Collaborating Authors


Machine Learning and Artificial Intelligence for Business Recovery after COVID 19


Currently, the world is facing the most challenging time and going through economic turmoil. One of the important priorities of many companies is to recover quickly from the current scenario and be operational as quickly as possible. The coronavirus has impacted many companies and this economic hit is very fast throughout the world. Companies around the globe are looking for stabilizing their business and the recovery. According to the Organization for Economic Co-operation and Development of the reports released on 14th April, consumer expenditure has dropped more than 25% in Canada, France, and Germany in many majority economies, thus causing the slowdown between 20-25%. The Machine Learning (ML) and Artificial Intelligence (AI) can play a major role in the business recovery after and during the COVID 19 pandemic.

Large expert-curated database for benchmarking document similarity detection in biomedical literature search


Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.