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B. John Oommen

B. John Oommen

D-Index & Metrics

Computer Science

D-Index
39
Citations
6351
World Ranking
9776
National Ranking
387

Research.com Recognitions

  • 2006 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to fundamental and applied problems in syntactic and statistical pattern recognition.

Overview

B. John Oommen is a researcher affiliated with Carleton University in Canada. Their research contributions span several areas of computer science and engineering, with a focus on optimization, machine learning, and decision sciences.

Their work has been published extensively in various academic venues. Frequent publication venues include:

  • Pattern Analysis and Applications
  • IEEE Transactions on Neural Networks and Learning Systems
  • Evolving Systems
  • Pattern Recognition Letters
  • The Knowledge Engineering Review

Oommen's main fields of study cover:

  • Computer Science
  • Engineering
  • Decision Sciences

The subfields in which they concentrate include:

  • Computer Networks and Communications
  • Artificial Intelligence
  • Management Science and Operations Research
  • Electrical and Electronic Engineering
  • Radiology, Nuclear Medicine and Imaging

The primary research topics associated with their work are:

  • Optimization and Search Problems
  • Machine Learning and Algorithms
  • Auction Theory and Applications
  • Distributed systems and fault tolerance
  • AI in cancer detection
  • Semigroups and automata theory
  • Advanced Bandit Algorithms Research

Frequent co-authors in their research collaborations include:

  • Lei Jiao
  • Rebekka Olsson Omslandseter
  • Anis Yazidi
  • Tahira Ghani
  • Xuan Zhang

Selected recent papers by B. John Oommen include:

  • "Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm," 2020, IEEE Transactions on Neural Networks and Learning Systems
  • "User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution," 2022, Pattern Analysis and Applications
  • "The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality," 2022, IEEE Transactions on Neural Networks and Learning Systems
  • "Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers," 2021, IEEE Transactions on Neural Networks and Learning Systems
  • "Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes," 2023, Pattern Analysis and Applications

Oommen has received recognition including the 2006 Fellow award from the International Association for Pattern Recognition (IAPR) for contributions to syntactic and statistical pattern recognition.

Best Publications

  • Robot navigation in unknown terrains using learned visibility graphs. Part I: The disjoint convex obstacle case

    B. Oommen;S. Iyengar;N. Rao;R. Kashyap

  • Generalized pursuit learning schemes: new families of continuous and discretized learning automata

    Unknown

  • Discretized pursuit learning automata

    Unknown

  • Continuous and discretized pursuit learning schemes: various algorithms and their comparison

    Unknown

  • The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem

    N. Aras;B. J. Oommen;I. K. Altinel

  • Deterministic Learning Automata Solutions to the Equipartitioning Problem

    Unknown

  • Learning Automata-Based Solutions to the Nonlinear Fractional Knapsack Problem With Applications to Optimal Resource Allocation

    Unknown

  • Graph Partitioning Using Learning Automata

    Unknown

  • A brief taxonomy and ranking of creative prototype reduction schemes

    S. W. Kim;B. John Oommen

  • Continuous Learning Automata Solutions to the Capacity Assignment Problem

    Unknown

  • Dynamic algorithms for the shortest path routing problem: learning automata-based solutions

    S. Misra;B.J. Oommen

  • Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments

    B. John Oommen;Luis Rueda

  • Discretized estimator learning automata

    Unknown

  • Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution

    S. Misra;B.J. Oommen;S. Yanamandra;M.S. Obaidat

  • Stochastic searching on the line and its applications to parameter learning in nonlinear optimization

    Unknown

  • On terrain acquisition by a point robot amidst polyhedral obstacles

    N.S.V. Rao;S.S. Iyengar;B.J. Oommen;R.L. Kashyap

  • Recognition of Noisy Subsequences Using Constrained Edit Distances

    B. John Oommen

  • Ε-optimal Discretized Linear Reward-penalty Learning Automata

    Unknown

  • Spelling correction using probabilistic methods

    R. L. Kashyap;B. J. Oommen

  • GPSPA: a new adaptive algorithm for maintaining shortest path routing trees in stochastic networks

    Sudip Misra;B. John Oommen

  • List organizing strategies using stochastic move-to-front and stochastic move-to-rear operations

    B. John Oommen;E. R. Hansen

  • Routing Bandwidth-Guaranteed Paths in MPLS Traffic Engineering: A Multiple Race Track Learning Approach

    B.J. Oommen;S. Misra;O.-C. Granmo

  • Absorbing and Ergodic Discretized Two-Action Learning Automata

    Unknown

  • Service selection in stochastic environments: a learning-automaton based solution

    Anis Yazidi;Ole-Christoffer Granmo;B. John Oommen

  • Parameter learning from stochastic teachers and stochastic compulsive liars

    B.J. Oommen;G. Raghunath;B. Kuipers

  • Topology-oriented self-organizing maps: a survey

    César A. Astudillo;B. John Oommen

  • Cybernetics and Learning Automata

    B. John Oommen;Sudip Misra

  • Anomaly Detection in Dynamic Systems Using Weak Estimators

    Justin Zhan;B. John Oommen;Johanna Crisostomo

  • A cryptosystem for data security

    B. John Oommen;Luis G. Rueda

  • An efficient dynamic algorithm for maintaining all-pairs shortest paths in stochastic networks

    S. Misra;B.J. Oommen

  • An efficient compression scheme for data communication which uses a new family of self-organizing binary search trees

    Luis Rueda;B. John Oommen

Frequent Co-Authors

Sudip Misra
Sudip Misra Indian Institute of Technology Kharagpur
Rangasami L. Kashyap
Rangasami L. Kashyap Purdue University West Lafayette
Yuanwei Liu
Yuanwei Liu University of Hong Kong
Mohammad S. Obaidat
Mohammad S. Obaidat University of Jordan
S. Sitharama Iyengar
S. Sitharama Iyengar Florida International University
Benjamin Kuipers
Benjamin Kuipers University of Michigan–Ann Arbor
Nageswara S. V. Rao
Nageswara S. V. Rao Oak Ridge National Laboratory
Stan Matwin
Stan Matwin Dalhousie University

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