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7- Full CourseMulti-modal Data Science and Engineering (MDSE)

Dalian University of Technology, China,  Mar - Jun 2024

Course organizer: Prof. Shihua Zhou

Course presenter: Prof Nikola Kasabov; Assistant: Doct. Iman AbouHassan.  

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

Course Content

1. Introduction to the course: What is MDSE and why we need it? (Lecture 7.1) 

2. Methods for MDSE (Lecture 7.2)

  • Budhraja et al., "Mosaic LSM: A Liquid State Machine Approach for Multimodal Longitudinal Data Analysis," 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 2023, pp. 1-8, doi: 10.1109/IJCNN54540.2023.10191256. (Paper 7.2)

3. MSDE for biomedical engineering (Lecture 7.3)

  • Paper 1: M. Doborjeh, N. Kasabov, Z. Doborjeh, R. Enayatollahi, E. Tu, A. H. Gandomi, Personalised modelling with spiking neural networks integrating temporal and static information," Neural Networks, 119 (2019),162-177 (Paper 7.3-1).

  • Paper 2: Sengupta, N., McNabb, C. B., Kasabov, N., & Russell, B. R. (2018). Integrating Space, Time, and Orientation in Spiking Neural Networks: A Case Study on Multimodal Brain Data Modelling. IEEE Transactions on Neural Networks and Learning Systems, 29(11). doi:10.1109/TNNLS.2018.2796023 (Paper 7.3-2)

  • Paper 3: Li, Jiawei; Liu, Jinyuan; Zhou, Shihua; Zhang, Qiang; Kasabov, Nikola, , "GeSeNet: A General Semantic-guided Network with Couple Mask Ensemble for Medical Image Fusion" , IEEE Transactions on Neural Networks and Learning Systems, DOI: https://doi.org/10.1109/TNNLS.2023.3293274, 21 July 2023 (Paper 7.3-3).

4-MDSE for predictive modelling of multisensory streaming data (Lecture 7.4(Chapter 19)

  • Paper 1: Maciag, Pi; Bembenik, R; Piekarzewicz A, Del Ser L, Javier; L, Lobo, J; N Kasabov;, Effective Air Pollution Prediction by Combining Time Series Decomposition with Stacking and Bagging Ensembles of Evolving Spiking Neural Networks, Environmental Modelling and Software, vol.170, on line: 16.10.2023, Dec 2023, 105851, https://doi.org/10.1016/j.envsoft.2023.105851; https://www.sciencedirect.com/science/article/pii/S1364815223002372

  • Paper 2: H Liu, G Lu,Y Wang, N Kasabov, Evolving spiking neural network model for PM2.5 hourly concentration prediction based on seasonal differences: A case study on data from Beijing and Shanghai, Aerosol and Air Quality Research, vol.21, Issue 2, Feb. 2021, 200247, https://doi.org/10.4209/aaqr.2020.05.0247

  • Paper 3: Laña I, Lobo JL, Capecci E, Del Ser J, Kasabov N, Adaptive long-term traffic state estimation with evolving spiking neural networks, Transportation rc.2019.02.011Research Part C: Emerging Technologies 101:126-144 2019, https://doi.org/10.1016/j.t

5. MDSE for integrated audio-visual information processing (Lecture 7.5

  • Paper 1: N. Kasabov et al, AVIS: a connectionist-based framework for integrated auditory and visual information processing. Inf. Sci. 133, 137–148 (2000) (Paper 7.5-1) (Paper 7.5-2)

  • Paper2: N Kasabov, B Bhattacharya, D Patel, N Aggarwal, T Bankar, I AbouHassan, a Cognitive Audio-Visual Associative Memories using Brain-inspired Spiking Neural Networks with Case Studies on Moving Object Recognition (IEEE Trans. Cognitive and Devel. Systems, 2023).(Paper 7.5-3)

6. MDSE for integrating times series and text data in finance and economics (Ms Iman AbouHassan) (Lecture 7.6

  • Paper: I AbouHassan, N Kasabov, V Jagtap, P Kulkarni, Spiking neural networks for predictive and explainable modelling of multimodal streaming data on the Case Study of Financial Time Series Data and on-line news, SREP, Springer-Nature, Sci Rep 13, 18367 (2023), https://doi.org/10.1038/s41598-023-42605-0. (Paper 7.6)

7. MDSE for integration of brain data and face image data for emotion recognition (Lecture 7.7)

8. Revision of the course (Lecture 7.8)

6- Full CourseCognitive System Engineering

Dalian University of Technology, China,  Mar - Jun 2024

Course organizer: Prof. Shihua Zhou

Course presenter: Prof Nikola Kasabov; Assistant: Doct. Iman AbouHassan.  

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

Course Content

1. Introduction to the course (Lecture 6.1) 

Part I: Learning systems

2. Deep learning and deep knowledge representation in the human brain (Lecture 6.2)

Chapter 3 from: N.Kasabov, ”Time-space, spiking neural networks and brain-inspired artificial intelligence," Springe-Nature, 2019 (Chapter Summary 6.2)

3. Modelling brain dynamics (Lecture 6.3)

4. Evolving learning systems (Lecture 6.4)

  • N. Kasabov, ”Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 31, no. 6, pp. 902-918, Dec. 2001, doi: 10.1109/3477.969494. (Paper 6.4)

5. Neuro-fuzzy learning and inference systems: DENFIS (Lecture 6.5)  (Chapter Summary 6.5)

  • Kasabov, N. K., & Song, Q. (2002).”DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction.” IEEE transactions on Fuzzy Systems, 10(2), 144-154. (Paper 6.5)

6. Spatio-temporal learning systems: SNN (Lecture 6.6)

7. Reservoir computing and Brain-inspired SNN (Lecture 6.7)

  • S. Schliebs, A. Mohemmed, N. Kasabov, ”Are probabilistic spiking neural networks suitable for reservoir computing? in International Joint Conference on Neural Networks" (USA, 2011), pp.3156–3163.(Paper- 6.7-1)

  • N. Kasabov, ”NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data .” Neural Netw. 52(2014), 62–76 (2014).(Paper- 6.7-2)

8. Integrated learning systems:

  • P. Koprinkova-Hristova, D. Penkov, S. Nedelcheva, S. Yordanov and N. Kasabov, ”On-line Learning, Classification and Interpretation of Brain Signals using 3D SNN and ESN," 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 2023, pp. 1-6, https://doi.org/10.1109/IJCNN54540.2023.10191974,(Paper- 6.8-2)

  • AbouHassan I., Kasabov N. (2024). ”NeuDen: A Framework for the Integration of Neuromorphic Evolving Spiking Neural Networks with Dynamic Evolving Neuro-Fuzzy Systems for Predictive and Explainable Modelling of Streaming Data," TechRxiv. Preprint.(Paper- 6.8-1)

Part II. Associative memories

9. Spatio-Temporal Associative Memories in SNN (Lecture 6.9)

10. Associative memories for neuroimaging data: EEG and fMRI 

11. Audio-visual associative memories (Lecture 6.11)

  • N Kasabov, B Sen Bhattacharya, D Patel, N Aggarwal, T Bankar, I AbouHassan, ”Cognitive Audio-Visual Associative Memories using Brain-inspired Spiking Neural Networks with Case Studies on Moving Object Recognition" (subm. IEEE Trans. Cognitive and Developm. Systems, 2023).

12. Predictive associative memories for time series (Lecture 6.12)

Part III. Software and Hardware Implementation of CogSys.

13. Neuromorphic hardware for CogSys implementations (Lecture 6.13)

  • J. Behrenbeck, Z. Tayeb, C. Bhiri, C. Richter, O. Rhodes, N. Kasabov, S. Furber, J. Conrad, Classification and Regression of SpatioTemporal EMG Signals using NeuCube Spiking Neural Network and its implementation on SpiNNaker Neuromorphic Hardware. J. Neural Eng. (IOP Press, Article reference: JNE-102499) (2018). http://iopscience.iop.org/journal/1741-2552. (Paper 6.13a

  • paper for CogSys on Loihi chip (Paper 6.13b)

14. Software implementations of CogSys. (Lecture 6.14)

  • NeuCubePy, NEST, PyNN for SpiNNaker, Lava for Loihi

15. Quantum computation

16. Revision of the course

5- Full CourseAdvanced Artificial Intelligence Technologies and Applications

Dalian University of Technology, China,  Sep- Dec 2023

Course organizer: A/Prof. Shihua Zhou

Course presenter: Prof Nikola Kasabov; Assistant: Doct. Iman AbouHassan.  

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

Course Content

1. AI and the evolution of its principles. Evolving processes in Time and Space (Ch1, 3-19)

2. From Data and Information to Knowledge. Fuzzy logic. (Ch1,19-33 + extra reading)

3. Artificial neural networks - fundamentals. (Ch2, 39-48).

4. Deep neural networks (Ch.2, 48-50 + extra reading)

5. Evolving connectionist systems (ECOS) (Ch2, 50-78). NeuCom software (IA)

6. Deep learning and deep knowledge representation in the human brain (Ch3)

7. Spiking neural networks (Ch4). Evolving spiking neural networks (Ch5)

8. Brain-inspired SNN. NeuCube. (Ch.6). NeuCube software (IA)

9. Evolutionary and quantum inspired computation (Ch.7)

10. AI applications in health (Ch.8-11)

11. AI applications for computer vision (Ch.12,13)

12. AI for brain-computer interfaces (BCI) (Ch.14)

13. AI for language modelling. ChatBots (extra reading)

14. AI in bioinformatics and neuroinformatics (Ch15,16, 17,18)

15. AI applications for multisensory environmental data (Ch.19)

16. AI in finance and economics (Ch19)

17. Neuromorphic hardware and neurocomputers (Ch20).

4- Full CourseAdvanced Artificial Intelligence Technologies and Applications

Dalian University of Technology, China,  Mar - Jun 2023

Course organizer: A/Prof. Shihua Zhou

Course presenter: Prof Nikola Kasabov; Assistants: A/Prof Wei Qi Yan and Ms. Iman AbouHassan.  

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

Course Content

  1. AI and the evolution of its principles. Evolving processes in Time and Space (Ch.1, p.3-19) (Lecture1)

  2. From Data and Information to Knowledge. Fuzzy logic. (Ch.1, p.19-33 + Time-Space&AI(Lecture2)

  3. Artificial neural networks - fundamentals. (Ch.2, p.39-48). Computational modeling with NN (Lecture3) (Tut1: NeuCom)

  4. Deep neural networks (Ch.2, p.48-50 + extra reading) (Lecture4) 

  5. Evolving connectionist systems (ECOS) (Ch.2, p.52-78). NeuCom software (IA) (Lecture5) (Tutorial2:ECOS in NeuCom)

  6. Deep learning and deep knowledge representation in the human brain (Ch.3) (Lecture6)

  7. Spiking neural networks (Ch.4). Evolving spiking neural networks (Ch.5) (Lecture7)

  8. Brain-inspired SNN. NeuCube. (Ch.6). NeuCube software (IA) (Lecture8 - Tutorial3: NeuCube Software)

  9. From von Neuman Machines to Neuromorphic Platforms (Ch.20, p.22) + v.Neumann-Atanas Neuromorphic (Lecture9)

  10. Other neurocomputers: Transformers+ Transformers paper (Lecture10)

  11. Evolutionary and quantum-inspired computation (Ch.7) (Lecture11)

  12. AI applications for brain data: EEG, fMRI (Ch.8-11) (Lecture12)

  13. Brain-computer interfaces (BCI) (Ch.14) (Lecture13)

  14. AI applications for audio-visual information (Ch.12,13). AI for language modeling (Lecture14)

  15. AI in bioinformatics and neuroinformatics (Ch.15,16,17,18) (Lecture15)

  16. AI in finance and economics (Ch19) (Lecture16)

  17. AI applications for multisensory environmental data (Ch19). Revision of the course. (Lecture17)

3- Short CourseNeural Networks, Deep Learning, and Brain-Inspired Artificial intelligence 

Dalian University of Technology, China, Aug 2022

Course organizer: A/Prof. Shihua Zhou

Course presenter: Prof Nikola Kasabov; Assistants: A/Prof Wei Qi Yan and Ms. Iman AbouHassan.

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

                    Wei Qi Yan, "Computational methods for deep learning", Springer 2022.

Course Content

  1. Artificial neural networks - fundamentals. Evolving connectionist systems (ECOS) (NK, Chapters 1,2)

  2. NeuCom software. Incremental and transfer learning in ECOS (IA, NK).

  3. Deep neural networks (DNN) (WQY, NK)

  4. Deep learning algorithms (WQY, NK)

  5. DNN for image and video (WQY, NK)

  6. Brain information processing (Chapter 3).

  7. Spiking neural networks and evolving SNN (NK, Chapters 4,5)

  8. Brain-inspired computational architectures. NeuCube software (NK, IA, Chapter 6).

  9. Evolutionary and quantum computation (NK, Chapter 7).

  10. Applications of NN and SNN for financial and economic data modeling (IA, NK, Chapter 19).

  11. Other applications of NN, DNN, and SNN. Overview of the course  (NK, WQY, IA)

2- Short Course: Deep Learning in Neural Networks and Applications

The Dalian University of Technology, China, Jun 2022

Course organizer: A/Prof. Shihua Zhou

Lecturers: Prof Nikola Kasabov; A/Prof Wei Qi Yan; Ms. Iman AbouHassan

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

                   Wei Qi Yan, "Computational methods for deep learning", Springer 2022.

Course Content

  1. Introduction to Neural Networks and Deep Neural Networks (DNN).

  2. Algorithms for Deep Learning 1.

  3. Algorithms for Deep Learning 2.

  4. Algorithms for Deep Learning 3.

  5. Reinforcement learning.

  6. Incremental Learning (IL) and Transfer Learning (TL).

    1. IL and TL of vector-based data in evolving neuro-fuzzy systems, exemplified by EFuNN & DENFIS in NeuCom.

    2. IL and TL in brain-inspired SNN, exemplified by NeuCube.

1- Short CourseAdvanced Neural Networks for AI: Methods, Systems, Applications

The Dalian University of Technology, China, May 2022

Course organizer: A/Prof. Shihua Zhou

Lecturers: Prof Nikola Kasabov; Ms. Iman AbouHassan

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

Course Content

  1. Artificial Neural Networks Fundamentals: NeuCom software.

  2. Spiking Neural Networks.

  3. Brain-Inspired Computational Architectures: NeuCube software.

  4. Evolutionary and Quantum Computation.

  5. Overview of applications of SNN for brain and genetic data modeling.

  6. Applications of SNN for streaming multisensory predictive data modeling.​

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