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John Basantkumar Oommen

John Basantkumar Oommen

Department of Information and Communication Technology >

 

+47 37 23 32 27

www.scs.carleton.ca/~oommen

Scientific publications

  • Hassan, Ismail; Oommen, John; Yazidi, Anis (2023). Adaptive learning with artificial barriers yielding Nash equilibria in general games. Knowledge engineering review (Print). ISSN: 0269-8889. 38doi:10.1017/S0269888923000103.
  • Oommen, John; Omslandseter, Rebekka Olsson; Lei, Jiao (2023). Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes. Pattern Analysis and Applications. ISSN: 1433-7541. 26s 751 - 772. doi:10.1007/s10044-023-01131-5.
  • Omslandseter, Rebekka Olsson; Lei, Jiao; Oommen, John (2023). Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions. Information Sciences. ISSN: 0020-0255. 647s 1 - 17. doi:10.1016/j.ins.2023.119487.
  • Oommen, John; Omslandseter, Rebekka Olsson; Lei, Jiao (2023). The object migration automata: its field, scope, applications, and future research challenges. Pattern Analysis and Applications. ISSN: 1433-7541. 26s 917 - 928. doi:10.1007/s10044-023-01163-x.
  • Oommen, John; Zhang, Xuan; Lei, Jiao (2022). A Comprehensive Survey of Estimator Learning Automata and Their Recent Convergence Results. Lecture Notes in Networks and Systems. ISSN: 2367-3370. 289s 33 - 52. doi:10.1007/978-3-030-87049-2_2.
  • Omslandseter, Rebekka Olsson; Jiao, Lei; Oommen, John (2022). Enhancing the Speed of Hierarchical Learning Automata by Ordering the Actions - A Pioneering Approach. Lecture Notes in Computer Science (LNCS). ISSN: 0302-9743. 13728s 775 - 788. doi:10.1007/978-3-031-22695-3_54.
  • Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis; Oommen, John (2022). The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements. Lecture Notes in Computer Science (LNCS). ISSN: 0302-9743. 13151s 507 - 518. doi:10.1007/978-3-030-97546-3_41.
  • Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis; Oommen, John (2022). The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme with Fast Convergence and Epsilon-Optimality. IEEE Transactions on Neural Networks and Learning Systems. ISSN: 2162-237X. 35 (6). s 8278 - 8292. doi:10.1109/TNNLS.2022.3226538.
  • Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei; Oommen, John (2022). User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution. Pattern Analysis and Applications. ISSN: 1433-7541. doi:10.1007/s10044-022-01091-2.
  • Omslandseter, Rebekka Olsson; Jiao, Lei; Oommen, John (2021). A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes. Lecture Notes in Computer Science (LNCS). ISSN: 0302-9743. 12799s 227 - 239. doi:10.1007/978-3-030-79463-7_19.
  • Yazidi, Anis; Hassan, Ismail; Hammer, Hugo Lewi; Oommen, John (2020). Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm. IEEE Transactions on Neural Networks and Learning Systems. ISSN: 2162-237X. 32 (8). s 3444 - 3457. doi:10.1109/TNNLS.2020.3010888.
  • Omslandseter, Rebekka Olsson; Jiao, Lei; Oommen, John (2021). Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes. IFIP Advances in Information and Communication Technology. ISSN: 1868-4238. doi:10.1007/978-3-030-79150-6_11.
  • Ghani, Tahira; Oommen, John (2021). On utilizing 2D features from 3D scans to enhance the prediction of lung cancer survival rates. Pattern Recognition Letters. ISSN: 0167-8655. 152s 56 - 62. doi:10.1016/j.patrec.2021.09.001.
  • Bisong, O. Ekaba; Oommen, John (2021). On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists. Evolving Systems. ISSN: 1868-6478. 12 (3). s 655 - 686. doi:10.1007/s12530-021-09378-1.
  • Yazidi, Anis; Silvestre, Daniel; Oommen, John (2021). Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers. IEEE Transactions on Neural Networks and Learning Systems. ISSN: 2162-237X. 34 (2). s 650 - 661. doi:10.1109/TNNLS.2021.3099095.
  • Helmy, Ibrahim; Oommen, John (2020). A Novel Learning Automata-Based Strategy to Generate Melodies from Chordal Inputs. Artificial Intelligence Applications and Innovations. AIAI 2020. ISBN: 978-3-030-49160-4. Springer Nature. chapter. s 203 - 215.
  • Ghani, Tahira; Oommen, John (2020). Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans. Image Analysis and Recognition. ICIAR 2020. ISBN: 978-3-030-50515-8. Springer Nature. chapter. s 202 - 215.
  • Mahmoudi, Fatemeh; Razmkhah, Mostafa; Oommen, John (2020). Nonparametric “anti-Bayesian” quantile-based pattern classification. Pattern Analysis and Applications. ISSN: 1433-7541. 24s 75 - 87. doi:10.1007/s10044-020-00903-7.
  • Ghani, Tahira; Oommen, John (2020). Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis. AI 2020: Advances in Artificial Intelligence. ISBN: 978-3-030-64983-8. Springer Nature. chapter. s 42 - 54.
  • Ghaleb, Omar; Oommen, John (2020). On solving single elevator-like problems using a learning automata-based paradigm. Evolving Systems. ISSN: 1868-6478. doi:10.1007/s12530-020-09325-6.
  • Bisong, O. Ekaba; Oommen, John (2020). On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists. Evolving Systems. ISSN: 1868-6478. doi:10.1007/s12530-020-09327-4.
  • Bisong, O. Ekaba; Oommen, John (2020). Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning. Artificial Intelligence Applications and Innovations. AIAI 2020. ISBN: 978-3-030-49160-4. Springer Nature. chapter. s 227 - 240.
  • Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei; Oommen, John (2020). User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution. Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. ISBN: 978-3-030-55789-8. Springer Nature. Artikkel. s 299 - 311.
  • Zhang, Xuan; Jiao, Lei; Oommen, John; Granmo, Ole-Christoffer (2019). A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton. IEEE Transactions on Neural Networks and Learning Systems. ISSN: 2162-237X. doi:10.1109/TNNLS.2019.2900639.
  • Ghaleb, Omar; Oommen, John (2019). Learning Automata-Based Solutions to the Multi-Elevator Problem. Intelligent Computing Methodologies. ICIC 2019. ISBN: 978-3-030-26765-0. Springer. chapter. s 130 - 141.
  • Ghaleb, Omar; Oommen, John (2019). Learning Automata-Based Solutions to the Single Elevator Problem. Artificial Intelligence Applications and Innovations. AIAI 2019. ISBN: 978-3-030-19822-0. Springer. chapter. s 439 - 450.
  • Perez, Nicolas; Oommen, John (2019). Multi-Minimax: A new AI paradigm for simultaneously-played multi-player games. AI 2019: Advances in Artificial Intelligence: 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2–5, 2019, Proceedings Editors. ISBN: 978-3-030-35288-2. Springer Nature. chapter. s 41 - 53.
  • Havelock, Jessica; Oommen, John; Granmo, Ole-Christoffer (2019). On Using “Stochastic Learning on the Line” to Design Novel Distance Estimation Methods for Three-Dimensional Environments. Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. ISBN: 978-3-030-22998-6. Springer. chapter. s 39 - 49.
  • Shirvani, Abdolreza; Oommen, John (2019). On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm. Pattern Analysis and Applications. ISSN: 1433-7541. doi:10.1007/s10044-019-00817-z.
  • Tavasoli, Hanane; Oommen, John; Yazidi, Anis (2019). On utilizing weak estimators to achieve the online classification of data streams. Engineering Applications of Artificial Intelligence. ISSN: 0952-1976. 86s 11 - 31. doi:10.1016/j.engappai.2019.08.015.
  • Yazidi, Anis; Zhang, Xuan; Lei, Jiao; Oommen, John (2019). The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions. IEEE Transactions on Neural Networks and Learning Systems. ISSN: 2162-237X. doi:10.1109/TNNLS.2019.2905162.
  • Shirvani, Abdolreza; Oommen, John (2019). The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data. IFIP Advances in Information and Communication Technology. ISSN: 1868-4238. 559s 3 - 16. doi:10.1007/978-3-030-19823-7_1.
  • McMahon, Thomas; Oommen, John (2018). Enhancing English-Japanese Translation Using Syntactic Pattern Recognition Methods. Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. ISBN: 9783319591612. Springer. chapter.
  • Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (2018). Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm. IEEE Access. ISSN: 2169-3536. 6s 24362 - 24374. doi:10.1109/ACCESS.2018.2820501.
  • Havelock, Jessica; Oommen, John; Granmo, Ole-Christoffer (2018). Novel Distance Estimation Methods Using 'Stochastic Learning on the Line' Strategies. IEEE Access. ISSN: 2169-3536. 6s 48438 - 48454. doi:10.1109/ACCESS.2018.2868233.
  • Yazidi, Anis; Oommen, John (2018). Novel Results on Random Walk-Jump Chains That Possess Tree-Based Transitions. Advances in Intelligent Systems and Computing. ISSN: 2194-5357. 578s 43 - 52. doi:10.1007/978-3-319-59162-9_5.
  • Taucer, Armando H.; Polk, Spencer; Oommen, John (2018). On Addressing the Challenges of Complex Stochastic Games Using “Representative” Moves. Artificial Intelligence Applications and Innovations. ISBN: 978-3-319-92006-1. Springer. chapter. s 3 - 13.
  • Shirvani, Abdolreza; Oommen, John (2018). On Invoking Transitivity to Enhance the Pursuit-Oriented Object Migration Automata. IEEE Access. ISSN: 2169-3536. 6s 21668 - 21681. doi:10.1109/ACCESS.2018.2827305.
  • Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre (2017). On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. Journal of Computational Science. ISSN: 1877-7503. 24s 290 - 312. doi:10.1016/j.jocs.2017.08.005.
  • Mohan, Ratish; Yazidi, Anis; Feng, Boning; Oommen, John (2018). On optimizing firewall performance in dynamic networks by invoking a novel swapping window-based paradigm. International Journal of Communication Systems. ISSN: 1074-5351. 31 (15). doi:10.1002/dac.3773.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2018). On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques. Pattern Recognition. ISSN: 0031-3203. 76s 108 - 124. doi:10.1016/j.patcog.2017.10.031.
  • Yazidi, Anis; Oommen, John (2018). On the analysis of a random walk-jump chain with tree-based transitions and its applications to faulty dichotomous search. Sequential Analysis. ISSN: 0747-4946. 37 (1). s 31 - 46. doi:10.1080/07474946.2018.1427971.
  • Havelock, Jessica; Oommen, John; Granmo, Ole-Christoffer (2018). On using "Stochastic learning on the line" to design novel distance estimation methods. Lecture Notes in Computer Science (LNCS). ISSN: 0302-9743. 10868 LNAIs 34 - 42. doi:10.1007/978-3-319-92058-0_4.
  • Yazidi, Anis; Zhang, Xuan; Lei, Jiao; Oommen, John (2018). The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions. Artificial Intelligence Applications and Innovations. ISBN: 978-3-319-92006-1. Springer. Chapter. s 451 - 461.
  • Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (2017). A higher-fidelity frugal quantile estimator. Lecture Notes in Computer Science (LNCS). ISSN: 0302-9743. 10604 LNAIs 76 - 86. doi:10.1007/978-3-319-69179-4_6.
  • Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (2017). A learning automaton-based scheme for scheduling domestic shiftable loads in smart grids. IEEE Access. ISSN: 2169-3536. 6s 5348 - 5361. doi:10.1109/ACCESS.2017.2788051.
  • Yazidi, Anis; Oommen, John (2017). A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search. Information Sciences. ISSN: 0020-0255. 393s 108 - 129. doi:10.1016/j.ins.2017.02.014.
  • Polk, Spencer; Oommen, John (2017). Challenging state-of-the-art move ordering with Adaptive Data Structures. Applied intelligence (Boston). ISSN: 0924-669X. s 1 - 20. doi:10.1007/s10489-017-1006-0.
  • Yazidi, Anis; Oommen, John; Goodwin, Morten (2017). Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments. Lecture Notes in Computer Science (LNCS). ISSN: 0302-9743. 10604s 741 - 753. doi:10.1007/978-3-319-69179-4_52.
  • Oommen, John; Kim, Sang-Woon (2017). Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information. Engineering Applications of Artificial Intelligence. ISSN: 0952-1976. 63s 69 - 84. doi:10.1016/j.engappai.2017.05.001.
  • Shirvani, Abdolreza; Oommen, John (2017). On Utilizing the Pursuit Paradigm to Enhance the Deadlock-Preventing Object Migration Automaton. International Conference on New Trends in Computing Sciences, ICTCS 2017. ISBN: 978-1-5386-0527-1. IEEE conference proceedings. chapter. s 295 - 302.
  • Shirvani, Abdolreza; Oommen, John (2017). On enhancing the object migration automaton using the Pursuit paradigm. Journal of Computational Science. ISSN: 1877-7503. 24s 329 - 342. doi:10.1016/j.jocs.2017.08.008.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2017). On using novel 'Anti-Bayesian' techniques for the classification of dynamical data streams. 2017 IEEE Congress on Evolutionary Computation (CEC). ISBN: 978-1-5090-4601-0. IEEE conference proceedings. Proceedings. s 1173 - 1182.
  • Shirvani, Abdolreza; Oommen, John (2017). Partitioning in signal processing using the object migration automaton and the pursuit paradigm. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). ISBN: 978-1-5090-6341-3. IEEE Signal Processing Society. chapter.
  • Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (2017). Scheduling domestic shiftable loads in smart grids: A learning automata-based scheme. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. ISSN: 1867-8211. 203s 58 - 68. doi:10.1007/978-3-319-61813-5_6.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2017). “Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids. Information Sciences. ISSN: 0020-0255. 418-419s 495 - 512. doi:10.1016/j.ins.2017.08.017.
  • Astudillo, César A.; Poblete, Jorge; Resta, Marina; Oommen, John (2016). A Cluster Analysis of Stock Market Data Using Hierarchical SOMs. AI 2016: Advances in Artificial Intelligence. ISBN: 978-3-319-50126-0. Springer. chapter. s 101 - 112.
  • Bell, Nathan; Oommen, John (2016). A novel abstraction for swarm intelligence: particle field optimization. Autonomous Agents and Multi-Agent Systems. ISSN: 1387-2532. 31 (2). s 362 - 385. doi:10.1007/s10458-016-9350-8.
  • Jobava, Akaki; Yazidi, Anis; Oommen, John; Begnum, Kyrre (2016). Achieving Intelligent Traffic-aware Consolidation of Virtual Machines in a Data Center Using Learning Automata. 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS). ISBN: 978-1-5090-2914-3. IEEE conference proceedings. chapter.
  • Polk, Spencer; Oommen, John (2016). Challenging Established Move Ordering Strategies with Adaptive Data Structures. Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. ISBN: 978-3-319-42006-6. Springer. chapter. s 862 - 872.
  • Astudillo, César A.; Gonzalez, Javier I.; Oommen, John; Yazidi, Anis (2016). Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers. AI 2016: Advances in Artificial Intelligence. ISBN: 978-3-319-50126-0. Springer. chapter. s 175 - 182.
  • Mohan, Ratish; Yazidi, Anis; Feng, Boning; Oommen, John (2016). Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm. Proceedings of the 6th International Conference on Communication and Network Security (ICCNS '16). ISBN: 978-1-4503-4783-9. Association for Computing Machinery (ACM). 1. s 11 - 20.
  • Oommen, John; Kim, Sang-Woon (2016). Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three. 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel. ISBN: 978-3-319-41501-7. Springer. chapter. s 243 - 253.
  • Yazidi, Anis; Oommen, John (2016). Novel Discretized Weak Estimators Based on the Principles of the Stochastic Search on the Line Problem. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 46 (12). s 2732 - 2744. doi:10.1109/TCYB.2015.2487338.
  • Polk, Spencer; Oommen, John (2016). Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games. Applied intelligence (Boston). ISSN: 0924-669X. s 1 - 19. doi:10.1007/s10489-016-0835-6.
  • Polk, Spencer; Oommen, John (2016). On Achieving History-Based Move Ordering in Adversarial Board Games using Adaptive Data Structures. Transactions on Computational Collective Intelligence. ISSN: 2190-9288. 9655s 10 - 44. doi:10.1007/978-3-662-49619-0_2.
  • Yazidi, Anis; Oommen, John; Goodwin, Morten (2016). On solving the problem of identifying unreliable sensors without a knowledge of the ground truth: the case of stochastic environments. IEEE Transactions on Cybernetics. ISSN: 2168-2267. 47 (7). s 1604 - 1617. doi:10.1109/TCYB.2016.2552979.
  • Oommen, John; Kim, Sang-Woon (2016). On the Foundations of Multinomial Sequence Based Estimation. Computational Collective Intelligence, 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part I. ISBN: 978-3-319-45242-5. Springer. chapter. s 218 - 229.
  • Tavasoli, Hanane; Oommen, John; Yazidi, Anis (2016). On the Online Classification of Data Streams Using Weak Estimators. Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. ISBN: 978-3-319-42006-6. Springer. chapter. s 68 - 79.
  • Lei, Jiao; Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer (2016). Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach. Applied intelligence (Boston). ISSN: 0924-669X. 44 (2). s 307 - 321. doi:10.1007/s10489-015-0682-x.
  • Yazidi, Anis; Oommen, John; Horn, Geir Henrik; Granmo, Ole-Christoffer (2016). Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments. Pattern Recognition. ISSN: 0031-3203. 60s 430 - 443. doi:10.1016/j.patcog.2016.05.001.
  • Oommen, John; Khoury, Richard; Schmidt, Aron (2016). Text Classification Using “Anti”-Bayesian Quantile Statistics-Based Classifiers. Transactions on Computational Collective Intelligence. ISSN: 2190-9288. doi:10.1007/978-3-662-53580-6_7.
  • Oommen, John; Qin, Ke; Calitoiu, Dragos (2016). The Science and Art of Chaotic Pattern Recognition. Applications of Chaos Theory. ISBN: 9781466590441. CRC Press. chapter. s 745 - 802.
  • Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer (2016). The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality. Pattern Analysis and Applications. ISSN: 1433-7541. s 1 - 12. doi:10.1007/s10044-016-0535-1.
  • Yazidi, Anis; Hammer, Hugo Lewi; Oommen, John (2016). “Anti-Bayesian” Flat and Hierarchical Clustering Using Symmetric Quantiloids. Trends in Applied Knowledge-Based Systems and Data Science. 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings. ISBN: 978-3-319-42006-6. Springer. Intelligent Systems in Modeling Phase of Information Mining Development Process. s 56 - 67.
  • Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (2015). A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm. 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. ISBN: 978-3-319-19066-2. Springer. Unsupervised Learning. s 536 - 545.
  • Polk, Spencer; Oommen, John (2015). Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures. Computational Collective Intelligence, 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I. ISBN: 978-3-319-24069-5. Springer. chapter. s 225 - 235.
  • Polk, Spencer; Oommen, John (2015). Novel AI Strategies for Multi-Player Games at Intermediate Board States. Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. ISBN: 978-3-319-19066-2. Springer. Kapittel. s 33 - 42.
  • Yazidi, Anis; Oommen, John; Goodwin, Morten (2015). On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015, Volume II.. ISBN: 978-1-4673-9618-9. IEEE. Volume II. s 104 - 111.
  • Bell, Nathan; Oommen, John (2015). Particle Field Optimization: A New Paradigm for Swarm Intelligence. AAMAS '15 Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. ISBN: 978-1-4503-3413-6. The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). Kapittel. s 257 - 265.
  • Astudillo, César A.; Oommen, John (2015). Pattern Recognition using the TTOCONROT. Current Approaches in Applied Artificial Intelligence, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. ISBN: 978-3-319-19066-2. Springer. kapittel. s 435 - 444.
  • Yazidi, Anis; Oommen, John (2015). Solving Stochastic Root-Finding with adaptive d-ary search. 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). ISBN: 978-1-4673-6698-4. IEEE. chapter.
  • Polk, Spencer; Oommen, John (2015). Space and depth-related enhancements of the history-ADS strategy in game playing. 2015 IEEE Conference on Computational Intelligence and Games. ISBN: 978-1-4799-8621-7. IEEE conference proceedings. kapittel. s 322 - 327.
  • Oommen, John; Khoury, Richard; Schmidt, Aron (2015). Text Classification Using Novel “Anti-Bayesian” Techniques. Computational Collective Intelligence, 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I. ISBN: 978-3-319-24069-5. Springer. Chapter.
  • Lei, Jiao; Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John (2014). A Bayesian Learning Automata-Based Distributed Channel Selection Scheme. Modern Advances in Applied Intelligence, 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014,Kaohsiung, Taiwan, June 3-6, 2014, Part II. ISBN: 978-3-319-07455-9. Springer. kapittel. s 48 - 57.
  • Qin, Ke; Oommen, John (2014). Cryptanalysis of a Cryptographic Algorithm that Utilizes Chaotic Neural Networks. Information Science and Systems - Proceedings of the 29th International Symposium on Computer and Information Science. ISBN: 978-3-319-09465-6. Springer. chapter. s 167 - 174.
  • Astudillo, César A.; Oommen, John (2014). Fast BMU Search in SOMs Using Random Hyperplane Trees. PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence. ISBN: 9783319135601. Springer. chapter. s 39 - 51.
  • Yazidi, Anis; Oommen, John; Granmo, Ole-Christoffer; Goodwin, Morten (2014). On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling. 17th IEEE International Conference on Computational Science and Engineering, CSE 2014. ISBN: 978-1-4799-7981-3. IEEE conference proceedings. kapittel. s 32 - 37.
  • Sakhravi, Rokhsareh; Omran, Masoud T.; Oommen, John (2014). On the Existence and Heuristic Computation of the Solution for the Commons Game. Transactions on Computational Collective Intelligence XIV. ISBN: 9783662445099. Springer. chapter. s 71 - 99.
  • Zhang, Xuan; Oommen, John; Granmo, Ole-Christoffer; Lei, Jiao (2014). Using the Theory of Regular Functions to Formally Prove the ε -Optimality of Discretized Pursuit Learning Algorithms. Modern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part I. ISBN: 978-3-319-07467-2. Springer. kapittel. s 379 - 388.
  • Thomas, A.; Oommen, John (2013). A Novel Border Identification Algorithm Based on an “Anti-Bayesian” Paradigm. Computer Analysis of Images and Patterns. ISBN: 978-3-642-40245-6. Springer. kapittel. s 196 - 203.
  • Li, Yifeng; Oommen, John; Ngom, Alioune; Rueda, Luis (2013). A new paradigm for pattern classification: nearest border techniques. AI 2013: Advances in Artificial Intelligence. ISBN: 978-3-319-03679-3. Springer. kapittel. s 441 - 446.
  • Zhang, Xuan; Lei, Jiao; Granmo, Ole-Christoffer; Oommen, John (2013). Channel selection in cognitive radio networks: A switchable Bayesian learning automata approach. 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). ISBN: 978-1-4673-6234-4. IEEE conference proceedings. kapittel. s 2362 - 2367.
  • Thomas, A.; Oommen, John (2013). Classification of multi-dimensional distributions using order statistics criteria. Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. ISBN: 978-3319009681. Springer Publishing Company. kapittel. s 19 - 29.
  • Qin, Ke; Oommen, John (2013). IdealChaotic Pattern Recognition Is Achievable: The Ideal-M-AdNN - Its Design and Properties. Transactions on Computational Collective Intelligence XI. ISBN: 978-3-642-41775-7. Springer Publishing Company. Kapittel. s 22 - 51.
  • Thomas, A.; Oommen, John (2013). On Achieving Near-optimal “Anti-Bayesian” Order Statistics-based Classification for Asymmetric Exponential Distributions. Computer Analysis of Images and Patterns. ISBN: 978-3-642-40245-6. Springer. kapittel. s 368 - 376.
  • Polk, Spencer; Oommen, John (2013). On Enhancing Recent Multi-Player Game Playing Strategies using a Spectrum of Adaptive Data Structures. Proceedings of the 2013 Conference on Technologies and Applications of Artificial Intelligence. ISBN: 978-1-4799-2528-5. IEEE. chapter. s 164 - 169.
  • Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, John; Lei, Jiao (2013). On Using the Theory of Regular Functions to Prove the Epsilon-Optimality of the Continuous Pursuit Learn- ing Automaton. Recent Trends in Applied Artificial Intelligence. ISBN: 978-3-642-38576-6. Springer. kapittel. s 262 - 271.
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