Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

Education acts as an important part of economic growth and improvement in human welfare.The educational sectors have transformed a lot in recent days,and Information and Communication Technology(ICT)is an effective part of the education field.Almost every action in university and college,right from the process fromcounselling to admissions and fee deposits has been automated.Attendance records,quiz,evaluation,mark,and grade submissions involved the utilization of the ICT.Therefore,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for CyberSecurity in Higher Education Institutions(AOD-CSHEI)technique.The AOD-CSHEI technique intends to determine the presence of intrusions or attacks in the HEIs.The AOD-CSHEI technique initially performs data pre-processing in two stages namely data conversion and class labelling.In addition,the Adaptive Synthetic(ADASYN)technique is exploited for the removal of outliers in the data.Besides,the sparrow search algorithm(SSA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs network.Finally,the SSA is utilized to effectually adjust the hyper parameters of the DNN approach.In order to showcase the enhanced performance of the AOD-CSHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CSHEI technique over its compared methods with higher accuracy of 0.9997.

An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of COVID-19.The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places.Therefore,it is very difficult to manually monitor people in overcrowded areas.This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19.This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked.The proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces.Moreover,this work applies to real-time and can be implemented in any public service area.

Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of attacks.To accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish security.The incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game changer.Consequently,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly effective.Thus,artificial intelligence(AI)and machine learning(ML)models have shown significant interest in HEIs.This study designs a novel Artificial Intelligence based Cybersecurity Intrusion Detection Model for Higher Education Institutions named AICIDHEI technique.The goal of the AICID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education institutes.The AICID-HEI technique encompassesmin-max normalization approach to preprocess the data.Besides,the AICID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-FS)technique is applied to choose the feature subsets.Moreover,the bidirectional long short-term memory(BiLSTM)model is utilized for the detection and classification of intrusions in the network.Furthermore,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational institutions.In order to validate the experimental results of the proposed AICID-HEI technique,the simulation results of the AICIDHEI technique take place by the use of benchmark dataset.The experimental results reported the betterment of the AICID-HEI technique over the other methods interms of different measures.

Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City

In recent years,Smart City Infrastructures(SCI)have become familiar whereas intelligent models have been designed to improve the quality of living in smart cities.Simultaneously,anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of pedestrians.The increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions,a laborious process.In this background,Deep Learning(DL)models can be used in the detection of anomalies found through video surveillance systems.The current research paper develops an Internet of Things Assisted Deep Learning Enabled Anomaly Detection Technique for Smart City Infrastructures,named(IoTAD-SCI)technique.The aim of the proposed IoTAD-SCI technique is to mainly identify the existence of anomalies in smart city environment.Besides,IoTAD-SCI technique involves Deep Consensus Network(DCN)model design to detect the anomalies in input video frames.In addition,Arithmetic Optimization Algorithm(AOA)is executed to tune the hyperparameters of the DCN model.Moreover,ID3 classifier is also utilized to classify the identified objects in different classes.The experimental analysis was conducted for the proposed IoTADSCI technique upon benchmark UCSD anomaly detection dataset and the results were inspected under different measures.The simulation results infer the superiority of the proposed IoTAD-SCI technique under different metrics.

Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds.So,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data prediction.In this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical data.The output of optimization is either 0 or 1 i.e.,odd or even in QC.From this output value,the data is identified according to the class.Meanwhile,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter method.After the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by LTSA.The motivation of the minimal objective is to remain faster.However,some computations can be performed more efficiently by the proposed model.In testing,the test data was evaluated by minimal loss function.The outcomes were assessed in terms of accuracy,computational time,and so on.For this,databases like Lymphography,Dermatology,and Arrhythmia were used.