In the present era of digital communication, secure data transfer is a challenging task in the case of open networks. Low-key-strength encryption techniques incur enormous security threats. Therefore, efficient cryptosystems are highly necessary for the fast and secure transmission of multimedia data. In this article, cryptanalysis is performed on an existing encryption scheme designed using elliptic curve cryptography (ECC) and a Hill cipher. The work shows that the scheme is vulnerable to brute force attacks and lacks both Shannon’s primitive operations of cryptography and Kerckchoff’s principle. To circumvent these limitations, an efficient modification to the existing scheme is proposed using an affine Hill cipher in combination with ECC and a 3D chaotic map. The efficiency of the modified scheme is demonstrated through experimental results and numerical simulations.

This paper proposed a novel futuristic approach to support the educational informatics and overcome the conventional system of attitude measure. For this, we presented a significant predictive model to identify the attitude of students towards technology. The present approach not only explored the impact of the technology but also predicted an opinion of students. The concept of an online awareness model may overcome the traditional method. We have performed the descriptive and inferential statistics to predict the attitude of Indian students towards the ICTMT in university education with primary data samples. Factor Analysis (FA) using Principal Component Analysis (PCA) has extracted the prominent two components with nine features for technology benefits and six features for the technology use. The SQuareRoot (SQRT) and Log transformations have been used to decrease the Skewness, and it has also improved the association between attitude and educational benefit. Overall reliability of the gathered data sample size of 163 calculated 0.957 provided with Cronbach alpha test. This study has used a Pearson Correlation (PC) for exploring the technology impact and the Linear Regression Model (LRM) for the prediction. The LRM has substantiated that the educational benefit explained the attitude significantly, and a significant positive association discovered using the PC with a value of 0.75. The LRM model projected nine most significant educational benefits of ICTMT, which affected the attitude of the Indian student towards technology. We have proposed technology aids in building online predictive model wires real-time prediction.

Distance learning has made learning possible for those who cannot attend traditional courses, especially in pandemic periods. This type of learning, however, faces a challenge in keeping students engaged and interested. Furthermore, it is important to identify students who are in need of help to ensure that their progress does not deteriorate. First, the research identifies students’ engagement based on their behaviors in Virtual Learning Environment (VLE) and their performances in assessments. This research goal is to investigate the association/relationship between demographic characteristics and engagement level. It identifies less engaged students by using an unsupervised clustering model based on VLE interactions and assessments of submission-derived features. According to results, the two-level clustering model outperforms other models in regard to cluster separation using silhouette coefficient. Apriori algorithm is utilized to obtain a set of rules that connect demographic features to student engagement. Results show gender, highest education, studied credits, and number of previous attempts have positive correlation with engagement level in distance-based learning.

This work is a new step towards the understanding of students’ opinions about the use of technology in learning and improvements to provide sustainable E-learning solutions. Every higher educational university tries to provide well-suited, updated, and trending technology-based education facilities to its students. The task of analyzing the student’s sentiment about technology delivers benefits not only to ICT administrators, but also to management to become aware of the technological concerns. The opinions of Hungarian university students were analyzed using the regression method. We investigated 165 primary samples supported by the four hypotheses. The reliability of the data sample was calculated as 0.91 with Cronbach alpha testing. The Pearson Momentum Correlation (PMC) proved that the suggested technology benefits had a linear positive association with the student’s opinion. Furthermore, technology usability was positively correlated with the benefits. The supporting results of the regression model evidenced the significant impact of technology usability and benefits on the opinions. Using Exploratory Factor Analysis (EFA), we proposed significant features for the model that predicted students’ opinions using the educational benefit and usability parameters. These parameters statistically significantly predicted student’s opinions: F (2, 162) = 104.9, p < 0.05, $R^2$ = 0.559. This study may be supportive of implementing the opinion mining model online and useful to university authorities to understand better the students’ sentiments about the current technological facilities provided. The authors proposed an opinion mining model to deploy on the university’s real-time “E-lection” sustainable technology.

The present study was conducted to explore both the differential and inferential analysis of technology applications and benefits in two country university students. In-depth statistical analysis with parametric and non-parametric tests was performed on primary samples collected from Indian and Hungarian universities. We assumed six null hypotheses and their corresponding alternative hypotheses in line three major objectives of our study. The first two null hypotheses were tested with the Student T-test, and the rest of the four hypotheses were tested with Correspondence Analysis (CA) advance dimension reduction approach. Supporting with results of Cramer’s V (CV) inclusive Fisher’s Exact (FE) and $Chi^{2}$ tests, an in-depth analysis was also performed. The result of the T-test proved that country variable not impacted by Technology Use due to insignificant ${p}$ value ( ${p}$ > 0.05). But, Technology Benefit affected two-country students with significant ${p}$ value ( ${p}$ < 0.05). Surprisingly, the Mann-Whitney U test explored the effect of Technology Benefit and Use on Indian and Hungarian students ( ${p}$ < 0.05). The CA method explored a significant association of Technology Use with Indian and Hungarian student’s responses ( ${p}$ < 0.05) yielded two dimensions. On the one hand, it did not find a significant association of Technology Benefit with Indian students ( ${p}$ < 0.05). Still, on the other hand, it explored a significant association of Technology Benefit with Hungarian student’s opinions ( ${p}$ > 0.05). The paper’s findings are evident in higher technology awareness and association among Hungarian students than Indian students. We also proposed a real-time conceptual design to estimate the differential and inferential analysis using a web application to avoid the conventional method. Our proposed models might be helpful not only for two countries but also for inter-continent institutions to work in a collaborative project to measure; exchange student’s and educators’ perceptions about the technology. Based on the results, we also proposed a futuristic real-time technology impact identification for a university to integrate with extant “E-lection,” a class response system.