ABSTRACT
In recent years, the number of online courses in India has skyrocketed especially due to the Covid pandemic. The most significant increments have happened in degree colleges, where 85% concur that internet based courses are important for their drawn-out procedure when contrasted with 60% in 2015. The distribution of online courses has evolved dramatically as technology has advanced. Web-based platform provides new challenges for both teachers and students. Teachers should be clear about the effectiveness of online learning in teaching students. For that, the possibilities of online learning should be compared with traditional learning. Students are evaluated based on their focus on online learning. This study aims to determine the efficacy of online courses by predicting student performance in an e-learning system. These research findings evaluate modern learning methods, highlight students' potential and help teachers understand how to assess and lead students on online platforms. © 2023 IEEE.
ABSTRACT
With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.
ABSTRACT
Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. © 2022 THE AUTHORS
ABSTRACT
Nowadays, achieving the objectives of sustainable development (SD) within a manufacturing company, through introducing and integrating sustainability into a development strategy, is a key parameter in gaining a competitive advantage in the market. The objective of this study was to develop a decision-tree based methodology to facilitate SD assessment in a manufacturing company, which consists of five main components: (1) Determination of SD indicators based on literature analysis, (2) Using the Analytic Hierarchy Process (AHP) method which determines the priority of the SD criteria, (3) Collecting data to determine the values of the key objectives SD, (4) Using a decision tree to build scenarios of possible actions to increase the level of SD, (5) Indicating recommended actions for continuous monitoring of progress towards reaching SD objectives. In the proposed approach, the use of the AHP method allowed for indicating the most important SD indicators, which made it possible to limit the number of queries to manufacturers on data from real companies regarding the values of SD indicators. Finally, the methodology was applied and verified within a real manufacturing company in order to assist the Management Board in making projections about future actions regarding an increase in SD level.
ABSTRACT
Information content that is inaccurate, misleading, or whose source cannot be verified is fake news. This content could be created to purposely harm people’s reputations, deceive them, or draw attention to themselves. Since December 2019, the epidemic of coronavirus disease has sparked considerable alarm and has had a significant impact on people’s lives. Also, misinformation on COVID-19 is frequently spread on social media. This project aims to use Machine learning algorithms to recognize fraudulent news. For this, we use seven essential algorithms, namely Logistic regression, Naïve Bayes, Support Vector Machine (SVM), Neural Network (NN), K-Nearest Neighbours (KNN), Decision tree, and Random forest. We compared the results of all the algorithms stated above and found that neural networks and random forest achieved the highest accuracy of 83%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.