Vol.72, No.1, 2022-Table of Contents
  • Human Pose Estimation and Object Interaction for Sports Behaviour
  • Abstract In the new era of technology, daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds. To understand the scenes and activities from human life logs, human-object interaction (HOI) is important in terms of visual relationship detection and human pose estimation. Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained. Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures, occluded regions, and unsatisfactory detection of objects, especially small-sized objects. The existing HOI detection techniques are instance-centric (object-based)… More
  •   Views:1294       Downloads:898        Download PDF
  • An Optimal Deep Learning for Cooperative Intelligent Transportation System
  • Abstract Cooperative Intelligent Transport System (C-ITS) plays a vital role in the future road traffic management system. A vital element of C-ITS comprises vehicles, road side units, and traffic command centers, which produce a massive quantity of data comprising both mobility and service-related data. For the extraction of meaningful and related details out of the generated data, data science acts as an essential part of the upcoming C-ITS applications. At the same time, prediction of short-term traffic flow is highly essential to manage the traffic accurately. Due to the rapid increase in the amount of traffic data, deep learning (DL) models… More
  •   Views:906       Downloads:617        Download PDF
  • Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network
  • Abstract Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between lesions and surrounding skin, visual… More
  •   Views:699       Downloads:465        Download PDF
  • Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis
  • Abstract Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature… More
  •   Views:615       Downloads:569        Download PDF
  • A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI
  • Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning… More
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  • Modeling and Simulation of Two Axes Gimbal Using Fuzzy Control
  • Abstract The application of the guided missile seeker is to provide stability to the sensor's line of sight toward a target by isolating it from the missile motion and vibration. The main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling approach. The paper is started by deriving the mathematical model for gimbals motion using Newton's second law, followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/SimMechanics. Then,… More
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  • Design of Machine Learning Based Smart Irrigation System for Precision Agriculture
  • Abstract Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model… More
  •   Views:694       Downloads:548        Download PDF
  • Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT
  • Abstract Numerous Internet of Things (IoT) systems produce massive volumes of information that must be handled and answered in a quite short period. The growing energy usage related to the migration of data into the cloud is one of the biggest problems. Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time, increase security, and reduce the congestion of networks. Therefore, in this paper, Optimized Energy Efficient Strategy (OEES) has been proposed for extracting, distributing, evaluating the data on the edge devices. In the initial stage of OEES,… More
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  • Intelligent DoS Attack Detection with Congestion Control Technique for VANETs
  • Abstract Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended… More
  •   Views:502       Downloads:423       Cited by:1        Download PDF
  • Multi-dimensional Security Range Query for Industrial IoT
  • Abstract The Internet of Things (IoT) has allowed for significant advancements in applications not only in the home, business, and environment, but also in factory automation. Industrial Internet of Things (IIoT) brings all of the benefits of the IoT to industrial contexts, allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy. The expansion of the IoT has been set by serious security threats and obstacles, and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control. A privacy-preserving multi-dimensional secure query technique for fog-enhanced IIoT… More
  •   Views:564       Downloads:427        Download PDF
  • Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme
  • Abstract The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such… More
  •   Views:563       Downloads:400        Download PDF
  • Computer-Vision Based Object Detection and Recognition for Service Robot in Indoor Environment
  • Abstract The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks. In this paper, we present a viable approach for a real-time computer vision based object detection and recognition for efficient indoor navigation of a mobile robot. The mobile robotic systems are utilized mainly for home assistance, emergency services and surveillance, in which critical action needs to be taken within a fraction of second or real-time. The object detection and recognition is enhanced with utilization of the proposed algorithm based on the modification of You Look Only Once (YOLO) algorithm, with… More
  •   Views:613       Downloads:547        Download PDF
  • Smart Anti-Pinch Window Simulation Using H-/H Criterion and MOPSO
  • Abstract Automobile power windows are mechanisms that can be opened and shut with the press of a button. Although these windows can comfort the effort of occupancy to move the window, failure to recognize the person's body part at the right time will result in damage and in some cases, loss of that part. An anti-pinch mechanism is an excellent choice to solve this problem, which detects the obstacle in the glass path immediately and moves it down. In this paper, an optimal solution is presented for fault detection of the anti-pinch window system. The anti-pinch makes it possible to detect… More
  •   Views:470       Downloads:395        Download PDF
  • Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks
  • Abstract Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning, allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be received. Despite the increasing literature on the line of sight with different scenarios, no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their accuracy. Therefore, this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances… More
  •   Views:505       Downloads:400        Download PDF
  • Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction
  • Abstract Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters… More
  •   Views:649       Downloads:442        Download PDF
  • Anomaly Detection for Internet of Things Cyberattacks
  • Abstract The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT cyberattacks. Although various detection techniques… More
  •   Views:783       Downloads:475       Cited by:1        Download PDF
  • Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques
  • Abstract This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and… More
  •   Views:556       Downloads:455        Download PDF
  • A Compact 28 GHz Millimeter Wave Antenna for Future Wireless Communication
  • Abstract This article presents a novel modified chuck wagon dinner bell shaped millimeter wave (mm-wave) antenna at 28 GHz. The proposed design has ultra-thin Rogers 5880 substrate with relative permittivity of 2.2. The design consists of T shaped resonating elements and two open ended side stubs. The desired 28 GHz frequency response is achieved by careful parametric modeling of the proposed structure. The maximum achieved single element gain at the desired resonance frequency is 3.45 dBi. The efficiency of the proposed design over the operating band is more than 88%. The impedance bandwidth achieved for −10 dB reference value is nearly… More
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  • A Skeleton-based Approach for Campus Violence Detection
  • Abstract In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method… More
  •   Views:516       Downloads:354        Download PDF
  • Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment
  • Abstract In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of… More
  •   Views:611       Downloads:413        Download PDF
  • An Adaptive Classifier Based Approach for Crowd Anomaly Detection
  • Abstract Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular… More
  •   Views:478       Downloads:405        Download PDF
  • A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms
  • Abstract The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD… More
  •   Views:641       Downloads:445        Download PDF
  • High Performance Classification of Android Malware Using Ensemble Machine Learning
  • Abstract Although Android becomes a leading operating system in market, Android users suffer from security threats due to malwares. To protect users from the threats, the solutions to detect and identify the malware variant are essential. However, modern malware evades existing solutions by applying code obfuscation and native code. To resolve this problem, we introduce an ensemble-based malware classification algorithm using malware family grouping. The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number. It also adopts unified feature extraction technique for… More
  •   Views:450       Downloads:371        Download PDF
  • A Framework for e-Voting System Based on Blockchain and Distributed Ledger Technologies
  • Abstract Election allows the voter of a country to select the most suitable group of candidates to run the government. Election in Pakistan is simply paper-based method but some certain political and socio-economic issues turn that simple process in complicated and disputes once. Solutions of such problems are consisting of many methods including the e-voting system. The e-voting system facilitates the voters to cast their votes by electronic means with very easy and convenient way. This also allows maintaining the security and secrecy of the voter along with election process. Electronic voting reduces the human-involvement throughout the process from start to… More
  •   Views:521       Downloads:436        Download PDF
  • An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System
  • Abstract Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided… More
  •   Views:454       Downloads:401        Download PDF
  • A Novel Framework for Windows Malware Detection Using a Deep Learning Approach
  • Abstract Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The deep learning-based approach is recently… More
  •   Views:488       Downloads:437        Download PDF
  • An Evolutionary Normalization Algorithm for Signed Floating-Point Multiply-Accumulate Operation
  • Abstract In the era of digital signal processing, like graphics and computation systems, multiplication-accumulation is one of the prime operations. A MAC unit is a vital component of a digital system, like different Fast Fourier Transform (FFT) algorithms, convolution, image processing algorithms, etcetera. In the domain of digital signal processing, the use of normalization architecture is very vast. The main objective of using normalization is to perform comparison and shift operations. In this research paper, an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer, Adder etc. The proposed normalization algorithm is further… More
  •   Views:564       Downloads:384        Download PDF
  • Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19
  • Abstract The most common alarming and dangerous disease in the world today is the coronavirus disease 2019 (COVID-19). The coronavirus is perceived as a group of coronaviruses which causes mild to severe respiratory diseases among human beings. The infection is spread by aerosols emitted from infected individuals during talking, sneezing, and coughing. Furthermore, infection can occur by touching a contaminated surface followed by transfer of the viral load to the face. Transmission may occur through aerosols that stay suspended in the air for extended periods of time in enclosed spaces. To stop the spread of the pandemic, it is crucial to… More
  •   Views:515       Downloads:405        Download PDF
  • Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method
  • Abstract Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily… More
  •   Views:521       Downloads:418        Download PDF
  • Robust Interactive Method for Hand Gestures Recognition Using Machine Learning
  • Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with a 100% recognition rate. We… More
  •   Views:699       Downloads:423        Download PDF
  • Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models
  • Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy.… More
  •   Views:481       Downloads:392       Cited by:1        Download PDF
  • A Template Matching Based Feature Extraction for Activity Recognition
  • Abstract Human activity recognition (HAR) can play a vital role in the monitoring of human activities, particularly for healthcare conscious individuals. The accuracy of HAR systems is completely reliant on the extraction of prominent features. Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities, thereby reducing recognition performance. In this paper, we propose a robust feature extraction method for HAR systems based on template matching. Essentially, in this method, we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette. In this regard, the template is placed on… More
  •   Views:461       Downloads:376        Download PDF
  • Improved Test Case Selection Algorithm to Reduce Time in Regression Testing
  • Abstract Regression testing (RT) is an essential but an expensive activity in software development. RT confirms that new faults/errors will not have occurred in the modified program. RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame. Earlier, several test case selection approaches have been introduced, but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture. To address these limitations, we recommend an improved and efficient… More
  •   Views:480       Downloads:399        Download PDF
  • Non-integer Order Control Scheme for Pressurized Water Reactor Core Power
  • Abstract Tracking load changes in a pressurized water reactor (PWR) with the help of an efficient core power control scheme in a nuclear power station is very important. The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR. To overcome the uncertainties, a non-integer-based fractional order control method is demonstrated to control the core power of PWR. The available dynamic model of the reactor core is used in this analysis. Core power is controlled using a modified state feedback approach with a non-integer integral… More
  •   Views:461       Downloads:418        Download PDF
  • An Efficient and Reliable Multicasting for Smart Cities
  • Abstract The Internet of thing (IoT) is a growing concept for smart cities, and it is compulsory to communicate data between different networks and devices. In the IoT, communication should be rapid with less delay and overhead. For this purpose, flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead, energy consumption and packet drop etc. This paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT concept.… More
  •   Views:498       Downloads:400        Download PDF
  • Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment
  • Abstract Internet of Things (IoT) with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications. At the same time, machine learning (ML) and data mining approaches are presented for accomplishing prediction and classification processes. With this motivation, this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection (IML-ELNVBD) in IoT environment. The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors, cameras, etc. which are then connected to the cloud server for further processing. In addition, the modelling and extraction of behaviour… More
  •   Views:462       Downloads:400        Download PDF
  • Classification of Desertification on the North Bank of Qinghai Lake
  • Abstract In this paper, RS, GIS and GPS technologies are used to interpret the remote sensing images of the north shore of Qinghai Lake from 1987 to 2014 according to the inversion results of vegetation coverage (FVC), albedo, land surface temperature (LST), soil moisture (WET) and other major parameters after image preprocessing, such as radiometric correction, geometric correction and atmospheric correction. On this basis, the decision tree classification method based on landsat8 remote sensing image is used to classify the desertification land in this area, and the development and change of desertification in this period are analyzed. The results show that… More
  •   Views:606       Downloads:412        Download PDF
  • Malware Detection Using Decision Tree Based SVM Classifier for IoT
  • Abstract The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware… More
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  • HARQ Optimization for PDCP Duplication-Based 5G URLLC Dual Connectivity
  • Abstract Packet duplication (PD) with dual connectivity (DC) was newly introduced in the 5G New Radio (NR) specifications to meet the stringent ultra reliable low latency communication (URLLC) requirements. PD technology uses duplicated packets in the packet data convergence protocol (PDCP) layer that are transmitted via two different access nodes (ANs) to the user equipment (UE) in order to enhance the reliability performance. However, PD can result in unnecessary retransmissions in the lower layers since the hybrid automatic retransmission request (HARQ) operation is unaware of the transmission success achieved through the alternate DC link to the UE. To overcome this issue,… More
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  • Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM
  • Abstract Remote sensing image processing engaged researchers’ attentiveness in recent years, especially classification. The main problem in classification is the ratio of the correct predictions after training. Feature extraction is the foremost important step to build high-performance image classifiers. The convolution neural networks can extract images’ features that significantly improve the image classifiers’ accuracy. This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’ outputs as a features extraction using two classic convolution models; these convolution models are the ResNet 50 and the DenseNet 169. These elicited features have been used by… More
  •   Views:554       Downloads:428        Download PDF
  • Variational Bayesian Based IMM Robust GPS Navigation Filter
  • Abstract This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise… More
  •   Views:565       Downloads:401        Download PDF
  • Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage
  • Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in… More
  •   Views:521       Downloads:432        Download PDF
  • Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images
  • Abstract The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles… More
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  • Identification and Classification of Crowd Activities
  • Abstract The identification and classification of collective people's activities are gaining momentum as significant themes in machine learning, with many potential applications emerging. The need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd congestion. This paper investigates the capability of deep neural network (DNN) algorithms to achieve our carefully engineered pipeline for crowd analysis. It includes three principal stages that cover crowd analysis challenges. First, individual's detection is represented using the You Only Look Once (YOLO) model for human detection and Kalman filter for multiple human tracking; Second, the density… More
  •   Views:471       Downloads:400        Download PDF
  • Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times
  • Abstract COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19. We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world.… More
  •   Views:547       Downloads:419        Download PDF
  • Hybrid GrabCut Hidden Markov Model for Segmentation
  • Abstract Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify in MRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images.… More
  •   Views:561       Downloads:392        Download PDF
  • Parkinson's Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals
  • Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM and optimized with adaptive moment… More
  •   Views:569       Downloads:402        Download PDF
  • Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks
  • Abstract Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN model is carried out with… More
  •   Views:456       Downloads:401        Download PDF
  • Compact Multibeam Array with Miniaturized Butler Matrix for 5G Applications
  • Abstract This paper presents the design and implementation of a miniaturized beam steering network that produces broadside beams when it is fed with a compact antenna array. Butler Matrix (BM) was used as the beam steering network. It was completely built from a miniaturized 3 dB hybrid-couplers in planar microstrip technology. It was configured by feeding the BM with a Planar Inverted-E Antenna (PIEA) array separated at 0.3 λ as against the 0.5 λ separation. This makes the BM produce a major radiation pattern at the broadside. Apart from the miniaturization, no modification was made from the BM side. However, employing effective… More
  •   Views:505       Downloads:419        Download PDF
  • Fusion-Based Deep Learning Model for Hyperspectral Images Classification
  • Abstract A crucial task in hyperspectral image (HSI) taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube. For classification of images, 3-D data is adjudged in the phases of pre-cataloging, an assortment of a sample, classifiers, post-cataloging, and accurateness estimation. Lastly, a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken. In topical years, sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands. Encouraged by those efficacious solicitations, sparse representation (SR) has likewise been presented… More
  •   Views:510       Downloads:426        Download PDF
  • A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics
  • Abstract There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and… More
  •   Views:540       Downloads:392        Download PDF
  • Directional Wideband Wearable Antenna with Circular Parasitic Element for Microwave Imaging Applications
  • Abstract This work proposes a wideband and unidirectional antenna consisting of dual layer of coplanar waveguide based on the circular parasitic element technique. The design procedure is implemented in three stages: Design A, which operates at 3.94 GHz with a bandwidth of 3.83 GHz and a fractional bandwidth (FBW) of 97.2%; Design B, which operates at 3.98 GHz with a bandwidth of 0.66 GHz (FBW of 56.53%); and Design C as the final antenna. The final Design C is designed to resonate at several frequencies between 2.89 and 7.0 GHz for microwave imaging applications with a bandwidth of 4.11 GHz (79.8%)… More
  •   Views:588       Downloads:370        Download PDF
  • Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices
  • Abstract Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino… More
  •   Views:582       Downloads:423        Download PDF
  • Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics
  • Abstract Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in… More
  •   Views:568       Downloads:397        Download PDF
  • Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks
  • Abstract This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the… More
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  • An Optimal Scheme for WSN Based on Compressed Sensing
  • Abstract Wireless sensor networks (WSNs) is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks, bio-medical engineering, agriculture, industry and many more. It has been used in the internet-of-things (IoTs) applications. A method for data collecting utilizing hybrid compressive sensing (CS) is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load. Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes, and then the cluster heads are selected in… More
  •   Views:697       Downloads:391        Download PDF
  • Triple-Band Metamaterial Inspired Antenna for Future Terahertz (THz) Applications
  • Abstract For future healthcare in the terahertz (THz) band, a triple-band microstrip planar antenna integrated with metamaterial (MTM) based on a polyimide substrate is presented. The frequencies of operation are 500, 600, and 880 GHz. The triple-band capability is accomplished by etching metamaterial on the patch without affecting the overall antenna size. Instead of a partial ground plane, a full ground plane is used as a buffer to shield the body from back radiation emitted by the antenna. The overall dimension of the proposed antenna is 484 × 484 μm2. The antenna's performance is investigated based on different crucial factors, and excellent results are… More
  •   Views:726       Downloads:469        Download PDF
  • Adaptive Multi-Cost Routing Protocol to Enhance Lifetime for Wireless Body Area Network
  • Abstract Wireless Body Area Network (WBAN) technologies are emerging with extensive applications in several domains. Health is a fascinating domain of WBAN for smart monitoring of a patient's condition. An important factor to consider in WBAN is a node's lifetime. Improving the lifetime of nodes is critical to address many issues, such as utility and reliability. Existing routing protocols have addressed the energy conservation problem but considered only a few parameters, thus affecting their performance. Moreover, most of the existing schemes did not consider traffic prioritization which is critical in WBANs. In this paper, an adaptive multi-cost routing protocol is proposed… More
  •   Views:1398       Downloads:438        Download PDF
  • Constructing Collective Signature Schemes Using Problem of Finding Roots Modulo
  • Abstract Digital signature schemes are often built based on the difficulty of the discrete logarithm problems, of the problem of factor analysis, of the problem of finding the roots modulo of large primes or a combination of the difficult problems mentioned above. In this paper, we use the new difficult problem, which is to find the root in the finite ground field to build representative collective signature schemes, but the chosen modulo p has a special structure distinct , where is an even number and are prime numbers of equal magnitude, about . The characteristics of the proposed scheme are: i)… More
  •   Views:667       Downloads:431        Download PDF
  • A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis
  • Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification… More
  •   Views:647       Downloads:424        Download PDF
  • Effective Frameworks Based on Infinite Mixture Model for Real-World Applications
  • Abstract Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications. In particular, recognizing human activities with accurate results have become a topic of high interest. Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions. In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some real-world applications. In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.… More
  •   Views:486       Downloads:401        Download PDF
  • Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment
  • Abstract Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell… More
  •   Views:624       Downloads:394        Download PDF
  • An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography
  • Abstract The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved Convolutional Neural Network (ICNN) model… More
  •   Views:484       Downloads:396        Download PDF
  • Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks
  • Abstract The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly… More
  •   Views:603       Downloads:419        Download PDF
  • User Recognition System Based on Spectrogram Image Conversion Using EMG Signals
  • Abstract Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of next-generation user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over… More
  •   Views:541       Downloads:389        Download PDF
  • Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)
  • Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More
  •   Views:613       Downloads:394        Download PDF
  • Windows 10's Browser Forensic Analysis for Tracing P2P Networks’ Anonymous Attacks
  • Abstract A web browser is the most basic tool for accessing the internet from any of the machines/equipment. Recently, data breaches have been reported frequently from users who are concerned about their personal information, as well as threats from criminal actors. Giving loss of data and information to an innocent user comes under the jurisdiction of cyber-attack. These kinds of cyber-attacks are far more dangerous when it comes to the many types of devices employed in an internet of things (IoT) environment. Continuous surveillance of IoT devices and forensic tools are required to overcome the issues pertaining to secure data and… More
  •   Views:603       Downloads:456        Download PDF
  • Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization
  • Abstract The recent aggrandizement of radio frequency (RF) signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service (QoS). In addition, it does not require any unnecessary alterations on the transmission hardware side. A hybridized global optimization technique uniting Global best and Local best (GL) based particle swarm optimization (PSO) and ant colony optimization (ACO) is proposed in this paper to optimally allocate resources in wireless powered communication networks (WPCN) through coordinated operation of communication… More
  •   Views:669       Downloads:407        Download PDF
  • Locomotion of Bioinspired Underwater Snake Robots Using Metaheuristic Algorithm
  • Abstract Snake Robots (SR) have been successfully deployed and proved to attain bio-inspired solutions owing to its capability to move in harsh environments, a characteristic not found in other kinds of robots (like wheeled or legged robots). Underwater Snake Robots (USR) establish a bioinspired solution in the domain of underwater robotics. It is a key challenge to increase the motion efficiency in underwater robots, with respect to forwarding speed, by enhancing the locomotion method. At the same time, energy efficiency is also considered as a crucial issue for long-term automation of the systems. In this aspect, the current research paper concentrates… More
  •   Views:487       Downloads:332        Download PDF
  • Design of Low Power Transmission Gate Based 9T SRAM Cell
  • Abstract Considerable research has considered the design of low-power and high-speed devices. Designing integrated circuits with low-power consumption is an important issue due to the rapid growth of high-speed devices. Embedded static random-access memory (SRAM) units are necessary components in fast mobile computing. Traditional SRAM cells are more energy-consuming and with lower performances. The major constraints in SRAM cells are their reliability and low power. The objectives of the proposed method are to provide a high read stability, low energy consumption, and better writing abilities. A transmission gate-based multi-threshold single-ended Schmitt trigger (ST) 9T SRAM cell in a bit-interleaving structure without… More
  •   Views:688       Downloads:340        Download PDF
  • Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment
  • Abstract Rapid advancements of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) pose serious security issues by revealing secret data. Therefore, security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real time. Practically, AI techniques can be utilized to design image steganographic techniques in IIoT. In addition, encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized access. In order to accomplish secure data transmission in IIoT environment, this study presents novel encryption with image steganography based data hiding technique (EIS-DHT) for… More
  •   Views:479       Downloads:362        Download PDF
  • A New Method for Scene Classification from the Remote Sensing Images
  • Abstract The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas. In recent years, a lot of interest has been generated in researching remote sensing image scene classification. Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research. In the last several years, the number of deep learning (DL) methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs, providing new research and development possibilities for RS… More
  •   Views:649       Downloads:400        Download PDF
  • Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform
  • Abstract This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage… More
  •   Views:741       Downloads:416        Download PDF
  • An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures
  • Abstract The Human-Centered Internet of Things (HC-IoT) is fast becoming a hotbed of security and privacy concerns. Two users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key agreement. Most of the early authenticated key agreement systems relied on pairing, hashing, or modular exponentiation processes that are computationally intensive and cost-prohibitive. In order to address this problem, this paper offers a new three-party authenticated key agreement technique based on fractional chaotic maps. The new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network… More
  •   Views:534       Downloads:324        Download PDF
  • Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification
  • Abstract In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner. In addition,… More
  •   Views:635       Downloads:421        Download PDF
  • Optimized Load Balancing Technique for Software Defined Network
  • Abstract Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission… More
  •   Views:588       Downloads:344        Download PDF
  • Machine Learning Empowered Electricity Consumption Prediction
  • Abstract Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes… More
  •   Views:687       Downloads:681        Download PDF
  • Detection of Lung Nodules on X-ray Using Transfer Learning and Manual Features
  • Abstract The well-established mortality rates due to lung cancers, scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion. To this end, we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health (NIH) chest X-Ray dataset comprised of 30,805 unique patients. The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer. For classification with both approaches, Support Vectors Machines (SVM) are used.… More
  •   Views:436       Downloads:330        Download PDF
  • Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection
  • Abstract Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex… More
  •   Views:814       Downloads:770       Cited by:1        Download PDF
  • A Perfect Knob to Scale Thread Pool on Runtime
  • Abstract Scalability is one of the utmost nonfunctional requirement of server applications, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) has been used extensively as a middleware service in server applications. The size of thread pool is the most significant factor, that affects the overall performance of servers. Determining the optimal size of thread pool dynamically on runtime is a challenging problem. The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request… More
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  • Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network
  • Abstract The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.… More
  •   Views:707       Downloads:414        Download PDF
  • Enhancement of Biomass Material Characterization Images Using an Improved U-Net
  • Abstract For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of… More
  •   Views:453       Downloads:368        Download PDF
  • Weighted-adaptive Inertia Strategy for Multi-objective Scheduling in Multi-clouds
  • Abstract One of the fundamental problems associated with scheduling workflows on virtual machines in a multi-cloud environment is how to find a near-optimum permutation. The workflow scheduling involves assigning independent computational jobs with conflicting objectives to a set of virtual machines. Most optimization methods for solving non-deterministic polynomial-time hardness (NP-hard) problems deploy multi-objective algorithms. As such, Pareto dominance is one of the most efficient criteria for determining the best solutions within the Pareto front. However, the main drawback of this method is that it requires a reasonably long time to provide an optimum solution. In this paper, a new multi-objective minimum… More
  •   Views:593       Downloads:354        Download PDF
  • Game Theory-Based IoT Efficient Power Control in Cognitive UAV
  • Abstract With the help of network densification, network coverage as well as the throughput can be improved via ultra-dense networks (UDNs). In tandem, Unmanned Aerial Vehicle (UAV) communications have recently garnered much attention because of their high agility as well as widespread applications. In this paper, a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal. Further, the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation. The quality of service (QoS) related to the cognitive node was considered as a utility function… More
  •   Views:959       Downloads:790        Download PDF
  • A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures
  • Abstract Due to the drastic increase in the number of critical infrastructures like nuclear plants, industrial control systems (ICS), transportation, it becomes highly vulnerable to several attacks. They become the major targets of cyberattacks due to the increase in number of interconnections with other networks. Several research works have focused on the design of intrusion detection systems (IDS) using machine learning (ML) and deep learning (DL) models. At the same time, Blockchain (BC) technology can be applied to improve the security level. In order to resolve the security issues that exist in the critical infrastructures and ICS, this study designs a… More
  •   Views:814       Downloads:764        Download PDF
  • Urdnet: A Cryo-EM Particle Automatic Picking Method
  • Abstract Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and pixel-level tags, used to accurately… More
  •   Views:446       Downloads:372        Download PDF
  • Analytical Model for Underwater Wireless Sensor Network Energy Consumption Reduction
  • Abstract In an Underwater Wireless Sensor Network (UWSN), extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor system. Systems based on clustering strategies, instead of each node sending information by itself, utilize cluster heads to collect information inside the clusters for forwarding collective information to sink. This can effectively minimize the total energy loss during transmission. The environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission bandwidth. Round base… More
  •   Views:501       Downloads:340        Download PDF
  • Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks
  • Abstract The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta scheme is implemented to form… More
  •   Views:538       Downloads:386        Download PDF
  • Hyperchaos and MD5 Based Efficient Color Image Cipher
  • Abstract While designing and developing encryption algorithms for text and images, the main focus has remained on security. This has led to insufficient attention on the improvement of encryption efficiency, enhancement of hyperchaotic sequence randomness, and dynamic DNA-based S-box. In this regard, a new symmetric block cipher scheme has been proposed. It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images. Our proposed scheme supports various size color images. It generates three DNA based S-boxes for substitution namely DNA_1_s-box, DNA_2_s-box and DNA_3_s-box, each of size . Next, the 4D hyperchaotic… More
  •   Views:538       Downloads:310        Download PDF
  • Robust Frequency Estimation Under Additive Mixture Noise
  • Abstract In many applications such as multiuser radar communications and astrophysical imaging processing, the encountered noise is usually described by the finite sum of -stable variables. In this paper, a new parameter estimator is developed, in the presence of this new heavy-tailed noise. Since the closed-form PDF of the -stable variable does not exist except and , we take the sum of the Cauchy () and Gaussian () noise as an example, namely, additive Cauchy-Gaussian (ACG) noise. The probability density function (PDF) of the mixed random variable, can be calculated by the convolution of the Cauchy's PDF and Gaussian's PDF. Because… More
  •   Views:484       Downloads:363        Download PDF
  • COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification
  • Abstract Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature… More
  •   Views:475       Downloads:377        Download PDF
  • DAVS: Dockerfile Analysis for Container Image Vulnerability Scanning
  • Abstract Container technology plays an essential role in many Information and Communications Technology (ICT) systems. However, containers face a diversity of threats caused by vulnerable packages within container images. Previous vulnerability scanning solutions for container images are inadequate. These solutions entirely depend on the information extracted from package managers. As a result, packages installed directly from the source code compilation, or packages downloaded from the repository, etc., are ignored. We introduce DAVS–A Dockerfile analysis-based vulnerability scanning framework for OCI-based container images to deal with the limitations of existing solutions. DAVS performs static analysis using file extraction based on Dockerfile information to… More
  •   Views:557       Downloads:450        Download PDF
  • Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone
  • Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural… More
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  • Self-Care Assessment for Daily Living Using Machine Learning Mechanism
  • Abstract Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.… More
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  • Cyber Security Analysis and Evaluation for Intrusion Detection Systems
  • Abstract Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world. Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues. It is crucial in offering a proactive security system in the field of cybersecurity. In real time, cybersecurity protects information, information systems, and networks from intruders. In the recent decade, several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches. At an increasing rate, intruders are breaching information security. Anomaly detection, software… More
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  • Classification of Images Based on a System of Hierarchical Features
  • Abstract The results of the development of the new fast-speed method of classification images using a structural approach are presented. The method is based on the system of hierarchical features, based on the bitwise data distribution for the set of descriptors of image description. The article also proposes the use of the spatial data processing apparatus, which simplifies and accelerates the classification process. Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure, for which the sets of descriptors are compared. The… More
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  • Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve
  • Abstract The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.… More
  •   Views:528       Downloads:369       Cited by:1        Download PDF
  • Fuzzy System Design Using Current Amplifier for 20 nm CMOS Technology
  • Abstract In the recent decade, different researchers have performed hardware implementation for different applications covering various areas of experts. In this research paper, a novel analog design and implementation of different steps of fuzzy systems with current differencing buffered amplifier (CDBA) are proposed with a compact structure that can be used in many signal processing applications. The proposed circuits are capable of wide input current range, simple structure, and are highly linear. Different electrical parameters were compared for the proposed fuzzy system when using different membership functions. The novelty of this paper lies in the electronic implementation of different steps for… More
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  • Efficient Load Balancing with MANET Propagation of Least Common Multiple Routing and Fuzzy Logic
  • Abstract Mobile Ad Hoc Network (MANET) is a group of node that would interrelate among each other through one multi-hop wireless link, wherein the nodes were able to move in response to sudden modifications. The objective of MANET routing protocol is to quantify the route and compute the best path, but there exists a major decrease in energy efficiency, difficulty in hop selection, cost estimation, and efficient load-balancing. In this paper, a novel least common multipath-based routing has been proposed. Multipath routing is used to find a multipath route from source and destination. Load balancing is of primary importance in the… More
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  • Modeling Reliability Engineering Data Using Scale-Invariant Quasi-Inverse Lindley Model
  • Abstract An important property that any lifetime model should satisfy is scale invariance. In this paper, a new scale-invariant quasi-inverse Lindley (QIL) model is presented and studied. Its basic properties, including moments, quantiles, skewness, kurtosis, and Lorenz curve, have been investigated. In addition, the well-known dynamic reliability measures, such as failure rate (FR), reversed failure rate (RFR), mean residual life (MRL), mean inactivity time (MIT), quantile residual life (QRL), and quantile inactivity time (QIT) are discussed. The FR function considers the decreasing or upside-down bathtub-shaped, and the MRL and median residual lifetime may have a bathtub-shaped form. The parameters of the… More
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  • Invariant of Enhanced AES Algorithm Implementations Against Power Analysis Attacks
  • Abstract The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of IoT networks. Side Channel Attacks (SCA) are one of the major concerns. The prime objective of SCA is to acquire the information by observing the power consumption, electromagnetic (EM) field, timing analysis, and acoustics of the device. Later, the attackers perform statistical functions to recover the key. Advanced Encryption Standard (AES) algorithm has proved to be a good security solution for constrained IoT devices. This paper implements a simulation model which is used to modify the AES algorithm using logical masking properties. This… More
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  • QoS in FANET Business and Swarm Data
  • Abstract This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks (FANET). Each drone has the ability to send and receive information (like a router); and can behave as a hierarchical node whit the intregration of three protocols: Multiprotocol Label Switch (MPLS), Fast Hierarchical AD Hoc Mobile (FHAM) and Internet Protocol version 6 (IPv6), in conclusion MPLS + FHAM + IPv6. The metrics analyzed in the FANET are: delay, jitter, throughput, lost and sent packets/received. Testing process was carried out with swarms composed of 10, 20,… More
  •   Views:590       Downloads:370        Download PDF
  • Comparative Study of Machine Learning Modeling for Unsteady Aerodynamics
  • Abstract Modern fighters are designed to fly at high angle of attacks reaching 90 deg as part of their routine maneuvers. These maneuvers generate complex nonlinear and unsteady aerodynamic loading. In this study, different aerodynamic prediction tools are investigated to achieve a model which is highly accurate, less computational, and provides a stable prediction of associated unsteady aerodynamics that results from high angle of attack maneuvers. These prediction tools include Artificial Neural Networks (ANN) model, Adaptive Neuro Fuzzy Logic Inference System (ANFIS), Fourier model, and Polynomial Classifier Networks (PCN). The main aim of the prediction model is to estimate the pitch… More
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  • Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model
  • Abstract Latest advancements in the integration of camera sensors paves a way for new Unmanned Aerial Vehicles (UAVs) applications such as analyzing geographical (spatial) variations of earth science in mitigating harmful environmental impacts and climate change. UAVs have achieved significant attention as a remote sensing environment, which captures high-resolution images from different scenes such as land, forest fire, flooding threats, road collision, landslides, and so on to enhance data analysis and decision making. Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs. This paper proposes a new multi-modal fusion based earth data classification (MMF-EDC)… More
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  • Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning
  • Abstract Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method of deep learning and the… More
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  • Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing
  • Abstract Generally, software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software. But, the quality of test cases has a considerable influence on fault revealing capability of software testing activity. Test Case Prioritization (TCP) remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected (APFD) and time spent upon execution results. TCP is mainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics. The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection… More
  •   Views:464       Downloads:390        Download PDF
  • Embedding Extraction for Arabic Text Using the AraBERT Model
  • Abstract Nowadays, we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task. In this work, we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking, essay grading, and question answering systems. We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset. The used schemes include lexical-based similarity features, frequency-based features, and pre-trained model-based features. Also, we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers (AraBERT). We… More
  •   Views:459       Downloads:367        Download PDF
  • An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling
  • Abstract Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity, capability, and capacity. Such tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or decision-making. Swarm intelligence techniques including Particle Swarm Optimization (PSO) have proved to be effective examples. Different recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling, machine scheduling, etc. However, having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is guaranteed. This research paper seeks… More
  •   Views:625       Downloads:450        Download PDF
  • Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques
  • Abstract As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while… More
  •   Views:571       Downloads:428        Download PDF
  • IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning
  • Abstract On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain. The absence or displacement of… More
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  • Spherical Fuzzy WASPAS-based Entropy Objective Weighting for International Payment Method Selection
  • Abstract In international trade, exporters prefer to receive payments as quickly as possible, and importers want to make payments as late as possible. In this respect, the payment field, an essential condition for trade transactions, also represents the positions of exporters and importers conflict. In addition, there are many cases in which various variables must be considered rather than only one specific variable representatively affecting payment, particularly in the case of import-export Small and Medium-Sized Enterprises (SMEs) from emerging countries. A selection of proper payment methods can be categorized as a Multi-Criteria Decision-Making (MCDM) issue. Therefore, this study aims to propose… More
  •   Views:545       Downloads:402       Cited by:1        Download PDF
  • Energy-Efficient Static Data Collector-based Scheme in Smart Cities
  • Abstract In the Internet of Things (IoT)-based smart city applications, employing the Data Collectors (DC) as the data brokers between the nodes and Base Station (BS) can be a promising solution to enhance the energy efficiency of energy-constrained IoT sensor nodes. There are several schemes that utilize mobile DCs to collect the data packets from sensor nodes. However, moving DCs along the hundreds of thousands of sensors sparsely distributed across a smart city is considered a design challenge in such schemes. Another concern lies in how these mobile DCs are being powered. Therefore, to overcome these limitations, we exploit multiple energy-limited… More
  •   Views:753       Downloads:660        Download PDF
  • Regulation Relatedness Map Creation Method with Latent Semantic Analysis
  • Abstract Regulatory authorities create a lot of legislation that must be followed. These create complex compliance requirements and time-consuming processes to find regulatory non-compliance. While the regulations establish rules in the relevant areas, recommendations and best practices for compliance are not generally mentioned. Best practices are often used to find a solution to this problem. There are numerous governance, management, and security frameworks in Information Technology (IT) area to guide businesses to run their processes at a much more mature level. Best practice maps can used to map another best practice, and users can adapt themselves by the help of this… More
  •   Views:939       Downloads:753        Download PDF
  • Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model
  • Abstract Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images. Thus, there were lots of efforts trying to automate the classification operation and retrieve similar images accurately. To reach this goal, we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically. Then, the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network. The Siamese model built and trained at first from scratch but, it… More
  •   Views:483       Downloads:370        Download PDF
  • An Energy-Efficient 12b 2.56 MS/s SAR ADC Using Successive Scaling of Reference Voltages
  • Abstract This paper presents an energy efficient architecture for successive approximation register (SAR) analog to digital converter (ADC). SAR ADCs with a capacitor array structure have been widely used because of its simple architecture and relatively high speed. However, conventional SAR ADCs consume relatively high energy due to the large number of capacitors used in the capacitor array and their sizes scaled up along with the number of bits. The proposed architecture reduces the energy consumption as well as the capacitor size by employing a new array architecture that scales down the reference voltages instead of scaling up the capacitor sizes.… More
  •   Views:825       Downloads:692        Download PDF
  • Energy Theft Identification Using Adaboost Ensembler in the Smart Grids
  • Abstract One of the major concerns for the utilities in the Smart Grid (SG) is electricity theft. With the implementation of smart meters, the frequency of energy usage and data collection from smart homes has increased, which makes it possible for advanced data analysis that was not previously possible. For this purpose, we have taken historical data of energy thieves and normal users. To avoid imbalance observation, biased estimates, we applied the interpolation method. Furthermore, the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing. By proposing an improved version… More
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