Journal Articles

  1. Bergmann, J. H. M., et al. (2017). “A Bayesian Assessment of Real-World Behavior During Multitasking.” Cognitive Computation.
  2. Cambria E, Howard N, Xia Y, Chua T-S. Computational Intelligence for Big Social Data Analysis [Guest Editorial]. IEEE Computational Intelligence Magazine. 2016;11:8-9.
  3. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., et al. (2016). Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study. IEEE Access, 4, 7940-7957.
  4. Malik, Z. K., Hussain, Z. U., Kobti, Z., Lees, C. W., Howard, N., & Hussain, A. (2016). A New Recurrent Neural Network Based Predictive Model for Faecal Calprotectin Analysis: A Retrospective Study. arXiv preprint arXiv:1612.05794.
  5. Elgendi M, Howard N, Lovell N, Cichocki A, Brearley M, Abbott D, Adatia I. A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives JMIR Biomed Eng 2016;1(1):e1
  6. Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016). Fusing Audio, Visual and Textual Clues for Sentiment Analysis from Multimodal Content. Neurocomputing, 174, Part A, 50-59.
  7. Jehel, L., Howard N., Pradem M., Simchowitz Y., Robert Y., Messiah A. (2015). Prendre en compte la dimension transculturelle dans l’évaluation du risque suicidaire et du psychotraumatisme. European Psychiatry, vol 30, issue 8, page S79.
  8. Wang, Y., Rolls, E. T., Howard, N., Raskin, V., Kinsner, W., Murtagh, F., et al. (2015). Cognitive Informatics and Computational Intelligence: From Information Revolution to Intelligence Revolution. International Journal of Software Science and Computational Intelligence (IJSSCI), 7(2), 50-69.
  9. Cambria E, White B, Durrani TS, Howard N. Computational Intelligence for Natural Language Processing [Guest Editorial]. IEEE Computational Intelligence Magazine. 2014;9:19-63.
  10. Howard N, Jehel L, Arnal R: Towards a Differntial Diagnostic of PTSD Using Cognitive Computing Methods. in 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing (ICCI CC). London, UK, IEEE; 2014, p 9-20.
  11. Dunn, J., Beltran de Heredia, J., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., … & Argamon, S. (2014, June). Language-Independent Ensemble Approaches to Metaphor Identification. In Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence.
  12. Poria, S., Agarwal, Basant., Gelbukh, A., Hussain, A., Howard, N. (2014). Dependency-Based Semantic Parsing for Concept-Level Text Analysis. Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, 8403, 113-127
  13. Hussain, A., Cambria, E., Schuller, B., Howard, N. (2014). Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis, Neural Networks, Special Issue, 58, 1-3.
  14. Cambria, E., Howard, N., Song, Y. & Wang, H. (2014). Semantic Multidimensional Scaling for Open Domain Sentiment Analysis. IEEE Intelligent Systems, 29 March/April.
  15. Bermann, J., Langdon, P., Mayagoita, R. & Howard, N. (2014). Exploring the Use of Sensors to Measure Behavioral Interactions: An Experimental Evaluation of Using Hand Trajectories. PLoS ONE, 9, e88080.
  16. Nave, O., Neuman, Y., Perlovsky, L. & Howard, N. (2014). How Much Information Should We Drop to Become Intelligent? Applied Mathematics and Computation, 245: 261-264.
  17. Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhyay S. Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining. IEEE Intelligent Systems. 2013;28:31-38.
  18. Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. Pattern Recognition. Springer Berlin Heidelberg, 7914: 254-263.
  19. Howard N, Leisman G. (2013) DIME (Diplomatic Information Military and Economic) Power) Effects Modeling System: Applications for the Modeling of the Brain. 3:2-3. Journal of Functional Neurology, Rehabilitation and Ergonomics. 2013;3:257-273.
  20. Howard, N., & Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information Management (IJPHIM), 1(1), 1-11. doi:10.4018/ijphim.2013010101
  21. Poria, S., Gelbukh, A., Agarwal, B., Cambria, E. & Howard, N. (2013). Common Sense Knowledge Based Personality Recognition from Text. Advances in Soft Computing and Its Applications,. Lecture Notes in Computer Science, Springer, 8266:2013, 484-496.
  22. Cambria, E., Mazzocco, T., Hussain, A., Howard, N. (2013). Sentic Neurons: A Biologically Inspired Cognitive Architecture for Affective Common Sense Reasoning. In Procedia Computer Science, 00, 1-6.
  23. Howard N, Cambria E. Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences. 2013;3:9.
  24. Howard, N. Intention Awareness Theory in Information Risk Engineering: Contrived Balance in Integrating Information Assurance and Situation Awareness Journal of Information Assurance and Security. Volume 8 (2013) pp. 009-016
  25. Howard, N. Intention Awareness Theory; Risk Engineering Architecture Integrating Situation Awareness and Intention Awareness in Network-Centric Information Policy Journal of Information Assurance and Security. Volume 8 (2013) pp. 001-008
  26. Howard N, Lieberman H. BrainSpace: Relating Neuroscience to Knowledge About Everyday Life. Cognitive Computation. 2014;6:35-44.
  27. Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). Metaphor Identification in Large Texts Corpora. PLoS One, 8.
  28. Howard, N., Bergmann, J. & Stein, J. (2013). Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
  29. Bergmann, J., Graham, S., Howard, N. & Mcgregor, A. (2013). Comparison of Median Frequency Between Traditional and Functional Sensor Placements During Activity Monitoring. Measurement, 46, 2193-2200.
  30. Howard, N., Stein, J. & Aziz, T. (2013). Early Detection of Parkinson’s Disease from Speech and Movement Recordings. Oxford Parkinson’s Disease Center Research Day 2013.
  31. Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2(1), 29-38
  32. Bergmann, J. & Howard, N. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease. BMC Neuroscience, 13(supp),
  33. Howard, N., Lieberman, H. (2012). Brain Space: Relating Neuroscience to Knowledge About Everyday Life. Cognitive Computation, published online August 2012.
  34. Howard N, Kanareykin S. Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction The Brain Sciences Journal. 2012;1:110-124
  35. Cambria, E., White, B., Durrani, T., & Howard, N. (2012). Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1), 19-63.
  36. Howard, N. (2012). Brain Language: The Fundamental Code Unit. The Brain Sciences Journal, 1(1), 4-45.
  37. Howard, N. (2012). Energy Paradox of the Brain. The Brain Sciences Journal, 1(1), 46-61.
  38. Howard, N., Lieberman, H. (2012). Brain Space: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience. Brain Sciences Journal, 1(1), 85-97.
  39. Howard N. Cognitive architecture: Integrating situational awareness and intention awareness. Brain Sciences Journal. 2012;1:62-84.
  40. Howard, N., Guidere, M. (2012). LXIO The Mood Detection Robopsych. The Brain Sciences Journal, 1(1), 98-109.
  41. Howard, N., Kanareykin, S. (2012). Transcranial Ultrasound Application Methods: Low-Frequency Ultrasound as a Treatment for Brain Dysfunction. The Brain Sciences Journal, 1(1), 110-124.
  42. Howard N, Guidere M. Computational Methods for Clinical Applications: An Introduction. Functional Neurology, Rehabilitation, and Ergonomics. 2011;1:237-250.


  1. Howard, N., (2015).  Approach to Study the Brain: Towards the Early Detection of Neurodegenerative Disease. University of Oxford Press.


  1. Howard, N., Poria, S., Hussain, A., Cambria, E. (2015). A Common Framework for Multimodal Emotion and Sentiment Analysis. Springer. In preparation.
  2. Howard, N. (2015). The Fundamental Code Unit of the Brain: Deciphering the DNA of Cognition. Frontiers in Systems Neuroscience (Commissioned, In Preparation).
  3. Bergmann, J., Fei, J., Green, D. & Howard, N. (2015). Effect of Everyday Living Behavior on Cognitive Processing. PLOS ONE, In Preparation.
  4. Howard, N. & Stein, J. F. (2015). Mathematical Review for Cortical Computation Proposition for Brain Code Hypothesis. Frontiers Systems Neuroscience. Commissioned, In Preparation.
  5. Howard, N., Rao, D., Fahlstrom, R., Bergmann, J. & Stein, J. (2015). The Fundamental Code Unit- Applying Neural Oscillation Detection Across Clinical Conditions. Frontiers, Commissioned, In Preparation.
  6. Howard, N. & Stein, J. (2015). Potential Neural and Clinical Measures of Parkinson’s Disease. In Preparation
  7. Howard, N. & Stein, J. (2015). Toward a New Diagnostic Approach for Parkinson’s Disease. In Preparation.
  8. Bergmann, J. H. M., Goodier, H., Howard, N. & Mcgreggor, A. (2015). An Integrated Clothing Sensing System for Measuring Knee Joint Stability. In Preparation.