Arvind Kumar

About

Arvind Kumar, received Master degree in Computer Science from the University of Allahabad, Prayagraj (India), in 2003, and Ph.D. degree in Computer Science Engineering from Bennett University Greater Noida (India). He comes with an extensive 18+ years of Software Product Development experience with a proven track record of building a high-value product that aligns with the organization’s business strategy. He is currently associated with Precisely Software, Noida (India), as a Principal Software Engineer and taking care of Location Intelligence (LI) product software development in an agile process environment. He has done ‘IT Project Management’ certification from IIT Bombay (India). Also, He has multiple industries-led certifications like Microsoft Certified Professional (MCP), Microsoft Certified Specialist (MCS), ScrumAlliance’s Certified Scrum Master (CSM), and Project Management Institute’s Agile Certified Practitioner (PMI-ACP). He has also made contributions to different Open-Source applications. His research interests include Evolutionary Computation, Computer Vision, Image Processing, and Location Intelligence.

Employment

• Tata Consultancy Services Ltd Technical Architect - AI & Machine Learning
  2022 - Present
• Precisely Software And Data India Private Limited Principal software Engineer
  2021 - 2022
• Pitney Bowes Sr. Technical Lead
  2009 - 2021

Education

• Bennett University Ph. D.
  2018 - 2022
• Amity University Master of Technology, Computer Science and Engineering
  2012 - 2015
• University of Allahabad Master of Science, Computer Science (Software)
  2000 - 2003

Projects & Funding

Projects & funding information is unavailable.

Publications (24)

• Predictive Driver Monitoring Using Multimodal AI for Road Safety
  International Journal of Innovative Science and Research Technology 2026 DOI: 10.38124/ijisrt/26mar176
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• A monocular vision-based framework for metric distance estimation of vehicles and pedestrians using a zero-shot depth estimation model
  Ain Shams Engineering Journal 2026 DOI: 10.1016/j.asej.2026.104246
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• A Comprehensive Review of Retrieval-Augmented Generation in Generative AI
  Journal of Applied Bioanalysis 2025 DOI: 10.69980/jab.v11si6.2890
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• A new fitness function in genetic programming for classification of imbalanced data
  Journal of Experimental & Theoretical Artificial Intelligence 2024 DOI: 10.1080/0952813X.2022.2120087
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• Classification of forest cover-type using ensemble of decision tree, random forest and K nearest neighbor
  JIMS8I International Journal of Information Communication and Computing Technology 2023 DOI: 10.5958/2347-7202.2022.00011.1
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• A Review on Unbalanced Data Classification
  Algorithms for Intelligent Systems 2022 DOI: 10.1007/978-981-19-0332-8_14
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• Assessment of Weight Factor in Genetic Programming Fitness Function for Imbalanced Data Classification
  Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021) 2022 DOI: 10.1007/978-3-030-97196-0_1
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• A Logarithmic Distance-Based Multi-Objective Genetic Programming Approach for Classification of Imbalanced Data
  Communications in Computer and Information Science 2022 DOI: 10.1007/978-3-030-95502-1_23
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• Performance Assessment of K-Nearest Neighbor Algorithm for Classification of Forest Cover Type
  Advances in Intelligent Systems and Computing 2022 DOI: 10.1007/978-981-16-4538-9_5
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• Predicting the Presence of Newt-Amphibian Using Genetic Programming
  Advances in Data and Information Sciences 2022 DOI: 10.1007/978-981-16-5689-7_19
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