Parmod Kumar

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I am Parmod Kumar, qualified Engineer offering 14 years of cross functional experience. I started my career as a Marine Engineer and worked with Mitsui O.S.K. Lines and Chevron Shipping Co. L.L.C. Presently I am working as an Assistant Professor with MWU, Ethiopia, a Data Science Trainer [URL] and volunteering as NPA Diversity Officer. I am at the same time working as a freelance research adviser to M.S. and Ph.D. students. I am further serving as a reviewer for various reputed international conferences and journals. I am passionate to listen and solve challenging problems. In Data Science training course, I teach 'Data Science Introduction and Toolbox', 'Data Extraction, Preparation, and Manipulation' 'Inferential Statistics', 'Principal Of Analytic Graph', 'Predictive Models and Machine Learning Algorithm – Supervised Regression', 'Predictive Models and Machine Learning Algorithm – Supervised Classification', 'Predictive Models and Machine Learning Algorithm – Unsupervised', 'Text Mining, Natural Language Processing, and Social Network Analysis', and 'Capstone Project.'My academic credentials include Post Graduate Diploma in industrial Automation (PGDIA)
Supervisors: Marco P. Schoen
Phone: +33-786382200
Address: Residence Sainte Marthe ALOMEA
40 A Boulevard Limbert
No. A326
Avignon 84000
France

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Adobe Systems

Universität Passau

Southeast University (Bangladesh)

Partha Dhar

Southeast University (Bangladesh)

Nancy Diniz

Central Saint Martins College of Art and Design, University of the Arts London

Sheelagh Carpendale

University of Calgary

gwadaboy Janlou

Shahzad Malik

Mario Ganzeboom

Radboud University Nijmegen

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Hand Data Glove: A Wearable Real-Time Device for Human-Computer Interaction

In this paper, a real-time Human-Computer Interaction (HCI) based on the hand data glove and K-NN... more In this paper, a real-time Human-Computer Interaction (HCI) based on the hand data glove and K-NN classifier for gesture recognition is proposed. HCI is moving more and more natural and intuitive way to be used. One of the important parts of our body is our hand which is most frequently used for the Interaction in Digital Environment and thus complexity and flexibility of motion of hands are the research topics. To recognize these hand gestures more accurately and successfully data glove is used. Here, gloves are used to capture current position of the hand and the angles between the joints and then these features are used to classify the gestures using K-NN classifier. The gestures classified are categorized as clicking, rotating, dragging, pointing and ideal position. Recognizing these gestures relevant actions are taken, such as air writing and 3D sketching by tracking the path helpful in virtual augmented reality (VAR). The results show that glove used for interaction is better than normal static keyboard and mouse as the interaction process is more accurate and natural in dynamic environment with no distance limitations. Also it enhances the user's interaction and immersion feeling.

Parmod Kumar

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