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Online learning has changed all elements of teaching of entire learning structure from primary to university level all around the world so that the challenges of online teaching are required to be optimized. The prominent objective of this manuscript is to optimize the issues of online teaching-learning in online education. Twelve issues of online teaching-learning are shortlisted by performing deep reviewing of the literature and grouping into three categories: “Students’ issues,” “Common issues,” and “Teachers’ issues” using the opinions of expert people. The analytical hierarchy process method is chosen for ranking of issues of online teaching. The findings can become effective in planning to get solution of the challenges of online teaching. These challenges of online teaching may lead to fragmental illness mentally over a long period of time. Because social media platforms may become an efficient tool for incorporating into online education, social media is a vital aspect of online learning. Over time, social media use may have an effect on the human brain in one way or another. The given work’s exploration of online teaching-learning challenges could lead to a social media-based examination of mental illness.

Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.

In this paper, we propose a semisupervised feature selection approach that is based on feature clustering and hypothesis margin maximization. The aim is to improve the classification accuracy by choosing the right feature subset and to allow building more interpretable models. Our approach handles the two core aspects of feature selection, i.e., relevance and redundancy, and is divided into three steps. First, the similarity weights between features are represented by a sparse graph where each feature can be reconstructed from the sparse linear combination of the others. Second, features are then hierarchically clustered identifying groups of the most similar ones. Finally, a semisupervised margin-based objective function is optimized to select the most data discriminative feature from within each cluster, hence maximizing relevance while minimizing redundancy among features. Eventually, we empirically validate our proposed approach on multiple well-known UCI benchmark datasets in terms of classification accuracy and representation entropy, where it proved to outperform four other semisupervised and unsupervised methods and competed with two widely used supervised ones.