Hybrid Machine Learning Model for Face Recognition Using SVM

Face recognition systems have enhanced human-computer interactions in the last ten years.However,the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations.Principal Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis techniques.Compared to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data samples.Apart from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN.PCA-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s face.This study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection.The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the deadline.To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time.In order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications.In this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier.The predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the system.Fromresults as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.

A Performance Study of Membership Inference Attacks on Different Machine Learning Algorithms

Nowadays,machine learning(ML)algorithms cannot succeed without the availability of an enormous amount of training data.The data could contain sensitive information,which needs to be protected.Membership inference attacks attempt to find out whether a target data point is used to train a certain ML model,which results in security and privacy implications.The leakage of membership information can vary from one machine-learning algorithm to another.In this paper,we conduct an empirical study to explore the performance of membership inference attacks against three different machine learning algorithms,namely,K-nearest neighbors,random forest,support vector machine,and logistic regression using three datasets.Our experiments revealed the best machine learning model that can be more immune to privacy attacks.Additionally,we examined the effects of such attacks when varying the dataset size.Based on our observations for the experimental results,we propose a defense mechanism that is less prone to privacy attacks and demonstrate its effectiveness through an empirical evaluation.