Cochin University of Science and Technology
Kochi - 682022,
Kerala, India
E-mail-
philips@cusat.ac.in
BRIEF BIO
Dr. Philip Samuel took Ph.D in Computer Science & Engineering from Indian Institute of Technology (IIT Kharagpur) in 2007. He has published more than 80 research papers in various International conferences and Journals. His research interests include Artificial Intelligence, Big Data Computing, Automated Software Engineering and UML.
He has more than 24 years teaching experience as faculty, CUSAT and has served as Head, Information Technology, School of Engineering, Cochin University of Science & Technology. Currently, he is Professor & Head, Department of Computer Science, Cochin University of Science & Technology.
RESEARCH INTERESTS
Artificial Intelligence, Big Data Computing, Automated Software Engineering and UML
Agent-based asynchronous training in distributed software development, International Journal of Learning and Change, Inderscience Vol. 12, 2020
Petri net model for resource scheduling with auto scaling in elastic cloud, International Journal of Networking and Virtual Organisations, Inderscience Vol. 22, 2020
Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter, Journal of Big Data, Springer Vol. 7, 2020
Feature intersection for agent-based customer churn prediction, Data Technologies and Applications, Emerald UK Vol. 53, 2019
Service-level agreement–aware scheduling and load balancing of tasks in cloud, Software: Practice and Experience, John Wiley & Sons, Ltd Vol. 49, 2019
Context aware reliable sensor selection in IoT, International Journal of Intelligent Systems Technologies and Applications, Inderscience Publishers Vol. 18, 2019
Automatic Code Generation From UML State Chart Diagrams, IEEE Access, IEEE Vol. 7, 2019
A Multinomial Naïve Bayes Classifier for identifying Actors and Use Cases from Software Requirement Specification documents, IEEE 2nd International Conference on Intelligent Technologies (CONIT), Hubli, 2022
Launch Overheads of Spark Applications on Standalone and Hadoop YARN Clusters, Advances in Electrical and Computer Technologies, Springer, 2020
Optimum Parallelism in Spark Framework on Hadoop YARN for Maximum Cluster Resource Utilization, International Conference on Sustainable Technologies for Computational Intelligence, Advances in Intelligent Systems and Computing, Springer, Vol. 1045 , 0, 2019
Randomized Agent-Based Model for Mobile Customer Retention Behaviour Prediction, Innovations in Communication and Computing, Springer, EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 2019
Analysis and Modeling of Resource Management Overhead in Hadoop YARN Clusters, Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence & Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2017 IEEE 15th Intl, 2017
Study of execution parallelism by resource partitioning in hadoop YARN , Advances in Computing, Communications and Informatics (ICACCI), 2017 International Conference on, 2017
Improving the productivity in global software development, Innovations in Bio-Inspired Computing and Applications, 2016
Optimal sensor selection from sensor pool in IoT environment, Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd International Conference on, 2016
Secure Cloud Multi-tenant Applications with Cache in PaaS, Innovations in Bio-Inspired Computing and Applications, 2016
PATENT
Bytecode generation from UML models - Granted US Patent,2020
