All Outputs (10)

Temporal graph convolutional autoencoder based fault detection for renewable energy applications. (2024)
Presentation / Conference Contribution
ARIFEEN, M. and PETROVSKI, A. 2024. Temporal graph convolutional autoencoder based fault detection for renewable energy applications. In Proceedings of the 7th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber-physical systems international conference 2024 (ICPS 2024), 12-15 May 2024, St. Louis, USA. Piscataway: IEEE [online], article number 10639998. Available from: https://doi.org/10.1109/ICPS59941.2024.10639998

Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently, researchers have shown an increasing interest in using data-driven models that utilize sensor data... Read More about Temporal graph convolutional autoencoder based fault detection for renewable energy applications..

HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks. (2024)
Presentation / Conference Contribution
AHMAD, Z., PETROVSKI, A., ARIFEEN, M., KHAN, A.S. and SHAH, S.A. 2024. HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks. In Iliadis, L., Maglogiannis, I., Papaleonidas, A., Pimenidis, E. and Jayne, C. (eds.) Engineering applications on neural networks: proceedings of the 25th International Engineering applications on neural networks 2024 (EANN 2024), 27-30 June 2024, Corfu, Greece. Communications in computer and information science, 2141. Cham: Springer [online], pages 178-190. Available from: https://doi.org/10.1007/978-3-031-62495-7_14

The rapid expansion of Internet-of-Things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due t... Read More about HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks..

Enhancing gas-pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions. (2024)
Presentation / Conference Contribution
HASAN, M.J., ARIFEEN, M., SOHAIB, M., ROHAN, A. and KANNAN, S. 2024. Enhancing gas pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions. To be published in Proceedings of the 20th International conference on condition monitoring and asset management 2024 (CM 2024), 18-20 June 2024, Oxford, UK. Northampton: BINDT [online], (accepted). To be made available at: https://doi.org/10.1784/cm2024.4b3

Traditional machine learning (ML) and deep learning (DL)-based acoustic emission (AE) data-driven condition monitoring models face several reliability issues due to factors such as fluid pressure changes, flange vibrations, inconsistent leak lengths,... Read More about Enhancing gas-pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions..

A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. (2024)
Presentation / Conference Contribution
OTOKWALA, U., ARIFEEN, M. and PETROVSKI, A. 2024. A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. In Panoutsos, G., Mihaylova, L.S. and Mahfouf, M. (eds.) Advances in computational intelligence systems: contributions presented at the 21st UK workshop on computational intelligence (UKCCI 2022), 7-9 September 2022, Sheffield, UK. Advances in intelligent systems and computing, 1454. Cham: Springer [online], pages 238-249. Available from: https://doi.org/10.1007/978-3-031-55568-8_20

The detection of zero-day attacks in the IoT network is a challenging task due to unknown security vulnerabilities. Also, the unavailability of the data makes it difficult to train a machine learning (ML) model about new vulnerabilities. The existing... Read More about A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things..

Gated recurrent unit autoencoder for fault detection in penicillin fermentation process. (2023)
Presentation / Conference Contribution
PETROVSKI, A., ARIFEEN, M. and PETROVSKI, S. 2023. Gated recurrent unit autoencoder for fault detection in penicillin fermentation process. In Kovalev, S., Kotenko, I. and Sukhanov, A. (eds.) Proceedings of the 7th Intelligent information technologies for industry international scientific conference 2023 (IITI'23), 20-25 September 2023, St. Petersburg, Russia, volume 1. Lecture notes in networks and systems (LNNS), 776. Cham: Springer [online], pages 86-95. Available from: https://doi.org/10.1007/978-3-031-43789-2_8

The penicillin fermentation process is a fed-batch system to generate industrial-scale penicillin for antibiotic production. Any fault in the fermentation tank can lead to low-quality penicillin products, which may cause a severe impact on final anti... Read More about Gated recurrent unit autoencoder for fault detection in penicillin fermentation process..

Bayesian optimized autoencoder for predictive maintenance of smart packaging machines. (2023)
Presentation / Conference Contribution
ARIFEEN, M. and PETROVSKI, A. 2023. Bayesian optimized autoencoder for predictive maintenance of smart packaging machines. In Proceedings of the 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber-physical systems 2023 (ICPS 2023), 8-11 May 2023, Wuhan, China. Piscataway: IEEE [online], 10128064. Available from: https://doi.org/10.1109/icps58381.2023.10128064

Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can... Read More about Bayesian optimized autoencoder for predictive maintenance of smart packaging machines..

Topology for preserving feature correlation in tabular synthetic data. (2022)
Presentation / Conference Contribution
ARIFEEN, M. and PETROVSKI, A. 2022. Topology for preserving feature correlation in tabular synthetic data. In Proceedings of the 15th IEEE (Institute of Electrical and Electronics Engineers) International conference on security of information and networks 2022 (SINCONF 2022), 11-13 November 2022, Sousse, Tunisia. Piscataway: IEEE [online], pages 61-66. Available from: https://doi.org/10.1109/SIN56466.2022.9970505

Tabular synthetic data generating models based on Generative Adversarial Network (GAN) show significant contributions to enhancing the performance of deep learning models by providing a sufficient amount of training data. However, the existing GAN-ba... Read More about Topology for preserving feature correlation in tabular synthetic data..

Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIoT). (2021)
Presentation / Conference Contribution
ARIFEEN, M., PETROVSKI, A. and PETROVSKI, S. 2021. Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIot). In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 28. Available from: https://doi.org/10.1109/SIN54109.2021.9699232

The integration of the IoT network with the Operational Technology (OT) network is increasing rapidly. However, this incorporation of IoT devices into the OT network makes the industrial control system vulnerable to various cyber threats. Hacking an... Read More about Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIoT)..

A next-generation telemedicine and health advice system. (2021)
Presentation / Conference Contribution
SIDDIQUI, S., HOPGOOD, A., GOOD, A., GEGOV, A., HOSSAIN, E., RAHMAN, W., FERDOUS, R., ARIFEEN, M. and KHAN, Z. 2021. A next-generation telemedicine and health advice system. In Yang, X.-S., Sherratt, S., Dey, N. and Joshi, A. (eds.) Proceedings of sixth International congress on information and communication technology, 25-26 February 2021, London, UK. Lecture notes in networks and systems, 236. Singapore: Springer [online], pages 981-989. Available from: https://doi.org/10.1007/978-981-16-2380-6_87

This project aims to create a real-time health advice platform andtelemedicine system that can reach healthcare providers and healthcare deprived people. A pragmatic approach is being used to understand the research problem of this study, which allow... Read More about A next-generation telemedicine and health advice system..

Performance analysis of different loss function in face detection architectures. (2020)
Presentation / Conference Contribution
FERDOUS, R.H., ARIFEEN, M.M., EIKO, T.S. and AL MAMUN, S. 2020. Performance analysis of different loss function in face detection architectures. In Kaiser, M.S., Bandyopadhyay, A., Muhmad, M. and Ray, K. (eds.) Proceedings of International conference on trends in computational and cognitive engineering 2020 (TCCE-2020), 17-18 December 2020, Dhaka, Bangladesh. Singapore: Springer [online], 659-669. Available from: https://doi.org/10.1007/978-981-33-4673-4_54

Masked face detection is a challenging task due to the occlusions created by the masks. Recent studies show that deep learning models can achieve effective performance for not only occluded faces but also for unconstrained environments, illuminations... Read More about Performance analysis of different loss function in face detection architectures..