PurposeEmergence of coronavirus disease 2019 (COVID-19) forced the world-wide education system to adopt online mode immediately. There are two main objectives of the paper: the first one is to know the acceptability of online mode of examination and learning amongst students by analysing the various aspects like difficulty, mental pressure, study pattern, etc. and the second one is to know the role of gender in adopting online education.Design/methodology/approachAn online survey is conducted amongst the students of Graphic Era Hill University, Dehradun, India. Stratified sampling method has been used to select the students. First, a simple statistical analysis of the responses is conducted, and then chi-square test of independence has been used to know the dependency of various aspects on gender.FindingsThe two main findings of the present study are as follows: first, the online examinations were accepted with ease and low pressure in comparison to regular examination and second, the gender has a significant role in adopting online education with the observations that female students were more adoptable with online education in terms of assignments, study patterns and comfort. The present work also focuses on the challenges of online education like Internet connectivity, class interactions, etc.Research limitations/implicationsThe present work was carried out during the initial time of pandemic in India when the focus was to continue the academic process by utilizing all the available resources in the absence of well-defined standards of online education.Practical implicationsThe findings of the paper can be used for making strategies for online education across the world.Social implicationsThe findings of the paper have shown that gender plays a significant role in adoptability of online education in Indian context.Originality/valueThe present work is conducted amid the environment of COVID-19. It reflects the analysis of students' responses towards the acceptability of online education under the difficult conditions developed due to the pandemic and subsequent lockdown.

Data clustering is one of the most popular techniques in data mining. It is a method of grouping data into clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The most popular clustering algorithm K-mean and other classical algorithms suffer from disadvantages of initial centroid selection, local optima, low convergence rate problem etc. Particle Swarm Optimization (PSO) is a population based globalized search algorithm that mimics the capability (cognitive and social behavior) of swarms. PSO produces better results in complicated and multi-peak problems.

Clustering is a widely used technique of finding interesting patterns residing in the dataset that are not obviously known. The K-Means algorithm is the most commonly used partitioned clustering algorithm because it can be easily implemented and is the most efficient in terms of the execution time. However, due to its sensitiveness to initial partition it can only generate a local optimal solution. Particle Swarm Optimization (PSO) technique offers a globalized search methodology but suffers from slow convergence near optimal solution. In this paper, we present a new Hybrid Sequential clustering approach, which uses PSO in sequence with K-Means algorithm for data clustering. The proposed approach overcomes drawbacks of both algorithms, improves clustering and avoids being trapped in a local optimal solution. Experiments on four kinds of data sets have been conducted. The obtained results are compared with K-Means, PSO, Hybrid, K-Means+Genetic Algorithm and it has been found that the proposed algorithm generates more accurate, robust and better clustering results.

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Vehicular ad hoc network (VANET) is emerging as one of the challenging research area because of the heavy dependency of human being into vehicles which tends to develop an intelligent transport system. VANET is treated as an extension of mobile ad hoc network (MANET) due to its behavior and its working mode. VANET is emerging as a new powerful tool to provide safety and security to the human beings during the time of traveling from one place to another. Routing is one of the challenging tasks for both MANET and VANET due to the frequent change in the topology. In this paper, we are evaluating the adaptability of existing MANET routing protocols for VANET. This paper analyze that what is the impact the vehicle density and speed on the packet delivery ratio, normalized routing load, average end-to-end delay, average throughput, average path length and average loss rate, which will help to design a new routing protocol or to have some improvement in the existing routing protocols.

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Learning has transcended into a life-long endeavor in the information age. It is no longer restricted to confines of formal classrooms. Consequently, a student is not restricted to traditional learning resources like teachers, textbooks or printed content. Digital resources available on the Internet form a very significant component of self-learning. Copious volumes of learning resources without legal barriers to self-learning reside in digital repositories, educational institution portals and on numerous websites. Learners wishing to utilize the web for personalized learning are faced with a daunting array of content to wade through and select the suitable ones to fulfill his/her learning objectives. Therefore, it is not a question of availability; it is one of relevance and suitability. Typically, in addition to time constraints, learners lack the expertise to screen content for effective eLearning. Adaptive hypermedia systems (AHSs) offer a path to harnessing this large volume of learning resources for personalized learning. This review paper provides a concise and coherent discussion about the evolution of AHSs along with the challenges that need to be addressed for effectively harnessing openly available educational resources referred to as open corpus resources (OCRs).