Education institutions like Schools, colleges, and universities in India are currently based on traditional learning methods and follow the conventional setting of face-to-face interaction/lectures in a classroom. Most of the academic sector started unified learning, still most of them struct with old steps. The unexpected Plague of a deadly infection called COVID-19 caused by (SARS-Cov-2) trembled the whole world. The WHO announced it as a disease outbreak. This circumstance challenged the whole education system worldwide and compelled educators to change to an online mode immediately. Many educational organizations that were earlier unwilling to change their traditional didactic practice had no choice but to move exclusively to online teaching–learning. This article provides an elaborate discussion about the education sector's impact during a disease outbreak in India. It offers a detailed discussion regarding how India adopts the e-learning approach in this critical situation. Further, it describes how to cope with the challenges related to e-learning.

Machine learning is the worldwide recent research technique for various systems as they are intelligent enough to find the solution for classification and prediction problems. The proposed work is about a hybrid genetic fuzzy algorithm that performs an optimal search as well as classification upon uncertain data. The data which is uncertain is suitable for fuzzy classifiers to predict the disease. The hybrid genetic fuzzy system applied on the attributes selects relevant attributes. The selected attributes are fed into the fuzzy classifier. The fuzzy rules are again generated using genetic algorithms. This algorithm is applied on three of the important and bench marking data sets taken from the UCI machine learning repository. The heart disease, Wisconsin breast cancer and Pima Indian diabetes datasets produce classification accuracy as 89.65%, 99.5% and 88.93% respectively. In this article there is a comparative study on few of the feature selection and feature reduction techniques.

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The elastic optical network (EON) fulfills the upcoming generation network requirements such as high-definition videos, high bandwidth demand services, and ultra-high-definition televisions. The key issues in EON are routing spectrum assignment and spectrum fragmentation for spectrum allocation. The spectrum fragmentation issues are resultant in poor consumption of spectrum resources and an increase in the new connection blocking. A flexible defragmentation algorithm must utilize more spectrum resources with a high transmission rate. This paper presents a new multiconstrained defragmentation algorithm (MCDFA) for elastic optical networks. The MCDFA addressed two key issues: spectrum allocation for new connections and then reconfiguring the existing connections in a nondisruptive manner. The first-last-exact fit spectrum allocation policy assigns the spectrum slots during the new connection request. It splits each light path request by disjoint/ nondisjoint and by efficiently handling the small fragmented slots in spectrum resources. The simulation results are evaluated using standard metrics such as bandwidth blocking probability, bandwidth fragmentation ratio, and spectrum utilization gain. The results also demonstrated that our proposed algorithm generates promised solution to EON’s routing, spectrum assessment, and fragmentation issues.