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USD 80 /hr
Hire Dr. M M Manjurul I.
United Kingdom
USD 80 /hr

PhD in AI | Predictive Maintenance & Data Analytics Expert | Helping Industry & Academia Turn Data into Results

Profile Summary
Subject Matter Expertise
Services
Writing Clinical Trial Documentation, Medical Writing, Technical Writing, General Proofreading & Editing
Research Market Research, User Research, Meta-Research, Feasibility Study, Technology Scouting, Gap Analysis, Gray Literature Search, Scientific and Technical Research, Systematic Literature Review, Secondary Data Collection
Consulting Business Strategy Consulting, Digital Strategy Consulting, Healthcare Consulting, Legal Consulting, Operations Consulting, Scientific and Technical Consulting, Manufacturing Consulting
Data & AI Predictive Modeling, Statistical Analysis, Image Processing, Image Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Data Visualization, Big Data Analytics, Text Mining & Analytics, Data Mining, Data Cleaning, Data Processing, Data Insights
Product Development Product Validation, Manufacturing, Quality Assurance & Control (QA/QC), Concept Development, Prototyping
Work Experience

Research Associate in Artificial Intelligence for Smart Manufacturing

Ulster University Magee Campus

January 2023 - Present

Assistant Professor

American International University-Bangladesh

February 2021 - January 2022

Postdoctoral researcher

Fondazione Bruno Kessler

September 2019 - January 2021

PhD Researcher

University of Ulsan

September 2014 - August 2019

Researcher

Accenture plc

January 2014 - August 2014

System Engineer

Grameenphone Ltd

August 2008 - December 2013

Education

PhD

Ulsan University - Korea, Republic of

September 2014 - August 2019

Certifications
Publications
CONFERENCE PAPER
Machine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in Semiconductor Production @inproceedings{8cf9fb6d2f5a4806a214a67d48d86f2c, title = "Machine Learning with Heuristic Search-based Hybrid Framework for Cycle Time Optimization in Semiconductor Production", abstract = "This paper aims to optimise cycle time (CT) in semiconductor wafer production, a critical factor for enhancing operational efficiency and competitiveness in the semiconductor manufacturing industry. A hybrid methodology, based on statistical analysis and machine learning (ML) techniques, is developed to identify the optimal combination of key performance indicators (KPIs) for individual tools to minimise CT. To achieve this, hyperparameter tuning and model optimisation are performed using Sequential Quadratic Programming (SQP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), with a focus on identifying the most effective optimisation technique. The optimisation process incorporates constraints on KPIs, introducing additional complexity and necessitating robust constraint-handling mechanisms. A hierarchical decomposition approach is employed to systematically address the problem, achieving significant reductions in production cycle time. The experimental study suggests that the random forest algorithm with GA significantly outperforms other techniques in terms of CT reduction.", keywords = "Cycle Time, Semiconductor, Manufacturing, Hybrid Algorithms, PSO, GA, SQP, Manufacturing Analytics, Semiconductor Manufacturing", author = "Rahman, {Mohammad Sharifur} and Islam, {M M Manjurul} and Girijesh Prasad and Saugat Bhattacharyya and Karl McCreadie and Tamas Reiter and Nuala Parker", note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; IEEE Conference on Artificial Intelligence, IEEE CAI 2025 ; Conference date: 05-05-2025 Through 07-05-2025", year = "2025", month = jul, day = "7", doi = "10.1109/CAI64502.2025.00224", language = "English", isbn = "979-8-3315-2401-2", pages = "1286--1291", booktitle = "2025 IEEE Conference on Artificial Intelligence (CAI)", publisher = "IEEE", address = "United States", url = "https://cai.ieee.org/2025/", } . 2025 IEEE Conference on Artificial Intelligence (CAI).
Enhancing Speech Emotion Recognition Using Deep Convolutional Neural Networks @inproceedings{61c7a79190844e269ceb56577c34559d, title = "Enhancing Speech Emotion Recognition Using Deep Convolutional Neural Networks", abstract = "Speech emotion recognition (SER) is considered a pivotal area of research that holds significant importance in a variety of real-time applications, such as assessing human behavior and analyzing the emotional states of speakers in emergency situations. This paper assesses the capabilities of deep convolutional neural networks (CNNs) in this context. Both CNNs and Long Short-Term Memory (LSTM) based deep neural networks are evaluated for voice emotion identification. In our empirical evaluation, we utilize the Toronto Emotional Speech Set (TESS) database, which comprises speech samples from both young and old individuals, encompassing seven distinct emotions: anger, happiness, sadness, fear, surprise, disgust, and neutrality. To augment the dataset, variations in voice are introduced along with the addition of white noise. The empirical findings indicate that the CNN model outperforms existing studies on SER using the TESS corpus, yielding a noteworthy 21% improvement in average recognition accuracy. This work underscores SER{\textquoteright}s significance and highlights the transformative potential of deep CNNs for enhancing its effectiveness in real-time applications, particularly in high-stakes emergency situations.", keywords = "Speech corpus, Human speech emotion recognition, Convolutional neural network applications, Long short-term memory neural networks", author = "Islam, {M M Manjurul} and Kabir, {Md Alamgir} and Alamin Sheikh and Muhammad Saiduzzaman and Abdelakram Hafid and Saad Abdullah", note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 2024 9th International Conference on Machine Learning Technologies, ICMLT 2024 ; Conference date: 24-05-2024 Through 26-05-2024", year = "2024", month = sep, day = "11", doi = "10.1145/3674029.3674045", language = "English", isbn = "9798400716379", series = "2024 9th International Conference on Machine Learning Technologies (ICMLT)", publisher = "Association for Computing Machinery (ACM)", pages = "95--100", booktitle = "Proceedings of the 2024 9th International Conference on Machine Learning Technologies", url = "https://www.icmlt.org/", } . Proceedings of the 2024 9th International Conference on Machine Learning Technologies.
M M Manjurul Islam, Md Alamgir Kabir, Alamin Sheikh, Muhammad Saiduzzaman, Abdelakram Hafid, Saad Abdullah (2024). Enhancing Speech Emotion Recognition Using Deep Convolutional Neural Networks .
Efficient Wafer Defect Patterns Recognition Using Deep Convolutional Neural Network @inproceedings{3fdfd5171eef40479fe754ae87cfd42f, title = "Efficient Wafer Defect Patterns Recognition Using Deep Convolutional Neural Network", abstract = "Defect recognition in semiconductor wafers is crucial in manufacturing process to identify the root cause. This paper presents an efficient wafer defects pattern recognition methodology based on deep convolutional neural networks (DCNN) to automate the classification. In DCNN design, we revamped LeNet-5 deep learning model. The proposed DCNN comprises seven layers including three convolutional and two pooling layers. We implemented an adaptive moment (Adam) optimizer to fine-tune model parameters in DCNN. We tested the proposed model on publicly available semiconductor wafers datasets to verify the effectiveness of the model. The experimental study suggests that the proposed model is highly efficient in classifying wafer defects with an accuracy of 99.22% in testing.", keywords = "Semiconductor wafer, smart manufacturing, pattern recognition, deep learning", author = "Islam, {M M Manjurul} and Cormac McAteer and Girijesh Prasad", note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Conference on Artificial Intelligence ; Conference date: 05-06-2023 Through 06-06-2023", year = "2023", month = aug, day = "2", doi = "10.1109/cai54212.2023.00102", language = "English", isbn = "979-8-3503-3985-7", series = "Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023", publisher = "IEEE", pages = "220--221", booktitle = "2023 IEEE Conference on Artificial Intelligence (CAI)", address = "United States", url = "https://cai.ieee.org/2023/", } . 2023 IEEE Conference on Artificial Intelligence (CAI).
Kabir, M.A., Islam, M.M.M., Mahmud, S.M.H., Elahe, M.F.(2022). Spectrum Impact Analysis of Fault Proneness Statement for Improved Fault Localization . ACM International Conference Proceeding Series. p. 59-66.
Sikder, Niloy, Bhakta, Kangkan, Al Nahid, Abdullah, Islam, M. M. Manjurul (2019). Fault Diagnosis of Motor Bearing Using Ensemble Learning Algorithm with FFT-based Preprocessing . International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST).
Sikder, N., Bhakta, K., Al Nahid, A., Islam, M.M.M.(2019). Fault diagnosis of motor bearing using ensemble learning algorithm with FFT-based preprocessing . 1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019. p. 564-569.
Kangkan Bhakta, Niloy Sikder, Abdullah Al Nahid, M M Manjurul Islam (2019). Fault Diagnosis of Induction Motor Bearing Using Cepstrum-based Preprocessing and Ensemble Learning Algorithm . International Conference on Electrical, Computer and Communication Engineering (ECCE).
Bhakta, K., Sikder, N., Nahid, A.-A., Islam, M.M.M.(2019). Rotating Element Bearing Fault Diagnosis Using Discrete Cosine Transform and Supervised Machine Learning Algorithm . 5th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2019.
Kangkan Bhakta, Niloy Sikder, Abdullah-Al Nahid, M M Manjurul Islam (2019). Rotating Element Bearing Fault Diagnosis Using Discrete Cosine Transform and Supervised Machine Learning Algorithm . International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2).
Bhakta, K., Sikder, N., Nahid, A.A., Islam, M.M.M.(2019). Fault Diagnosis of Induction Motor Bearing Using Cepstrum-based Preprocessing and Ensemble Learning Algorithm . 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019.
Islam, Md.R., Islam, M.M.M., Kim, J.-M.(2017). Feature selection techniques for increasing reliability of fault diagnosis of bearings . Proceedings of 9th International Conference on Electrical and Computer Engineering, ICECE 2016. p. 396-399.
Lee, S.-M., Manjurul Islam, M.M., Kim, J., Kim, Y.-H., Jeong, I., Kim, J.-M.(2017). Filter and wrapper-based feature selection using mutual information for rolling elements bearing diagnosis . 2017 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2017 - Proceedings of the 2017 International Conference on Artificial Intelligence, ICAI 2017. p. 187-190.
Jeong, I., Appana, D.K., Islam, M., Kim, C.-H., Kim, J.-M.(2017). Optimized mobile robotic navigation based on fuzzy logic control . 2017 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2017 - Proceedings of the 2017 International Conference on Artificial Intelligence, ICAI 2017. p. 183-186.
Kim, Y.-H., Islam, M.M.M., Islam, R., Kim, J.-M.(2017). Genetic algorithm based discriminant feature selection for improved fault diagnosis of induction motor . 2017 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2017 - Proceedings of the 2017 International Conference on Artificial Intelligence, ICAI 2017. p. 21-27.
Md. Rashedul Islam, M M Manjurul Islam, Jong-Myon Kim(2016). Feature selection techniques for increasing reliability of fault diagnosis of bearings . 2016 9th International Conference on Electrical and Computer Engineering (ICECE). {IEEE}
Islam, Md. Rashedul, Islam, M. M. Manjurul, Kim, Jong-Myon (2016). Feature Selection Techniques for Increasing Reliability of Fault Diagnosis of Bearings . International Conference on Electrical and Computer Engineering (ICECE).
(2011). Performance measurement of real time object detection and recognition . Innovative Applications of Information Technology for the Developing World.
(2011). Real time video and audio transmition across the network . Innovative Applications of Information Technology for the Developing World.
M. M. MANJURUL ISLAM, MIR MD. JAHANGIR KABIR, MD. ROBIUR RAHMAN, BOSHIR AHMED(2007). REAL TIME VIDEO AND AUDIO TRANSMITION ACROSS THE NETWORK . Innovative Applications of Information Technology for the Developing World. {PUBLISHED} {BY} {IMPERIAL} {COLLEGE} {PRESS} {AND} {DISTRIBUTED} {BY} {WORLD} {SCIENTIFIC} {PUBLISHING} {CO}.
MIR MD. JAHANGIR KABIR, SAMIR HALDER, MD. ROBIUR RAHMAN, MD. W. H. SADID, M. M. MANJURUL ISLAM, MD. NAZRUL ISLAM MONDAL(2007). PERFORMANCE MEASUREMENT OF REAL TIME OBJECT DETECTION AND RECOGNITION . Innovative Applications of Information Technology for the Developing World. {PUBLISHED} {BY} {IMPERIAL} {COLLEGE} {PRESS} {AND} {DISTRIBUTED} {BY} {WORLD} {SCIENTIFIC} {PUBLISHING} {CO}.
Manjurul Islam, M.M., Kabir, J., Rahman, R., Ahmed, B.(2007). Real time video and audio transmition across the network . Advances in Computer Science and Eng.: Reports and Monographs - Innovative Applications of Information Technology for the Developing World - Proc. of the 3rd Asian Applied Comput. Conf., AACC 2005. 2. p. 238-239.
Kabir, J., Halder, S., Rahman, R., Sadid, W.H., Manjurul Islam, M.M., Mondal, N.I.(2007). Performance measurement of real time object detection and recognition . Advances in Computer Science and Eng.: Reports and Monographs - Innovative Applications of Information Technology for the Developing World - Proc. of the 3rd Asian Applied Comput. Conf., AACC 2005. 2. p. 349-352.
Md Musfequs Salehin, Md Rabiul Islam, MMM Islam(2006). An Efficient Approach to Develop an Artificial Intelligent Robot Based on Real Time Task Specifications Using Genetic Programming. International Conference on Computer & Information Technology. p. 421--426.
Islam, M.M.M., Sadid, Md.W.H., Rashid, S.M.M.A., Kabir, M.Md.J.(2006). An implementation of ACO system for solving NP-complete problem; TSP . Proceedings of 4th International Conference on Electrical and Computer Engineering, ICECE 2006. p. 304-307.
(2006). An implementation of ACO system for solving NP-complete problem; TSP . International Conference on Electrical and Computer Engineering.
Prosvirin, Alexander and Islam, MM Manjurul and Kim, Cheolhong and Kim, Jong Myon Fault Prediction of Rolling Element Bearings Using One Class Least Squares SVM .
BOOK CHAPTER
Artificial Intelligence in Smart Manufacturing @inbook{05bc2de01e544634a3baa69761483fea, title = "Artificial Intelligence in Smart Manufacturing: Emerging Opportunities and Prospects", abstract = "This chapter presents the significant role of Artificial Intelligence (AI)Artificial Intelligence (AI) in advancing smart manufacturing, emphasizing its potential to improve efficiency, quality, and sustainability. We explore how AI integrates with critical manufacturing processes and supports the transition toIndustry 4.0 Industry 4.0. The discussion includes an in-depth look at essential technologies like machine learning, deep learningDeep learning, big dataBig data analytics, immersive technologies, digital twinsDigital twins, internet of things, and their applications in areas such as predictive maintenancePredictive maintenance, quality control, and supply chainLogistics and supply chain management. Through real-world examples, we demonstrate AI{\textquoteright}s effectiveness in enhancing manufacturing operations and discuss opportunities for further technological improvements. Additionally, the chapter identifies challenges manufacturers face, including data security, system integration, and the need for a skilled workforce, offering practical advice on overcoming these obstacles. We not only highlight the advantages of AI in manufacturing but also address the ethical and practical complexities of its broad adoption.", keywords = "Advanced technologies, Human resources and ethics, Industry X.0, Machines and equipment, Manufacturing processes, Smart factory, Smart manufacturing", author = "Islam, {M. M.Manjurul} and Emon, {Jakaria Islam} and Ng, {Kok Yew} and Abdoreza Asadpour and Aziz, {M. M.Rafi Al} and Baptista, {Marcia L.} and Kim, {Jong Myon}", note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.", year = "2025", month = mar, day = "6", doi = "10.1007/978-3-031-80154-9_2", language = "English", series = "Springer Series in Advanced Manufacturing", publisher = "Springer Nature", pages = "9--36", booktitle = "Springer Series in Advanced Manufacturing", address = "United States", } . Springer Series in Advanced Manufacturing.
Md Alamgir Kabir, M. M. Manjurul Islam, Narayan Ranjan Chakraborty, Sheak Rashed Haider Noori(2025). Trustworthy Artificial Intelligence for Industrial Operations and Manufacturing: Principles and Challenges . Springer Series in Advanced Manufacturing. p. 179–197. Springer Nature Switzerland
Marcia L. Baptista, Nan Yue, M. M. Manjurul Islam, Helmut Prendinger(2025). Large Language Models (LLMs) for Smart Manufacturing and Industry X.0 . Springer Series in Advanced Manufacturing. p. 97–119. Springer Nature Switzerland
M. M. Manjurul Islam, Jakaria Islam Emon, Kok Yew Ng, Abdoreza Asadpour, M. M. Rafi Al Aziz, Marcia L. Baptista, Jong-Myon Kim(2025). Artificial Intelligence in Smart Manufacturing: Emerging Opportunities and Prospects . Springer Series in Advanced Manufacturing. p. 9–36. Springer Nature Switzerland
M. M. Manjurul Islam(2025). Improved Wafer Defect Pattern Classification in Semiconductor Manufacturing Using Deep Learning and Explainable AI . Springer Series in Advanced Manufacturing. p. 147–164. Springer Nature Switzerland
Faisal Tariq, M. M. Manjurul Islam, Marcia L. Baptista(2025). Introduction to Smart Manufacturing . Springer Series in Advanced Manufacturing. p. 1–7. Springer Nature Switzerland
Jakaria Islam Emon, M. M. Manjurul Islam, Syeda Amina Abedin, Shakhawat Hossain, Rupam Kumar Das(2023). Real-Time Facemask Detection Using Deep Convolutional Neural Network-Based Transfer Learning . Artificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare. p. 243--254. {CRC} Press
Hossain Shakhawat, Sakir Hossain, Alamgir Kabir, S. M. Hasan Mahmud, M. M. Manjurul Islam, Faisal Tariq(2023). Review of Artifact Detection Methods for Automated Analysis and Diagnosis in Digital Pathology . Artificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare. p. 177--202. {CRC} Press
Manjurul Islam, Alexander Prosvirin, Jong-Myon Kim(2018). Intelligent Rub-Impact Fault Diagnosis Based on Genetic Algorithm-Based IMF Selection in Ensemble Empirical Mode Decomposition and Diverse Features Models . 147--155Springer International Publishing
JOURNAL ARTICLE
Md Alamgir Kabir, Atiq Ur Rehman, M. M. Manjurul Islam, Nazakat Ali, Marcia L. Baptista (2023). Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting . Symmetry.
Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting @article{116847ec515e49bdae3a12e72438dea2, title = "Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting", abstract = "Concept drift (CD) refers to a phenomenon where the data distribution within datasets changes over time, and this can have adverse effects on the performance of prediction models in software engineering (SE), including those used for tasks like cost estimation and defect prediction. Detecting CD in SE datasets is difficult, but important, because it identifies the need for retraining prediction models and in turn improves their performance. If the concept drift is caused by symmetric changes in the data distribution, the model adaptation process might need to account for this symmetry to maintain accurate predictions. This paper explores the impact of CD within the context of cross-version defect prediction (CVDP), aiming to enhance the reliability of prediction performance and to make the data more symmetric. A concept drift detection (CDD) approach is further proposed to identify data distributions that change over software versions. The proposed CDD framework consists of three stages: (i) data pre-processing for CD detection; (ii) notification of CD by triggering one of the three flags (i.e., CD, warning, and control); and (iii) providing guidance on when to update an existing model. Several experiments on 30 versions of seven software projects reveal the value of the proposed CDD. Some of the key findings of the proposed work include: (i) An exponential increase in the error-rate across different software versions is associated with CD. (ii) A moving-window approach to train defect prediction models on chronologically ordered defect data results in better CD detection than using all historical data with a large effect size (Formula presented.).", keywords = "concept drift, software defect prediction, cross-version defect prediction, chronological splitting", author = "Kabir, {Md Alamgir} and Rehman, {Atiq Ur} and Islam, {M. M. Manjurul} and Nazakat Ali and Baptista, {Marcia L.} and Simos, {Theodore E.}", note = "Publisher Copyright: {\textcopyright} 2023 by the authors.", year = "2023", month = oct, day = "18", doi = "10.3390/sym15101934", language = "English", volume = "15", pages = "1--25", journal = "Symmetry", issn = "2073-8994", publisher = "MDPI", number = "10", } . Symmetry.
Md Alamgir Kabir, Atiq Ur Rehman, M. M. Manjurul Islam, Nazakat Ali, Marcia L. Baptista (2023). Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting . Symmetry.
Kabir, Md Alamgir, Rehman, Atiq Ur, Islam, M. M. Manjurul, Ali, Nazakat, Baptista, Marcia L. (2023). Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting . Symmetry.
Islam, M. M. Manjurul, McAteer, Cormac, Prasad, Girijesh (2023). Efficient Wafer Defect Patterns Recognition Using Deep Convolutional Neural Network . Ieee Conference on Artificial Intelligence, Cai.
Sohaib, M., Munir, S., Islam, M.M.M., Shin, J., Tariq, F., Ar Rashid, S.M.M., Kim, J.-M.(2022). Gearbox fault diagnosis using improved feature representation and multitask learning . Frontiers in Energy Research. 10.
Sohaib, Muhammad, Munir, Shahid, Islam, M. M. Manjurul, Shin, Jungpil, Tariq, Faisal, Ar Rashid, S. M. Mamun, Kim, Jong-Myon (2022). Gearbox fault diagnosis using improved feature representation and multitask learning . Frontiers in Energy Research.
Hasan, M.J., Manjurul Islam, M.M., Kim, J.-M.(2022). Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning . Sensors. 22. (1).
Md Junayed Hasan, M. M. Manjurul Islam, Jong-Myon Kim (2021). Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning . Sensors.
Md Junayed Hasan, M. M. Manjurul Islam, Jong-Myon Kim (2021). Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning . Sensors.
M.M. Manjurul Islam, Alexander E. Prosvirin, Jong-Myon Kim(2021). Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines . Mechanical Systems and Signal Processing. 160. p. 107853. Elsevier {BV}
Niloy Sikder, Abu Shamim Mohammad Arif, M. M. Manjurul Islam, Abdullah-Al Nahid(2021). Induction Motor Bearing Fault Classification Using Extreme Learning Machine Based on Power Features . Arabian Journal for Science and Engineering. Springer Science and Business Media {LLC}
Shiza Mushtaq, M. M. Manjurul Islam, Muhammad Sohaib (2021). Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review . Energies.
Shiza Mushtaq, M. M. Manjurul Islam, Muhammad Sohaib (2021). Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review . Energies.
Md Junayed Hasan, M.M Manjurul Islam, Jong-Myon Kim(2021). Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions . Measurement. 168. p. 108478. Elsevier {BV}
Hasan, M.J., Islam, M.M.M., Kim, J.-M.(2021). Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions . Measurement: Journal of the International Measurement Confederation. 168.
Sikder, N., Mohammad Arif, A.S., Islam, M.M.M., Nahid, A.-A.(2021). Induction Motor Bearing Fault Classification Using Extreme Learning Machine Based on Power Features . Arabian Journal for Science and Engineering. 46. (9). p. 8475-8491.
Mushtaq, S., Manjurul Islam, M.M., Sohaib, M.(2021). Deep learning aided data-driven fault diagnosis of rotatory machine: A comprehensive review . Energies. 14. (16).
Mushtaq, Shiza, Islam, M. M. Manjurul, Sohaib, Muhammad (2021). Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review . Energies.
Manjurul Islam, M.M., Prosvirin, A.E., Kim, J.-M.(2021). Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines . Mechanical Systems and Signal Processing. 160.
M. M. Manjurul Islam, Jong-Myon Kim(2019). Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network . Sensors. 19. (19). p. 4251. {MDPI} {AG}
M. M. Manjurul Islam, Jong-Myon Kim (2019). Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network . Sensors.
Md. Nazmul Hasan, Rafia Nishat Toma, Abdullah-Al Nahid, M M Manjurul Islam, Jong-Myon Kim (2019). Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach . Energies.
Md Junayed Hasan, M.M. Manjurul Islam, Jong-Myon Kim(2019). Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions . Measurement. 138. p. 620--631. Elsevier {BV}
Wasim Ahmad, Sheraz Ali Khan, M M Manjurul Islam, Jong-Myon Kim (2019). A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models . Reliability Engineering & System Safety.
M.M. Manjurul Islam, Jong-Myon Kim(2019). Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network . Computers in Industry. 106. p. 142--153. Elsevier {BV}
M.M. Manjurul Islam, Jong-Myon Kim(2019). Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines . Reliability Engineering & System Safety. 184. p. 55--66. Elsevier {BV}
Muhammad Sohaib, Manjurul Islam, Jaeyoung Kim, Duck-Chan Jeon, Jong-Myon Kim(2019). Leakage Detection of a Spherical Water Storage Tank in a Chemical Industry Using Acoustic Emissions . Applied Sciences. 9. (1). p. 196. {MDPI} {AG}
Muhammad Sohaib, Manjurul Islam, Jaeyoung Kim, Duck-Chan Jeon, Jong-Myon Kim (2019). Leakage Detection of a Spherical Water Storage Tank in a Chemical Industry Using Acoustic Emissions . Applied Sciences.
Alexander E. Prosvirin, M. M. Manjurul Islam, Jong-Myon Kim(2019). An Improved Algorithm for Selecting IMF Components in Ensemble Empirical Mode Decomposition for Domain of Rub-Impact Fault Diagnosis . IEEE Access. 7. p. 121728--121741. Institute of Electrical and Electronics Engineers ({IEEE})
Alexander E. Prosvirin, M. M. Manjurul Islam, Jong-Myon Kim(2019). An Improved Algorithm for Selecting IMF Components in Ensemble Empirical Mode Decomposition for Domain of Rub-Impact Fault Diagnosis . IEEE Access. 7. p. 121728--121741. Institute of Electrical and Electronics Engineers ({IEEE})
Prosvirin, A.E., Manjurul Islam, M.M., Kim, J.-M.(2019). An improved algorithm for selecting IMF components in ensemble empirical mode decomposition for domain of rub-impact fault diagnosis . IEEE Access. 7. p. 121728-121741.
Islam, M. M. Manjurul, Kim, Jong-Myon (2019). Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines . Reliability Engineering & System Safety.
Manjurul Islam, M.M., Kim, J.-M.(2019). Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines . Reliability Engineering and System Safety. 184. p. 55-66.
Sohaib, M., Islam, M., Kim, J., Jeon, D.-C., Kim, J.-M.(2019). Leakage detection of a spherical water storage tank in a chemical industry using acoustic emissions . Applied Sciences (Switzerland). 9. (1).
Piltan, Farzin, Islam, Manjurul, Kim, Jong-Myon (2019). Input-Output Fault Diagnosis in Robot Manipulator Using Fuzzy LMI-Tuned PI Feedback Linearization Observer Based on Nonlinear Intelligent ARX Model . Advances in Intelligent Systems and Computing.
Ahmad, Wasim, Khan, Sheraz Ali, Islam, M. M. Manjurul, Kim, Jong-Myon (2019). A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models . Reliability Engineering & System Safety.
Ahmad, W., Khan, S.A., Islam, M.M.M., Kim, J.-M.(2019). A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models . Reliability Engineering and System Safety. 184. p. 67-76.
Hasan, M.J., Islam, M.M.M., Kim, J.-M.(2019). Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions . Measurement: Journal of the International Measurement Confederation. 138. p. 620-631.
Nazmul Hasan, Md., Toma, R.N., Nahid, A.-A., Manjurul Islam, M.M., Kim, J.-M.(2019). Electricity theft detection in smart grid systems: A CNN-LSTM based approach . Energies. 12. (17).
Islam, M.M.M., Kim, J.-M.(2019). Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network . Computers in Industry. 106. p. 142-153.
Manjurul Islam, M.M., Kim, J.-M.(2019). Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder–decoder network . Sensors (Switzerland). 19. (19).
Manjurul Islam, Muhammad Sohaib, Jaeyoung Kim, Jong-Myon Kim (2018). Crack Classification of a Pressure Vessel Using Feature Selection and Deep Learning Methods . Sensors.
Manjurul Islam, Muhammad Sohaib, Jaeyoung Kim, Jong-Myon Kim (2018). Crack Classification of a Pressure Vessel Using Feature Selection and Deep Learning Methods . Sensors.
Alexander E. Prosvirin, Manjurul Islam, Jaeyoung Kim, Jong-Myon Kim (2018). Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models . Sensors.
Alexander Prosvirin, Manjurul Islam, Jaeyoung Kim, Jong-Myon Kim(2018). Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models . Sensors. 18. (7). p. 2040. {MDPI} {AG}
(2018). Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models . Sensors.
Prosvirin, A.E., Islam, M., Kim, J., Kim, J.-M.(2018). Rub-impact fault diagnosis using an effective imf selection technique in ensemble empirical mode decomposition and hybrid feature models . Sensors (Switzerland). 18. (7).
Prosvirin, Alexander E., Islam, Manjurul, Kim, Jaeyoung, Kim, Jong-Myon (2018). Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models . Sensors.
Islam, Manjurul, Prosvirin, Alexander, Kim, Jong-Myon (2018). Intelligent Rub-Impact Fault Diagnosis Based on Genetic Algorithm-Based IMF Selection in Ensemble Empirical Mode Decomposition and Diverse Features Models . Lecture Notes in Computer Science.
Islam, M.M.M., Jong-Myon Kim (2018). Motor Bearing Fault Diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal . Advances in Artificial Intelligence. Canadian Conference on Artificial Intelligence, Canadian AI. Proceedings: LNAI 10832.
Islam, M., Sohaib, M., Kim, J., Kim, J.-M.(2018). Crack classification of a pressure vessel using feature selection and deep learning methods . Sensors (Switzerland). 18. (12).
M. M. Manjurul Islam, Jong-Myon Kim(2017). Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings . Journal of Ambient Intelligence and Humanized Computing. Springer Nature
M. M. Manjurul Islam, Jong-Myon Kim (2017). Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings . Journal of Ambient Intelligence and Humanized Computing.
Islam, M.M.M., Kim, J.-M.(2017). Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings . Journal of Ambient Intelligence and Humanized Computing. p. 1-16.
Islam, M.M.M., Kim, J., Khan, S.A., Kim, J.-M.(2017). Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines . Journal of the Acoustical Society of America. 141. (2). p. EL89-EL95.
Islam, M. M. Manjurul, Kim, Jaeyoung, Khan, Sheraz A., Kim, Jong-Myon (2017). Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines . The Journal of the Acoustical Society of America.
Islam, M. M. Manjurul, Islam, Md. Rashedul, Kim, Jong-Myon (2017). A Hybrid Feature Selection Scheme Based on Local Compactness and Global Separability for Improving Roller Bearing Diagnostic Performance . Lecture Notes in Computer Science.
Barman, S., Gope, H., Manjurul Islam, M.M., Hasan, M., Salma, U.(2016). Clustering techniques for software engineering . Indonesian Journal of Electrical Engineering and Computer Science. 4. (2). p. 465-472.
Shohag Barman, Hira Lal Gope, M M Manjurul Islam, Md Mehedi Hasan, Umme Salma (2016). Clustering Techniques for Software Engineering . Indonesian Journal of Electrical Engineering and Computer Science.
Islam, M. M. Manjurul, Khan, Sheraz A., Kim, Jong-Myon (2015). Multi-fault Diagnosis of Roller Bearings Using Support Vector Machines with an Improved Decision Strategy . Lecture Notes in Computer Science.
OTHER
M. M. Manjurul Islam, Cormac McAteer, Girijesh Prasad(2023). Efficient Wafer Defect Patterns Recognition Using Deep Convolutional Neural Network . 2023 IEEE Conference on Artificial Intelligence (CAI). {IEEE}
Kangkan Bhakta, Niloy Sikder, Abdullah-Al Nahid, M M Manjurul Islam(2019). Rotating Element Bearing Fault Diagnosis Using Discrete Cosine Transform and Supervised Machine Learning Algorithm . 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). {IEEE}
Se-Jong Kang, Jae-Young Kim, In-Kyu Jeong, M. M. Manjurul Islam, Kichang Im, Jong-Myon Kim(2019). An Improved Gas Classification Technique Using New Features and Support Vector Machines . Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). p. 158--166. Springer International Publishing
Niloy Sikder, Kangkan Bhakta, Abdullah Al Nahid, M M Manjurul Islam(2019). Fault Diagnosis of Motor Bearing Using Ensemble Learning Algorithm with FFT-based Preprocessing . 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST). {IEEE}
Farzin Piltan, Manjurul Islam, Jong-Myon Kim(2018). Input-Output Fault Diagnosis in Robot Manipulator Using Fuzzy LMI-Tuned PI Feedback Linearization Observer Based on Nonlinear Intelligent ARX Model . Advances in Computer Communication and Computational Sciences. p. 305--315. Springer Singapore
M. M. Manjurul Islam, Jong-Myon Kim(2018). Motor Bearing Fault Diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal . Advances in Artificial Intelligence. p. 144--155. Springer International Publishing
M. M. Manjurul Islam, Md. Rashedul Islam, Jong-Myon Kim(2016). A Hybrid Feature Selection Scheme Based on Local Compactness and Global Separability for Improving Roller Bearing Diagnostic Performance . Artificial Life and Computational Intelligence. p. 180--192. Springer International Publishing
M. M. Manjurul Islam, Sheraz A. Khan, Jong-Myon Kim(2015). Multi-fault Diagnosis of Roller Bearings Using Support Vector Machines with an Improved Decision Strategy . Advanced Intelligent Computing Theories and Applications. p. 538--550. Springer International Publishing
BOOK
Kang, S.-J., Kim, J.-Y., Jeong, I.-K., Islam, M.M.M., Im, K., Kim, J.-M.(2020). An Improved Gas Classification Technique Using New Features and Support Vector Machines . Advances in Intelligent Systems and Computing. 942. p. 158-166.
Piltan, F., Islam, M., Kim, J.-M.(2019). Input-output fault diagnosis in robot manipulator using fuzzy LMI-tuned PI feedback linearization observer based on nonlinear intelligent ARX model . Advances in Intelligent Systems and Computing. 759. p. 305-315.
Islam, M., Prosvirin, A., Kim, J.-M.(2018). Intelligent Rub-Impact Fault Diagnosis Based on Genetic Algorithm-Based IMF Selection in Ensemble Empirical Mode Decomposition and Diverse Features Models . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11314 LNCS. p. 147-155.
Islam, M.M.M., Kim, J.-M.(2018). Motor bearing fault diagnosis using deep convolutional neural networks with 2D analysis of vibration signal . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10832 LNAI. p. 144-155.
Islam, M.M.M., Islam, M.R., Kim, J.-M.(2017). A hybrid feature selection scheme based on local compactness and global separability for improving roller bearing diagnostic performance . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10142 LNAI. p. 180-192.
Manjurul Islam, M.M., Khan, S.A., Kim, J.-M.(2015). Multi-fault diagnosis of roller bearings using support vector machines with an improved decision strategy . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9227. p. 538-550.