The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

The aim of this study is to evaluate the impact of quick response (QR) codes on customer satisfaction and purchase intention in the online shopping context on the basis of perceived flow. This study also showed the impact of QR codes on perceived flow, followed by perceived flow on purchase intention and customer satisfaction, and finally the combination impact of perceived flow and customer satisfaction on purchase intention. Stimulus–Organism–Response theoretical model was used in this research. Data were collected by online questionnaires from 420 valid respondents who purchased a product online via QR code. The covariance-based structural equation modeling approach was used to analyze the structural model and measurements. This study showed that QR codes have a great impact on purchase intention and customer satisfaction. The findings of this article confirmed that QR codes influence perceived flow, combined effect of them, in turn, influenced online shoppers’ satisfaction and, finally, purchase intention. These findings are practically significant for both marketers and customers. Marketers should use QR codes as an embedded advertising tool with particular URLs that will lead to more profitability for shopping agency. Besides customers can also learn their behavior regarding QR codes in the online shopping context. This research has great real implications in the field of Internet shopping, online marketing, retailing, marketing promotion, and consumer behavior. Embedded QR codes with advertising are very helpful for advertisers to share and influence customer satisfaction and purchase intention. Sharing information via QR codes will influence customers to engage and share information with other customers in the online community, which will help to increase the volume of sales rapidly. To the best of our knowledge there is no research to evaluate the real impact of QR codes on purchase intention and customer satisfaction on the basis of perceived flow.

The study’s goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages using a variety of machine learning methods, which could help companies better understand how customers respond to their products and services. As a result, companies can learn how to use machine learning methods to better understand the behaviour of their customers.