Terms Of Performance Metrics Research Articles

Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks

Insects are among the important causes of significant losses in crops such as rice, wheat, corn, soybeans, sugarcane, chickpeas, potatoes. Identification of insect species in the early period is crucial so that the necessary precautions can be taken to keep losses at a low level. However, accurate identification of various types of crop insects is a challenging task for the farmers due to the similarities among insect species and also their lack of knowledge. To address this problem, computerized methods, especially based on Convolutional Neural Networks (CNNs), can be employed. CNNs have been used successfully in many image classification problems due to their ability to learn data-dependent features automatically from the data.Throughout the study, seven different pre-trained CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, SqueezeNet) were modified and re-trained using appropriate transfer learning and fine-tuning strategies on publicly available D0 dataset with 40 classes. Later, the top three best performing CNN models, Inception-V3, Xception, and MobileNet, were ensembled via sum of maximum probabilities strategy to increase the classification performance, the model was named SMPEnsemble. After that, these models were ensembled using weighted voting. The weights were determined by the genetic algorithm that takes the success rate and predictive stability of three CNN models into account, the model was named GAEnsemble.GAEnsemble achieved the highest classification accuracy of 98.81% for D0 dataset. For the sake of robustness ensembled model, without changing the initial best performing CNN models on D0, the process was repeated by using two more datasets such that SMALL dataset with 10 classes and IP102 dataset with 102 classes. The accuracy values for GAEnsemble are 95.15% for SMALL dataset and 67.13% for IP102. In terms of performance metrics, GAEnsemble is competitive compared to the literature for each of these three datasets.

Ultrasonic Indexed Modulation and Multiple Access for Intra-Body Networks

Intra-body communication (IBC) plays a significant role in future health care systems. This article proposes a pulse-based ultrasonic index modulation (UsIM) to enable high data rate and low power IBCs. Two grouping-based UsIM strategies, namely ultrasonic grouped-chip indexed modulation (UsCIM) and ultrasonic chip-group indexed modulation (UsGIM), are proposed to implement multiple access for intra-body networks (IBNs). UsCIM uses the chip indices in a group to convey additional information bits for a biosensor node, while UsGIM uses the group indices and each node occupies one chip in a group. Three receivers, including the maximum likelihood (ML), the maximum ratio combination (MRC), and the energy detection (ED) receivers, are then proposed and studied. The theoretical bit-error rate (BER) upper bound for the ML receiver, and theoretical BER expressions for the MRC and ED receivers are derived. The system performance metrics in terms of complexity, throughput and energy efficiency are analyzed. The analytical and simulation results reveal that UsGIM achieves a lower BER than UsCIM under channel fading and multipath effect. Both UsCIM and UsGIM achieve higher throughput and energy efficiency than the existing IBC systems, and both strategies are important for energy-constrained implanted IBNs.

Performance and Security Challenges Digital Rights Management (DRM) Approaches Using Fog Computing for Data Provenance: A Survey

Over the past few years, the cloud based storage providers such as Google drive, OneDrive, Box and Dropbox are increasingly being used to store users personal profile based data, which users could share over cloud storage devices. Immediately, the users data leaves the user premises and enters the cloud storage, the user has lost all the control over its data, and even when that data is further shared for the purposes of collaboration. Digital rights management (DRM) for Content hosted in Cloud based storage covers technologies related devices that protects the digital assets stored in cloud by enforcing the permission regulator, and when the data is in collaboration. This is important to protect healthcare (Fiaz, M., et al., 2019. Social media news classification in healthcare communication. Journal of Medical Imaging and Health Informatics, 9(6), pp.1215–1223; Irfan, R., et al., 2019. Ontology learning in text mining for handling big data in healthcare systems. Journal of Medical Imaging and Health Informatics, 9(4), pp.649–661; Ikram Ullah, L. and Ali Shariq, I., 2019. A special section on effectiveness of search engine and social networking data in healthcare systems. Journal of Medical Imaging and Health Informatics, 9(4), pp.647–648; Preethi, P. and Asokan, R., 2019. A high secure medical image storing and sharing in cloud environment using hex code cryptography methodsecure genius. Journal of Medical Imaging and Health Informatics, 9(7), pp.1337–1345; Shahzad, B., et al., 2017. Finding healthcare issues with search engine queries and social network data. International Journal of Semantic Web and Information System, 13(1), pp.48–62; Saleem, K., et al., 2015. Human-oriented design of secure machine-to-machine communication system for e-Healthcare society. Computers in Human Behaviour, 51, pp.977–985.) data from being misused by authorized and unauthorized entities. To implement DRM in cloud, these Authors have proposed encryption based solution. Due to the distributed nature of cloud it is suitable for the real time data transportation. However, DRM process requires performance that is close to real time. This paper summarized the current state of the research in Digital Rights Management and leveraged Fog computing in DRM. This is to meet the new performance requirements for next-generation distribution networks, in terms of performance metrics, such as energy consumption, traffic delay, and network throughput, as required by in large and heterogonous Cloud solutions (Preethi, P. and Asokan, R., 2019. A high secure medical image storing and sharing in cloud environment using hex code cryptography methodsecure genius. Journal of Medical Imaging and Health Informatics, 9(7), pp.1337–1345.) as data storage system.

Mitigation of mutual coupling in 2 X 2 dual slant polarized MIMO antennas using periodic array of SRRs loaded with transmission line for LTE band 40

2 X 2 dual slant polarized multiple input multiple output (MIMO) antennas with reduced mutual coupling is presented for polarization and spatial diversity. The design of printed periodic array of split ring resonators (SRRs) loaded with horizontal strip transmission line is proposed to mitigate the mutual coupling among the MIMO antenna elements. An equivalent circuit model, characterizes the effect of coupling between the array of SRRs and the loaded transmission line, which together reduces the near field coupling between the adjacent antenna elements. The additional horizontal strip at the bottom is utilized to seek the return current path to the ground plane. Polarization and spatial diversity is achieved by utilizing dual slant 450 polarized antenna elements with eight independent channels. All four dual polarized antenna elements are designed to operate with a VSWR of <2 for (2.3-2.4 GHz) Band 40. Dual slant 450 polarization is achieved by utilizing two printed planar bow-tie antennas in orthogonal orientation. Measured and simulated results shows by incorporating periodic array of SRRs loaded with transmission line a considerable mutual coupling reduction of 25 to 50 dB is achieved in E-plane, H-plane, and D-plane over the required band 40. An isolation of 30 dB and an average gain of 7.5 dB is measured for dual slant 450 polarized antenna elements. MIMO performance metrics in terms of envelope correlation coefficient and diversity gain are also investigated.

FPGA implementations of Grain v1, Mickey 2.0, Trivium, Lizard and Plantlet

In this paper, three eSTREAM Portfolio 2 ciphers: Grain v1, Mickey 2.0 and Trivium, and two Grain-like stream ciphers: Lizard and Plantlet are implemented in three versions aimed at different hardware application purposes. The hardware platform adopts Xilinx’s Spartan7 serial, and the simulations, syntheses and implementations are conducted in Vivado using the Verilog hardware design language. These implementations are compared with each other and those presented in existing literature in terms of performance metrics including: throughput, area consumption and throughput-area ratio. The basic version of Trivium achieves the highest frequency reaching a maximum of 416 Mbps and the serial version consumes the smallest area of 13 slices. In the parallel version, the maximum throughput-area ratio is 165.5 Mbps/Slice achieved by Trivium. At the same time, the basic version of the Mickey 2.0 algorithm achieved the second highest frequency of 384 Mbps, and the serial version of the Grain v1 algorithm achieved the second smallest area with 26 slices.

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

AbstractIn this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.

Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Image Stitching is becoming more popular in field of computer vision because of rapid development of efficient algorithms that replaces the high cost wider lens cameras and commercial image stitching tools. The existing methods used global geometric transformation in registration stage and hence suffered from object deformation, parallax error, ghosting effect and motion blur in output result. In this paper, newly developed Hybrid Warping of weighted linearized homography matrix and similarity transform matrix is implemented over standard image stitching database. The visual quality of stitched image using proposed method has been examined in terms of performance metrics of Full Reference Image Quality Assessment (FRIQA) such as Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Reduced Reference Image Quality Assessment (RRIQA). Also, the performance analysis of proposed method is compared against existing image stitching methods in terms of field of view and stitching time. This analysis has ascertained the outperformance of Novel Hybrid Image Stitching method.

Dyme: Dynamic Microservice Scheduling in Edge Computing Enabled IoT

In recent years, the rapid development of mobile edge computing (MEC) provides an efficient execution platform at the edge for Internet-of-Things (IoT) applications. Nevertheless, the MEC also provides optimal resources to different microservices, however, underlying network conditions and infrastructures inherently affect the execution process in MEC. Therefore, in the presence of varying network conditions, it is necessary to optimally execute the available task of end users while maximizing the energy efficiency in edge platform and we also need to provide fair Quality-of-Service (QoS). On the other hand, it is necessary to schedule the microservices dynamically to minimize the total network delay and network price. Thus, in this article, unlike most of the existing works, we propose a dynamic microservice scheduling scheme for MEC. We design the microservice scheduling framework mathematically and also discuss the computational complexity of the scheduling algorithm. Extensive simulation results show that the microservice scheduling framework significantly improves the performance metrics in terms of total network delay, average price, satisfaction level, energy consumption rate (ECR), failure rate, and network throughput over other existing baselines.

Link Optimization in Software Defined IoV Driven Autonomous Transportation System

Due to the high mobility, dynamic nature, and legacy vehicular networks, the seamless connectivity and reliability become a new challenge in software-defined internet of vehicles based intelligent transportation systems (ITS). Thus, effieicnt optimization of the link with proper monitoring of the high speed of vehicles in ITS is very vital to promote the error-free and trustable platform. Key issues related to reliability, connectivity and stability optimization for vehicular networks are addressed. Thus, this study proposes a novel reliable connectivity framework by developing a stable, and scalable link optimization (SSLO) algorithm, state-of-the-art system model. In addition, a Use-case of smart city with stable and reliable connectivity is proposed by examining the importance of vehicular networks. The numerical experimental results are extracted from software defined-Internet of Vehicle (SD-IoV) platform which shows high stability and reliability of the proposed SSLO under different test scenarios, such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to anything (V2X). The proposed SSLO and Baseline algorithms are compared in terms of performance metrics e.g. packet loss ratio, transmission power (i.e., stability), average throughput, and average delay transfer. Finally, the validated results reveal that SSLO algorithm optimizes connectivity (95%), energy efficiency (67%), throughput (4Kbps) and delay (3 sec).

TAQE: Tweet Retrieval-Based Infrastructure Damage Assessment During Disasters

Twitter is an active communication channel for the spreading of updated information in emergency situations. Retrieving specific information related to infrastructure damage offers the situational views to the concerned authorities, who can take necessary action to disburse help. However, such usages of Twitter demand significant accuracy of the retrieved information. Previous techniques on IR have not been able to capture the semantic variations satisfactorily in the tweets, due to low content quality and vocabulary gap, and consequently have failed to yield considerable performance. This has left ample scope for further improvement in this area of research. There are two major contributions of our work: 1) developing a relevant tweet retrieval framework that provides information about infrastructure damage and 2) assignment of a relative damage score to the affected regions so that the severity of the damage can be assessed. Our proposed technique involves a novel split-query-based mechanism with topic aligned query expansion (TAQE) to retrieve relevant tweets that are subsequently used for measuring the infrastructure damage across different locations. We report empirical results on multiple-crisis-related data sets to establish the efficacy of our approach to these events at different locations. Empirical validation of our proposed approach on manually annotated ground-truth data reveals considerably better performance metrics in terms of precision, recall, Bpref, and MAP over several state-of-the-art techniques.

Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia. The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems. The novelty of the proposed approach is that it employs only the historical values of the GHI itself as explanatory variables and a fast training algorithm (resilient-propagation). In terms of performance metrics, the rp-trained FFANNs provided better results than Quasi-Newton (bfg) algorithm trained FFANNs for almost all the studied combinations of the FFANN structure. It has been also shown that increasing the number of neurons per layer didn’t improve the performance. Medium structures with fast training algorithms are recommended.

Assessment of current rotor design comparison practices based on high-fidelity CFD methods

ABSTRACTThis paper provides an assessment of current rotor design comparison practices. First, the employed CFD method is validated for a number of rotor designs and is shown to achieve accurate performance predictions in hover and high-speed forward flight. Based on CFD results, a detailed investigation is performed in terms of comparing different rotor designs. The CFD analysis highlighted the need of high fidelity methods due to the subtle aerodynamics involved in advanced planforms. Nevertheless, the paper suggests that the correct basis for comparison in terms of performance metrics must be used to inform decisions about the suitability of the rotor blades designs for specific applications. In particular, when comparing blades of advanced planforms, direct torque and thrust comparisons are better than the commonly used lift to drag ratio and figure of merit.

Location Verification Technique for Cluster Based Geographical Routing in MANET

In mobile ad hoc network (MANET), routing has been the main issue because its high mobility and maintaining its routing structures are important requirements. Geographical routing mostly relies on real time location information, however, there exist lags in correctness of location information, and malicious nodes can cause troubles in accurate location tracking in the network. In order to ensure the correctness of location update information, in this paper, we propose a novel design based on a cluster based geographic routing (CBGR) formulation (Muthusenthil and Murugavalli, 2014), wherein we add a position verification technique based on a direct symmetry test (DST) to securely verify the location claims. We further introduce a new noise threshold parameter in the CBGR formulation to evaluate the correctness of location information based on a DST. Then a location based encryption scheme is employed to protect the estimated location against the eavesdropping attacks. With our simulation results, we show that the proposed location verification technique for CBGR (LVT-CBGR) network enhances the network security and performs better compared to other protocols in terms of performance metrics. The experimental outcomes illustrate the fact that our approach is well-geared to scale down the overall network expenditure.

Outage Performance Analysis of Reconfigurable Intelligent Surfaces-Aided NOMA Under Presence of Hardware Impairment

The future of wireless communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) wireless networks. Since the traditional wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces (RIS) in wireless communications enables, on the other hand, network operators to control the radio waves (the scattering, reflection, and refraction characteristics) to eliminate the negative effects of natural wireless propagation. Recent results have revealed that non-orthogonal multiple access (NOMA) benefits from RIS mechanism which can effectively provide effective transmissions. Motivated by the potential of these emerging technologies, we study the impact of hardware impairment in RIS-aided NOMA system in term of performance metrics. We then derive analytical expressions of outage probability and throughput as main performance metrics. Simulations are conducted to validate the analytical expressions. We find that the number of meta-surfaces in RIS, transmit power at the base station, power allocation factors play important role to demonstrate improvement in system performance of RIS relying on NOMA compared with orthogonal multiple access (OMA). Numerical results are presented to validate the effectiveness of the proposed RIS-aided NOMA transmission strategies.

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images.

Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers’ predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

Lung Cancer Detection using Bio-Inspired Algorithm in CT Scans and Secure Data Transmission through IoT Cloud

Primary recognition of pulmonary cancer nodules eloquently increases the odds of survival, also leads it solider problem to resolve, as it often relies on a tomography scan filmic examination. By increasing the possibility of effective treatment, earlier tumor diagnosis decreases lung cancer mortality. Radiologists usually diagnose lung cancer on medical images by a systematic analysis that consumes more time and is unreliable often, because of the substantial improvement in the transmission of data in the healthcare sector, the protection and integrity of medical data has been a huge problem for healthcare applications. This study utilizes computational intelligence techniques. For detection and data transmission, a novel Hybrid model is therefore proposed in this paper. Two steps are involved in the proposed method where diverse image processing procedures are used to detect cancer in the first step using MATLAB and data transfer to authorized persons via the IoT cloud in the second stage. The simulated steps include pre-processing, segmentation by Otsu thresholding along with swarm intelligence algorithm, extraction of features by local binary pattern and classification using the support vector machine (SVM). This work demonstrates the dominance of swarm-intelligent framework over the conventional algorithms in terms of performance metrics like sensitivity, accuracy and specificity as well as training time. The tests carried out show that the model built can achieve up to 92.96 percent sensitivity, 93.53 percent accuracy and 98.52 percent specificity.

An updated dashboard of complete search FSM implementations in centralized graph transaction databases

Frequent subgraph mining algorithms are widely used in various areas for information analysis. As yet, a handful of algorithms have been proposed and defined in the literature. While several experimental studies were reported, these experiments lack critical information which are important for selecting an implementation of an algorithm for a specific case of use. In this paper, we report on experiments that we carried out on available implementations of complete search Frequent Subgraph Mining (FSM) algorithms. These experiments are conducted in order to choose a suitable FSM solution (i.e., implementation). We identified 32 algorithms in the literature, six of them were selected for our experiments, through a filtering process relying on a set of criteria. Thirteen working implementations of these 6 algorithms are experimented. In this paper, we provide details of the experiments in terms of performance metrics and input variation effect. We propose a preliminary selection of the most efficient FSM solutions for end users, based on the most tested centralized graph-transaction datasets of the literature.

Multiobjective Design Optimization of a Biconcave Mobile-Bearing Lumbar Total Artificial Disk Considering Spinal Kinematics, Facet Joint Loading, and Metal-on-Polyethylene Contact Mechanics.

Total disk arthroplasty (TDA) using an artificial disk (AD) is an attractive surgical technique for the treatment of spinal disorders, since it can maintain or restore spinal motion (unlike interbody fusion). However, adverse surgical outcomes of contemporary lumbar TDAs have been reported. We previously proposed a new mobile-bearing AD design concept featuring a biconcave ultrahigh-molecular-weight polyethylene (UHMWPE) mobile core. The objective of this study was to develop an artificial neural network (NN) based multiobjective optimization framework to refine the biconcave-core AD design considering multiple TDA performance metrics, simultaneously. We hypothesized that there is a tradeoff relationship between the performance metrics in terms of range of motion (ROM), facet joint force (FJF), and polyethylene contact pressure (PCP). By searching the resulting three-dimensional (3D) Pareto frontier after multiobjective optimization, it was found that there was a "best-tradeoff" AD design, which could balance all the three metrics, without excessively sacrificing each metric. However, for each single-objective optimum AD design, only one metric was optimal, and distinct sacrifices were observed in the other two metrics. For a commercially available biconvex-core AD design, the metrics were even worse than the poorest outcomes of the single-objective optimum AD designs. Therefore, multiobjective design optimization could be useful for achieving native lumbar segment biomechanics and minimal PCPs, as well as for improving the existing lumbar motion-preserving surgical treatments.