Security Implementation in WSN with Symmetric and Matrix Mapping on Asymmetric ECC Cryptographic Techniques

As the wireless sensor networks are easily deployable, the volume of sensor applications has been increased widely in various fields of military and commercial areas. In order to attain security on the data exchanged over the network, a hybrid cryptographic mechanism which includes both symmetric and asymmetric cryptographic functions is used. The public key cryptographic ECC security implementation in this paper performs a matrix mapping of data’s at the points on the elliptical curve, which are further encoded using the private symmetric cipher cryptographic algorithm. This security enhancement with the hybrid mechanism of ECC and symmetric cipher cryptographic scheme achieves efficiency in energy conservation of about 7% and 4% compared to the asymmetric and symmetric cipher security implementations in WSN.

An Intelligent Predictive Model-Based Multi-Response Optimization of EDM Process

Electrical Discharge Machining(EDM)is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials.It does not require a cutting tool and can machine complex geometries easily.However,it suffers from drawbacks like a poor rate of machining and excessive tool wear.In this research,an attempt is made to address these issues by using an intelligent predictive model coupled global optimization approach to predict suitable combinations of input parameters(current,pulse on-time and pulse off-time)that would effectively increase the material removal rate and minimize the tool wear.The predictive models,which are based on the symbolic regression approach exploit the machine intelligence of Genetic Programming(GP).As compared to traditional polynomial response surface(PRS)predictive models,the GP predictive models show compactness as well as better prediction capability.The developed GP predictive models are deployed in conjunction with NSGA-II to predict Pareto optimal solutions.

Optimized Image Multiplication with Approximate Counter Based Compressor

The processor is greatly hampered by the large dataset of picture or multimedia data.The logic of approximation hardware is moving in the direction of multimedia processing with a given amount of acceptable mistake.This study proposes various higher-order approximate counter-based compressor(CBC)using input shuffled 6:3 CBC.In the Wallace multiplier using a CBC is a significant factor in partial product reduction.So the design of 10-4,11-4,12-4,13-4 and 14-4 CBC are proposed in this paper using an input shuffled 6:3 compressor to attain two stage multiplications.The input shuffling aims to reduce the output combination of the 6:3 compressor from 64 to 27.Design of 15-4,10-4,9-4,and 7-3 CBCs are performed using the proposed 6:3 compressor and the results obtained are compared with the existing models.These existing models are constructed using multiplexers and 5-3 CBC.When compared to input shuffled 5-3 the proposed 6:3 compressor shows better results in terms of area,power and delay.An approximation is performed on the 6:3 compressor to further reduce the computational energy of the system which is optimal for multimedia applications.The major contribution of this work is the development of two stage multiplier using various proposed CBC.All designs of the approximate compressor(AC)and true compressor(TC)are analysed with 8 ×8 and 16 × 16 imagemultiplication.The proposed multipliers also provide adequate levels of accuracy,according to the MATLAB simulations,in addition to greater hardware efficiency.As the result approximate circuits over image processing shows the stunning performance in many deep learning network in the current research which is only oriented to multimedia.

Multi-Objective High-Fidelity Optimization Using NSGA-III and MO-RPSOLC

Optimizing the performance of composite structures is a real-world application with significant benefits.In this paper,a high-fidelity finite element method(FEM)is combined with the iterative improvement capability of metaheuristic optimization algorithms to obtain optimized composite plates.The FEM module comprises of ninenode isoparametric plate bending element in conjunction with the first-order shear deformation theory(FSDT).A recently proposed memetic version of particle swarm optimization called RPSOLC is modified in the current research to carry out multi-objective Pareto optimization.The performance of the MO-RPSOLC is found to be comparable with the NSGA-III.This work successfully highlights the use of FEM-MO-RPSOLC in obtaining highfidelity Pareto solutions considering simultaneous maximization of the fundamental frequency and frequency separation in laminated composites by optimizing the stacking sequence.

Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods.It is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed work.Using Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor.Using two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise estimates.The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on.Using the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region.Then,the estimates was made by two separate models that were put together to produce the final prediction.In comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things(IoMT)enabled e-healthcare has the potential to greately improve conventional healthcare services significantly.However,security and privacy become major issues of IoMT because of the restricted processing abilities,storage,and energy constraints of the sensors.Therefore,it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors.In order to ensure security on sensitive medical data,effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers.In this view,this study designs an effective metaheuristic optimization based encryption with user authentication(EMOE-UA)technique for IoMT environment.This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity.Moreover,the EMOE-UA technique employs optimal multikey homomorphic encryption(OMKHE)technique to encrypt the IoMT data.Furthermore,the improved social spider optimization algorithm(ISSOA)was employed for the optimal multikey generation of the MKHE technique.The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects.The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

In contemporary medicine,cardiovascular disease is a major public health concern.Cardiovascular diseases are one of the leading causes of death worldwide.They are classified as vascular,ischemic,or hypertensive.Clinical information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart illness.Heart failure rates have risen drama-tically in recent years as a result of changes in modern lifestyles.Heart diseases are becoming more prevalent in today’s medical setting.Each year,a substantial number of people die as a result of cardiac pain.The primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of diseases.To improve the efficiency of the classification algo-rithms,we construct a data pre-processing stage using feature selection.Experi-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modified Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classification performance on the UCI Machine Learning Heart Disease dataset.The DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease dataset.We demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease dataset.Our proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

Indirect Output Voltage Control in Negative Output Elementary Super Lift Luo Converter Using PIC plus FLC in Discontinuous Conduction Mode

In this paper, the design of a proportional integral controller (PIC) plus fuzzy logic controller (FLC) for the negative output elementary super lift Luo converter (NOESLLC) operated in discontinuous conduction mode (DCM) is presented. In spite of the many benefits viz. the high voltage transfer gain, the high efficiency, and the reduced inductor current and the capacitor voltage ripples, it natured with non-minimum phase. This characteristic makes the control of NOESLLC cumbersome. Any attempt of direct controlling the output voltage may erupt to instability. To overcome this problem, indirect regulation of the output voltage based on the two-loop controller is devised. The savvy in the inductor current control improves the dynamic response of the output voltage. The FLC is designed for the outer (voltage) loop while the inner (current) loop is controlled by the PIC. For the developed ?19.6 V NOESLLC, the dynamic performances for different perturbations (line, load and component variations) are obtained for PIC plus FLC and compared with PIC plus PIC. The study of two cases is performed at various operating regions by developing the MATLAB/Simulink model.