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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..

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..

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..

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..

LDDNet: a deep learning framework for the diagnosis of infectious lung diseases. (2023)
Journal Article
PODDER, P., RANI DAS, S., MONDAL, M.R.H., BHARATI, S., MALIHA, A., HASAN, M.J. and PILTAN, F. 2023. LDDNet: a deep learning framework for the diagnosis of infectious lung diseases. Sensors [online], 23(1), article 480. Available from: https://doi.org/10.3390/s23010480

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The pro... Read More about LDDNet: a deep learning framework for the diagnosis of infectious lung diseases..

Automated analysis of sleep study parameters using signal processing and artificial intelligence. (2022)
Journal Article
SOHAIB, M., GHAFFAR, A., SHIN, J., HASAN. M.J. and SULEMAN, M.T. 2022. Automated analysis of sleep study parameters using signal processing and artificial intelligence. International journal of environmental research and public health [online], 19(20), article number 13256. Available from: https://doi.org/10.3390/ijerph192013256

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG sign... Read More about Automated analysis of sleep study parameters using signal processing and artificial intelligence..

A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. (2021)
Journal Article
HASAN, M.J., RAI, A., AHMAD, Z. and KIM, J.-Y. 2021. A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. IEEE access [online], 9, pages 58052-58066. Available from: https://doi.org/10.1109/ACCESS.2021.3072854

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagno... Read More about A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning..

Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. (2021)
Journal Article
HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2022. Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. Sensors [online], 22(1): sensing technologies for fault diagnostics and prognosis, article 56. Available from: https://doi.org/10.3390/s22010056

Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-do... Read More about Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning..

A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. (2021)
Journal Article
RAI, A., AHMAD, Z., HASAN, M.J. and KIM, J.-M. 2021. A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test. Sensors [online], 21(24): intelligent systems for fault diagnosis and prognosis, article 8247. Available from: https://doi.org/10.3390/s21248247

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and ent... Read More about A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test..

A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis. (2021)
Journal Article
AHMAD, Z., RAI, A., HASAN, M.J., KIM, C.H. and KIM, J.-M. 2021. A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of walsh transform and cosine linear discriminant analysis. IEEE access [online], 9, pages 150128-150141. Available from: https://doi.org/10.1109/ACCESS.2021.3124903

In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty n... Read More about A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis..

A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. (2021)
Journal Article
HABIB, M.A., HASAN, M.J. and KIM, J.-M. 2021. A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals. IEEE access [online], 9, pages 104029-104050. Available from: https://doi.org/10.1109/ACCESS.2021.3099124

This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a sig... Read More about A lightweight deep learning-based approach for concrete crack characterization using acoustic emission signals..

An explainable AI-based fault diagnosis model for bearings. (2021)
Journal Article
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2021. An explainable AI-based fault diagnosis model for bearings. Sensors [online], 21(12): sensing technologies for fault diagnostics and prognosis, article 4070. Available from: https://doi.org/10.3390/s21124070

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for var... Read More about An explainable AI-based fault diagnosis model for bearings..

A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. (2020)
Journal Article
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2020. A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. Sensors [online], 20(24): deep learning, artificial neural networks and sensors for fault diagnosis, article 7205. Available from: https://doi.org/10.3390/s20247205

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequ... Read More about A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions..

Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. (2020)
Journal Article
HASAN, M.J., SHON, D., IM, K., CHOI, H.-K., YOO, D.-S. and KIM, J.-M. 2020. Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. Applied sciences [online], 10(21): medical signal and image processing, article 7639. Available from: https://doi.org/10.3390/app10217639

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., t... Read More about Sleep state classification using power spectral density and residual neural network with multichannel EEG signals..

Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. (2020)
Journal Article
HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2021. Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. Measurement [online], 168, article 108478. Available from: https://doi.org/10.1016/j.measurement.2020.108478

In this paper, a crack diagnosis framework is proposed that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process. The objective of the signal-to-imaging technique is to conv... Read More about Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions..