Supervised news topic detection

With the advancement of technology, there has been much improvement in the automatic recording of broadcast news by utilizing speech recognition. However the continually increasing dynamic information pool is posing challenges for efficient information retrieval techniques. This pain-point creates the need to develop systems that can automatically categorize this information under relevant topics for the purpose of easy information retrieval. In recent years, much focus has been given to the subject of topic detection of broadcast news more through unsupervised techniques such as clustering as a few studies focusing on supervised classification techniques. In this project, we propose a simple yet effective approach for this purpose by drawing inspiration from previously conducted studies. In this thesis, we experiment with a supervised machine learning algorithm namely Logistic Regression along with language processing techniques to automatically detect topics from broadcast news comprised in the TDT2 English corpus. We consider the input documents, as a stream of sentences and use the trained classifier to predict the topics they are associated with and accordingly assign these news documents to the most appropriate topic. This approach includes various pre-processing techniques along with feature selection and natural language processing. It can be inferred from the results obtained that the chosen model is able to detect relevant topics of new articles by adopting a simplistic topic detection approach that uses the Logistic Regression classifier while taking inspiration from conducted studies. The proposed model performs in comparison to some state-of-the-art topic classifiers.Bachelor of Engineering (Computer Science

Medical students' attitudes toward psychiatry at a teaching institute in North India: A qualitative pilot study

Background: Medical students must embrace psychiatry and psychiatric illnesses with a positive outlook if we are to bridge the massive treatment gap for psychiatric illnesses. The opinions of medical students toward psychiatry have been investigated in several quantitative research carried out in India. Qualitative research is lacking, making it difficult to comprehend students' perspectives. Hence, we created this qualitative study. Methods: We conducted online one-on-one in-depth interviews with three final-year students and two interns in this pilot study. The data were examined using content analysis. In addition, the most frequent codes were found using word cloud analysis. Results: Participants reported that students would get more knowledge about psychiatry if it is made a compulsory examination subject in the MBBS. Conclusion: The preliminary findings suggest that medical graduates mostly perceive psychiatry as a promising branch and feel that psychiatry as a branch will get its due importance if it is made a major subject in the medical curriculum

Book of Abstracts of the 2nd International Conference on Applied Mathematics and Computational Sciences (ICAMCS-2022)

It is a great privilege for us to present the abstract book of ICAMCS-2022 to the authors and the delegates of the event. We hope that you will find it useful, valuable, aspiring, and inspiring. This book is a record of abstracts of the keynote talks, invited talks, and papers presented by the participants, which indicates the progress and state of development in research at the time of writing the research article. It is an invaluable asset to all researchers. The book provides a permanent record of this asset. Conference Title: 2nd International Conference on Applied Mathematics and Computational SciencesConference Acronym: ICAMCS-2022Conference Date: 12-14 October 2022Conference Organizers: DIT University, Dehradun, IndiaConference Mode: Online (Virtual