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This article explores the trends in computer science research in India from 2019 to 2022 based on research coming out of leading Indian technology institutes, including Chennai Mathematical Institute, Indian Institute of Science and Indian Institute of Technology Delhi. Topics include research on automata theory, formal verification, computational complexity research, and Algorithms research.

A recent study conducted at the Central University of Haryana in India has proposed a privacy-preserving collaborative model for the classification of brain tumors using magnetic resonance imaging (MRI) images. The study utilized deep learning and transfer learning algorithms to analyze an open-source dataset of MRI images, which included four types of tumors and no tumor. The researchers found that federated deep learning models, involving multiple clients, outperformed conventional pretrained models in terms of accuracy. The proposed framework offers a solution for early diagnosis of brain tumors while ensuring data privacy for edge devices with limited resources. [Extracted from the article]

The article discusses the automated translation of Indian languages. The 2001 census in India states that there are 122 languages spoken in India with 22 designated as official by the Indian government. Sampark, a multipart machine translation architecture, is being constructed by 11 different institutions under the leadership of the Language Technologies Research Center at the International Institute of Information Technology in Hyderabad. Sampark utilizes dictionary-based algorithms and traditional rules alongside statistical machine learning to translate and it could be available to the public on the Internet in January 2010.

A study conducted by researchers at VIT-AP University in Andhra Pradesh, India, has developed a deep learning algorithm called RU-Net2+ for accurate brain tumor segmentation and survival rate prediction. The algorithm was applied to a dataset of brain tumor images and achieved impressive results, surpassing current benchmarks in classification accuracy, tumor segmentation precision, and survival rate prediction. The framework shows promise for automating brain tumor diagnosis and enhancing patient care, providing valuable insights for medical professionals making treatment decisions. The study was published in IEEE Access. [Extracted from the article]