Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.

Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data traffic.The system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT infrastructure.An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers.Nowadays,machine learning(ML)-related techniques were used for detecting intrusion in IoTs IDSs.But,the IoT IDS mechanism faces significant challenges because of physical and functional diversity.Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult.This study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for IDS.The proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT environment.To attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input dataset.For feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)technique.In the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLSTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the MM-WMVEDL technique.The comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective exploitation.The traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical models.The recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)models.In this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)model.The presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the environment.To attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic flow.In order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

Industrial Control Systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively.Internet of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent processes.The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,etc.Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes essential.This study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial infrastructures.The proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE approach.The primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial infrastructures.TheMFROFS-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter tuning.Besides,the MRFO based feature selection approach is designed for the optimal selection of feature subsets.Moreover,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

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.