In recent past years, Deep Learning presented an excellent performance in different areas like image recognition, pattern matching, and even in cybersecurity. The Deep Learning has numerous advantages including fast solving complex problems, huge automation, maximum application of unstructured data, ability to give high quality of results, reduction of high costs, no need for data labeling, and identification of complex interactions, but it also has limitations like opaqueness, computationally intensive, need for abundant data, and more complex algorithms. In our daily life, we used many applications that use Deep Learning models to make decisions based on predictions, and if Deep Learning models became the cause of misprediction due to internal/external malicious effects, it may create difficulties in our real life. Furthermore, the Deep Learning training models often have sensitive information of the users and those models should not be vulnerable and expose security and privacy. The algorithms of Deep Learning and machine learning are still vulnerable to different types of security threats and risks. Therefore, it is necessary to call the attention of the industry in respect of security threats and related countermeasures techniques for Deep Learning, which motivated the authors to perform a comprehensive survey of Deep Learning security and privacy security challenges and countermeasures in this paper. We also discussed the open challenges and current issues.

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All applications are developed for context adaptation and provide communication with users through their interfaces. These applications offer new opportunities for developers as well as users by collecting context data and adapting systems behavior accordingly. Particularly, in mobile devices, these mechanisms provide usability increment tremendously. Rigid and non-adaptive interface blocks the features of context awareness. In this paper, we study methods, technologies and criteria which have been proposed specifically for adaptive interfaces. Based on these guidelines, we elaborate the intelligence of adaptivity and usage of context according to user mental model. Further, we have proposed a model to develop user context ontology (UCO) and adaptive interface ontology (AIO) to optimize the use of adaptive mobile interfaces in the context of user preferences. These ontologies organize the perceptions and thoughts of user. The philosophy of User Centered Design (UCD) is proposed to analyze the usability and validity of mobile device interfaces according to user contexts.

Coronaviruses are a family of viruses that can be transmitted from one person to another. Earlier strains have only been mild viruses, but the current form, known as coronavirus disease 2019 (COVID-19), has become a deadly infection. The outbreak originated in Wuhan, China, and has since spread worldwide. The symptoms of COVID-19 include a dry cough, sore throat, fever, and nasal congestion. Antimicrobial drugs, pathogen-host interaction, and 2 weeks of isolation have been recommended for the treatment of the infection. Safe operating procedures, such as the use of face masks, hand sanitizer, handwashing with soap, and social distancing, are also suggested. Moreover, travel bans for cities, states, and countries have been put in place, along with lockdowns to control the outbreak. Travel restrictions, mask use, sanitizer or soap use, and avoidance of touching the face and nose have produced encouraging results, whereas the effectiveness of antibiotics has not been proved. The results of isolation for the recovery of infected people have also been promising. Travel bans and lockdowns have caused a slump in economies, and unemployment has risen sharply, resulting in an increase in mental health cases globally. To date, vaccines have been developed and are in use in certain countries, but following standard operating procedures remain critical. The countries following the guidelines can eradicate this virus. New Zealand was the rst country to eliminate the virus from their territory.

Bioinformatics education has been a hot topic in South Asia, and the interest in this education peaks with the start of the 21st century. The governments of South Asian countries had a systematic effort for bioinformatics. They developed the infrastructures to provide maximum facility to the scientific community to gain maximum output in this field. This article renders bioinformatics, measures, and its importance of implementation in South Asia with proper ways of improving bioinformatics education flaws. It also addresses the problems faced in South Asia and proposes some recommendations regarding bioinformatics education. The information regarding bioinformatics education and institutes was collected from different existing research papers, databases, and surveys. The information was then confirmed by visiting each institution’s website, while problems and solutions displayed in the article are mostly in line with South Asian bioinformatics conferences and institutions’ objectives. Among South Asian countries, India and Pakistan have developed infrastructure and education regarding bioinformatics rapidly as compared to other countries, whereas Bangladesh, Sri Lanka, and Nepal are still in a progressing phase in this field. To advance in a different sector, the bioinformatics industry has to be revolutionized, and it will contribute to strengthening the pharmaceutical, agricultural, and molecular sectors in South Asia. To advance in bioinformatics, universities’ infrastructure needs to be on a par with the current international standards, which will produce well-trained professionals with skills in multiple fields like biotechnology, mathematics, statistics, and computer science. The bioinformatics industry has revolutionized and strengthened the pharmaceutical, agricultural, and molecular sectors in South Asia, and it will serve as the standard of education increases in the South Asian countries. A framework for developing a centralized database is suggested after the literature review to collect and store the information on the current status of South Asian bioinformatics education. This will be named as the South Asian Bioinformatics Education Database (SABE). This will provide comprehensive information regarding the bioinformatics in South Asian countries by the country name, the experts of this field, and the university name to explore the top-ranked outputs relevant to queries.