Dr Mufti Mahmud is a Senior Lecturer of Computer Science at the School of Science and Technology. Dr Mahmud leads the MSc in Data Analytics for Business and the Data Analytics strand of the Online MBA courses. Dr Mahmud is a member of the NTU Distance Learning Governance, Operation and Steering committee as well as the International Mobility committee.
Also, Dr Mahmud serves as an independent end-point assessor for the Level 6 BSc (Hons) in Digital & Technology Solutions Professional Degree Apprenticeship.
Dr Mahmud is highly experienced in blended learning and online learning in higher education. He has several years of experience in developing contents for asynchronous learning of online postgraduate (level - 7) courses in the field of (big) data analytics and machine learning. As a competent educator and a frequent speaker in events with heterogeneous audiences, Dr Mahmud is well versed in teaching technical subjects like big data infrastructure and machine learning to students from a non-technical background (such as enrolled in business administration and computer science conversion courses).
Dr Mahmud is also heavily involved in organising conferences such as the coordinator of the local organising committee chair of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020 held in Glasgow, UK from 19 to 24 July 2020.
Dr Mahmud is responsible for the following modules:
- Big Data and its Infrastructures (Postgraduate – on campus)
- Big Data and its Infrastructures (Postgraduate – online delivery)
- Practical Machine Learning Methods for Data Mining (Postgraduate – online delivery)
- Data Analysis (Undergraduate)
Dr Mahmud is also involved in teaching the following modules:
- Foundations of Computing & Technology (Undergraduate)
- Information & Database Engineering (Undergraduate)
- Practical Project Management & Professional Development (Undergraduate)
- System Analysis & Design with Professional Development (Undergraduate)
Also, Dr Mahmud previously taught the following:
- Neural Signal Processing, Scientific Computing using MATLAB at Postgraduate level;
- Software Engineering, Software Design and Development, Object-Oriented Programming, Structured Computer Programming with C/C++, Professional Programming with C#.NET, Data Structures, Computing Systems, Computer Graphics, Computer Networking, Data & Tele Communication at Undergraduate level; and
- Developing Windows Applications using Microsoft .NET Platform (C#), Developing Web Applications using Microsoft .NET Platform (ASP.NET & C#), and Web Database Programming using SQL Server 2000 at a professional level.
Dr Mahmud’s research vision is to contribute towards a secure, smart, healthy and better world to live in. In today's digitized world, converting the ever-expanding amount of raw data to smart data, and building predictive, secure and adaptive systems aiming personalized services are essential and challenging, which require cross-disciplinary and multi-stakeholder collaborations. Towards these goals, Dr Mahmud conducts problem-driven `Brain Informatics' research where he works with problem domain experts to find multidisciplinary solutions to real-world problems. Dr Mahmud’s research involves Computational-, Health- and Social- sciences, and uses Neuroscience, Healthcare, Applied Data Science, Computational Neuroscience, Big Data Analytics, Cyber Security, Machine Learning, Cloud Computing, and Software Engineering; and plans to develop secure computational tools to advance healthcare access in low-resource settings.
Prior to joining NTU Dr Mahmud served as:
- Senior Postdoctoral Research Fellow at the NeuroChip Lab of Department of Biomedical Sciences, University of Padova, Padova, Italy from 03/2015 to 03/2018.
- Marie-Curie Research Fellow at the Theoretical Neurobiology & Neuroengineering Lab of Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium from 02/2013 to 01/2015.
- Postdoctoral Research Fellow at the NeuroChip Lab of Department of Human Anatomy & Physiology, University of Padova, Padova, Italy from 01/2011 to 01/2013.
- Assistant Professor at the Institute of Information Technology of Jahangirnagar University, Savar, Bangladesh from 09/2011 to 01/2016.
- Research Assistant at the NeuroChip Lab of Department of Human Anatomy & Physiology, University of Padova, Padova, Italy from 01/2008 till 12/2010.
- Staff Scientist at the Molecular Stamping S.r.l., Trento, Italy from 06/2007 till 01/2008.
- Lecturer of Computer Science & Engineering and Administrator of Microsoft IT Academy at the Daffodil International University, Dhaka, Bangladesh from 02/2006 till 10/2011.
- Lecturer of Computer Science & Engineering and .NET Curriculum Coordinator at the Prime University, Dhaka, Bangladesh from 03/2005 till 01/2006.
- Andre Luis Debiaso Rossi topic: Analysis of Clinical Data using Machine Learning for Disease Prediction; currently at Universidade Estadual Paulista, Brazil;
- Cosimo Ieracitano topic: Understanding Neural & Neuromuscular Behaviour through Modelling; currently at Università degli Studi Mediterranea di Reggio Calabria, Italy.
- Marcos Ignacio Fabietti (main supervisor with Prof. A. Lotfi as co-supervisor), topic: Analysis of Healthcare Big Data using Machine Learning for Disease Monitoring and Management;
- Oluwatamilore O. Orojo (main supervisor with Dr J. Teppar and Prof. M. McGinnity as co-supervisors), topic: Computational Architectures for Extracting Intelligence from Unstructured Data for Healthcare Applications;
- Salisu W. Yahaya (Co-supervisor with Prof. A. Lotfi as the main supervisor), topic: User-Centric Anomaly Detection in Activities of Daily Living.
Prospective students may obtain detailed information on opportunities to carry out postgraduate research towards an MPhil / PhD from the NTU Doctoral School.
Dr Mahmud’s research expertise and interest revolve around the following:
- Advanced Machine Learning for Biological Data Analysis: Recent research in Deep and Reinforcement Learning, and their combination promise to revolutionize Artificial Intelligence. And, multimodal data from various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/ Body]-Machine Interfaces) are piling up which require novel data-intensive machine learning techniques. In this project, we have been applying DL based methods to analyse data coming from different biological sources.
- Advanced Machine Learning for Crowd Analysis: This project aims in developing cutting-edge tools for multisource crowd data analysis to develop a real-time automated transportation system for smart city applications.
- Distributed Biosignal Analysis Framework: With the rapidly growing brain data, scientists require automated and intelligent tools to infer meaningful conclusions from them which, for a normal desktop PC, is becoming increasingly difficult. This project aims to harness the powers of distributed and cloud computing for the job.
- Early Detection of Brain Network Dysfunction in Alzheimer’s Disease (AD) & Mild Cognitive Impairment (MCI): AD & MCI are characterized by altered brain network activity which is currently detected at a mature stage. In this project, we aim to characterize a stable biomarker detectable at the disease onset to facilitate their early diagnosis and treatment. Sophisticated frequency-based analyses, of in vivo LFP from genetically modified mouse models at different pathological stages, are applied to detect network dysfunctions.
- High-Resolution Brain-Chip Interfacing: This project aims to develop high-resolution CMOS based neuronal probes to acquire neuronal signals at (sub-)cellular resolution (7.5-100 micron) across different brain structures.
- Neuroinformatics Tools for Extracellular Neural Signal Processing & Analysis: Automated analysis of multi-channel neural data has always been a huge challenge, especially when they are recorded in a low SNR setup. Under this umbrella project, we develop novel tools for automated analysis of multi-channel brain signals.
- Neuroprosthetics & Rehabilitation Engineering: The aim of this project is to reduce rehabilitation costs and to provide independent mobility to the severely disabled through automatic and smart assistive devices controllable with bio-signals (e.g., EEG and EMG). These signals are mainly triggered by imagination, physical activities, and even just by looking at the way to follow. Also, fuzzy-based controllers are optimized to operate with minimal numbers of channels.
- Secure Cyber-Physical Systems and Internet of Things: Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel healthcare data to better assess health conditions, diagnose diseases, and devise treatments. This project aims to ensure secure and reliable data communication between end-to-end devices supported by current IoT and cloud infrastructure through trust management as well as the development of novel secure IoHT frameworks.
- Understanding Neural & Neuromuscular Behaviour through Modelling: To design appropriate therapy and realistic assistive devices for neurodegenerative disorders we need to understand in detail the neural transmission mechanisms. In this project, we develop detailed models of neural networks suitable for electroceutical therapy and neuromuscular junction mimicking the neurobiological phenomena.
- Wireless Sensor Network for Healthcare Application: With the rise of service automation, access to digitized data, and growing network speed, this project aims to apply wireless sensor network to provide e-healthcare solutions.
Dr Mahmud’s external activities include discharging editorial responsibilities for prominent academic journals and organising important scientific conferences.
Grant review panel member:
- Engineering and Physical Sciences Research Council (EPSRC) the UK.
- Ministry of Education, University and Research (MIUR) Italy.
- Human Frontier Science Program (HFSP) France.
- Qatar Foundation (QF) Qatar.
- Fellow of the Higher Education Academy (UK).
- Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).
- Senior Member of the Association for Computing Machinery (ACM).
- Professional Member of the British Computer Society (BCS).
- Member of the International Association of Engineers (IAENG).
- Regional editor (Europe) of Brain Informatics (Springer-Nature) journal.
- Associate Editor of IEEE Access (IEEE) and Frontiers in Neuroscience (Frontiers) journals.
- Editorial board member of Cognitive Computation (Springer-Nature) and Big Data Analytics (BioMed Central, Springer-Nature).
- Guest editor of Applied Sciences, Brain Informatics, Cognitive Computation, IEEE Transactions on Emerging Topics in Computational Intelligence, and Sensors.
- Referee of many renowned journals from computing, neuroscience, health informatics disciplines.
Keynotes and invited talks (selected, since 2019):
- Keynote Speaker at the 10th Annual International Conference of Information and Communication Technology (ICICT2020), Virtual, 13-15 Nov. 2020.
- Keynote Speaker at the ICT Academy Global Technology Forum - A conference by the Govt. ofIndia (GTF-2020), Virtual, 14-18 Oct. 2020.
- Keynote Speaker at the 14th IEEE International Conference on Application of Information and Communication Technologies (AICT-2020), Tashkent, Uzbekistan, 7-9 Oct. 2020.
- Keynote Speaker at the International Conference on Intelligent and Smart Computing in DataAnalytics (ISCDA’2020), Vaddeswaram, India, 3-5 Oct. 2020.
- Keynote Speaker at the BGC Trust Summer School, Chittagong, Bangladesh, 29 Aug. 2020.
- Keynote Speaker at the Third International Conference on Soft Computing and Signal Processing(ICSCSP-2020), Hyderabad, India, 20-22 Aug. 2020.
- Keynote Speaker at the International COVID-19 Congress (ICC’20), Dhaka, Bangladesh, 9-10 Aug.2020.
- Keynote Speaker at the Information and Communication Technology for Intelligent Systems (ICTIS2020), Ahmedabad, India, 15 May 2020.
- Keynote Speaker at the International Conference of Advanced Computing and Informatics(ICACIn’20), Casablanca, Morocco, 13-14 Apr. 2020.
- Keynote Speaker at the IEEE Symposium Series on Computational Intelligence (IEEE-SSCI2019), Xiamen, China, 6-9 Dec. 2019.
- Plenary Speaker at the 5th Annual MarketsandMarkets Neuroscience R&D TechnologiesConference, London, UK, 3-4 Oct. 2019.
Conference organisation (selected):
- General co-chair of the 14th International Conference on Brain Informatics, September 17-19, 2020, Padova, Italy.
- General co-chair of the 1st International Conference on Artificial Intelligence and Informatics, April 9-10, Dhaka, Bangladesh.
- Programme chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2021), December 5-8, 2021, Orlando, Florida, USA.
- General chair of the International Conference on Camera-based Analysis and Recognition (CAR2020), December 28-29, 2020, Mysore, India.
- General co-chair of the 13th International Conference on Brain Informatics, September 18-20, 2020, Padova, Italy.
- Programme chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2020), December 1-4, 2020, Canberra, Australia.
- Coordinator of Local arrangement committee of the IEEE World Congress on Computational Intelligence (IEEE-WCCI2020), July 24-29, 2020, Glasgow, UK
- Programme co-chair of the IEEE Symposium on Computational Intelligence in Healthcare and E-health (IEEE CICARE 2019), December 6-9, 2019, Xiamen, China.
- Workshop chair of the 12th International Conference on Brain Informatics, December 13-15, 2019, Hainan, China.
- Publicity chair of the 11th International Conference on Brain Informatics, December 7-9, 2018, Arlington, Texas, USA.
- Special Session chair of BI2018 (Texas, USA; Dec. 7-9), IEEE-EMBC 2015 (Milano, Italy; Aug. 25-29), IEEE CICARE 2015 (Cape Town, South Africa; Dec. 7-10), IEEE CICARE 2014 (Orlando, USA; Dec. 9-12), IEEE CICARE 2013 (Singapore; Apr. 15-19), ICONIP2012 (Doha, Qatar; Nov. 12-15).
- Track chair of IEEE MECBME 2018 (Neuroengineering track, Mar. 20-30), IEEE R10HTC 2017 (Healthcare and Biomedical Engineering track, Dec. 21-23), IEEE CICARE 2015 (All tracks, Dec. 7-10).
- Publication chair of BICS 2015 (Hefei, China; Dec. 11-13).
- Local organiser of CSN School on Neurotechniques at NeuroChip Lab of University of Padova, Italy (2011, 2014, 2015, and 2016 editions).
- International Advisory Committee member of ICEEICT 2018 (Dhaka, Bangladesh; Sept. 13-15), ICEEICT 2015 (Dhaka, Bangladesh; May 21-23), ICEEICT 2014 (Dhaka, Bangladesh; Apr. 10-12), E2IC2 2015 (Coimbatore, India; Dec. 18-19.
- PC member of ICTP - since 2015, ICONIP - since 2012, IEEE-CICARE - since 2013, BICS - since 2013.
- Steering Committee member of IEEE-CICARE 2016 (Athens, Greece; Dec. 06-09).
Sponsors and collaborators
Since 2008, Dr Mahmud has been funded largely by the European Commission through the following projects:
- DIVERSASIA (Euro 1M through H2020, Erasmus+): Embracing diversity in ASIA through the adoption of Inclusive Open Practice.
- AITOP (Euro ~460k through H2020, Erasmus+): n AI Tool to Predict Engagement and ’Meltdown’ Events in Students with Autism.
- ML-SELECT (GBP ~36k through MTIF, NTU): Machine Learning Augmented Selection of Eligible People for Lung Cancer Screening using Electronic Primary Care Data.
- Ramp (Euro 362k through FP7, ICT-2013.9.6 FET Proactive, 612058): A new biohybrid architecture of natural and artificial neurons endowed with plasticity properties was developed. Communication between artificial and natural worlds was established through new nano- and micro transducers to directly interface network of neurons in culture to an artificial CMOS-based counterpart.
- NeuroAct (Euro 948k through FP7, PEOPLE-2011-IAPP Marie-Curie Action, 286403): Novel tools and platforms were developed to advance current understanding of the molecular and microcircuit bases for nervous system (dys)function and perform high-throughput in-vitro drug screening.
- Realnet (Euro 643k through FP7, ICT-2009.8.8 FET Proactive, 270434): Specific imaging techniques to record from multiple neurons in the cerebellar network was developed. From the data, realistic real-time model of the cerebellum was obtained and connected to robotic systems under closed-loop conditions. Using "adaptable filter theory", sensorimotor control and cognitive systems were investigated.
- CyberRat (Euro 372k through FP7, ICT-2007.8.3 Bio-ICT convergence, 216528): An innovative brain-chip interface, with CMOS chip featuring a large-scale matrix of stimulation and recording microelectrodes integrated at high-spatial-density (~1000 sites at <10-micron separation), was developed. I was in charge of processing and analysis of the data recorded from these chips.
- Euro ~57k from UNIPD, IT for the `Development of novel software tools to study neuronal populations Activity recorded using high-resolution multi-site neuronal probes' (03/2015-02/2017).
- Euro ~39k from UNIPD, IT for the `Use of capacitors and transistors for recording & stimulation of neuronal activity in the cortex and deep nuclei of rat brain' (01/2011-12/2012).
- Euro ~67k from Fondazione CARIPARO for `Developing novel neuronal signal processing and analysis tools' (01/2008-12/2010).
Dr Mahmud’s external collaborators include:
- Prof. Stefano Vassanelli from University of Padova, Italy on the development of novel high-resolution brain-chip interfacing and smart neuroengineering systems for distributed artificial intelligence
- Prof. Cristina Fasolato from the University of Padova, Italy on the characterization of a stable biomarker for early detection of Alzheimer’s disease.
- Prof. Roland Thewes from the Technical University of Berlin, Germany on the development of microelectronic devices.
- Prof. Amir Hussain from the Edinburgh Napier University, UK on the development of intelligent signal analysis tools for [e/m]Health applications.
- Prof. Michele Giugliano from the SISSA, Italy on the development of novel cloud-based neuroinformatics tools.
- Prof. M. Shamim Kaiser from IIT, Jahangirnagar University, Bangladesh on the development of low cost and wireless assistive/rehabilitation devices.
The list below shows selected Journal publications only. It excludes book chapters and conference proceeding papers. For a complete list of publication - please consult my Google Scholar profile.
Journal articles (selected):
- M. Mahmud, M. S. Kaiser, M. McGinnity, and A. Hussain. (2020). Deep Learning in Mining Biological Data, Cognitive Computation, pp. 1–33, Dec. 2020, doi: 10.1007/s12559-020-09773-x [epub ahead of print].
- R. L. S. Sharpe, M. Mahmud, M. S. Kaiser, and J. Chen. (2020). Gamma entrainment frequency affects mood, memory and cognition: an exploratory pilot study, Brain Informatics, vol. 7, no. 1, p. 17, Nov. 2020.
- G. Rabby, S. Azad, M. Mahmud, K. Z. Zamli, and M. M. Rahman. (2020). TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique, Cognitive Computation, vol. 12, no. 4, pp. 811–833, Jul. 2020.
- M. B. T. Noor, N. Z. Zenia, M. S. Kaiser, S. A. Mamun, and M. Mahmud. (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimers disease, Parkinsons disease and schizophrenia, Brain Informatics, vol. 7, no. 1, p. 11, Oct. 2020.
- Z. Ju, L. Gun, A. Hussain, M. Mahmud, and C. Ieracitano. (2020). ‘A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications’, Applied Sciences, vol. 10, no. 19, Art. no. 19, Sep. 2020.
- A. Leparulo, M. Mahmud, E. Scremin, T. Pozzan, S. Vassanelli, and C. Fasolato. (2020). Dampened Slow Oscillation Connectivity Anticipates Amyloid Deposition in the PS2APP Mouse Model of Alzheimer's Disease, Cells, vol. 9, no. 1, Art. no. 1, Jan. 2020.
- L. Farah, A. Hussain, A. Kerrouche, C. Ieracitano, J. Ahmad, and M. Mahmud. (2020). A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System, IEEE Access, vol. 8, pp. 163225–163237, 2020..
- N. Dey, V. Rajinikanth, S. J. Fong, M. S. Kaiser, and M. Mahmud. (2020). Social Group Optimization–Assisted Kapurs Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images, Cognitive Computation, vol. 12, no. 5, pp. 1011–1023, Sep. 2020.
- L. Chen, J. Yan, J. Chen, Y. Sheng, Z. Xu, and M. Mahmud. (2020). An event based topic learning pipeline for neuroimaging literature mining, Brain Informatics, vol. 7, no. 1, p. 18, Nov. 2020.
- M. H. A. Banna, K. A. Taher, M. S. Kaiser, M. Mahmud, M. S. Rahman, A.S.M.S. Hosen, G. H. Cho (2020). Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges, IEEE Access, vol. 8, pp. 192880–192923, 2020.
- F. I. Adiba, T. Islam, M. S. Kaiser, M. Mahmud, and M. A. Rahman. (2020). Effect of Corpora on Classification of Fake News using Naive Bayes Classifier, International Journal of Automation, Artificial Intelligence and Machine Learning, vol. 1, no. 1, Art. no. 1, Oct. 2020.
- S. W. Yahaya, A. Lotfi, and M. Mahmud. (2020). A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living, Applied Soft Computing, vol. 83, p. 105613, Oct. 2019.
- S. Azad*, N. E. A. C. Nordin, N. N. A. Rasul, M. Mahmud*, and K. Z. Zamli. (2020). A Secure Hybrid Authentication Scheme Using Passpoints and Press Touch Code. IEEE Access, vol. 7, pp. 166043–166053, 2019. [* Co-senior authors]
- M. Asif-Ur-Rahman, F. Afsana, M. Mahmud*, M.S. Kaiser*, M.R. Ahmed, O. Kaiwartya, A. James-Taylor. (2019). Towards a Heterogeneous Mist, Fog, and Cloud based Framework for the Internet of Healthcare Things. IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4049–4062. Doi: 10.1109/JIOT.2018.2876088 [* Co-senior authors]
- A. Aliyu, A.H. Abdullah, N. Aslam, A. Altameem, R.Z. Radzi, R. Kharel, M. Mahmud, S. Prakash, U.M. Joda. (2018). Interference-aware Multipath Video Streaming in Vehicular Environments. IEEE Access, Vol. 6, pp. 47610-47626. Doi: 10.1109/ACCESS.2018.2854784
- M. Mahmud*, M.S. Kaiser*, M.M. Rahman, M.A. Rahman, A. Shabut, S. Al Mamun, A. Hussain. (2018). A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications. Cognitive Computation, Vol. 10, No. 5, pp. 864–873. Doi: 10.1007/s12559-018-9543-3 [*: equal contributors.]
- M. Mahmud*, M.S. Kaiser*, A. Hussain, S. Vassanelli. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 6, pp. 2063 - 2079. Doi: 10.1109/TNNLS.2018.2790388 [*: equal contributors.]
- F. Afsana, M.A. Rahman, M.R. Ahmed, M. Mahmud*, M.S. Kaiser*. (2018). An Energy Conserving Routing Scheme for Wireless Body Sensor Nanonetwork Communication. IEEE Access, Vol. 6, pp. 9186-9200. Doi: 10.1109/ACCESS.2018.2789437 [*: Co-senior authors.]
- M.S. Kaiser, K. Lwin, M. Mahmud, D. Hajializadeh, T. Chaipimonplin, A. Sarhan, M. A. Hossain. (2018). Advances in Crowd Analysis for Urban Applications through Urban Event Detection. IEEE Transactions on Intelligent Transport Systems, Vol. 19, No. 10, pp. 3092 - 3112. Doi: 10.1109/TITS.2017.2771746
- R. Fontana, M. Agostini, E. Murana, M. Mahmud, M. Rubega, G. Sparacino, S. Vassanelli, C. Fasolato. (2017). Early Hippocampal Hyperexcitability in PS2APP Mice: Role of Mutant PS2 and AP. Neurobiology of Aging, Vol. 50, pp. 64-76. Doi: 10.1016/j.neurobiolaging.2016.10.027.
- S. Vassanelli, M. Mahmud. (2016). Trends and Challenges in Neuroengineering: Towards ‘Intelligent’ Neuroprostheses through Brain-‘Brain Inspired Systems’ Communication. Frontiers in Neuroscience, Vol. 10, art. no. 438. Doi: 10.3389/fnins.2016.00438.
- M. Mahmud, S. Vassanelli. (2016). Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-art and Challenges. Frontiers in Neuroscience, Vol.10, art. no. 248. Doi10.3389/fnins.2016.00248.
- M.S. Kaiser, Z. Chowdhury, S. Al Mamun, A. Hussain, M. Mahmud. (2016). A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair. Cognitive Computation, Vol. 8, No. 5, pp. 946-954. Doi: 10.1007/s12559-016-9398-4.
- M. Mahmud, R. Pulizzi, E. Vasilaki, M. Giugliano. (2014). QSpike Tools: a Generic Framework for Parallel Batch Preprocessing of Extracellular Neuronal Signals Recorded by Substrate Microelectrode Arrays. Frontiers in Neuroinformatics, Vol. 8, art. no. 26. Doi: 10.3389/fninf.2014.00026.
- M. Mahmud, A. Bertoldo, et al. (2012). SigMate: A Matlab–Based Automated Tool for Extracellular Neuronal Signal Processing and Analysis. Journal of Neuroscience Methods, Vol. 207, No. 1, pp. 97-112. Doi: 10.1016/j.jneumeth.2012.03.009.