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155 results on '"Kabir, Sohag"'

1. Reliability Assessment of IoT-Enabled Systems Using Fault Trees and Bayesian Networks

Author

Abdulhamid, Alhassan, Kabir, Sohag, Ghafir, Ibrahim, Lei, Ci, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Umakanta, editor, Tripathy, Asis Kumar, editor, Sahoo, Jyoti Prakash, editor, Sarkar, Mahasweta, editor, and Li, Kuan-Ching, editor

Published
2024
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2. Adaptation of Model Transformation for Safety Analysis of IoT-Based Applications

Author

Abdulhamid, Alhassan, Kabir, Sohag, Ghafir, Ibrahim, Lei, Ci, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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3. Developing Dependable IoT Systems: Safety Perspective

Author

Abdulhamid, Alhassan, Kabir, Sohag, Ghafir, Ibrahim, Lei, Ci, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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4. Online Dynamic Reliability Evaluation of Wind Turbines based on Drone-assisted Monitoring

Author

Kabir, Sohag, Aslansefat, Koorosh, Gope, Prosanta, Campean, Felician, and Papadopoulos, Yiannis

Subjects
Computer Science - Artificial Intelligence, Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach., Comment: A modified version of this work has been published in the 2022 International Conference on Computing, Electronics & Communications Engineering (iCCECE). This work is a draft author version

Published
2022

7. Reliability Analysis of Process Systems Using Intuitionistic Fuzzy Set Theory

Author

Yazdi, Mohammad, Kabir, Sohag, Kumar, Mohit, Ghafir, Ibrahim, Islam, Farhana, Gowda, G. D. Veerappa, Editor-in-Chief, Kesavan, S., Editor-in-Chief, Nekka, Fahima, Editor-in-Chief, Khan, Akhtar A., Editorial Board Member, Rangarajan, Govindan, Editorial Board Member, Balachandran, K., Editorial Board Member, Sreenivasan, K. R., Editorial Board Member, Brokate, Martin, Editorial Board Member, Nashed, M. Zuhair, Editorial Board Member, Gupta, N. K., Editorial Board Member, Zahra, Noore, Editorial Board Member, Manchanda, Pammy, Editorial Board Member, Lozi, René Pierre, Editorial Board Member, Aslan, Zafer, Editorial Board Member, and Garg, Harish, editor

Published
2023
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11. A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262

Author

Vasudevan, Vinod, Abdullatif, Amr, Kabir, Sohag, Campean, Felician, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Jansen, Thomas, editor, Jensen, Richard, editor, Mac Parthaláin, Neil, editor, and Lin, Chih-Min, editor

Published
2022
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12. A Model-Based Reliability Analysis Method Using Bayesian Network

Author

Kabir, Sohag, Campean, Felician, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Jansen, Thomas, editor, Jensen, Richard, editor, Mac Parthaláin, Neil, editor, and Lin, Chih-Min, editor

Published
2022
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14. Certifiability Analysis of Machine Learning Systems for Low-Risk Automotive Applications

Author

Vasudevan, Vinod, Abdullatif, Amr, Kabir, Sohag, and Campean, Felician

Abstract

We analyze the existing safety standard, ISO 26262, for automotive applications, determining the certifiability of machine learning (ML) approaches used in low-risk automotive applications. This can help assuring the security and safety of ML-based autonomous driving systems, gaining the trust of regulators, certification agencies, and stakeholders.

Published
2024
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17. Compositional dependability analysis of dynamic systems with uncertainty

Author

Kabir, Sohag and Walker, Martin

Subjects
004, Computer science
Abstract

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in. MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model. One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs. Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components. The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn.

Published
2016

18. A Runtime Safety Analysis Concept for Open Adaptive Systems

Author

Kabir, Sohag, Sorokos, Ioannis, Aslansefat, Koorosh, Papadopoulos, Yiannis, Gheraibia, Youcef, Reich, Jan, Saimler, Merve, Wei, Ran, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Papadopoulos, Yiannis, editor, Aslansefat, Koorosh, editor, Katsaros, Panagiotis, editor, and Bozzano, Marco, editor

Published
2019
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19. A Conceptual Framework to Incorporate Complex Basic Events in HiP-HOPS

Author

Kabir, Sohag, Aslansefat, Koorosh, Sorokos, Ioannis, Papadopoulos, Yiannis, Gheraibia, Youcef, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Papadopoulos, Yiannis, editor, Aslansefat, Koorosh, editor, Katsaros, Panagiotis, editor, and Bozzano, Marco, editor

Published
2019
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26. Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis.

Author

Abdulhamid, Alhassan, Rahman, Md Mokhlesur, Kabir, Sohag, and Ghafir, Ibrahim

Subjects
FAULT trees (Reliability engineering), SYSTEM safety, SYSTEM failures, HAZARD mitigation, SYSTEM analysis, IRRIGATION
Abstract

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation. [ABSTRACT FROM AUTHOR]

Published
2024
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28. IoT Integrated Fire Prevention and Alert Systems for Garments Industries in Bangladesh.

Author

Karim, Mohammad Rezaul, Kabir, Sohag, Ci Lei, Lefticaru, Raluca, and Baset, Mohammad Abdul

Subjects
FIRE prevention, CLOTHING industry, INTERNET of things, ECONOMIC development
Abstract

The garments industry in Bangladesh is a significant contributor to the country’s economic growth. However, it faces severe challenges in terms of fire safety. Yearly fire-related incidents not only cause harm to individuals but also result in substantial economic losses. This paper addresses the insufficient fire risk assessment in the garments industry and proposes the implementation of an Internet of Things (IoT) integrated fire prevention and alert system. By leveraging IoT devices and their integration into fire safety systems, this solution aims to save lives, protect property, and preserve the environment. [ABSTRACT FROM AUTHOR]

Published
2023

29. A Model-Based Extension to HiP-HOPS for Dynamic Fault Propagation Studies

Author

Kabir, Sohag, Papadopoulos, Yiannis, Walker, Martin, Parker, David, Aizpurua, Jose Ignacio, Lampe, Jörg, Rüde, Erich, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bozzano, Marco, editor, and Papadopoulos, Yiannis, editor

Published
2017
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37. Evaluation of Antioxidant, Antibacterial and Analgesic Activities of Syzygium cumini used in Bangladesh

Author

Margia Sultana, Jamiuddin Ahmed, Md Abdul Barek, Shaidul Kabir Sohag, Md Safayet Hossain, and Umme Habiba

Subjects
Pharmaceutical Science, Pharmacology (medical)
Abstract

Syzygium cumini (L.) skeels (Family: Myrtaceae) is a renowned medicinal plant traditionally used in various diseases and available in Bangladesh. This investigation was aimed to explore whether this plant has any potential antioxidant, antibacterial, and analgesic effects of methanolic extracts of S. cumini (leaves and bark). Quantitative determination of methanolic extracts of leaves and bark found the total phenolic contents as 199.11 mg GAE/g DW and 204.03 mg GAE/g DW, respectively. The leaves extract possessed mild antibacterial activity (leaves: 7.00 mm at 1 mg/disc, 9.15 mm at 5 mg/disc) against two different bacteria at different concentration whereas the bark extract showed no effect. A significant writhing (leaves: 23.17 ± 1.80, p≤0.001; bark: 24.33 ± 1.15, p≤0.001) and licking effects (leaves: 8.17 ± 1.49 sec, p≤0.05; bark: 9.08 ± 1.96 sec, p≤0.05) were found at 400 mg/kg of leaves and bark extracts of this plant which is very close to standard drugs mentioned in the tables. Results of this study demonstrated that methanolic extracts of S. cumini leaf and bark possessed significant antioxidant, analgesic, and mild antibacterial like activities which tend to suggest medicinal aspects. Dhaka Univ. J. Pharm. Sci. 21(1): 15-24, 2022 (June)

Published
2022

38. Machine learning for malware detection in network traffic

Author

Omopintemi, Ayorinde Henry, Ghafir, Ibrahim, Eltanani, Shadi, Kabir, Sohag, Lefoane, Moemedi, Omopintemi, Ayorinde Henry, Ghafir, Ibrahim, Eltanani, Shadi, Kabir, Sohag, and Lefoane, Moemedi

Abstract

Developing advanced and efficient malware detection systems is becoming significant in light of the growing threat landscape in cybersecurity. This work aims to tackle the enduring problem of identifying malware and protecting digital assets from cyber-attacks. Conventional methods frequently prove ineffective in adjusting to the ever-evolving field of harmful activity. As such, novel approaches that improve precision while simultaneously taking into account the ever-changing landscape of modern cybersecurity problems are needed. To address this problem this research focuses on the detection of malware in network traffic. This work proposes a machine-learning-based approach for malware detection, with particular attention to the Random Forest (RF), Support Vector Machine (SVM), and Adaboost algorithms. In this paper, the model’s performance was evaluated using an assessment matrix. Included the Accuracy (AC) for overall performance, Precision (PC) for positive predicted values, Recall Score (RS) for genuine positives, and the F1 Score (SC) for a balanced viewpoint. A performance comparison has been performed and the results reveal that the built model utilizing Adaboost has the best performance. The TPR for the three classifiers performs over 97% and the FPR performs < 4% for each of the classifiers. The created model in this paper has the potential to help organizations or experts anticipate and handle malware. The proposed model can be used to make forecasts and provide management solutions in the network’s everyday operational activities.

Published
2023

43. An Extended Function-Behaviour-Structure Ontology to support FMEA within a System Engineering Context

Author

Younus, Haytham, Doikin, Aleksandr, Campean, Felician, Kabir, Sohag, Abdullatif, Amr, Delaux, David, and Bonnaud, Pascal

Abstract

Failure Mode and Effects Analysis is a core tool for the systems engineering design assurance, facilitating the identification of design risks and their management throughout the lifecycle of the system. However, the development and management of the FMEA within a systems engineering context faces many challenges, including effectiveness of the process and integrity of knowledge capture, and the lack of a consistent integration with the systems lifecycle. The research underpinning this contribution aims to establish an intelligent FMEA environment supported by an ontology as the basis for knowledge representation of complex systems function failure underpinning the FMEA analysis and documentation. We propose to extend the Function-Behaviour-Structure (FBS) ontology of system design, with representation of function failure reasoning supported by a function failure taxonomy. This provides a consistent ontological basis underpinning the FMEA analysis of a system. We also discuss the expansion of the ontology with the levels of design decomposition of a complex system, to support the management of traceability of the FMEA across the design process. We illustrate the deployment of the proposed ontology framework, implemented in Protégé environment.

Published
2024
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44. Ontology Development for Detecting Complex Events in Stream Processing: Use Case of Air Quality Monitoring.

Author

Yemson, Rose, Kabir, Sohag, Thakker, Dhavalkumar, and Konur, Savas

Subjects
AIR quality monitoring, ONTOLOGIES (Information retrieval), INDOOR air quality, SEMANTIC Web, ASTHMATICS, ONTOLOGY
Abstract

With the increasing amount of data collected by IoT devices, detecting complex events in real-time has become a challenging task. To overcome this challenge, we propose the utilisation of semantic web technologies to create ontologies that structure background knowledge about the complex event-processing (CEP) framework in a way that machines can easily comprehend. Our ontology focuses on Indoor Air Quality (IAQ) data, asthma patients' activities and symptoms, and how IAQ can be related to asthma symptoms and daily activities. Our goal is to detect complex events within the stream of events and accurately determine pollution levels and symptoms of asthma attacks based on daily activities. We conducted a thorough testing of our enhanced CEP framework with a real dataset, and the results indicate that it outperforms traditional CEP across various evaluation metrics such as accuracy, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

Published
2023
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