Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.

MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly dynamic.Clustering is one of the promising solutions to maintain the route stability in the dynamic network.However,existing algorithms consume a considerable amount of time in the cluster head(CH)selection process.Thus,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of vehicles.The MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean distance.The CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.

Characterization and optimization of pulse electrodeposition of Ni/nano-Al_2O_3 composite coatings

Nickel/nano-A1203 composite coatings produced by the pulse electrodeposition method and the influence of pulse parameters, i.e., pulse frequency, duty cycle, and current density on the microstructure, hardness, and corrosion resistance, were critically investigated on an AISI 1018 mild steel specimen electroplated in a Watt＇s type bath. The experiments were carried out with different combinations of pulse parameters using Taguchi＇s L27 orthogonal array, and 27 trials were conducted to study the effect of pulse parameters in view to maximize the hardness of the specimen. The assessment results clearly reveal that the specimen exhibits the maximum hardness at the pulse frequency of 20 Hz, duty cycle of 30%, and peak current density of 0.4 A/cm2, which are designated as the optimal parameters herein. Furthermore, the influences of those optimized pulse parameters over the microstructure and corrosion resistance were investigated, and some conclusions were drawn. Also, from the ANOVA examination, it is clear that duty cycle is predominant in affecting the hardness, while current density has relatively low impact.

SYNTHESIS AND CHARACTERIZATION OF TRIAZOLE CONTAINING LIQUID CRYSTALLINE POLYMERS THROUGH 1,3-DIPOLAR CYCLOADDITION POLYMERIZATION

Liquid crystalline polymers containing 1,2,3-triazole units as linking groups have been synthesized from the monomers containing triad ester diazide and flexible dialkyne ester by 1,3-cycloaddition reaction and were characterized. Click reaction of azide and alkyne functionals catalyzed by Cu(I) yielded target polyesters with 1,2,3-triazole groups.The structure of the polymer was confirmed by spectral techniques.GPC analysis reveals that the polymers have moderate molecular weight with narrow distribution.H...

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Comparison of High Performance Fly Ash Concrete Using Nano Silica Fume on Different Mixes

This paper investigates the experimental results on the effect of nano silica fume on compressive strength development of concretes containing high volume fly ash (HVFA). The effect of various silica fumes contents such as 1%, 2% & 3% (wt. %) as partial replacement of cement on the compressive strength of cements is evaluated in the first part. The nano silica fume content which exhibits the highest compressive strength above is used in high volume fly ash concretes containing 30% and 50% class F fly ash. The results show that among three different silica fumes contents, the addition of 1% increases the compressive strength of concretes. The addition of 1% silica fume also increases the early age and 28 days compressive strengths of HVFA concretes.

Design and Implementation of Bi-Directional DC-DC Converter for Wind Energy System

This paper proposes a design and implementation of the bi-directional DC-DC converter for Wind Energy Conversion System. The proposed project consists of boost DC/DC converter, bi-directional DC/DC converter (BDC), permanent magnet DC generator and batteries. A DC-DC boost converter is interface with proposed wind system to step up the initial generator voltage and maintain constant output voltage. The fluctuation nature of wind makes them unsuitable for standalone operation. To overcome the drawbacks an energy storage device is used in the proposed system to compensate the fluctuations and to maintain a smooth and continuous power flow in all operating modes to load. Bi-directional DC-DC converter (BDC) is capable of transforming energy between two DC buses. It can operate as a boost converter which supplies energy to the load when the wind generator output power is greater than the required load power. It also operates in buck mode which charges from DC bus when output power is less than the required load power. The proposed converter reduces the component losses and increases the performance of the overall system. The complete system is implemented in MATLAB/SIMULINK and verified with hardware.

Properties of Sustainable High Strength Concrete with Waste Copper Slag

The present work investigates copper slag as a substitute for river sand in high-strength concrete.The concrete mixtures were manufactured with 10%,30%,50%,70%,and 100%of copper slag to evaluate the mechanical and durability properties.The experimental results indicate that replacing copper slag above 50%affects the performance characteristics of the concrete due to its high angularity and lower water absorption characteristics.The strength of concrete with 50%copper slag is improved by 5.6%,whereas the strength of concrete with 100%copper slag is reduced by 2.75%at 28 days.However,increased curing to 90days improves the strength of the former by 7.16%and reduces the latter by only 0.23%.The water absorption,porosity,and rapid chloride penetration of the concrete mixtures with 100%copper slag are increased by 10.44%,13.20%,and 19.56%compared to control concrete.Micro-structural investigations through SEM infer higher replacement of copper results in higher void formation due to its reduced water absorption.

Evaluation of Codebook Design Using SCMA Scheme Based on A_(n) and D_(n) Lattices

The sparse code multiple access(SCMA)scheme is a Non-Orthogonal Multiple Access(NOMA)type of scheme that is used to handle the uplink com-ponent of mobile communication in the current generation.A need of the 5G mobile network is the ability to handle more users.To accommodate this,the SCMA allows each user to deploy a variety of sub-carrier broadcasts,and several consumers may contribute to the same frequency using superposition coding.The SCMA approach,together with codebook design for each user,is used to improve channel efficiency through better management of the available spectrum.How-ever,developing a codebook with a greater number of value sets is still another challenge.With enhanced techniques of encoding and decoding for 5G networks,mapping the multidimensional constellations in the SCMA system plays a signif-icant role in improving the system performance and enhancing the overall system performance.The creation of a codebook utilizing the SCMA approach in con-junction with the lattice theory is suggested in this study.The prototype is shaped using a popular lattice,such as A n and D n,as the basis.Afterward,from the primary lattice constellation,the multidimensional complex mother constellation with the most noticeable variance in power is discovered.The lattice-based cod-ing is generated by combining the codebooks with the mother constellation,and the codes in the matrices are mapped by rotating the constellations in this context.The suggested technique,in conjunction with the investigation of novel SCMA codebook sets,provides improved performance in terms of Bit Error Rate(BER)and complexity with regard to Signal to Noise Ratio(SNR).Finally,the bit error rate is reduced for various SNRs during transmission in the channel.

Fault Recognition of Multilevel Inverter Using Artificial Neural Network Approach

This paper focuses on the development of a diagnostic tool for detect-ing insulated gate bipolar transistor power electronic switchflaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications.High-resolution laboratory virtual instrument engineering work-bench software testing tool with a sample rate data collection system,as well as specialized signal processing and soft computing technologies,are used in this proposed method.On a single-phase cascaded H-bridge multilevel inverter,simu-lation and experimental investigations of both open and short issues of the insu-lated gate bipolar transistor components are performed out.In all conceivable switch issues,the output voltage signals are evaluated for different modulation index values.Fast fourier transform and discrete wavelet transform methods are used to investigate the frequency domain properties of output voltage signals.In the artificial neural network,the back propagation training technique was employed,and the generated neural parameter values were used in the Laboratory Virtual Instrumentation Engineering Workbench real-time fault diagnosis model.

Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines

Virtualization is the backbone of cloud computing,which is a developing and widely used paradigm.Byfinding and merging identical memory pages,memory deduplication improves memory efficiency in virtualized systems.Kernel Same Page Merging(KSM)is a Linux service for memory pages sharing in virtualized environments.Memory deduplication is vulnerable to a memory disclosure attack,which uses covert channel establishment to reveal the contents of other colocated virtual machines.To avoid a memory disclosure attack,sharing of identical pages within a single user’s virtual machine is permitted,but sharing of contents between different users is forbidden.In our proposed approach,virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch,with memory deduplication performed inside that batch,to improve the memory pages sharing efficiency.When compared to memory deduplication applied to the entire host,implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU(Central processing unit)consumption also increased.

LOA-RPL:Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things(IoT)because IoT involves highly resource-constrained devices.Clustering is an effective technique for saving energy by reducing duplicate data.In a clustering protocol,the selection of a cluster head(CH)plays a key role in prolonging the lifetime of a network.However,most cluster-based protocols,including routing protocols for low-power and lossy networks(RPLs),have used fuzzy logic and probabilistic approaches to select the CH node.Consequently,early battery depletion is produced near the sink.To overcome this issue,a lion optimization algorithm(LOA)for selecting CH in RPL is proposed in this study.LOA-RPL comprises three processes:cluster formation,CH selection,and route establishment.A cluster is formed using the Euclidean distance.CH selection is performed using LOA.Route establishment is implemented using residual energy information.An extensive simulation is conducted in the network simulator ns-3 on various parameters,such as network lifetime,power consumption,packet delivery ratio(PDR),and throughput.The performance of LOA-RPL is also compared with those of RPL,fuzzy rule-based energyefficient clustering and immune-inspired routing(FEEC-IIR),and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm(RISARPL).The performance evaluation metrics used in this study are network lifetime,power consumption,PDR,and throughput.The proposed LOARPL increases network lifetime by 20%and PDR by 5%–10%compared with RPL,FEEC-IIR,and RISA-RPL.LOA-RPL is also highly energy-efficient compared with other similar routing protocols.

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

Investigations on Tracking and Erosion Resistance of Nano Silicone Composite for High Voltage Outdoor Insulation

In recent times, silicone rubber insulating material is used for power transmission line and substation insulation applications. In the present work, tracking and erosion resistance of the micro size filled and nano size filled silicone rubber material has been studied under the AC voltage, with ammonium chloride as a contaminant, as per IEC 60587 test procedures. The characteristic changes in the tracking resistance of the micro size and nano size filled specimens are analyzed through leakage current measurement. Comparative Tracking Index （CTI） is also evaluated in order to understand the relative behavior of solid electrical insulating material with regard to their susceptibility to surface tracking. Trend followed by the fundamental, third harmonic and fifth harmonic components of the leakage current during the tracking study are analyzed using moving average current technique. It is observed that the harmonic components of leakage current show good correlation with the tracking and erosion resistance of the material. It is noticed that 5 % wt ofnano size filler gives similar performance to that of 30 % wt of micro size filler in silicone composites.

EFFECT OF RECYCLED COARSE AGGREGATES IN PROPERTIES OF CONCRETE

The properties of concrete containing coarse recycled aggregates were investigated.Laboratory trials were conducted to investigate the possibility of using recycled aggregates from the demolition wastes available locally as the replacement of natural coarse aggregates in concrete.A series of tests were carried out to determine the density,compressive strength,split tensile strength,flexural strength and modulus of elasticity of concrete with and without recycled aggregates.The water cement ratio was kept constant for all the mixes.The coarse aggregate in concrete was replaced with 0%,20%,40%,60%,80%and 100%recycled coarse aggregates.The test results indicated that the replacement of natural coarse aggregates by recycled aggregates up to 40%had little effect on the compressive strength,but higher levels of replacement reduced the compressive strength.A replacement level of 100%causes a reduction of 28%in compressive strength,36%in split tensile strength and 50%in flexural strength.For strength characteristics,the results showed a gradual decrease in compressive strength,split tensile strength,flexural strength and modulus of elasticity as the percentage of recycled aggregate used in the specimens increased.100%replacement of natural coarse aggregate by recycled aggregate resulted in 43%savings in the cost of coarse aggregates and 9%savings in the cost of concrete.

Soft Computing: Fuzzy Logic Approach in Wireless Sensors Networks

The recent growth of communication and sensor technology results in the enlargement of a new attractive and challenging area-wireless sensor networks (WSNs). A network comprising of several minute wireless sensor nodes which are organized in a dense manner is called as a Wireless Sensor Network (WSN). Every node estimates the state of its surroundings in this network. The estimated results are then converted into the signal form in order to determine the features related to this technique after the processing of the signals. It’s high computational environment with limited and controlled broadcast range, processing, as well as limited energy. The embedded soft computing approach in wireless sensor networks is suggested. This approach means a grouping of embedded fuzzy logic and neural networks models for information processing in complex environment with unsure, rough, fuzzy measuring data. It is generalization of soft computing concept for the embedded, distributed, adaptive systems.