Dr Md Junayed Hasan's Outputs (44)

Enhancing acoustic emission driven smart gas-pipeline monitoring with graph neural network. (2025)
Book Chapter
ARIFEEN, M., HASAN, M.J., ROHAN, A., KANNAN, S. and PRATHURU, A. 2025. Enhancing acoustic emission driven smart gas-pipeline monitoring with graph neural network. In Manjurul Islam, M.M., Baptista, M.L. and Tariq, F. (eds.) Artificial intelligence for smart manufacturing and industry X.0. Springer series in Advanced Manufacturing, Cham: Springer [online], pages 165-178. Available from: https://doi.org/10.1007/978-3-031-80154-9_8

Conventional machine learning (ML) and deep learning (DL) strategies for acoustic emission (AE) data-driven condition monitoring exhibit a range of reliability concerns. These include variations due to fluid pressure, flange vibrations, varying leak... Read More about Enhancing acoustic emission driven smart gas-pipeline monitoring with graph neural network..

Effective marine monitoring with multimodal sensing and improved underwater robotic perception towards environmental protection and smart energy transition. (2024)
Journal Article
FARHADI TOLIE, H., REN, J., HASAN, M.J., MA, P, KENNAN, S. and LI, Y. 2024. Effective marine monitoring with multimodal sensing and improved underwater robotic perception towards environmental protection and smart energy transition. Journal of geodesy and geoinformation science [online], 7(4), pages 19-35. Available from: https://doi.org/10.11947/j.JGGS.2024.0403

Effective underwater sensing is crucial for environmental protection and sustainable energy transitions, particularly as we face growing challenges in marine ecosystem monitoring, resource management, and the need for efficient energy infrastructure.... Read More about Effective marine monitoring with multimodal sensing and improved underwater robotic perception towards environmental protection and smart energy transition..

Exponential degradation model based remaining life prediction for tools of milling machine. (2024)
Presentation / Conference Contribution
ARIFEEN, M., PETROVSKI, A., HASAN, M.J. and AHMAD, Z. 2024. Exponential degradation model based remaining life prediction for tools of milling machine. In Kovalev, S., Kotenko, I., Sukhanov, A., Li, Y. and Li Y. (eds.) Proceedings of the 8th Intelligent information technologies for industry international scientific conference (IITI'24), 1-7 November 2024, Harbin, China. Lecture notes in networks and systems, 1209. Cham: Springer [online], volume 1, pages 355-365. Available from: https://doi.org/10.1007/978-3-031-77688-5_34

Cutting tools of milling machines are prone to failure, and it is essential to predict their remaining useful life to ensure cost-effective maintenance in the manufacturing industry. Recent studies have shown that deep learning techniques can effecti... Read More about Exponential degradation model based remaining life prediction for tools of milling machine..

Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays. (2024)
Journal Article
ARIFEEN, M., PETROVSKI, A., HASAN, M.J., NOMAN, K., NAVID, W.U. and HARUNA, A. 2024. Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays. Machines [online], 12(12), article 894. Available from: https://doi.org/10.3390/machines12120894

Solar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance. Diagnosing and... Read More about Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays..

Adaptive path-planning for AUVs in dynamic underwater environments using sonar data. (2024)
Presentation / Conference Contribution
B, B., HASAN, M.J., KANNAN, S. and PRABHU, R. 2024. Adaptive path-planning for AUVs in dynamic underwater environments using sonar data. In Bouma, H., Prabhu, R., Yitzhahy, Y. and Kuijf, H.J. (eds.) Advanced materials, biomaterials, and manufacturing technologies for security and defence II: proceedings of the 2024 SPIE Security + defence, 16-20 September 2024, Edinburgh, UK. Proceedings of SPIE, 13206. Bellingham, WA: SPIE [online], paper 1320616. Available from: https://doi.org/10.1117/12.3031644

This paper presents an innovative approach to path-planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments, leveraging single-beam sonar data. Recognizing the limitations of traditional sonar systems in providing detaile... Read More about Adaptive path-planning for AUVs in dynamic underwater environments using sonar data..

Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., HASAN, M.J. and KANNAN, S. 2024. Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement. In Bouma, H., Prabhu, R., Yitzhaky, Y. and Kuijf, H.J. (eds.) Artificial intelligence for security and defence applications II: proceedings of the 2024 SPIE Security + defence, 16-20 September 2024, Edinburgh, UK. Proceedings of SPIE, 13206. Bellingham, WA; SPIE [online], paper 1320605. Available from: https://doi.org/10.1117/12.3030972

This work presents a depth image refinement technique designed to enhance the usability of a commercial camera in underwater environments. Stereo vision-based depth cameras offer dense data that is well-suited for accurate environmental understanding... Read More about Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement..

Promptable sonar image segmentation for distance measurement using SAM. (2024)
Presentation / Conference Contribution
TOLIE, H.F., REN, J., HASAN, M.J., KANNAN, S. and FOUGH, N. 2024. Promptable sonar image segmentation for distance measurement using SAM. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024), 14-16 October 2024, Portorose, Slovenia. Piscataway: IEEE [online], pages 229-233. Available from: https://doi.org/10.1109/metrosea62823.2024.10765703

The subsea environment presents numerous challenges for robotic vision, including non-uniform light attenuation, backscattering, floating particles, and low-light conditions, which significantly degrade underwater images. This degradation impacts rob... Read More about Promptable sonar image segmentation for distance measurement using SAM..

Application of deep learning for livestock behaviour recognition: a systematic literature review. (2024)
Journal Article
ROHAN, A., RAFAQ, M.S., HASAN, M.J., ASGHAR, F., BASHIR, A.K. and DOTTORINI, T. 2024. Application of deep learning for livestock behaviour recognition: a systematic literature review. Computers and electronics in agriculture [online], 224, article number 109115. Available from: https://doi.org/10.1016/j.compag.2024.109115

Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techni... Read More about Application of deep learning for livestock behaviour recognition: a systematic literature review..

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 robust self-supervised approach for fine-grained crack detection in concrete structures. (2024)
Journal Article
SOHAIB, M., HASAN, M.J., SHAH, M.A. and ZHENG, Z. 2024. A robust self-supervised approach for fine-grained crack detection in concrete structures. Scientific reports [online], 14(1), article number 12646. Available from: https://doi.org/10.1038/s41598-024-63575-x

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies,... Read More about A robust self-supervised approach for fine-grained crack detection in concrete structures..

Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. (2024)
Presentation / Conference Contribution
HASAN, M.J., ELYAN, E., YAN, Y., REN, J. and SARKER, M.M.K. 2024. Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 220-228. Available from: https://doi.org/10.1007/978-981-97-1417-9_21

Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identify the heat los... Read More about Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies..

A multichannel analysis of imbalanced computed tomography data for lung cancer classification. (2024)
Journal Article
SOHAIB, M., HASAN, M.J. and ZHENG, Z. 2024. A multichannel analysis of imbalanced computed tomography data for lung cancer classification. Measurement science and technology [online], 35(8), article number 085401. Available from: https://doi.org/10.1088/1361-6501/ad437f

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especia... Read More about A multichannel analysis of imbalanced computed tomography data for lung cancer classification..

Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning. (2024)
Journal Article
HAQUE, R., AL SAKIB, A., HOSSAIN, M.F., ISLAM, F., AZIZ, F.I., AHMED, M.R., KANNAN, S., ROHAN, A. and HASAN, M.J. 2024. Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning. BioMedInformatics [online], 4(2), pages 966-991. Available from: https://doi.org/10.3390/biomedinformatics4020054

Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood... Read More about Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning..

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures. (2024)
Journal Article
SOHAIB, M., HASAN, M.J., CHEN, J. and ZHENG, Z. 2024. Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures. Measurement science and technology [online], 35(5): AI-driven measurement methods for resilient infrastructure and communities, article number 055402. Available from: https://doi.org/10.1088/1361-6501/ad296c

Identification of damage and selection of a restoration strategy in concrete structures is contingent upon automatic inspection for crack detection and assessment. Most research on deep learning models for autonomous inspection has focused solely on... Read More about Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures..

Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis. (2023)
Journal Article
AHMMED, S., PODDER, P., MONDAL, M.R.H., RAHMAN, S.M.A., KANNAN, S., HASAN, M.J., ROHAN, A. and PROSVIRIN, A.E. 2023. Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis. Biomedinformatics [online], 3(4), pages 1124-1144. Available from: https://doi.org/10.3390/biomedinformatics3040068

This study focuses on leveraging data-driven techniques to diagnose brain tumors through magnetic resonance imaging (MRI) images. Utilizing the rule of deep learning (DL), we introduce and fine-tune two robust frameworks, ResNet 50 and Inception V3,... Read More about Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis..

Vision based relative position estimation in surgical robotics. (2023)
Presentation / Conference Contribution
MUTHUKRISHNAN, R., KANNAN, S., PRABHU, R., ZHAO, Y., BHOWMICK, P. and HASAN, M.J. 2023. Vision based relative position estimation in surgical robotics. In Proceedings of the 2023 Network, multimedia and information technology international conference (NMITCON 2023), 1-2 September 2023, Bengaluru, India. Piscataway: IEEE [online], article number 10275973. Available from: https://doi.org/10.1109/NMITCON58196.2023.10275973

Teleoperation-based Robotic-Assisted Minimally In-vasive Surgery (RAMIS) has gained immense popularity in medical field. However, limited physical interaction between the surgeon and patient poses a significant challenge. In RAMIS, the surgeon operat... Read More about Vision based relative position estimation in surgical robotics..

Tracking and estimation of surgical instrument position and angle in surgical robot using vision system. (2023)
Presentation / Conference Contribution
MUTHUKRISHNAN, R., KANNAN, S., PRABHU, R., ZHAO, Y., BHOWMICK, P. and HASAN, M.J. 2023. Tracking and estimation of surgical instrument position and angle in surgical robot using vision system. In Proceedings of the 2023 Network, multimedia and information technology international conference (NMITCON 2023), 1-2 September 2023, Bengaluru, India. Piscataway: IEEE [online], article number 10275983. Available from: https://doi.org/10.1109/NMITCON58196.2023.10275983

A da Vinci robot endoscopic-camera gives surgeons a magnified 2D view of the operating area, but additional time is required to detect and estimate the location of the surgical-instrument during an operation. The main focus and novelty of this resear... Read More about Tracking and estimation of surgical instrument position and angle in surgical robot using vision system..

Person recognition based on deep gait: a survey. (2023)
Journal Article
KHALILUZZAMAN, M., UDDIN, A., DEB, K. and HASAN, M.J. 2023. Person recognition based on deep gait: a survey. Sensors [online], 23(10), article 4875. Available from: https://doi.org/10.3390/s23104875

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its po... Read More about Person recognition based on deep gait: a survey..

Rethinking densely connected convolutional networks for diagnosing infectious diseases. (2023)
Journal Article
PODDER, P., ALAM, F.B., MONDAL, M.R.H., HASAN, M.J., ROHAN, A. and BHARATI, S. 2023. Rethinking densely connected convolutional networks for diagnosing infectious diseases. Computers [online], 12(5), article 95. Available from: https://doi.org/10.3390/computers12050095

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. I... Read More about Rethinking densely connected convolutional networks for diagnosing infectious diseases..

Data-driven solution to identify sentiments from online drug reviews. (2023)
Journal Article
HAQUE, R., LASKAR, S.H., KHUSHBU, K.G., HASAN, M.J. and UDDIN, J. 2023. Data-driven solution to identify sentiments from online drug reviews. Computers [online], 12(4), article 87. Available from: https://doi.org/10.3390/computers12040087

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicin... Read More about Data-driven solution to identify sentiments from online drug reviews..