Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Fake reviews,also known as deceptive opinions,are used to mislead people and have gained more importance recently.This is due to the rapid increase in online marketing transactions,such as selling and purchasing.E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased.New customers usually go through the posted reviews or comments on the website before making a purchase decision.However,the current challenge is how new individuals can distinguish truthful reviews from fake ones,which later deceives customers,inflicts losses,and tarnishes the reputation of companies.The present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the reviewer.The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency(TF-IDF)approach for extracting features and their representation.For detection and classification,n-grams of review texts were inputted into the constructed models to be classified as fake or truthful.However,the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website.The classification results of these experiments showed that na飗e Bayes(NB),support vector machine(SVM),adaptive boosting(AB),and random forest(RF)received 88%,93%,94%,and 95%,respectively,based on testing accuracy and tje F1-score.The obtained results were compared with existing works that used the same dataset,and the proposed methods outperformed the comparable methods in terms of accuracy.

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents.In other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new pandemic.To focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)algorithms.According to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and anxiety.The health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age factors.The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period.The results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.

MNN-XSS:Modular Neural Network Based Approach for XSS Attack Detection

The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic.In recent years,researchers have used different machine learning methods to detect network attacks without relying on those signatures.The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product.In this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system.Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample.The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks.The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.