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Research activities across all the emerging fields of Computer Science pave the way for the industrial world to strive forward with huge advancements. As an educational institution, encouragement and support to research can be provided by establishing a suitable platform for the research community, to interact with each other and to share the knowledge. Having this objective, the Department of Computer Science and applications, right from its inception, has been active in research and Organizes International Conference on HUMAN MACHINE INTERACTION IN THE DIGITAL ERA (ICHMIDE -2023)- In Association with AIMIST University, Malaysia, and Computer Society of India, Chennai Chapter. It is our privilege to share that this Conference has been supported by Taylor and Francis as publication partner. The Conference aims to bring different ideologies under one roof and provide opportunities to exchange ideas face to face, to establish research relations and to find global partners for future collaboration. Major cornerstone has been the number of key persons/researchers present for key notes and discussions in the conference.
Our previous editions of International Conferences -Research Essential in Machine Learning and Computational Intelligence - ECREMLACI2020 and ECREMLACI2021 were organized in the last two years which received an overwhelming response
Research Essential in Machine Learning and Computational Intelligence -
Research Essential in Machine Learning and Computational Intelligence -
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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