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Ajith Kumar Parlikad
Researcher at University of Cambridge
Publications - 225
Citations - 3522
Ajith Kumar Parlikad is an academic researcher from University of Cambridge. The author has contributed to research in topics: Asset management & Computer science. The author has an hindex of 23, co-authored 188 publications receiving 1934 citations. Previous affiliations of Ajith Kumar Parlikad include Rutgers University.
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The impact of government measures and human mobility trend on COVID-19 related deaths in the UK
TL;DR: The study shows that human-mobility reduction had a significant impact on reducing COVID-19-related deaths, thus providing crucial evidence in support of such government measures.
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Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus
Qiuchen Lu,Ajith Kumar Parlikad,Philip Woodall,Gishan Don Ranasinghe,Xiang Xie,Zhenglin Liang,Eirini Konstantinou,James T. Heaton,Jennifer Schooling +8 more
TL;DR: A digital twin (DT) refers to a digital replica of physical assets, processes, and systems that integrate artificial intelligence, machine learning, and data analytics to create living digital twins.
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AI for Next Generation Computing: Emerging Trends and Future Directions
Sukhpal Singh Gill,Minxian Xu,Carlo Ottaviani,Panos Patros,Rami Bahsoon,Arash Shaghaghi,Muhammed Golec,Vlado Stankovski,Huaming Wu,Ajit Varghese Abraham,Manmeet Singh,Harshit Mehta,Soumya Ghosh,Thar Baker,Ajith Kumar Parlikad,Hanan Lutfiyya,Salil S. Kanhere,Rizos Sakellariou,Schahram Dustdar,Omer Rana,Ivona Brandic,Steve Uhlig +21 more
TL;DR: In this article , the authors discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
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RFID-based product information in end-of-life decision making
TL;DR: Qualitatively it is shown qualitatively that the availability of product information has a positive impact on product recovery decisions, and how radio-frequency identification-based product identification technologies can be employed to provide the necessary information is discussed.
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Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance
TL;DR: A novel IFC-based data structure is presented, using which a set of monitoring data that carries diagnostic information on the operational condition of assets is extracted from building DTs, which contributes to efficient and automated asset monitoring in O&M.