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Professor Karim Ouazzane is a computer scientist and Professor of Computing and Knowledge Exchange in the School of Computing within the Faculty of Life Sciences and Computing at London Metropolitan University in London. [1]
| This section of a biography of a living person does not include any references or sources .(June 2023) |
| This section of a biography of a living person does not include any references or sources .(June 2023) |
| This section of a biography of a living person does not include any references or sources .(June 2023) |
| This section of a biography of a living person does not include any references or sources .(June 2023) |
M. Malcangi, K.Ouazzane, P.Patel (2013) 'Audio visual fuzzy fusion for robust speech recognition' Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, 4–9 August 2013, pp 284.
M. Malcangi, M.Riva, K.Ouazzane (2013) 'Hard and soft computing methods for capturing and processing phonocardiogram' International Journal of Circuits, Systems and Signal Processing, Issue 1, Volume 7, pp 34.
J.Li, K.Ouazzane, H.Kazemian, M.S.Afzal (2013) ' Neural Network Approaches for Noisy Modelling', IEEE Transactions on Neural Networks and Learning system, Volume 24 page 1–12. DOI: 10:1109/TNNLS.2013.2263557
M.Aigbodi, K.Ouazzane, D.Mitchell, V.Vassilev and J.Li (2013) ‘ Defence in-depth for Cyber Security With Custom Anti-Virus Signature Definition' The third conference on digital information processing and communication (ICDIPC2013) ISBN 978-0-9853483-3-5, pp 150.
V.Vassilev, M.Ulman, K.Ouazzane, H.Kazemian, M.Aigbodi and R.Boyd (2013) 'OntoCarer; An ontological framework for assistive agents for the disabled' The third conference on digital information processing and communication (ICDIPC2013) ISBN 978-0-9853483-3-5, pp 406.
K.Ouazzane, M.Aigbodi and J.Li (2013) ‘ Real life pilot solution with artificial intelligence for disabled computer users' The third conference on digital information processing and communication (ICDIPC2013) ISBN 978-0-9853483-3-5, pp 310.
H.kazemian and K.Ouazzane (2012) 'Neuro-fuzzy approach to video transmission over Zigbee; Neurocomputing journal, DOI: 10.1016/j.neucom.2012.10.006
K. Ouazzane, J. Li, H. Kazemian, Y. Jing and R. Boyd (2012) ‘ An artificial intelligence language modelling framework' International Journal of expert systems with applications; DOI: 10.1016/j.eswa.2011.11.121
J. Li, K. Ouazzane, H. Kazemian, Y. Jing, R. Boyd (2011) ‘ A neural Network Based Solution for Automatic Typing Errors Correction', Journal of Neural Computing Applications; DOI: 10.1007/s00521-010-0492-3
K.Ouazzane, Jun Li and H.B. Kazemian (2011) An Intelligent Keyboard Framework for Improving Disabled People Computer Accessibility, 12th Engineering Applications of Neural Networks and 7th Artificial Intelligence Applications and Innovations Joint Conferences, Corfu, Greece, Springer, Part I, International Federation for Information Processing AICT 363, pp. 382–391, 15th – 18 September 2011.
K.Ouazzane, S.Afzal, H.Kazemian and J. Li (2011) 'An E-business framework design using enhanced web 2.0 technology' ICEIS 2011–13th International Conference on Enterprise Information systems; Vol. 2, pp. 97, 2011.
J.Li, K.Ouazzane, S.Afzal and H.Kazemian (2011) 'Patterns identification for hitting adjacent key errors correction using neural network models' ICEIS 2011– 13th International Conference on Enterprise Information systems; Vol. 3 pp. 5, 2011.
K.Ouazzane, J. Li and H. Kazemian (2011) ‘ An Intelligent Keyboard Framework for Improving Disabled People Computer Accessibility' EANN/AIAI 2011, Part 1, IFIP AICT, pp. 382–391.
C. Althaff-Irfan, K. Ouazzane, S. Nomura and F. Yoshimi (2010) ‘ An Access Control System for e-learning System Management System' Proc. 47th the Japan Society of Mechanical Engineers; Hokuriku-etsu, pp. 59.
C. Althaff-Irfan, K. Ouazzane, S. Nomura and F. Yoshimi (2010) 'An access Control for e-learning management systems' IEEJ journal (ID C10 -174).
J. Li, K. Ouazzane, H. Kazemian, Y. Jing and R. Boyd (2009) ‘ Focused Time Delay Neural Network Modelling Towards Typing Stream Prediction' IADIS multiple on computer sciences and Information system, Proceedings of Intelligent Systems and Agents, pp. 189, ISBN 978-972-8924-87-4, Algarve, Portugal.
C. Althaff-Irfan, S. Nomura, K. Ouazzane, Y. Takashi (2009) 'Faced-based Access Control and Invigilation Tool For e-learning systems' International conference on biometrics and Kasei Engineering, pp. 40, IEEE computer society, Cieszyn, Poland.
J. Li, K. Ouazzane, Y. Jing, H. Kazemian and R. Boyd (2009) ‘ Evolutionary Ranking on Multiple Word Correction Algorithms Using Neural Network Approach' The 11th International on Engineering Applications of Neural Networks, EANN 2009, pp. 409 London.
C. Althaff-Irfan, S. Nomura, K. Ouazzane and F. Yoshimi (2009) ‘ Real Time Invigilation and Chronic Authentication Tool for e-learning Management System' International Journal of 3-Dimensional Images, Vol. 23 N2, July 2009.
In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.
Theoretical computer science is a subfield of computer science and mathematics that focuses on the abstract and mathematical foundations of computation.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the other type, the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that cannot be unrolled.
Vasant G. Honavar is an Indian-American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor.
Unconventional computing is computing by any of a wide range of new or unusual methods.
Sami Erol Gelenbe, a Turkish and French computer scientist, electronic engineer and applied mathematician, pioneered the field of Computer System and Network Performance. Currently Professor in the Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences, he is also a Visiting Professor at King's College London, Honorary Researcher in the I3S Laboratory, and the Abraham de Moivre Laboratory. Fellow of several National Academies, he Chairs the Informatics Section of Academia Europaea since 2023. His previous Professorial Chairs include the University of Liège (1974-1979), University Paris-Saclay (1979-1986), University Paris Descartes (1986-2005), NJIT (1991–93), ECE Chair at Duke University (1993-1998), University Chair Professor and Director of EECS, University of Central Florida (1998-2003), and Dennis Gabor Professor and Head of Intelligent Systems and Networks, Imperial College (2003-2019).
In data analysis, anomaly detection is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.
An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can produce or reproduce specific temporal patterns. The main interest of this network is that although its behavior is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system.
There are many types of artificial neural networks (ANN).
In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
An AI accelerator, deep learning processor or neural processing unit (NPU) is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2024, a typical AI integrated circuit chip contains tens of billions of MOSFETs.
Amir Hussain is a cognitive scientist, the director of Cognitive Big Data and Cybersecurity (CogBID) Research Lab at Edinburgh Napier University He is a professor of computing science. He is founding Editor-in-Chief of Springer Nature's internationally leading Cognitive Computation journal and the new Big Data Analytics journal. He is founding Editor-in-Chief for two Springer Book Series: Socio-Affective Computing and Cognitive Computation Trends, and also serves on the Editorial Board of a number of other world-leading journals including, as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics (Systems) and the IEEE Computational Intelligence Magazine.
Zygmunt J. Haas is a professor and distinguished chair in computer science, University of Texas at Dallas (UTD) also the professor emeritus in electrical and computer engineering, Cornell University. His research interests include ad hoc networks, wireless networks, sensor networks, and zone routing protocols.
Chan-Jin Chung, commonly known as CJ Chung, is a full professor of computer science at Lawrence Technological University (LTU) in Michigan, USA. He founded an international autonomous robotics competition called Robofest in the 1999–2000 academic year as well as numerous educational programs for youth by integrating STEM, arts, autonomous robotics, and computer science. He also served as the founding USA National Organizer of World Robot Olympiad (WRO) in 2014 and 2015. He also started the WISER conference in 2014. He is working on developing a computer science curriculum for connected and autonomous vehicles (CAV) with a support from National Science Foundation . His research areas include evolutionary computation, cultural algorithms, intelligent systems & autonomous mobile robotics, software engineering, machine learning & deep learning, computer science education, and educational robotics.
Lyle Norman Long is an academic, and computational scientist. He is a Professor Emeritus of Computational Science, Mathematics, and Engineering at The Pennsylvania State University, and is most known for developing algorithms and software for mathematical models, including neural networks, and robotics. His research has been focused in the fields of computational science, computational neuroscience, cognitive robotics, parallel computing, and software engineering.
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