Amir Hussain
amir hussain

Prof Amir Hussain

Professor

Biography

Amir Hussain received his B.Eng (highest 1st Class Honours with distinction) and Ph.D degrees, from the University of Strathclyde, Glasgow, U.K., in 1992 and 1997, respectively.

Following postdoctoral and academic positions at the University of West of Scotland (EPSRC postdoctoral fellow: 1996-98), University of Dundee (Research Lecturer: 1998-2000) and University of Stirling (Lecturer: 2000-4; Senior Lecturer: 2004-8; Reader: 2008-12; Chair Professor: 2012-18) respectively, he joined Edinburgh Napier University (ENU) in 2018 as a Chair Professor in the School of Computing. He is founding Director of the Centre for AI and Robotics (CAIR) and Heads the Trustworthy Data Science and Cyber Analytics Research Group (managing over 40 academics and research staff). He is also founding Head of the Cognitive Big Data Analytics (CogBiD) Research Lab.

He currently holds a number of Visiting Professorships, including at the University of Electronic Science and Technology of China (UESTC), Xi’an Jiaotong-Liverpool University, Shanghai University and Anhui University. He has previously held Visiting Professorships at the Massachusetts Institute of Technology (MIT - Synthetic Intelligence Lab), USA and the University of Oxford (Oxford Computational Neuroscience), UK.

He is Turing Academic Lead for the University Network of the Alan Turing Institute (the UK's National Institute for AI and Data Science). He is elected Executive Committee member of the UK Computing Research Committee (UKCRC) - the National Expert Panel of the Institution of Engineering and Technology (IET) and the BCS, The Chartered Institute for IT - promoting quality and impact of UK computing research, including formulating policy and presenting views to Government and Parliamentary Committees through submissions and responses to consultations. He is also a member of the Association for BME Engineers (ABFE-UK) and strives to collaboratively explore new ways to connect and inspire young professionals, women and other members of under-represented groups in computing and engineering.

He regularly serves as advisor for national and global industry and for international government organizations. For example, he was recently appointed international advisor for Kuwait Government’s Institute for Scientific Research (KISR) to develop the country’s National AI Strategy and the National AI Centre of Excellence. He advised on strategy, capacity building and development of high-impact AI research and innovation programmes to deliver priority objectives of Kuwait Vision 2035.

He is currently an invited member of the Distinguished Scientific Committee of Saudi Arabia's Research Development and Innovation Authority (RDIA). He also serves on the Advisory Board of the National Centre of Big Data and Cloud Computing (NCBC), established by Pakistan Government, offering strategic advice on harnessing the transformative potential of AI and data science through the development of a national AI strategy for the country. He is Chair of the IEEE UK and Ireland Industry Applications Society Chapter, which has been recognised as one of the most productive Chapters in the IEEE UK and Ireland Section.

RESEARCH PROFILE AND TRACK RECORD:

Prof Hussain's research and innovation interests are cross-disciplinary and industry-led, aimed at developing and commercializing responsible AI and cognitive data science technologies to engineer smart healthcare and industrial systems of tomorrow.

He has (co)authored three international patents and over 500 publications, including nearly 300 international journal papers, 20 Books/monographs and 100+ Book chapters (with a current h-index of 77, i10-index of 309, and ~25,000 citations ). His key patent on functionally expanded neural networks (FENN methodology) was acquired by a US company (Patent no. US6453206B1).

He has led major cross-disciplinary research projects (including current grants totalling over GBP 5 Million), as Principal Investigator, funded by national and European research councils, local and international charities and industry. He has supervised over 35 PhD students to-date - his high-profile PhD graduates include (amongst others): Prof Erik Cambria (NTU, Singapore, IEEE Fellow and the world’s most highly-cited AI researcher in sentiment analysis) and Dr Soujanya Poria (2018 Presidential Young Investigator Award Winner, Singapore).

INTERNATIONAL DISTINCTIONS AND AWARDS (Examples)

• He is founding Editor-in-Chief of Cognitive Computation (Springer Nature: SCI Impact Factor (IF): 5.4) and the Springer Book Series on Socio-Affective Computing, and Cognitive Computation Trends.

• He has been appointed invited Associate Editor/Editorial Board member for a number of prestigious journals, including: the IEEE Transactions on Artificial Intelligence (AI), the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Fusion (Elsevier), AI Review (Springer), IEEE Computational Intelligence Magazine,, and the IEEE Transactions on Emerging Topics in Computational Intelligence.

• Since 2017, he has been consistently ranked in independent surveys published in leading international journals (including in Elsevier’s Information Processing & Management Journal), as one of the world’s top two most productive. AI researchers in NLP (sentiment analysis) since 2000. Many of his works on biologically-inspired natural language processing for sentiment and opinion mining are ISI highly-cited papers (e.g. https://link.springer.com/article/10.1007/s12559-021-09861-6) which have also been commercialised by his co-founded spinout company (SenticNet).

• He was co-Leader of the Winning Team (jointly with Tsinghua University) at the 2nd World Intelligent Driving Challenge (WIDC), held in Tianjin, China, 15-17 May 2018 , demonstrating the outstanding commercial impact of their winning vehicle’s cognitive system architecture which is being globally cited (https://link.springer.com/article/10.1007/s12559-019-09692-6). His team secured second-position in the 2019 WIDC Challenge. WIDC is an annual, internationally leading competition comprising contests in autonomous driving, intelligent assistance, information security and virtual testing. The Challenge attracts 90-100 teams from industry, universities and research institutes across the world, and is broadcast by over 100 international media outlets.

• His pioneering research on Sentic Computing for Big Data sentiment analytics and Natural Language Processing, was awarded the top “4* (Outstanding)” (industrial) Impact evaluation by UK Government’s Research Enhancement Framework (REF2014) exercise, and also the Best Performing Approach Award for 'Semantic Parsing' Task at the joint-industry & academic-led 'Concept-Level Sentiment Analysis Challenge’, organized as part of the 11th Extended Semantic Web Conference.

• He organised, as General Chair, the 2020 IEEE WCCI (the world's largest and top technical event in Computational Intelligence, comprising IJCNN, IEEE CEC and FUZZ-IEEE, attended by over 2,000 delegates from academia and industry).

• He is an Executive Committee member of the UK Computing Research Committee (UKCRC) - the National Expert Panel of the Institution of Engineering and Technology (IET) and the BCS, The Chartered Institute for IT, for computing research in the UK. He has also been elected Vice-Chair of the Emergent Technologies Technical Committee of the IEEE Computational Intelligence Society (CIS) and (founding) Vice-Chair for the IEEE CIS Task Force on Intelligence Systems for e-Health. He is currently IEEE Chapter Chair of the UK and Ireland IEEE Industry Applications Society, and member of the IEEE CIS Conference Committee.

MAJOR INTERNATIONAL CONFERENCE ORGANIZING (examples)

He is Founding/General/Organizing Chair for over 50 leading international conferences, including:

General Chair: IEEE World Congress on Computational Intelligence (WCCI’2020) – world’s largest, top biennial Conference on Computational Intelligence (comprising IJCNN, FUZZ-IEEE, IEEE-CEC, attended by over 2,000 delegates), Glasgow, Scotland, 19-25 July 2020 .

General Chair, 2023 IEEE Smart World Congress, Portsmouth, UK, 25-28 August 2023 (http://ieee-smart-world-congress.org/)

Founding General Chair: Annual IEEE Symposium on Computational Intelligence in Healthcare & e-Health (IEEE CICARE) - organized as part of the flagship IEEE SSCI (since 2013) – IEEE SSCI CICARE 2023 in Mexico, 5-8 Dec 2023.

Founding General Chair: International Conference on Brain Inspired Cognitive Systems (BICS – since 2004: 1st-11th), BICS 2018 co-hosted with IEEE Brain Data Bank (BDB) Challenge - BICS’2022 was organised at UESTC, Chengdu, China, 22-23 April 2022.

General co-Chair: 3rd IEEE International Conference on Smart Data, Exeter, UK, 21-23 June 2017

Program Chair: 4th IEEE International Conference on Data Science and Systems (IEEE DSS’2018), Exeter, UK, 28-20 June 2018

MAJOR RESEARCH GRANTS (Examples):

Current funded Projects (examples - currently managing research grants totalling >£5million):

1. Lead Chief/Principal Investigator (PI) and Programme Director for the prestigious large (~GBP 4million) EPSRC Programme Grant (2021-2025): “Towards cognitively-inspired 5G-IoT enabled, multi-modal Hearing Aids (COG-MHEAR)” (EP/T021063/1). The proposal was ranked second under the UK Government’s Engineering and Physical Sciences Research Council (EPSRC) Programme Grants Call: Transformative Healthcare Technologies 2050. The cross-disciplinary COG-MHEAR consortium includes partner co-investigators (CIs) from the Universities of Edinburgh, Heriot-Watt, Glasgow, Nottingham and Manchester, and a strong User Group comprising industrial and clinical collaborators and end-user engagement organisations (including the UK Royal National Institute for Deaf People, Deaf Scotland, National Robotarium, global hearing-aid manufacturers: Phonak and Nokia Bell Labs, providing ~GBP1 million match-funding).

COG-MHEAR has developed the world’s first multi-modal hearing-assistive technology demonstrators including real-time software and hardware prototypes. These are radically exploiting and integrating the transformative potential of privacy-assuring, explainable and energy-efficient AI with 5G/6G communications, IoT/Edge and cybersecurity, coupled with wireless and wearable sensing and flexible (skin-based) electronics and robotics. These are enabling commercialization of personalized, multi-modal hearing assistive technologies with reduced end-user cognitive load/listening effort for smart social and work spaces of tomorrow. The adventurous research outputs are also being exploited to develop multimodally-enhanced, speech-enabled communication aids to support e.g. autistic people with speech disorders, and more natural human-robot interaction in both real-world and immersive AR/VR/MR environments. For updates and job opportunities, please see the COG-MHEAR website: (http://cogmhear.org)

3. Lead PI for new Scottish Government's Chief Scientist Office (CSO) funded research project (~GBP 0.14 Million: Grant Ref. COV-NAP-20-07), joint with the Usher Institute, University of Edinburgh, as part of the CSO Rapid Research in Covid -19 Programme Call (2020). The interdisciplinary project has developed and validated the world’s first AI-enabled ‘live’ dashboard for real-time analysis of Covid-19 related public sentiment & attitudes on social media platforms to inform national policy considerations. Exemplar case studies focussed on COVID-19 vaccination and tracing apps, and ethnic minorities experiences during the pandemic. The Dashboard is being used by the Scottish Government (SG) to better understand public mood & wellbeing during COVID-19. Novel ‘expert-in-loop’ AI tools for topic/theme & sentiment classification enabled rapid analysis of SG COVID-19 Public Dialogue datasets, at scale. (http://covidtracker.cloud). Project report available at: https://www.cso.scot.nhs.uk/wp-content/uploads/COV-NAP-20-07.pdf

4. Lead PI for the Innovate UK grant (with Ace Aquatec Ltd) (~GBP 0.3million, 2021-24: UK Government’s Research and Innovation funded Knowledge Transfer Partnership (KTP) Project Ref: 512070), on novel multi-modal and IoT-enabled AI tools and platforms for real-time detection of fish diseases/pathogens and automated feeding systems to improve fish health. This research and innovation project has funded a Postdoctoral Data Scientist (Dr A Anwary) under my principal supervision and led to development of ground-breaking AI-powered fish biomass measuring technology. The innovative prototype achieved more than 99percent accuracy in commercial farm trials. The practically validated and commercialised technology (A-BIOMASS) has transformed the way biomass estimation is currently conducted, based on calculations and manual sampling (which are often inaccurate and lead to higher production costs as well as increased fish handling). This has paved the way to provide faster data to farmers in Scotland and globally, with fewer systems required on site, significantly saving both time and cost. https://aceaquatec.com/news-and-resources/news/scottish-farm-trials-validate-biomass-technology / https://aceaquatec.com/aquaculture-products/grow/a-biomass).

5. Lead CI and co-Director of new H2020 EIT Digital and industry funded (~GBP 0.8 Million) Centre for Doctoral Training (CDT) on IoT Trust: Privacy, Blockchain and Cryptography (2024-28). The CDT is funding the training of 18 Doctoral students with a focus on development of privacy-preserving AI and Blockchain-enabled trustworthy citizen-centric systems. Key application areas include digital health and well-being, energy supply systems, e-Commerce, Fintech and public services.

Previous funded Projects (examples):

1. Lead PI for EPSRC grant (~GBP 0.5million), 2015-2019 (EP/M026981/1: Towards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devices: AV-COGHEAR). This pioneering project has been extensively covered in media, including the BBC (http://www.bbc.co.uk/news/uk-scotland-tayside-central-33098322) and developed novel deep learning based speech enhancement algorithms for next-generation multi-modal hearing aids, listening devices and assistive technology. Project partners included: Psychology (CI: Prof. R. Watt, Stirling University), Speech & Hearing Research Group (CI: Prof J. Barker, Sheffield University), major hearing-aid manufacturers: Phonak AG/ Sonova (Dr P. Derleth) & UK MRC Institute of Hearing Research (Dr W. Whitmer).

2. Lead PI for UK EPSRC grant (~GBP 0.5million), 2011-2014 (EP/I009310/1: Dual Process Control Models in the Brain and Machines with Application to Autonomous Vehicle Control). This interdisciplinary project funded a post-doctoral RA to develop cognitive, multi-modal AI algorithms and decision support systems for complex autonomous systems, in the context of regular road driving and planetary rovers - in collaboration with CI: Prof K. Gurney (Psychology, Sheffield), SciSys Ltd & Industrial Systems Control Ltd.

3. Project CI for Chief Scientist Office (CSO) & industry co-funded (~GBP 0.5million) grant, 2015-18 (led by Dr C. Lees, Lead PI, Edinburgh) on “Prognostic Modeling of multi-level Big Data genetic & environmental factors in Crohn’s and Colitis”. The consortium comprised 12 UK Universities and Hospitals.

4. Lead PI for two UK EPSRC & industry co-funded grants (~GBP 200k, 2009-14: EP/H501584/1, EP/G501750/1).), in partnership with Harvard Medical School (Prof. C. Macrae, Prof. W. Slack), MIT Media Lab (Prof. N. Howard), USA. and the Chinese Academy of Sciences (Prof C-L. Liu). These projects funded the development of machine learning and natural language processing (NLP)-powered tools for patient opinion mining and semantic web applications, along with real-time decision support systems to enhance cardiovascular care and pre-operative risk assessment. The EPSRC projects led to the pioneering development of sentic computing technologies and applications and founding of a successful spinout company (SenticNet: https://www.business.sentic.net/). This is providing commercialised sentic technology based innovative services and solutions to global industry.

5. Lead PI for EPSRC funded Research Network (2003-4: GR/S63779/01): Novel Computation - Towards multiple-model based learning & optimized control paradigms for complex systems. This Network project funded a postdoctoral RA, and involved multiple academic and industrial partners. It was awarded an (Overall) Assessment of “Tending to Outstanding” by the EPSRC post-Grant Review Panel.

EXAMPLE (RECENT) PUBLICATIONS:

Prof. Hussain regularly publishes in prestigious top-ranked journals - examples include the IEEE Transactions on Neural Networks and Learning Systems; Nature Communications; IEEE Transactions on Cybernetics; Information Fusion; IEEE Journal on Selected Areas in Communications and others.

(Up-to-date publications list is available on Google Scholar: https://scholar.google.co.uk/citations?hl=en&user=Qg47-BsAAAAJ&view_op=list_works&sortby=pubdate)

1. Hameed, H., Usman, M., Tahir, A. Hussain A. et al. Pushing the limits of remote RF sensing by reading lips under the face mask. Nature Communications 13, 5168 (2022). https://doi.org/10.1038/s41467-022-32231-1. This demonstrates how the radical integration of wireless RF sensing with machine learning technologies can overcome key limitations of current camera based lip-reading approaches for future multimodal hearing-aid technologies, e.g. occlusion, ambient lighting and privacy concerns. In addition, the pioneering technology is shown to deliver revolutionary li- reading capabilities in the presence of face masks.

2. Y. Zhou, K. Huang, C. Cheng, X. Wang, A. Hussain and X. Liu, "FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity," in IEEE Transactions on Neural Networks and Learning Systems, 2023, doi: 10.1109/TNNLS.2022.3143554

3. L. Zhao, Z. Zhao; E. Zhang; A. Hawbani; A. Y. Al-Dubai; Z. Tan, A. Hussain, "A Digital Twin-Assisted Intelligent Partial Offloading Approach for Vehicular Edge Computing," in IEEE Journal on Selected Areas in Communications, vol. 41, no. 11, pp. 3386-3400, Nov. 2023, doi: 10.1109/JSAC.2023.3310062.

4. G. Varone, W. Boulila, M. Driss, S. Kumari, M. K. Khan, T. R. Gadekallu, A. Hussain, Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications,
(Elsevier) Information Fusion, Volume 101, 2024, pp. 1566-2535,
https://doi.org/10.1016/j.inffus.2023.102006

5. A. Diwali, K. Saeedi, K. Dashtipour, M. Gogate, E. Cambria and A. Hussain, "Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis," in IEEE Transactions on Affective Computing, 2023, doi: 10.1109/TAFFC.2023.3296373

6. A. Adeel, A. Adetomi, K. Ahmed, A. Hussain, T. Arslan and W. A. Phillips, "Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 3, pp. 818-828, June 2023, doi: 10.1109/TETCI.2022.3228537.

7. U. Anwar, T. Arslan, A. Hussain, T. C. Russ and P. Lomax, "Design and Evaluation of Wearable Multimodal RF Sensing System for Vascular Dementia Detection," in IEEE Transactions on Biomedical Circuits and Systems, vol. 17, no. 5, pp. 928-940, Oct. 2023, doi: 10.1109/TBCAS.2023.3282350.

8. D. Comminiello, A. Nezamdoust, S. Scardapane, M. Scarpiniti, A. Hussain and A. Uncini, "A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, doi: 10.1109/TSMC.2022.3202656.

9. L. A. Passos, J. P Papa, J D Ser, A Hussain, A Adeel, Multimodal audio-visual information fusion using canonical-correlated Graph Neural Network for energy-efficient speech enhancement,
Information Fusion, Volume 90, 2023, Pages 1-11, https://doi.org/10.1016/j.inffus.2022.09.006.

10. Hussain A, Sheikh A, Opportunities for artificial intelligence-enabled social media analysis of public attitudes towards Covid-19 vaccines, NEJM Catalyst Innovations in Care Delivery, 2021, https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0649

11 Gogate M, Dashtipour K, Hussain A: CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement, Information Fusion, 63: 273-285 (2020)

12. Adeel A, Gogate M, Hussain A: Contextual Deep Learning-based Audio-Visual Switching For Speech Enhancement in Real-World Environments, (Elsevier) Information Fusion, Vol.59:163-170 (2020)

13. Al-Ghadir A.I, Azmi A.M, Hussain A, A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments, (Elsevier) Information Fusion, Volume 67, Pages 29-40. (2021)

14. Ieracitano C, Mammone N, Hussain A, Morabito F.C, A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia, (Elsevier) Neural Networks, Vol.123:176-190 (2020)

15. Xiong F, Sun B, Yang X, Qiao H, Huang K, Hussain A, Liu Z, "Guided Policy Search for Sequential Multitask Learning," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1): 216-226 (2019)

16. Alsarhan A.A, Kilani Y, Al-Dubai A.Y, Zomaya A.Y., Hussain A: Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs. IEEE Transactions on Vehicular Technology 69(2): 1568-1581 (2020).

17. Mahmud, M; Kaiser, M. S; Hussain, A: Vassanelli, S, Applications of Deep Learning and Reinforcement Learning to Biological Data, IEEE Transactions in Neural Networks and Learning Systems, 29(6):2063-2079 (2018)

18. Yang X, Huang K, Zhang R, Hussain A: Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence, 2(5): 450-463, (2018)

19. Hussain A, Cambria C: Semi-Supervised Learning for Big Social Data Analysis, (Elsevier) Neurocomputing, 275: 1662-1673 (2018)

20. Malik Z.K., Hussain A, Wu J: Multi-Layered Echo State Machine: A novel Architecture and Algorithm, IEEE Transactions on Cybernetics, 47(4):946 - 959 (2017)

Date


452 results

Preprocessing based solution for the vanishing gradient problem in recurrent neural networks

Conference Proceeding
Squartini, S., Hussain, A., & Piazza, F. (2003)
Preprocessing based solution for the vanishing gradient problem in recurrent neural networks. https://doi.org/10.1109/ISCAS.2003.1206412
In this paper, a possible solution to the vanishing gradient problem in recurrent neural networks (RNN) is proposed. The main idea consists of pre-processing the signal (a tim...

A recurrent multiscale architecture for long-term memory prediction task

Conference Proceeding
Squartini, S., Hussain, A., & Piazza, F. (2003)
A recurrent multiscale architecture for long-term memory prediction task. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), (789-792). https://doi.org/10.1109/ICASSP.2003.1202485
In the past few years, researchers have been extensively studying the application of recurrent neural networks (RNNs) to solving tasks where detection of long term dependencie...

Nonlinear speech processing: Overview and applications

Journal Article
Faúndez-Zanuy, M., McLaughlin, S., Esposito, A., Hussain, A., Schoentgen, J., Kubin, G., …Maragos, P. (2002)
Nonlinear speech processing: Overview and applications. Control and Intelligent Systems, 30, 1-10
This article presents an overview of various nonlinear processing techniques applied to speech signals. Evidence relating to the existence of nonlinearities in speech is prese...

Higher-order statistics-based nonlinear speech analysis

Journal Article
Soraghan, J. J., Hussain, A., Alkulabi, A., & Durrani, T. (2002)
Higher-order statistics-based nonlinear speech analysis. Control and Intelligent Systems, 30, 11-18
A fast and robust three-level binary higher order statistics (HOS) based algorithm for simultaneous voiced/unvoiced detection and pitch estimation of speech signals in coloure...

Introduction

Journal Article
Hussain, A. (2002)
Introduction. Control and Intelligent Systems, 30,
Introduction to Special Issue Number 1 of the international journal: Control and intelligent systems, nonlinear speech processing.

Stochastic resonance and finite resolution in a network of leaky integrate-and-fire neurons

Conference Proceeding
Mtetwa, N., Smith, L., & Hussain, A. (2002)
Stochastic resonance and finite resolution in a network of leaky integrate-and-fire neurons. In Artificial Neural Networks — ICANN 2002. , (117-122). https://doi.org/10.1007/3-540-46084-5_20
This paper discusses the effect of stochastic resonance in a network of leaky integrate-and-fire (LIF) neurons and investigates its realisation on a Field Programmable Gate Ar...

A new neural network and pole placement based adaptive composite controller

Conference Proceeding
Hussain, A., Zayed, A. S., & Smith, L. (2002)
A new neural network and pole placement based adaptive composite controller. In Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century, (267-271). https://doi.org/10.1109/INMIC.2001.995349
The paper describes a new composite control method combining a neural network estimator with a conventional pole-placement based adaptive controller. The neural network estima...

A modified generalised minimum-variance stochastic self-tuning controller with pole-zero placement

Conference Proceeding
Zayed, A., Hussain, A., & Smith, L. (2001)
A modified generalised minimum-variance stochastic self-tuning controller with pole-zero placement. In Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century, (252-256). https://doi.org/10.1109/INMIC.2001.995346
The paper proposes a modified robust self-tuning controller, which minimises a cost function, incorporating system input, system output and set-point. It provides an adaptive ...

An epidemological study of smoking at Abbottabad

Journal Article
Jaleel, M., Noreen, R., Hameed, A., Hussain, A., Rauf, S., Ali, L., & Tanoli, T. (2001)
An epidemological study of smoking at Abbottabad. Journal of Ayub Medical College, Abbottabad, 13(1), 34-36
BACKGROUND: Smoking is a type of environmental pollution and is injurious to health. We conducted an epidemiological study of different forms of smoking like cigarette, Hookah...

Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century: Foreword

Conference Proceeding
Hassan, S., Mirza, H., Aziz, S., Babri, H., Iqbal, M., Maud, M., …Iqbal, Z. (2001)
Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century: Foreword. In Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century, (iv

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