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Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

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Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

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Emergence and reemergence of infectious diseases pose significant public health risks that are continuously haunting human civilization in the past several decades. Such emerging pathogens should be considered as a high threat to humans, animals, and environmental health. The year 2020 was welcomed by another significant virus from family Coronaviridae called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the coronavirus disease 2019 (COVID-19). The disease was first reported in the city of Wuhan, Hubei province, China. Within a short time, this disease attained the status of the Public Health Emergency of International Concern. Presently, COVID-19 has spread to more than 150 countries, therefore, the World Health Organization (WHO) called it a pandemic. The Chinese government, along with WHO, other health agencies, and many nations, are monitoring the current situation closely to analyze the impact of SARS-CoV-2/COVID-19 on humans, animals, and environmental health. In the context of the current situation, biosafety and biosecurity measure that focus on One Health aspects of the disease outbreaks and the SARS-CoV-2 spread are of great importance to restrain this pathogen. Along with these efforts, standard precaution and control measures should also be taken at personal and community level to prevent the spreading of any contagion diseases, including COVID-19. Researchers are putting their very high efforts to develop suitable vaccines and therapeutics/drugs to combat COVID-19. This review aims to highlight the importance of biosafety, biosecurity, One Health approach, and focusing on recent developments and the ways forward to prevent and control COVID-19 in a useful way.