Hybridized Artificial Neural Network for Automated Software Test Oracle

Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality.These two characteristics are very critical in the software applications of present times.When testers want to perform scenario evaluations,test oracles are generally employed in the third phase.Upon test case execution and test outcome generation,it is essential to validate the results so as to establish the software behavior’s correctness.By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application,leads to a reduction in the fault detection work with minimal loss of information and would also greatly reduce the cost for clearing up.A hybrid Particle Swarm Optimization(PSO)with Stochastic Diffusion Search(PSO-SDS)based Neural Network,and a hybrid Harmony Search with Stochastic Diffusion Search(HS-SDS)based Neural Network has been proposed in this work.Further to evaluate the performance,it is compared with PSO-SDS based artificial Neural Network(PSO-SDS ANN)and Artificial Neural Network(ANN).The Misclassification of correction output(MCO)of HS-SDS Neural Network is 6.37 for 5 iterations and is well suited for automated testing.

Performance Enhancement of PV Based Boost Cascaded Fifteen Level Inverter for AC Loads

In this research work,single-stagefifteen levels cascaded DC-interface converter(CDDCLC)is proposed for sun arranged photovoltaic technology(PV)applications.The proposed geography is joined with help DC chopper and H-associate inverter to upgrade the power converter to accomplish the diminished harmonic profile.In assessment with the customary inverter structures,the proposed system is used with diminished voltage stress,decreased switch count and DC source tally.The proposed research work with cascaded DC link conver-ter design requires three DC sources for combiningfifteen-level AC output.This investigation structure switching technique is phase opposition and disposition pulse width modulation technique(POPD)which results in improved quality of obtained output AC power with 6.73%THD and also determinedly recommended for power converters used in UPS and drive applications since it is extremely affordable.A simulation and prototype model offifteen-level CDDCLC system is deployed and its performance is analyzed for various operating conditions.

Optimization of Disjoints Using WDM-PON in an Optical Network

An optical network is a type of data communication network built with optical fibre technology. It utilizes optical fibre cables as the primary communication medium for converting data and passing data as light pulses between sender and receiver nodes. The major issue in optical networking is disjoints that occur in the network. A polynomial time algorithm Wavelength Division Multiplexing-Passive Optical Networking (WDM-PON) computes disjoints of an optical network and reduces the count of disjoints that occur in the network by separating Optical Network Units (ONU) into several virtual point-to-point connections. The Arrayed Waveguide Grating (AWG) filter is included in WDM-PON to avoid the traffic in the network thereby increasing the bandwidth capacity. In case of a failure or disjoint Ant Colony Optimization (ACO) algorithm is used to find the optimized shortest path for re-routing. For enhanced security, modified Rivert Shamir Adleman (RSA) algorithm encrypts the message during communication between the nodes. The efficiency is found to be improved in terms of delay in packet delivery, longer optical reach, optimized shortest path, packet error rate.

Proposal of High Gain, Reduced Stress with Low-Duty-Cycle Two-Input Boost Converter for Renewable Energy Systems

A two-input boost converter with voltage multiplier cell is proposed in this paper. Then a family of two-input converters with and without voltage multiplier cell are derived and their results are compared to achieve high voltage gain, low duty cycle, and reduced voltage stress. From the analysis of different topologies, a modified two-input converter with two-stage voltage multiplier cell has good operating characteristics. The switch voltage stress and duty cycle of the modified converter is significantly very less than that of the other converter topologies. The modified DC-DC converter with 50% duty cycle achieves a voltage gain of 10 and the results are verified by using MATLAB/Simulink software.

Performance Analysis of Double Boost Converter Powered FSTPI Fed Induction Motor

Pulse Width Modulated (PWM) inverter-fed induction motor drives are most common in industrial applications. This paper aims at development of double boost converter for PWM inverter-fed three-phase induction motor. The inverter topology is designed with four switches. The proposed drive system has been simulated using Matlab/Simulink and the performance of has been assessed in terms of output voltage, output current, power factor and THD. From the simulation results, it is evident that the three-phase voltage waveforms of the proposed system are less distorted, with their currents being more sinusoidal. A comparative analysis has been made with the conventional six-switch inverter fed drive. The proposed system offered a THD of 1.84%, whereas for the conventional system it was 13.96%. These results inferred that the proposed double boost converter with four-switch based drive scheme exhibits superior performance.

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrast-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical techniques.With this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved regions.To perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful vectors.Finally,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

Intelligent Vehicular Communication Using Vulnerability Scoring Based Routing Protocol

Internet of Vehicles(IoV)is an intelligent vehicular technology that allows vehicles to communicate with each other via internet.Communications and the Internet of Things(IoT)enable cutting-edge technologies including such self-driving cars.In the existing systems,there is a maximum communication delay while transmitting the messages.The proposed system uses hybrid Cooperative,Vehicular Communication Management Framework called CAMINO(CA).Further it uses,energy efficient fast message routing protocol with Common Vulnerability Scoring System(CVSS)methodology for improving the communication delay,throughput.It improves security while transmitting the messages through networks.In this research,we present a unique intelligent vehicular infrastructure communication management framework.This framework includes additional stability for both short and long-range mobile communications.It also includes built-in cooperative intelligent transport system(C-ITS)capabilities for experimental verification in real-world contexts.In addition,an energy efficient-fast message distribution routing protocol(EE-FMDRP)has been presented.This combines the benefits between both temporal and direction oriented routing methods.This has been suggested for distributing information from the origin ends to the predetermined objective in a quick,accurate,and effective manner in the event of an emergency.The critical value scale score(CVSS)employ ratings to measure the assault probability in Markov chains.Probabilities of chained transitions allow us to statistically evaluate the integrity of a group of IoVassets.Thus the proposed method helps to enhance the vehicular systems.The CAMINO with energy efficient fast protocol using CVSS(CA-EEFP-CVSS)method outperforms in terms of shortest transmission latency achieves 2.6 sec,highest throughput 11.6%,and lowest energy usage 17%and PDR 95.78%.

Multisensor Information Fusion for Condition Based Environment Monitoring

Destructive wildfires are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the envir-onment.The result,however,is disastrous,causing irreversible damage to the ecosystem.The location of the incident and the hotspot can sometimes have an impact on earlyfire detection systems.With the advancement of intelligent sen-sor-based control technologies,the multi-sensor data fusion technique integrates data from multiple sensor nodes.The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence,allowingfire units to respond as soon as possible.Thus to predict the occurrence offire in forests,a fast and effective intelligent control system is proposed.The proposed algorithm with decision tree classification determines whetherfire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex environment.The experimental results of the proposed model have a detection rate of 98.3.Thus,providing real-time monitoring of certain environ-mental variables for continuous situational awareness and instant responsiveness.

Ensemble Based Learning with Accurate Motion Contrast Detection

Recent developments in computer vision applications have enabled detection of significant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalflows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced significantly.Thus,consideration of object motions in videos efficiently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efficiently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple significant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.

Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

Cloud computing(CC)is an advanced technology that provides access to predictive resources and data sharing.The cloud environment represents the right type regarding cloud usage model ownership,size,and rights to access.It introduces the scope and nature of cloud computing.In recent times,all processes are fed into the system for which consumer data and cache size are required.One of the most security issues in the cloud environment is Distributed Denial of Ser-vice(DDoS)attacks,responsible for cloud server overloading.This proposed sys-tem ID3(Iterative Dichotomiser 3)Maximum Multifactor Dimensionality Posteriori Method(ID3-MMDP)is used to overcome the drawback and a rela-tively simple way to execute and for the detection of(DDoS)attack.First,the pro-posed ID3-MMDP method calls for the resources of the cloud platform and then implements the attack detection technology based on information entropy to detect DDoS attacks.Since because the entropy value can show the discrete or aggregated characteristics of the current data set,it can be used for the detection of abnormal dataflow,User-uploaded data,ID3-MMDP system checks and read risk measurement and processing,bug ratingfile size changes,orfile name changes and changes in the format design of the data size entropy value.Unique properties can be used whenever the program approaches any data error to detect abnormal data services.Finally,the experiment also verifies the DDoS attack detection capability algorithm.

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

In social data analytics,Virtual Community(VC)detection is a primary challenge in discovering user relationships and enhancing social recommenda-tions.VC formation is used for personal interaction between communities.But the usual methods didn’t find the Suspicious Behaviour(SB)needed to make a VC.The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking(GJSBS-RDNNCR)Model addresses these issues.The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks(SN).In the GJSBS-RDNNCR model,the SN is given as an input at the input layer.After that,the User’s Behaviors(UB)are extracted in the first Hidden Layer(HL),and the Generalized Jaccard Similarity coefficient calculates the similarity value at the second HL based on the SB.In the third HL,the similarity values are examined,and SB tendency is classified using the Activation Function(AF)in the Output Layer(OL).Finally,the ranking process is performed with classified users in SN and their SB.Results analysis is performed with metrics such as Classification Accuracy(CA),Time Complexity(TC),and False Positive Rate(FPR).The experimental setup consid-ers 250 tweet users from the dataset to identify the SBs of users.

Decision Tree Based Key Management for Secure Group Communication

Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive systems.Secured group communication plays a vital role in case of providing the integrity,authenticity,confidentiality,and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group.In secure group communications,the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service,particularly in the large groups with highly active membership.Hence,the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group communication.During this process,each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory key.Each of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group communication.The rekeying process is performed when a member leaves or adds into the group.The performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost,rekeying process and formation of the group.It is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods.

Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire network.Another phishing issue is the broadening malware of the entire network,thus highlighting the demand for their detection while massive datasets(i.e.,big data)are processed.Despite the application of boosting mechanisms in phishing detection,these methods are prone to significant errors in their output,specifically due to the combination of all website features in the training state.The upcoming big data system requires MapReduce,a popular parallel programming,to process massive datasets.To address these issues,a probabilistic latent semantic and greedy levy gradient boosting(PLS-GLGB)algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection.Here,the missing data in each URL are identified and discarded for further processing to ensure data quality.Subsequently,with the preprocessed features(URLs),feature vectors are updated by the greedy levy divergence gradient(model)that selects the optimal features in the URL and accurately detects the websites.Thus,greedy levy efficiently differentiates between phishing websites and legitimate websites.Experiments are conducted using one of the largest public corpora of a website phish tank dataset.Results show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods.Significant amounts of phishing detection time and errors are also saved during the detection of website phishing.

Monarch Butterfly Optimization for Reliable Scheduling in Cloud

Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources.The scheduling of the cloud tasks is a well-recognized NP-hard problem.The Task scheduling problem is convoluted while convincing different objectives,which are dispute in nature.In this paper,Multi-Objective Improved Monarch Butterfly Optimization(MOIMBO)algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions.Three different dispute objectives,such as makespan,reliability,and resource utilization,are deliberated for task scheduling problems.The Epsilonfuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost solutions from the Pareto optimal solution set.MOIMBO,together with the Self Adaptive and Greedy Strategies,have been incorporated to enrich the performance of the proposed algorithm.The capability and effectiveness of the proposed algorithm are measured with NSGA-II and MOPSO algorithms.The simulation results prompt that the proposed MOIMBO algorithm extensively diminishes the makespan,maximize the reliability,and guarantees the appropriate resource utilization when associating it with identified existing algorithms.

Data Intelligent Low Power High Performance TCAM for IP-Address Lookup Table

This paper represents current research in low-power Very Large Scale Integration (VLSI) domain. Nowadays low power has become more sought research topic in electronic industry. Power dissipation is the most important area while designing the VLSI chip. Today almost all of the high speed switching devices include the Ternary Content Addressable Memory (TCAM) as one of the most important features. When a device consumes less power that becomes reliable and it would work with more efficiency. Complementary Metal Oxide Semiconductor (CMOS) technology is best known for low power consumption devices. This paper aims at designing a router application device which consumes less power and works more efficiently. Various strategies, methodologies and power management techniques for low power circuits and systems are discussed in this research. From this research the challenges could be developed that might be met while designing low power high performance circuit. This work aims at developing Data Aware AND-type match line architecture for TCAM. A TCAM macro of 256 × 128 was designed using Cadence Advanced Development Environment (ADE) with 90 nm technology file from Taiwan Semiconductor Manufacturing Company (TSMC). The result shows that the proposed Data Aware architecture provides around 35% speed and 45% power improvement over existing architecture.

BELBIC Tuned PI Controller Based Chopper Driven PMDC Motor

In orthopaedic surgeries, permanent magnet DC motors are used to drill the bone and fix the screws. The Motor drive employs an inner current and outer speed control loop with a conventional or modern controller. To enhance the performance of the drive, this paper proposes a Brain Emotional Logic Based Intelligent Controller based chopper drive. The proposed drive scheme has been simulated using Matlab/Simulink and physically realized for validation. A comparative analysis has been made between the conventional PI controller based drive and the proposed system in order to prove that the proposed scheme has an edge over the traditional PI controller scheme counterpart.