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.

Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of lives.This study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray radiographs.To test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS images.Two scenarios were considered to develop a system for predicting COVID-19.In the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs.After segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19.These features were processed using SVM and K-NN.In the second scenario,a CNN transfer model(ResNet 50)was used to detect COVID-19.The system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,respectively.Thus,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.

Human-Animal Affective Robot Touch Classification Using Deep Neural Network

Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and realistic.The novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning algorithm.The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015.The dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor devices.The leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is presented.Findings show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same dataset.Therefore,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification.