This issue consists of five papers. During the Covid-19 pandemic, the mode of education moved from the traditional classroom setting and leaned on technologies. Even before the pandemic, communication technology was used in education, but its use has now increased. The teachers' passion, inventiveness and inclination to transfer knowledge to the students form their attitudes. They have direct impact on their overall teaching performance. The available technologies have to be utilized effectively by the teaching faculty, which depends on their attitudes, skills in ICT and perception and practice towards online teaching. The first paper, "Attitudes and ICT Skills as Predictors of the Perspectives of Teaching Faculty Towards Online Learning During the Covid-19 Pandemic", by Grace Inbavathi M and Ananthi Sheshasaayee, studies the attitudes and ICT skills of the teaching faculty as predictors of their perspectives towards online learning. The following standardized tools-Attitude Scale, ICT Skills Scale and Perceptions and Practices of E-instructors toward Online Instruction Questionnaire (PPEOIQ)-were used for the study. Fifty-two teaching faculty from various institutions participated in the study. The findings reveal that teacher attitude and ICT skills are the predictors of all dimensions of the perspectives and practices of e-learning.

The second paper, "Texture-Based Palmprint Recognition Using Discrete Wavelet Transformation", by Dhulipalla Nagajyothi and K Venkata Ramaiah, presents a palmprint recognition system based on texture features using wavelet transformation for keeping personnel entry records in large enterprises. Using simple approaches-Discrete Wavelet Transformation (DWT) and Standard Deviation (s)-the study extracts some texture characteristics from ROI images which are then matched using Canberra, Euclidean and Manhattan distance methods. Among all captured images of datasets, K images per user are used to create the training set, with K ranging from 1 to 4. In image recognition mode, they are compared with the remaining images. The true acceptance rate is used to represent the results. In the study, it takes approximately 0.9643 to 1.2643 ms to extract the feature and recognition. If the dataset contains a huge number of vectors, then it will easily consume a lot of time for recognition. So, in future, methods to reduce the time consumption for recognition and at the same time, improve the recognition rate are needed.

The third paper, "Serverless Computing Systems: A Review", by Agbaje Michael O, Adebayo A O, Udosen Alfred A, Daniel Gogfrey and Bamidele Raymond O, pertains to Web Application Programming Interface (Web API) deployed on Virtual Machines (VM) and as FaaS. Despite the benefits of serverless computing, the study found that there is no obvious option between serverless and VMs for a Web API application, since prices must be carefully calculated and all components of FaaS must be included. Furthermore, the paper reviews some of the unique opportunities and potentials that serverless computing presents.

The fourth paper, "Deep Learning Approach to Automated Fish Egg Counting: A Systematic Literature Review", by R Ajikaran, P Vigneshwaran and J Charles, reviews the survival rate of fish eggs, stock evaluation and monitoring fish population increase. In most cases, counting fish eggs is done in a sequential procedure that requires a lot of time and effort, making it challenging to employ big datasets. Traditional counting methods have been used for many years, but many researchers have pointed out that they have several disadvantages, including requiring more time, exceptional human skills and excessive human interference. A number of researchers are working on developing several strategies to simplify the process of fish counting and assessment. The authors have reviewed several factors which help to automate fish egg counting, using deep learning algorithms and Convolution Neural Network (CNN) from standard databases, and discussed the most suitable features of fish egg counting, deep learning algorithms, and the future direction of fish egg counting.

The Covid-19 pandemic has revolutionized education for ever with a dramatic rise in e-learning imparted through digital platforms. It is needless to say that innovations in and development of web technology have a perceptible influence on the quality of online learning and performance of students. In the light of these developments, the last paper, "Intelligent E-Learning with New Web Technologies", by Veselina Nedeva and Snejana Dineva, summarizes the characteristics of e-learning in conjunction with the rapid technological changes taking place in the fields of Web 4.0, Web 5.0, AI and IoT, as revealed by the current literature.