Vol.70, No.1, 2022-Table of Contents
  • Measuring End-to-End Delay in Low Energy SDN IoT Platform
  • Abstract In this paper, we developed a new customizable low energy Software Defined Networking (SDN) based Internet of Things (IoT) platform that can be reconfigured according to the requirements of the target IoT applications. Technically, the platform consists of a set of low cost and energy efficient single-board computers, which are interconnected within a network with the software defined configuration. The proposed SDN switch is deployed on Raspberry Pi 3 board using Open vSwitch (OvS) software, while the Floodlight controller is deployed on the Orange Pi Prime board. We firstly presented and implemented the method for measuring a delay introduced by… More
  •   Views:345       Downloads:263        Download PDF
  • Deep Optimal VGG16 Based COVID-19 Diagnosis Model
  • Abstract Coronavirus (COVID-19) outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up Robust Features (SURF), Features from… More
  •   Views:358       Downloads:250        Download PDF
  • Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic
  • Abstract During COVID-19, the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand. This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals, pharmacies, and retail stores as its customers. Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers. A questionnaire has been… More
  •   Views:432       Downloads:213        Download PDF
  • A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data
  • Abstract Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify… More
  •   Views:288       Downloads:212        Download PDF
  • A Hybrid Approach for Network Intrusion Detection
  • Abstract Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination… More
  •   Views:363       Downloads:212        Download PDF
  • Joint Channel and Multi-User Detection Empowered with Machine Learning
  • Abstract The numbers of multimedia applications and their users increase with each passing day. Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems. In this article, a fuzzy logic empowered adaptive backpropagation neural network (FLeABPNN) algorithm is proposed for joint channel and multi-user detection (CMD). FLeABPNN has two stages. The first stage estimates the channel parameters, and the second performs multi-user detection. The proposed approach capitalizes on a neuro-fuzzy hybrid system that combines the competencies of both fuzzy logic and neural networks. This study analyzes the… More
  •   Views:270       Downloads:168        Download PDF
  • Amino Acid Encryption Method Using Genetic Algorithm for Key Generation
  • Abstract In this new information era, the transfer of data and information has become a very important matter. Transferred data must be kept secured from unauthorized persons using cryptography. The science of cryptography depends not only on complex mathematical models but also on encryption keys. Amino acid encryption is a promising model for data security. In this paper, we propose an amino acid encryption model with two encryption keys. The first key is generated randomly using the genetic algorithm. The second key is called the protein key which is generated from converting DNA to a protein message. Then, the protein message… More
  •   Views:295       Downloads:185        Download PDF
  • A Robust MPPT Control Based on Double Ended Forward Converter Architecture
  • Abstract

    In this paper, a stand-alone photovoltaic (PV) system based on a Double Ended Forward Converter (DEFC) is presented. The proposed converter is specified for 48 V, 100 W applications as most of the equipment used in telecommunication and aircraft fall in this range. The literature has limited potential application of DEFC in PV systems. The research work deals with an in-depth study of DEFC and proposes an improved DEFC for PV applications with battery backup. Besides, a bi-directional dc-dc converter for the battery is integrated to track the Maximum Power Point (MPP) of the PV generator. The converter is examined… More

  •   Views:204       Downloads:156        Download PDF
  • Design of a Low-Cost Air Quality Monitoring System Using Arduino and ThingSpeak
  • Abstract The impact of daily emissions of gaseous and particulate pollutants of machines and industries on human health and the environment has attracted increasing concerns. This impact has significantly led to a notable increase in mortality in the highly industrialized zones. Therefore, monitoring air quality and creating public awareness are important for a safer future, which led the governments globally to invest multi-billion in policymaking and solution stratification to address the problem. This study aims to design a real-time Internet of Things low-cost air quality monitoring system. The system utilizes air quality and carbon monoxide sensors for monitoring gaseous pollutants. Moreover,… More
  •   Views:296       Downloads:253        Download PDF
  • Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems
  • Abstract This paper addresses improvements in fractional order (FO) system performance. Although the classical proportional–integral–derivative (PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems, the FOPID fuzzy controller has been proven to provide better results. This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques. This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer, social spider optimization, in order to improve the response of fractional dynamical systems. This group of systems had… More
  •   Views:242       Downloads:168        Download PDF
  • Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks
  • Abstract The development of intelligent algorithms for controlling autonom- ous mobile robots in real-time activities has increased dramatically in recent years. However, conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories. The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time. The proposed system is implemented using the AlphaBot platform, and the performance of the system is evaluated according to the obstacle prediction accuracy, path detection accuracy, time-lapse,… More
  •   Views:327       Downloads:188        Download PDF
  • Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA
  • Abstract Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by… More
  •   Views:199       Downloads:145        Download PDF
  • Empirical Assessment of Bacillus Calmette-Guérin Vaccine to Combat COVID-19
  • Abstract COVID-19 has become one of the critical health issues globally, which surfaced first in latter part of the year 2019. It is the topmost concern for many nations’ governments as the contagious virus started mushrooming over adjacent regions of infected areas. In 1980, a vaccine called Bacillus Calmette-Guérin (BCG) was introduced for preventing tuberculosis and lung cancer. Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory. This paper’s initial research shows that the countries with a long-term compulsory BCG vaccination system are less affected by… More
  •   Views:380       Downloads:182        Download PDF
  • A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment
  • Abstract Nowadays, healthcare has become an important area for the Internet of Things (IoT) to automate healthcare facilities to share and use patient data anytime and anywhere with Internet services. At present, the host-based Internet paradigm is used for sharing and accessing healthcare-related data. However, due to the location-dependent nature, it suffers from latency, mobility, and security. For this purpose, Named Data Networking (NDN) has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet paradigm. Unfortunately, the novel breed lacks a secure framework for healthcare. This article constructs an NDN-Based Internet of Medical Things… More
  •   Views:246       Downloads:175        Download PDF
  • Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks
  • Abstract Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN’s current and future… More
  •   Views:265       Downloads:172        Download PDF
  • Stock Prediction Based on Technical Indicators Using Deep Learning Model
  • Abstract Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the… More
  •   Views:291       Downloads:214        Download PDF
  • Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks
  • Abstract Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find… More
  •   Views:238       Downloads:158        Download PDF
  • Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets
  • Abstract Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it… More
  •   Views:225       Downloads:152        Download PDF
  • Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids
  • Abstract Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their… More
  •   Views:366       Downloads:233        Download PDF
  • Improving Routine Immunization Coverage Through Optimally Designed Predictive Models
  • Abstract Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to… More
  •   Views:401       Downloads:148        Download PDF
  • Optimization Model for Selecting Temporary Hospital Locations During COVID-19 Pandemic
  • Abstract The two main approaches that countries are using to ease the strain on healthcare infrastructure is building temporary hospitals that are specialized in treating COVID-19 patients and promoting preventive measures. As such, the selection of the optimal location for a temporary hospital and the calculation of the prioritization of preventive measures are two of the most critical decisions during the pandemic, especially in densely populated areas where the risk of transmission of the virus is highest. If the location selection process or the prioritization of measures is poor, healthcare workers and patients can be harmed, and unnecessary costs may come… More
  •   Views:287       Downloads:154        Download PDF
  • Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems
  • Abstract The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach… More
  •   Views:286       Downloads:202        Download PDF
  • Isomorphic 2D/3D Objects and Saccadic Characteristics in Mental Rotation
  • Abstract Mental rotation (MR) is an important aspect of cognitive processing in gaming since transformation and manipulation of visuospatial information are necessary in order to execute a gaming task. This study provides insights on saccadic characteristics in gaming task performance that involves 2D and 3D isomorphic objects with varying angular disparity. Healthy participants (N =60) performed MR gaming task. Each participant was tested individually in an acoustic treated lab environment. Gaze behavior data of all participants were recorded during task execution and analyzed to find the changes in spatiotemporal characteristics of saccades associated with the variation in angular disparity and dimensionality. There… More
  •   Views:190       Downloads:152        Download PDF
  • Deep Learning Approach for Analysis and Characterization of COVID-19
  • Abstract Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images… More
  •   Views:368       Downloads:239        Download PDF
  • Hybrid Computational Modeling for Web Application Security Assessment
  • Abstract Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era. This has led to digital revolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remains a serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses as well. Most of the data breaches are financially motivated, especially in the healthcare sector. The cyber invaders continuously penetrate the… More
  •   Views:324       Downloads:177        Download PDF
  • Efficient Gauss-Seidel Precoding with Parallel Calculation in Massive MIMO Systems
  • Abstract A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output (MIMO) systems. The inter user interference (IUI) which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is used. In this paper, the horizontal Gauss-Seidel (HGS) method is proposed as precoding scheme in massive MIMO systems. In massive MIMO systems, the exact inversion of channel matrix is impractical due to the severe computational complexity. Therefore, the conventional Gauss-Seidel (GS) method is used to approximate the inversion of channel matrix. The GS has good performance by using previous calculation results as… More
  •   Views:208       Downloads:142        Download PDF
  • Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator
  • Abstract Image translation plays a significant role in realistic image synthesis, entertainment tasks such as editing and colorization, and security including personal identification. In Edge GAN, the major contribution is attribute guided vector that enables high visual quality content generation. This research study proposes automatic face image realism from freehand sketches based on Edge GAN. We propose a density variant image synthesis model, allowing the input sketch to encompass face features with minute details. The density level is projected into non-latent space, having a linear controlled function parameter. This assists the user to appropriately devise the variant densities of facial sketches… More
  •   Views:219       Downloads:158        Download PDF
  • Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI
  • Abstract For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental… More
  •   Views:256       Downloads:160        Download PDF
  • BHGSO: Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem
  • Abstract In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the… More
  •   Views:506       Downloads:175        Download PDF
  • Ambiguity Resolution in Direction of Arrival Estimation with Linear Antenna Arrays Using Differential Geometry
  • Abstract Linear antenna arrays (LAs) can be used to accurately predict the direction of arrival (DOAs) of various targets of interest in a given area. However, under certain conditions, LA suffers from the problem of ambiguities among the angles of targets, which may result in misinterpretation of such targets. In order to cope up with such ambiguities, various techniques have been proposed. Unfortunately, none of them fully resolved such a problem because of rank deficiency and high computational cost. We aimed to resolve such a problem by proposing an algorithm using differential geometry. The proposed algorithm uses a specially designed doublet… More
  •   Views:267       Downloads:212        Download PDF
  • A Secure Communication Protocol for Unmanned Aerial Vehicles
  • Abstract Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles. Multiple UAVs and autopilot systems support it, and it provides bi-directional communication between the UAV and Ground Control Station. The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS. In order to increase the transfer speed and efficiency, the Mavlink does not encrypt the messages. As a result, the protocol is vulnerable to various security attacks such as Eavesdropping, GPS Spoofing, and DDoS. In this study, we tackle the problem and secure… More
  •   Views:204       Downloads:351        Download PDF
  • Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks
  • Abstract As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore,… More
  •   Views:226       Downloads:173        Download PDF
  • Towards Aspect Based Components Integration Framework for Cyber-Physical System
  • Abstract Cyber-Physical Systems (CPS) comprise interactive computation, networking, and physical processes. The integrative environment of CPS enables the smart systems to be aware of the surrounding physical world. Smart systems, such as smart health care systems, smart homes, smart transportation, and smart cities, are made up of complex and dynamic CPS. The components integration development approach should be based on the divide and conquer theory. This way multiple interactive components can reduce the development complexity in CPS. As reusability enhances efficiency and consistency in CPS, encapsulation of component functionalities and a well-designed user interface is vital for the better end-user's Quality… More
  •   Views:234       Downloads:142        Download PDF
  • Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages
  • Abstract In the field of natural language processing (NLP), the advancement of neural machine translation has paved the way for cross-lingual research. Yet, most studies in NLP have evaluated the proposed language models on well-refined datasets. We investigate whether a machine translation approach is suitable for multilingual analysis of unrefined datasets, particularly, chat messages in Twitch. In order to address it, we collected the dataset, which included 7,066,854 and 3,365,569 chat messages from English and Korean streams, respectively. We employed several machine learning classifiers and neural networks with two different types of embedding: word-sequence embedding and the final layer of a… More
  •   Views:288       Downloads:159        Download PDF
  • Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread
  • Abstract The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA… More
  •   Views:249       Downloads:153        Download PDF
  • Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network
  • Abstract The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data… More
  •   Views:316       Downloads:175        Download PDF
  • Droid-IoT: Detect Android IoT Malicious Applications Using ML and Blockchain
  • Abstract One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries. Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021. Because of the popularity of the Android OS, it has been targeted by cybercriminals who have introduced a number of issues, such as stealing private information. As reported by one of the recent studies Android malware… More
  •   Views:239       Downloads:173        Download PDF
  • Comparison of Missing Data Imputation Methods in Time Series Forecasting
  • Abstract Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluate and compare the effects of imputation methods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared using each imputation method. Subsequently,… More
  •   Views:253       Downloads:159        Download PDF
  • AI Cannot Understand Memes: Experiments with OCR and Facial Emotions
  • Abstract

    The increasing capabilities of Artificial Intelligence (AI), has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans, which may not always have a positive impact on the society. AI gone rogue, and Technological Singularity are major concerns in academia as well as the industry. It is necessary to identify the limitations of machines and analyze their incompetence, which could draw a line between human and machine intelligence. Internet memes are an amalgam of pictures, videos, underlying messages, ideas, sentiments, humor, and experiences, hence the way an internet… More

  •   Views:196       Downloads:155        Download PDF
  • Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network
  • Abstract Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity… More
  •   Views:214       Downloads:180        Download PDF
  • An Artificial Intelligence Approach for Solving Stochastic Transportation Problems
  • Abstract Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution. Since the parameters are stochastic, the corresponding constraints are probabilistic. They… More
  •   Views:212       Downloads:158        Download PDF
  • Utilization of HEVC ChaCha20-Based Selective Encryption for Secure Telehealth Video Conferencing
  • Abstract Coronavirus (COVID-19) is a contagious disease that causes exceptional effect on healthcare organizations worldwide with dangerous impact on medical services within the hospitals. Because of the fast spread of COVID-19, the healthcare facilities could be a big source of disease infection. So, healthcare video consultations should be used to decrease face-to-face communication between clinician and patients. Healthcare video consultations may be beneficial for some COVID-19 conditions and reduce the need for face-to-face contact with a potentially positive patient without symptoms. These conditions are like top clinicians who provide remote consultations to develop treatment methodology and follow-up remotely, patients who consult… More
  •   Views:249       Downloads:149        Download PDF
  • Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment
  • Abstract Project scheduling is a key objective of many models and is the proposed method for project planning and management. Project scheduling problems depend on precedence relationships and resource constraints, in addition to some other limitations for achieving a subset of goals. Project scheduling problems are dependent on many limitations, including limitations of precedence relationships, resource constraints, and some other limitations for achieving a subset of goals. Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required, which are known and stable during the implementation process. The concept… More
  •   Views:219       Downloads:146        Download PDF
  • Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack
  • Abstract In the design and planning of next-generation Internet of Things (IoT), telecommunication, and satellite communication systems, controller placement is crucial in software-defined networking (SDN). The programmability of the SDN controller is sophisticated for the centralized control system of the entire network. Nevertheless, it creates a significant loophole for the manifestation of a distributed denial of service (DDoS) attack straightforwardly. Furthermore, recently a Distributed Reflected Denial of Service (DRDoS) attack, an unusual DDoS attack, has been detected. However, minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem. Moreover, recently the high frequencies of DDoS attacks have… More
  •   Views:895       Downloads:283        Download PDF
  • Secure and Robust Optical Multi-Stage Medical Image Cryptosystem
  • Abstract Due to the rapid growth of telemedicine and healthcare services, color medical image security applications have been expanded precipitously. In this paper, an asymmetric PTFrFT (Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested. Two different phases in the fractional Fourier and output planes are provided as deciphering keys. Accordingly, the ciphering keys will not be employed for the deciphering procedure. Thus, the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSH (Optical Scanning Holography) and DRPE (Double Random Phase Encoding) algorithms. One of the principal impacts of the introduced… More
  •   Views:204       Downloads:142        Download PDF
  • Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents
  • Abstract In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and… More
  •   Views:348       Downloads:231        Download PDF
  • Stock Market Trading Based on Market Sentiments and Reinforcement Learning
  • Abstract Stock market is a place, where shares of different companies are traded. It is a collection of buyers’ and sellers’ stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends… More
  •   Views:292       Downloads:207        Download PDF
  • Education and the Fourth Industrial Revolution: Lessons from COVID-19
  • Abstract The COVID-19 pandemic has prompted educators to rethink educational practices, especially with regard to technology. The COVID-19 pandemic is a huge challenge to education systems around the world. This Viewpoint offers guidance to teachers, institutional heads, and officials on addressing the crisis. This study investigated technology use in teaching during the COVID-19 lockdown in Malaysia, focusing on technology-based teaching methods, modifications necessitated by this new teaching style, and challenges teachers faced when using technology. Using purposive sampling, a qualitative study was undertaken with a sample of 10 English language teachers from Arabic schools in Malaysia. The results indicated that a… More
  •   Views:336       Downloads:203        Download PDF
  • Enhancing the Robustness of Visual Object Tracking via Style Transfer
  • Abstract The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy… More
  •   Views:521       Downloads:230        Download PDF
  • Improved Bi-Directional Three-Phase Single-Relay Selection Technique for Cooperative Wireless Communications
  • Abstract Single-relay selection techniques based on the max-min criterion can achieve the highest bit error rate (BER) performance with full diversity gain as compared to the state-of-the-art single-relay selection techniques. Therefore, in this work, we propose a modified max-min criterion by considering the differences among the close value channels of all relays while selecting the best relay node. The proposed criterion not only enjoys full diversity gain but also offers a significant improvement in the achievable coding gain as compared to the conventional one. Basically, in this article, an improved bi-directional three-phase single-relay selection technique using the decode-and-forward protocol for wireless… More
  •   Views:170       Downloads:155        Download PDF
  • A Secure and Efficient Cluster-Based Authentication Scheme for Internet of Things (IoTs)
  • Abstract IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) provides IP connectivity to the highly constrained nodes in the Internet of Things (IoTs). 6LoWPAN allows nodes with limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard, thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network. The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES (Advanced Encryption Standard), but the 6LoWPAN standard lacks and has omitted the security and privacy requirements at higher layers. The sensor nodes in… More
  •   Views:424       Downloads:179        Download PDF
  • Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids
  • Abstract Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper… More
  •   Views:229       Downloads:147        Download PDF
  • An Optimized Fuzzy Based Ant Colony Algorithm for 5G-MANET
  • Abstract The 5G demonstrations in a business has a significant role in today's fast-moving technology. Manet in 5G, drives a wireless system intended at an enormously high data rate, lower energy, low latency, and cost. For this reason, routing protocols of MANET have the possibility of being fundamentally flexible, high performance, and energy-efficient. The 5G communication aims to afford higher data rates and significantly low Over-The-Air latency. Motivated through supplementary ACO routing processes, a security-aware, fuzzy improved ant colony routing optimization protocol is proposed in MANETs. The goal is to develop a MANET routing protocol that could provide a stable packet… More
  •   Views:270       Downloads:154        Download PDF
  • Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus
  • Abstract Enterprise Resource Planning (ERP) software is extensively used for the management of business processes. ERP offers a system of integrated applications with a shared central database. Storing all business-critical information in a central place raises various issues such as data integrity assurance and a single point of failure, which makes the database vulnerable. This paper investigates database and Blockchain integration, where the Blockchain network works in synchronization with the database system, and offers a mechanism to validate the transactions and ensure data integrity. Limited research exists on Blockchain-based solutions for the single point of failure in ERP. We established in… More
  •   Views:288       Downloads:174        Download PDF
  • An Efficient Energy Aware Routing Mechanism for Wireless Body Area Networks
  • Abstract The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network (WBAN), which is a technology commonly used in the medical field. WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician, to monitor the patients remotely. These nodes typically have limited battery energy that led to a shortage of network lifetime. Therefore, energy efficiency is considered one of the most demanding challenges in routing design for WBAN. Many proposed routing mechanisms in WBAN did not cover… More
  •   Views:349       Downloads:212        Download PDF
  • QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model
  • Abstract Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network, and users can use them at any time. After assessing and choosing cloud providers, however, customers confront the variety and difficulty of quality of service (QoS). To increase customer retention and engagement success rates, it is critical to research and develops an accurate and objective evaluation model. Cloud is the emerging environment for distributed services at various layers. Due to the benefits of this environment, globally cloud is being taken as a standard environment for individuals as well as for… More
  •   Views:247       Downloads:155        Download PDF
  • Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models
  • Abstract In developing countries, medical diagnosis is expensive and time consuming. Hence, automatic diagnosis can be a good cheap alternative. This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks (CNNs). These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists. The deep CNNs allow direct learning from the medical images. However, the accessibility of classified data is still the largest challenge, particularly in the field of medical imaging. Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs… More
  •   Views:331       Downloads:174        Download PDF
  • Optimized Convolutional Neural Network for Automatic Detection of COVID-19
  • Abstract The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing followed by CNN-based feature… More
  •   Views:257       Downloads:175        Download PDF
  • Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen
  • Abstract The increasing use of fossil fuels has a significant impact on the environment and ecosystem, which increases the rate of pollution. Given the high potential of renewable energy sources in Yemen and the absence of similar studies in the region, this study aims to examine the potential of wind energy in Socotra Island. This was done by analyzing and evaluating wind properties, determining available energy density, calculating wind energy extracted at different altitudes, and then computing the capacity factor for a number of wind turbines and determining the best. The average wind speed in Socotra Island was obtained from the… More
  •   Views:210       Downloads:148        Download PDF
  • Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing
  • Abstract Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%–50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying… More
  •   Views:261       Downloads:210        Download PDF
  • Novel Ransomware Hiding Model Using HEVC Steganography Approach
  • Abstract Ransomware is considered one of the most threatening cyberattacks. Existing solutions have focused mainly on discriminating ransomware by analyzing the apps themselves, but they have overlooked possible ways of hiding ransomware apps and making them difficult to be detected and then analyzed. Therefore, this paper proposes a novel ransomware hiding model by utilizing a block-based High-Efficiency Video Coding (HEVC) steganography approach. The main idea of the proposed steganography approach is the division of the secret ransomware data and cover HEVC frames into different blocks. After that, the Least Significant Bit (LSB) based Hamming Distance (HD) calculation is performed amongst the… More
  •   Views:399       Downloads:163        Download PDF
  • A Modified Search and Rescue Optimization Based Node Localization Technique in WSN
  • Abstract Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN.… More
  •   Views:195       Downloads:157        Download PDF
  • Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment
  • Abstract Internet of Things (IoT) is a technological revolution that redefined communication and computation of modern era. IoT generally refers to a network of gadgets linked via wireless network and communicates via internet. Resource management, especially energy management, is a critical issue when designing IoT devices. Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment. In this point of view, the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e., EECBRM in IoT environment. The proposed EECBRM model has three stages namely, fuzzy logic-based clustering, Lion Whale… More
  •   Views:216       Downloads:161        Download PDF
  • Hierarchical Stream Clustering Based NEWS Summarization System
  • Abstract News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based news collection, news content extraction,… More
  •   Views:196       Downloads:144        Download PDF
  • Network Analysis for Projects with High Risk Levels in Uncertain Environments
  • Abstract The critical path method is one of the oldest and most important techniques used for planning and scheduling projects. The main objective of project management science is to determine the critical path through a network representation of projects. The critical path through a network can be determined by many algorithms and is useful for managing, monitoring, and controlling the time and cost of an entire project. The essential problem in this case is that activity durations are uncertain; time presents considerable uncertainty because the time of an activity is not always easily or accurately estimated. This issue increases the need… More
  •   Views:188       Downloads:144        Download PDF
  • Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model
  • Abstract Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application… More
  •   Views:289       Downloads:153        Download PDF
  • An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications
  • Abstract Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the… More
  •   Views:239       Downloads:147        Download PDF
  • RFID Adaption in Healthcare Organizations: An Integrative Framework
  • Abstract Radio frequency identification (RFID), also known as electronic label technology, is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals. Medical equipment information management is an important part of the construction of a modern hospital, as it is linked to the degree of diagnosis and care, as well as the hospital’s benefits and growth. The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare, as well as to conduct an empirical review of the… More
  •   Views:217       Downloads:164        Download PDF
  • Dynamic Routing Optimization Algorithm for Software Defined Networking
  • Abstract Time and space complexity is the most critical problem of the current routing optimization algorithms for Software Defined Networking (SDN). To overcome this complexity, researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow (OF) based large scale SDNs. This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs. Due to the dynamic nature of SDNs, the proposed algorithm uses a mutation operator to overcome the memory-based problem of the ant colony algorithm. Besides, it uses the box-covering method and the k-means clustering method to divide the SDN network to… More
  •   Views:189       Downloads:136        Download PDF
  • Effectively Handling Network Congestion and Load Balancing in Software-Defined Networking
  • Abstract The concept of Software-Defined Networking (SDN) evolves to overcome the drawbacks of the traditional networks with Internet Protocol (I.P.) packets sending and packets handling. The SDN structure is one of the critical advantages of efficiently separating the data plane from the control plane to manage the network configurations and network management. Whenever there are multiple sending devices inside the SDN network, the OpenFlow switches are programmed to handle the limited number of requests for their interface. When the recommendations are exceeded from the specific threshold, the load on the switches also increases. This research article introduces a new approach named… More
  •   Views:239       Downloads:155        Download PDF
  • A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19
  • Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal… More
  •   Views:264       Downloads:206        Download PDF
  • Classification of Citrus Plant Diseases Using Deep Transfer Learning
  • Abstract In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning… More
  •   Views:246       Downloads:248        Download PDF
  • High Gain of UWB Planar Antenna Utilising FSS Reflector for UWB Applications
  • Abstract In this paper, a high gain and directional coplanar waveguide (CPW)-fed ultra-wideband (UWB) planar antenna with a new frequency selective surface (FSS) unit cells design is proposed for UWB applications. The proposed UWB antenna was designed based on the Mercedes artistic-shaped planar (MAP) antenna. The antenna consisted of a circular ring embedded with three straight legs for antenna impedance bandwidth improvement. The modelled FSS used the integration of a two parallel conductive metallic patch with a circular loop structure. The FSS provided a UWB stopband filter response covering a bandwidth of 10.5 GHz, for frequencies from 2.2 to 12.7 GHz.… More
  •   Views:260       Downloads:212        Download PDF
  • Flow Management Mechanism in Software-Defined Network
  • Abstract Software-defined networking (SDN) is a paradigm shift in modern networking. However, centralised controller architecture in SDN imposed flow setup overhead issue as the control plane handles all flows regardless of size and priority. Existing frameworks strictly reduce control plane overhead and it does not focus on rule placement of the flows itself. Furthermore, existing frameworks do not focus on managing elephant flows like RTSP. Thus, the proposed mechanism will use the flow statistics gathering method such as random packet sampling to determine elephant flow and microflow via a pre-defined threshold. This mechanism will ensure that the control plane works at… More
  •   Views:220       Downloads:168        Download PDF
  • Soil-Structure Interaction Effects on Dynamic Behaviour of Transmission Line Towers
  • Abstract As inferred from earthquake engineering literature, considering soil structure interaction (SSI) effects is important in evaluating the response of transmission line towers (TLT) to dynamic loads such as impulse loads. The proposed study investigates the dynamic effects of SSI on TLT behavior. Linear and non-linear models are studied. In the linear model, the soil is represented by complex impedances, dependent of dynamic frequency, determined from numerical simulations. The nonlinear model considers the soil non-linear behavior in its material constitutive law and foundation uplift in a non-linear time history analysis. The simplified structure behavior of a typical lattice transmission tower is… More
  •   Views:191       Downloads:143        Download PDF
  • Load Balancing Framework for Cross-Region Tasks in Cloud Computing
  • Abstract Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between them. To maximize various performance parameters in cloud computing, researchers suggested various load balancing approaches. To store and access data and services provided by the different service providers through the network over different regions, cloud computing is one of the latest technology systems for both end-users and service providers. The volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been experienced. Users of the cloud are looking for services that are intelligent, and,… More
  •   Views:215       Downloads:165        Download PDF
  • A Cascaded Design of Best Features Selection for Fruit Diseases Recognition
  • Abstract Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms of different fruit diseases and… More
  •   Views:261       Downloads:164        Download PDF
  • High Throughput Scheduling Algorithms for Input Queued Packet Switches
  • Abstract The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications. These switching fabrics are efficiently driven by the deployed scheduling algorithms. In this paper, we proposed two scheduling algorithms for input queued switches whose operations are based on ranking procedures. At first, we proposed a Simple 2-Bit (S2B) scheme which uses binary ranking procedure and queue size for scheduling the packets. Here, the Virtual Output Queue (VOQ) set with maximum number of empty queues receives higher rank than other VOQ’s. Through simulation, we showed S2B has better throughput performance than Highest Ranking First… More
  •   Views:188       Downloads:152        Download PDF
  • Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques
  • Abstract Like the Covid-19 pandemic, smallpox virus infection broke out in the last century, wherein 500 million deaths were reported along with enormous economic loss. But unlike smallpox, the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement in medical aid and diagnostics. Data analytics, machine learning, and automation techniques can help in early diagnostics and supporting treatments of many reported patients. This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques. Our study suggests that using the Prediction and Deconvolutional Modules in combination… More
  •   Views:233       Downloads:179        Download PDF
  • Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification
  • Abstract With the modernization of machine learning techniques in healthcare, different innovations including support vector machine (SVM) have predominantly played a major role in classifying lung cancer, predicting coronavirus disease 2019, and other diseases. In particular, our algorithm focuses on integrated datasets as compared with other existing works. In this study, parallel-based SVM (P-SVM) and multiclass-based multiple submodels (MMSM-SVM) were used to analyze the optimal classification of lung diseases. This analysis aimed to find the optimal classification of lung diseases with id and stages, such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses, respectively. For… More
  •   Views:179       Downloads:140        Download PDF
  • Hyper-Convergence Storage Framework for EcoCloud Correlates
  • Abstract Cloud computing is an emerging domain that is capturing global users from all walks of life—the corporate sector, government sector, and social arena as well. Various cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very swiftly. This enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security, availability, accessibility, and latency. Emerging technology is providing robust solutions to many of our problems, the cloud platform is one of them.… More
  •   Views:201       Downloads:149        Download PDF
  • A Novel Database Watermarking Technique Using Blockchain as Trusted Third Party
  • Abstract With widespread use of relational database in various real-life applications, maintaining integrity and providing copyright protection is gaining keen interest of the researchers. For this purpose, watermarking has been used for quite a long time. Watermarking requires the role of trusted third party and a mechanism to extract digital signatures (watermark) to prove the ownership of the data under dispute. This is often inefficient as lots of processing is required. Moreover, certain malicious attacks, like additive attacks, can give rise to a situation when more than one parties can claim the ownership of the same data by inserting and detecting… More
  •   Views:233       Downloads:161        Download PDF
  • Secure Audio Transmission Over Wireless Uncorrelated Rayleigh Fading Channel
  • Abstract Audio communications and computer networking play essential roles in our daily lives, including many domains with different scopes. Developments in these technologies are quick. In consequence, there is a dire need to secure these technologies up to date. This paper presents an efficient model for secure audio signal transmission over the wireless noisy uncorrelated Rayleigh fading channel. Also, the performance of the utilized multiple secret keys-based audio cryptosystem is analyzed in different transformation domains. The discrete cosine transform (DCT), the discrete sine transform (DST), and the discrete wavelet transform (DWT) are investigated in the utilized multiple secret key-based audio cryptosystem.… More
  •   Views:176       Downloads:156        Download PDF
  • A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion
  • Abstract Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized… More
  •   Views:270       Downloads:168        Download PDF
  • Real-Time Network Intrusion Prevention System Using Incremental Feature Generation
  • Abstract Security measures are urgently required to mitigate the recent rapid increase in network security attacks. Although methods employing machine learning have been researched and developed to detect various network attacks effectively, these are passive approaches that cannot protect the network from attacks, but detect them after the end of the session. Since such passive approaches cannot provide fundamental security solutions, we propose an active approach that can prevent further damage by detecting and blocking attacks in real time before the session ends. The proposed technology uses a two-level classifier structure: the first-stage classifier supports real-time classification, and the second-stage classifier… More
  •   Views:168       Downloads:142        Download PDF
  • Design of Computer Methods for the Solution of Cervical Cancer Epidemic Model
  • Abstract Nonlinear modelling has a significant role in different disciplines of sciences such as behavioral, social, physical and biological sciences. The structural properties are also needed for such types of disciplines, as dynamical consistency, positivity and boundedness are the major requirements of the models in these fields. One more thing, this type of nonlinear model has no explicit solutions. For the sake of comparison its computation will be done by using different computational techniques. Regrettably, the aforementioned structural properties have not been restored in the existing computational techniques in literature. Therefore, the construction of structural preserving computational techniques are needed. The… More
  •   Views:256       Downloads:155        Download PDF
  • Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images
  • Abstract The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes… More
  •   Views:244       Downloads:162        Download PDF
  • Recognition and Tracking of Objects in a Clustered Remote Scene Environment
  • Abstract Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based MACH filter fails, the algorithms… More
  •   Views:205       Downloads:207        Download PDF
  • An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors
  • Abstract Rumors regarding epidemic diseases such as COVID 19, medicines and treatments, diagnostic methods and public emergencies can have harmful impacts on health and political, social and other aspects of people’s lives, especially during emergency situations and health crises. With huge amounts of content being posted to social media every second during these situations, it becomes very difficult to detect fake news (rumors) that poses threats to the stability and sustainability of the healthcare sector. A rumor is defined as a statement for which truthfulness has not been verified. During COVID 19, people found difficulty in obtaining the most truthful news… More
  •   Views:211       Downloads:184        Download PDF
  • Model Identification and Control of Evapotranspiration for Irrigation Water Optimization
  • Abstract Water conservation starts from rationalizing irrigation, as it is the largest consumer of this vital source. Following the critical and urgent nature of this issue, several works have been proposed. The idea of most researchers is to develop irrigation management systems to meet the water needs of plants with optimal use of this resource. In fact, irrigation water requirement is only the amount of water that must be applied to compensate the evapotranspiration loss. Penman-Monteith equation is the most common formula to evaluate reference evapotranspiration, but it requires many factors that cannot be available in many cases. This leads to… More
  •   Views:180       Downloads:149        Download PDF
  • Data Traffic Reduction with Compressed Sensing in an AIoT System
  • Abstract To provide Artificial Intelligence (AI) services such as object detection, Internet of Things (IoT) sensor devices should be able to send a large amount of data such as images and videos. However, this inevitably causes IoT networks to be severely overloaded. In this paper, therefore, we propose a novel oneM2M-compliant Artificial Intelligence of Things (AIoT) system for reducing overall data traffic and offering object detection. It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing (CS) rate, an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing, and a… More
  •   Views:217       Downloads:186        Download PDF
  • Multi-Factor Authentication for Secured Financial Transactions in Cloud Environment
  • Abstract The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network (VPN) backbone. This prominent application of VPN evades the hurdles involved in physical money exchange. The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server. The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014, Capital One Data Breach in 2019, and many more cloud server… More
  •   Views:637       Downloads:242        Download PDF
  • Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks
  • Abstract To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multiple-input multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami- distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the MultiBoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating… More
  •   Views:204       Downloads:137        Download PDF
  • ETM-IoT: Energy-Aware Threshold Model for Heterogeneous Communication in the Internet of Things
  • Abstract The internet of things (IoT) has a wide variety of applications, which in turn raises many challenging issues. IoT technology enables devices to closely monitor their environment, providing context-aware intelligence based on the real-time data collected by their sensor nodes. The IoT not only controls these devices but also monitors their user's behaviour. One of the major issues related to IoT is the need for an energy-efficient communication protocol which uses the heterogeneity and diversity of the objects connected through the internet. Minimizing energy consumption is a requirement for energy-constrained nodes and outsourced nodes. The IoT nodes deployed in different… More
  •   Views:215       Downloads:144        Download PDF
  • A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management
  • Abstract Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’ stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and arousal to elicit customer insights.… More
  •   Views:285       Downloads:189        Download PDF
  • IoT Information Status Using Data Fusion and Feature Extraction Method
  • Abstract The Internet of Things (IoT) role is instrumental in the technological advancement of the healthcare industry. Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0. Healthcare IoT systems are the emergence of this foresight. The communication systems between the sensing nodes and the processors; and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies. At present, many new technologies supplement these empowering technologies. So, in this research work, a practical feature extraction and classification technique is suggested for handling… More
  •   Views:186       Downloads:176        Download PDF
  • Malaria Blood Smear Classification Using Deep Learning and Best Features Selection
  • Abstract Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of… More
  •   Views:238       Downloads:152        Download PDF
  • A Hybrid Feature Selection Framework for Predicting Students Performance
  • Abstract Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions, for the improvement of quality of education and to meet the dynamic needs of society. The selection of features for student's performance prediction not only plays significant role in increasing prediction accuracy, but also helps in building the strategic plans for the improvement of students’ academic performance. There are different feature selection algorithms for predicting the performance of students, however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features. In this… More
  •   Views:552       Downloads:204        Download PDF
  • Improved RC6 Block Cipher Based on Data Dependent Rotations
  • Abstract This paper introduces an Improved RC6 (IRC6) cipher for data encryption based on data-dependent rotations. The proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard (AES). Four parameters are used to characterize the proposed scheme. These parameters are the size of the word (w) in bits, the number of rounds (r), the length of the secret key (b) in bytes, and the size of the block (L) in bits. The main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6, resulting in a variable… More
  •   Views:212       Downloads:142        Download PDF
  • Helix Inspired 28 GHz Broadband Antenna with End-Fire Radiation Pattern
  • Abstract This paper presents the design and characterization of a via free planar single turn helix for 28 GHz broadband applications. The proposed antenna is designed using ROGERS RO4003 material, having a simple structure and end-fire radiation pattern. The antenna comprises of a compact dimension of 1.36 λ0 × 0.9 λ0 with a thickness of 0.0189 λ0 (where λ0 is the free-space wavelength at the central frequency of 28 GHz). Parametric study has been carried out to investigate the impact of key design parameters and to achieve an optimum design. Results show a good agreement between the simulated and measured results.… More
  •   Views:245       Downloads:171        Download PDF
  • An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data
  • Abstract Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and… More
  •   Views:259       Downloads:138        Download PDF
  • Secure Rotation Invariant Face Detection System for Authentication
  • Abstract Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid… More
  •   Views:271       Downloads:190        Download PDF
  • Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks
  • Abstract In a wireless sensor network (WSN), data gathering is more effectually done with the clustering process. Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network. Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station. Moreover, existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes. Here, an improved clustering approach is anticipated to attain energy efficiency by implementing MapReduction for regulating mapping and reducing complexity in routing… More
  •   Views:223       Downloads:148        Download PDF
  • Using Link-Based Consensus Clustering for Mixed-Type Data Analysis
  • Abstract A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, and that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm… More
  •   Views:163       Downloads:141        Download PDF
  • Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications
  • Abstract As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT)… More
  •   Views:231       Downloads:167        Download PDF
  • Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease
  • Abstract Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected… More
  •   Views:220       Downloads:171        Download PDF
  • Deep Learning Based License Plate Number Recognition for Smart Cities
  • Abstract Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.… More
  •   Views:313       Downloads:192        Download PDF
  • A Position-Aware Transformer for Image Captioning
  • Abstract Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features.… More
  •   Views:220       Downloads:165        Download PDF
  • Power Domain Multiplexing Waveform for 5G Wireless Networks
  • Abstract Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier (NOMA-UFMC) has the potential to cope with fifth generation (5G) unprecedented challenges. NOMA employs power-domain multiplexing to support several users, whereas UFMC is robust to timing and frequency misalignments. Unfortunately, NOMA-UFMC waveform has a high peak-to-average power (PAPR) issue that creates a negative affect due to multicarrier modulations, rendering it is inefficient for the impending 5G mobile and wireless networks. Therefore, this article seeks to presents a discrete Hartley transform (DHT) pre-coding-based NOMA enabled universal filter multicarrier (UFMC) (DHT-NOMA-UFMC) waveform design for lowering the high PAPR. Additionally, DHT precoding… More
  •   Views:292       Downloads:171        Download PDF