load Balancing Algorithm for Migrating Switches in Software-Defined Vehicular Networks
  • Abstract In Software-Defined Networks (SDN), the divergence of the control interface from the data plane provides a unique platform to develop a programmable and flexible network. A single controller, due to heavy load traffic triggered by different intelligent devices can not handle due to it’s restricted capability. To manage this, it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness. The flow of data through the multiple controllers also varies, resulting in an unequal distribution of load between different controllers. One major drawback of the multiple controllers is their constant configuration of the… More
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  • Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks
  • Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More
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