Publications | Md Abrar Jahin

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2024

AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis

Md Abrar Jahin, Saleh Akram Naife, Anik Kumar Saha, and M. F. Mridha

Under review in International Journal of Machine Learning and Cybernetics, Jan 2024

arXiv:2401.10895 [cs]

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Supply chain risk assessment (SCRA) has witnessed a profound evolution through the integration of artificial intelligence (AI) and machine learning (ML) techniques, revolutionizing predictive capabilities and risk mitigation strategies. The significance of this evolution stems from the critical role of robust risk management strategies in ensuring operational resilience and continuity within modern supply chains. Previous reviews have outlined established methodologies but have overlooked emerging AI/ML techniques, leaving a notable research gap in understanding their practical implications within SCRA. This paper conducts a systematic literature review combined with a comprehensive bibliometric analysis. We meticulously examined 1,717 papers and derived key insights from a select group of 48 articles published between 2014 and 2023. The review fills this research gap by addressing pivotal research questions, and exploring existing AI/ML techniques, methodologies, findings, and future trajectories, thereby providing a more encompassing view of the evolving landscape of SCRA. Our study unveils the transformative impact of AI/ML models, such as Random Forest, XGBoost, and hybrids, in substantially enhancing precision within SCRA. It underscores adaptable post-COVID strategies, advocating for resilient contingency plans and aligning with evolving risk landscapes. Significantly, this review surpasses previous examinations by accentuating emerging AI/ML techniques and their practical implications within SCRA. Furthermore, it highlights the contributions through a comprehensive bibliometric analysis, revealing publication trends, influential authors, and highly cited articles.
@article{jahin_ai_2024, journal = {Under review in International Journal of Machine Learning and Cybernetics}, title = {{AI} in {Supply} {Chain} {Risk} {Assessment}: {A} {Systematic} {Literature} {Review} and {Bibliometric} {Analysis}}, shorttitle = {{AI} in {Supply} {Chain} {Risk} {Assessment}}, doi = {10.48550/arXiv.2401.10895}, urldate = {2024-02-02}, publisher = {arXiv}, author = {Jahin, Md Abrar and Naife, Saleh Akram and Saha, Anik Kumar and Mridha, M. F.}, month = jan, year = {2024}, note = {arXiv:2401.10895 [cs]}, keywords = {Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science}, }
Anthropometric Data of KUET students

Md Abrar Jahin, and Anik Kumar Saha

Feb 2024

Publisher: Mendeley Data

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Number of male students: 300 Number of female students: 80 The data was collected from the students, including batches 2k18, 2k19, 2k20, and 2k21. Confidentiality of participant responses was strictly maintained. All data collected were anonymized and stored securely. Only the research team has access to the raw data, and findings will be reported in aggregate form to ensure the anonymity of participants. Participants were provided with informed consent forms detailing the purpose of the study, their rights as participants, and procedures for data handling. Participation in the survey was voluntary, and participants had the right to withdraw at any time without penalty.
@misc{jahin_anthropometric_2024, title = {Anthropometric {Data} of {KUET} students}, doi = {10.17632/kw7fd465v7.1}, language = {en}, urldate = {2024-03-03}, publisher = {Mendeley Data}, author = {Jahin, Md Abrar and Saha, Anik Kumar}, month = feb, year = {2024}, note = {Publisher: Mendeley Data}, }
Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

Md Abrar Jahin, Md Sakib Hossain Shovon, Jungpil Shin, Istiyaque Ahmed Ridoy, and M. F. Mridha

Archives of Computational Methods in Engineering, Mar 2024

Publisher: Springer Nature

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This article systematically identifies and comparatively analyzes state-of-the-art supply chain (SC) forecasting strategies and technologies within a specific timeframe, encompassing a comprehensive review of 152 papers spanning from 1969 to 2023. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research.
@article{jahin_big_2024, title = {Big {Data}—{Supply} {Chain} {Management} {Framework} for {Forecasting}: {Data} {Preprocessing} and {Machine} {Learning} {Techniques}}, issn = {1886-1784}, shorttitle = {Big {Data}—{Supply} {Chain} {Management} {Framework} for {Forecasting}}, doi = {10.1007/s11831-024-10092-9}, language = {en}, urldate = {2024-03-24}, journal = {Archives of Computational Methods in Engineering}, note = {Publisher: Springer Nature}, author = {Jahin, Md Abrar and Shovon, Md Sakib Hossain and Shin, Jungpil and Ridoy, Istiyaque Ahmed and Mridha, M. F.}, month = mar, year = {2024}, }

Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm

Md. Mahfuzur Rahman*, Md Abrar Jahin*, Md. Saiful Islam, M. F. Mridha, and Jungpil Shin

Under review in European Journal of Operational Research, Apr 2024

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@article{rahman_qcdc-dr-ga_2024,
  journal = {Under review in European Journal of Operational Research},
  title = {{Optimizing} {Container} {Loading} and {Unloading} through {Dual}-{Cycling} and {Dockyard} {Rehandle} {Reduction} {Using} a {Hybrid} {Genetic} {Algorithm}},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  shorttitle = {{QCDC}-{DR}-{GA}},
  doi = {https://doi.org/10.48550/arXiv.2406.08534},
  urldate = {2024-04-06},
  author = {Rahman*, Md. Mahfuzur and Jahin*, Md Abrar and Islam, Md. Saiful and Mridha, M. F. and Shin, Jungpil},
  month = apr,
  year = {2024},
}

TRABSA: Interpretable Sentiment Analysis of Tweets using Attention-based BiLSTM and Twitter-RoBERTa

Md Abrar Jahin, Md Sakib Hossain Shovon, and M. F. Mridha

Under review in Nature Scientific Reports, Mar 2024

arXiv:2404.00297 [cs]

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Sentiment analysis is crucial for understanding public opinion and consumer behavior. Existing models face challenges with linguistic diversity, generalizability, and explainability. We propose TRABSA, a hybrid framework integrating transformer-based architectures, attention mechanisms, and BiLSTM networks to address this. Leveraging RoBERTa-trained on 124M tweets, we bridge gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy. Augmenting datasets with tweets from 32 countries and US states, we compare six word-embedding techniques and three lexicon-based labeling techniques, selecting the best for optimal sentiment analysis. TRABSA outperforms traditional ML and deep learning models with 94% accuracy and significant precision, recall, and F1-score gains. Evaluation across diverse datasets demonstrates consistent superiority and generalizability. SHAP and LIME analyses enhance interpretability, improving confidence in predictions. Our study facilitates pandemic resource management, aiding resource planning, policy formation, and vaccination tactics.
@article{jahin_trabsa_2024, journal = {Under review in Nature Scientific Reports}, title = {{TRABSA}: {Interpretable} {Sentiment} {Analysis} of {Tweets} using {Attention}-based {BiLSTM} and {Twitter}-{RoBERTa}}, shorttitle = {{TRABSA}}, doi = {10.48550/arXiv.2404.00297}, urldate = {2024-04-06}, publisher = {arXiv}, author = {Jahin, Md Abrar and Shovon, Md Sakib Hossain and Mridha, M. F.}, month = mar, year = {2024}, note = {arXiv:2404.00297 [cs]}, keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language}, }
Analyzing Male Domestic Violence through Exploratory Data Analysis and Explainable Machine Learning Insights

Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, and Jungpil Shin

Under review in Nature Scientific Reports, Mar 2024

arXiv:2403.15594 [cs]

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Domestic violence, which is often perceived as a gendered issue among female victims, has gained increasing attention in recent years. Despite this focus, male victims of domestic abuse remain primarily overlooked, particularly in Bangladesh. Our study represents a pioneering exploration of the underexplored realm of male domestic violence (MDV) within the Bangladeshi context, shedding light on its prevalence, patterns, and underlying factors. Existing literature predominantly emphasizes female victimization in domestic violence scenarios, leading to an absence of research on male victims. We collected data from the major cities of Bangladesh and conducted exploratory data analysis to understand the underlying dynamics. We implemented 11 traditional machine learning models with default and optimized hyperparameters, 2 deep learning, and 4 ensemble models. Despite various approaches, CatBoost has emerged as the top performer due to its native support for categorical features, efficient handling of missing values, and robust regularization techniques, achieving 76% accuracy. In contrast, other models achieved accuracy rates in the range of 58-75%. The eXplainable AI techniques, SHAP and LIME, were employed to gain insights into the decision-making of black-box machine learning models. By shedding light on this topic and identifying factors associated with domestic abuse, the study contributes to identifying groups of people vulnerable to MDV, raising awareness, and informing policies and interventions aimed at reducing MDV. Our findings challenge the prevailing notion that domestic abuse primarily affects women, thus emphasizing the need for tailored interventions and support systems for male victims. ML techniques enhance the analysis and understanding of the data, providing valuable insights for developing effective strategies to combat this pressing social issue.
@article{jahin_analyzing_2024, journal = {Under review in Nature Scientific Reports}, title = {Analyzing {Male} {Domestic} {Violence} through {Exploratory} {Data} {Analysis} and {Explainable} {Machine} {Learning} {Insights}}, doi = {10.48550/arXiv.2403.15594}, urldate = {2024-04-06}, publisher = {arXiv}, author = {Jahin, Md Abrar and Naife, Saleh Akram and Lima, Fatema Tuj Johora and Mridha, M. F. and Shin, Jungpil}, month = mar, year = {2024}, note = {arXiv:2403.15594 [cs]}, keywords = {Computer Science - Machine Learning, Computer Science - Computers and Society}, }
Patient Comments and Specialist Types Dataset

Md Abrar Jahin

Apr 2024

Publisher: Mendeley Data

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This dataset contains patient comments, associated patient categories, and specialist types. Each entry in the dataset corresponds to a patient comment along with the category of the patient’s condition and the specialist type recommended for that category. The specialist types are mapped to the patient categories using a predefined dictionary. This dataset can be used for sentiment analysis, patient category classification, and specialist recommendation systems in healthcare. The dataset is provided in CSV format and can be used for research and analysis in the healthcare domain.
@misc{jahin_patient_2024, title = {Patient {Comments} and {Specialist} {Types} {Dataset}}, doi = {10.17632/2twgjzpn82.1}, language = {en}, urldate = {2024-04-22}, publisher = {Mendeley Data}, author = {Jahin, Md Abrar}, month = apr, year = {2024}, note = {Publisher: Mendeley Data}, }
A Natural Language Processing-Based Classification and Mode-Based Ranking of Musculoskeletal Disorder Risk Factors

Md Abrar Jahin, and Subrata Talapatra

Decision Analytics Journal, Jun 2024

Publisher: Elsevier

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This research explores the intricate landscape of Musculoskeletal Disorder (MSD) risk factors, employing a novel fusion of Natural Language Processing (NLP) techniques and mode-based ranking methodologies. Enhancing knowledge of MSD risk factors, their classification, and their relative severity is the main goal of enabling more focused preventative and treatment efforts. The study benchmarks eight NLP models, integrating pre-trained transformers, cosine similarity, and various distance metrics to categorize risk factors into personal, biomechanical, workplace, psychological, and organizational classes. Key findings reveal that the Bidirectional Encoder Representations from Transformers (BERT) model with cosine similarity attains an overall accuracy of 28%, while the sentence transformer, coupled with Euclidean, Bray–Curtis, and Minkowski distances, achieves a flawless accuracy score of 100%. Using a 10-fold cross-validation strategy and performing rigorous statistical paired t-tests and Cohen’s d tests (with a 5% significance level assumed), the study provides the results with greater validity. To determine the severity hierarchy of MSD risk variables, the research uses survey data and a mode-based ranking technique parallel to the classification efforts. Intriguingly, the rankings align precisely with the previous literature, reaffirming the consistency and reliability of the approach. "Working posture" emerges as the most severe risk factor, emphasizing the critical role of proper posture in preventing MSD. The collective perceptions of survey participants underscore the significance of factors like "Job insecurity", "Effort reward imbalance", and "Poor employee facility" in contributing to MSD risks. The convergence of rankings provides actionable insights for organizations aiming to reduce the prevalence of MSD. The study concludes with implications for targeted interventions, recommendations for improving workplace conditions, and avenues for future research. This holistic approach, integrating NLP and mode-based ranking, contributes to a more sophisticated comprehension of MSD risk factors and opens the door for more effective strategies in occupational health.
@article{jahin_natural_2024, title = {A {Natural} {Language} {Processing}-{Based} {Classification} and {Mode}-{Based} {Ranking} of {Musculoskeletal} {Disorder} {Risk} {Factors}}, volume = {11}, issn = {2772-6622}, doi = {10.1016/j.dajour.2024.100464}, urldate = {2024-04-27}, journal = {Decision Analytics Journal}, publisher = {Elsevier}, author = {Jahin, Md Abrar and Talapatra, Subrata}, month = jun, year = {2024}, keywords = {Machine learning, Risk factors, Occupational health and safety, Natural Language Processing (NLP), Musculoskeletal Disorder (MSD)}, pages = {100464}, note = {Publisher: Elsevier}, }
Bangladeshi Male Domestic Abuse Dataset

Md Abrar Jahin

Feb 2024

Publisher: Mendeley Data

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The dataset comprises responses from diverse individuals, addressing demographic factors (residence type, age, education level, family structure), monthly income, initial experience of torture, current abuse situation, marital duration, extramarital involvement, primary abuse location, stance on male torture legislation, abuse victimization status, among others. Collected through a survey consisting of 22 questions, predominantly offering binary responses, it encompasses quantitative data derived from individual male responses. The survey targeted 2000 residents from Bangladesh’s 9 major cities, prioritizing professionals across sectors and ensuring representation of unemployed individuals, employees, and business owners.
@misc{jahin_bangladeshi_2024, title = {Bangladeshi {Male} {Domestic} {Abuse} {Dataset}}, doi = {10.17632/97xnx8nf22.1}, language = {en}, urldate = {2024-04-27}, publisher = {Mendeley Data}, author = {Jahin, Md Abrar}, month = feb, year = {2024}, note = {Publisher: Mendeley Data}, }
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model

Md Abrar Jahin*, Asef Shahriar*, and Md Al Amin

Under review in Engineering Applications of Artificial Intelligence, May 2024

arXiv:2405.15598 [cs] version: 1

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Accurate demand forecasting is crucial for optimizing supply chain management. Traditional methods often fail to capture complex patterns from seasonal variability and special events. Despite advancements in deep learning, interpretable forecasting models remain a challenge. To address this, we introduce the Multi-Channel Data Fusion Network (MCDFN), a hybrid architecture that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU) to enhance predictive performance by extracting spatial and temporal features from time series data. Our rigorous benchmarking demonstrates that MCDFN outperforms seven other deep-learning models, achieving superior metrics: MSE (23.5738%), RMSE (4.8553%), MAE (3.9991%), and MAPE (20.1575%). Additionally, MCDFN’s predictions were statistically indistinguishable from actual values, confirmed by a paired t-test with a 5% p-value and a 10-fold cross-validated statistical paired t-test. We apply explainable AI techniques like ShapTime and Permutation Feature Importance to enhance interpretability. This research advances demand forecasting methodologies and offers practical guidelines for integrating MCDFN into supply chain systems, highlighting future research directions for scalability and user-friendly deployment.
@article{jahin_mcdfn_2024, journal = {Under review in Engineering Applications of Artificial Intelligence}, title = {{MCDFN}: {Supply} {Chain} {Demand} {Forecasting} via an {Explainable} {Multi}-{Channel} {Data} {Fusion} {Network} {Model}}, shorttitle = {{MCDFN}}, doi = {https://doi.org/10.48550/arXiv.2405.15598}, urldate = {2024-05-27}, publisher = {arXiv}, author = {Jahin*, Md Abrar and Shahriar*, Asef and Amin, Md Al}, month = may, year = {2024}, note = {arXiv:2405.15598 [cs] version: 1}, keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning}, }
Analysis of Internet of Things Implementation Barriers in the Cold Supply Chain: An Integrated ISM-MICMAC and DEMATEL Approach

Kazrin Ahmad, Md Saiful Islam, Md Abrar Jahin, and M. F. Mridha

PLOS ONE, May 2024

Publisher: Public Library of Science (PLOS), arXiv:2402.01804 [cs]

Abs arXiv Bib HTML

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimize operating procedures, and increase productivity. The integration of the IoT in this complicated setting is hindered by specific barriers that require thorough examination. Prominent barriers to IoT implementation in a cold supply chain, which is the main objective, are identified using a two-stage model. After reviewing the available literature on IoT implementation, 13 barriers were identified. The survey data were cross-validated for quality, and Cronbach’s alpha test was employed to ensure validity. This study applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among these barriers, "regulatory compliance" and "cold chain networks" are the key drivers of IoT adoption strategies. MICMAC’s driving and dependence power element categorization helps evaluate barrier interactions. In the second phase of this study, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system benefits as a whole. The findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.
@article{ahmad_analysis_2024, journal = {PLOS ONE}, title = {Analysis of {Internet} of {Things} {Implementation} {Barriers} in the {Cold} {Supply} {Chain}: {An} {Integrated} {ISM}-{MICMAC} and {DEMATEL} {Approach}}, shorttitle = {Analysis of {Internet} of {Things} {Implementation} {Barriers} in the {Cold} {Supply} {Chain}}, doi = {10.1371/journal.pone.0304118}, urldate = {2024-06-25}, publisher = {PLOS ONE}, author = {Ahmad, Kazrin and Islam, Md Saiful and Jahin, Md Abrar and Mridha, M. F.}, month = may, year = {2024}, note = {Publisher: Public Library of Science (PLOS), arXiv:2402.01804 [cs]}, keywords = {Computer Science - Artificial Intelligence, Computer Science - Computers and Society}, }
Ergonomic Design of Computer Laboratory Furniture: Mismatch Analysis Utilizing Anthropometric Data of University Students

Anik Kumar Saha*, Md Abrar Jahin*, Md Rafiquzzaman, and M. F. Mridha

Heliyon, Mar 2024

Publisher: Elsevier, arXiv:2403.05589 [cs]

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Many studies have shown how ergonomically designed furniture improves productivity and well-being. As computers have become a part of students’ academic lives, they will grow further in the future. We propose anthropometric-based furniture dimensions suitable for university students to improve computer laboratory ergonomics. We collected data from 380 participants and analyzed 11 anthropometric measurements, correlating them to 11 furniture dimensions. Two types of furniture were studied: a non-adjustable chair with a non-adjustable table and an adjustable chair with a non-adjustable table. The mismatch calculation showed a significant difference between furniture dimensions and anthropometric measurements. The one-way ANOVA test with a significance level of 5% also showed a significant difference between proposed and existing furniture dimensions. The proposed dimensions were found to be more compatible and reduced mismatch percentages for both males and females compared to existing furniture. The proposed dimensions of the furniture set with adjustable seat height showed slightly improved results compared to the non-adjustable furniture set. This suggests that the proposed dimensions can improve comfort levels and reduce the risk of musculoskeletal disorders among students. Further studies on the implementation and long-term effects of these proposed dimensions in real-world computer laboratory settings are recommended.
@article{saha_ergonomic_2024, journal = {Heliyon}, title = {Ergonomic {Design} of {Computer} {Laboratory} {Furniture}: {Mismatch} {Analysis} {Utilizing} {Anthropometric} {Data} of {University} {Students}}, shorttitle = {Ergonomic {Design} of {Computer} {Laboratory} {Furniture}}, doi = {10.48550/arXiv.2403.05589}, urldate = {2024-03-18}, publisher = {Elsevier}, author = {Saha*, Anik Kumar and Jahin*, Md Abrar and Rafiquzzaman, Md and Mridha, M. F.}, month = mar, year = {2024}, note = {Publisher: Elsevier, arXiv:2403.05589 [cs]}, keywords = {Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction}, }

2023

Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

Abrar Morshed*, Abdulla Al Shihab*, Md Abrar Jahin*, Md Jaber Al Nahian*, Md Murad Hossain Sarker*, and 14 more authors

Under review in Multimedia Tools and Applications, May 2023

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The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.
@article{morshed_ultrasound-based_2023, journal = {Under review in Multimedia Tools and Applications}, title = {Ultrasound-{Based} {AI} for {COVID}-19 {Detection}: {A} {Comprehensive} {Review} of {Public} and {Private} {Lung} {Ultrasound} {Datasets} and {Studies}}, shorttitle = {Ultrasound-{Based} {AI} for {COVID}-19 {Detection}}, doi = {10.20944/preprints202303.0296.v3}, language = {en}, urldate = {2023-07-26}, publisher = {Preprints}, author = {Morshed*, Abrar and Shihab*, Abdulla Al and Jahin*, Md Abrar and Nahian*, Md Jaber Al and Sarker*, Md Murad Hossain and Wadud*, Md Sharjis Ibne and Uddin*, Mohammad Istiaq and Siraji*, Muntequa Imtiaz and Anjum*, Nafisa and Shristy*, Sumiya Rajjab and Rahman*, Tanvin and Khatun, Mahmuda and Dewan, Md Rubel and Hossain, Mosaddeq and Sultana, Razia and Chakma, Ripel and Emon, Sonet Barua and Islam, Towhidul and Hussain, Mohammad}, month = may, year = {2023}, keywords = {Deep learning, COVID-19, Artificial Intelligence, Review, Ultrasound}, }

Extended Covid Twitter Datasets

Md Abrar Jahin

May 2023

Publisher: Mendeley Data

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@misc{jahin_extended_2023,
  title = {Extended {Covid} {Twitter} {Datasets}},
  doi = {10.17632/2ynwykrfgf.1},
  language = {en},
  urldate = {2023-07-26},
  publisher = {Mendeley Data},
  author = {Jahin, Md Abrar},
  month = may,
  year = {2023},
  note = {Publisher: Mendeley Data},
}

QAmplifyNet: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network

Md Abrar Jahin, Md Sakib Hossain Shovon, Md Saiful Islam, Jungpil Shin, M. F. Mridha, and 1 more author

Scientific Reports, Oct 2023

Number: 1 Publisher: Nature Publishing Group

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Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression’s performance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting "Not Backorder" and 75% for predicting "backorder," outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
@article{jahin_qamplifynet_2023, title = {{QAmplifyNet}: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network}, volume = {13}, copyright = {2023 Springer Nature Limited}, issn = {2045-2322}, shorttitle = {{QAmplifyNet}}, doi = {10.1038/s41598-023-45406-7}, language = {en}, number = {1}, urldate = {2023-10-25}, journal = {Scientific Reports}, author = {Jahin, Md Abrar and Shovon, Md Sakib Hossain and Islam, Md Saiful and Shin, Jungpil and Mridha, M. F. and Okuyama, Yuichi}, month = oct, year = {2023}, note = {Number: 1 Publisher: Nature Publishing Group}, keywords = {Mathematics and computing, Engineering}, pages = {18246}, }
Exploring Internet of Things Adoption Challenges in Manufacturing Firms: A Delphi Fuzzy Analytical Hierarchy Process Approach

Hasan Shahriar*, Md Saiful Islam, Md Abrar Jahin*, Istiyaque Ahmed Ridoy, Raihan Rafi Prottoy, and 2 more authors

Under review in PLOS ONE, Dec 2023

arXiv:2309.12350 [cs]

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Innovation is crucial for sustainable success in today’s fiercely competitive global manufacturing landscape. Bangladesh’s manufacturing sector must embrace transformative technologies like the Internet of Things (IoT) to thrive in this environment. This article addresses the vital task of identifying and evaluating barriers to IoT adoption in Bangladesh’s manufacturing industry. Through synthesizing expert insights and carefully reviewing contemporary literature, we explore the intricate landscape of IoT adoption challenges. Our methodology combines the Delphi and Fuzzy Analytical Hierarchy Process, systematically analyzing and prioritizing these challenges. This approach harnesses expert knowledge and uses fuzzy logic to handle uncertainties. Our findings highlight key obstacles, with "Lack of top management commitment to new technology" (B10), "High initial implementation costs" (B9), and "Risks in adopting a new business model" (B7) standing out as significant challenges that demand immediate attention. These insights extend beyond academia, offering practical guidance to industry leaders. With the knowledge gained from this study, managers can develop tailored strategies, set informed priorities, and embark on a transformative journey toward leveraging IoT’s potential in Bangladesh’s industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and equips industry leaders to navigate them effectively. This strategic navigation, in turn, enhances the competitiveness and sustainability of Bangladesh’s manufacturing sector in the IoT era.
@article{shahriar_exploring_2023, journal = {Under review in PLOS ONE}, title = {Exploring {Internet} of {Things} {Adoption} {Challenges} in {Manufacturing} {Firms}: {A} {Delphi} {Fuzzy} {Analytical} {Hierarchy} {Process} {Approach}}, shorttitle = {Exploring {Internet} of {Things} {Adoption} {Challenges} in {Manufacturing} {Firms}}, doi = {10.48550/arXiv.2309.12350}, urldate = {2024-02-02}, publisher = {arXiv}, author = {Shahriar*, Hasan and Islam, Md Saiful and Jahin*, Md Abrar and Ridoy, Istiyaque Ahmed and Prottoy, Raihan Rafi and Abid, Adiba and Mridha, M. F.}, month = dec, year = {2023}, note = {arXiv:2309.12350 [cs]}, keywords = {Computer Science - Human-Computer Interaction}, }
Perfectly Conserved Sequences (PCS) Between Human and Mouse Are Significantly Enriched for Small-protein Coding Sequence

Lucia Žifčáková, and Md Abrar Jahin

In Society for Molecular Biology and Evolution (SMBE), 2023), Jul 2023

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We extracted PCS from the UCSC human and mouse genome alignment after the removal of repetitive sequences. We leveraged RefSeq, SmProt, and Enhanceratlas databases for PCS annotation by all known human genes, small proteins, and enhancers, respectively. We have created 1000 sets of "random PCS," each with the same length distribution as natural PCS but randomly located in the nonrepetitive part of the genome. To test for enrichment of small proteins in PCS, we applied Fisher’s exact test, and the hypergeometric test, phyper, in R. gProfiler (for coding regions) and GREAT (for non-coding, cisregulatory regions) was used to find enriched Gene Ontology (GO) terms in PCS annotated as small proteins. Both enrichment analyses use correction of the p-value for multiple hypothesis testing.
@inproceedings{zifcakova_perfectly_2023, address = {Ferrara, Emilia-Romagna, Italy}, title = {Perfectly {Conserved} {Sequences} ({PCS}) {Between} {Human} and {Mouse} {Are} {Significantly} {Enriched} for {Small}-protein {Coding} {Sequence}}, doi = {10.2139/ssrn.4533875}, language = {en}, urldate = {2024-02-03}, booktitle = {Society for {Molecular} {Biology} and {Evolution} ({SMBE}), 2023)}, author = {Žifčáková, Lucia and Jahin, Md Abrar}, month = jul, year = {2023}, keywords = {comparative genomics, human-mouse genomes, pcs, perfectly conserved sequences, ucsc}, }

Survey Data on Ranking of Musculoskeletal Disorder Risk Factors - Mendeley Data

Md Abrar Jahin

May 2023

Publisher: Mendeley Data

Bib HTML

@misc{jahin_survey_2023,
  title = {Survey {Data} on {Ranking} of {Musculoskeletal} {Disorder} {Risk} {Factors} - {Mendeley} {Data}},
  doi = {10.17632/kr33mvtw63.1},
  language = {en},
  urldate = {2024-03-29},
  publisher = {Mendeley Data},
  author = {Jahin, Md Abrar},
  month = may,
  year = {2023},
  note = {Publisher: Mendeley Data},
}

2022

Perfectly conserved sequences (PCS) between human and mouse are significantly enriched for small proteins

Lucia Žifčáková, Md Abrar Jahin, and Jonathan Miller

In Bioinformatics and Computational Biology Conference (BBCC), 2022, Dec 2022

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@inproceedings{zifcakova_perfectly_2022,
  title = {Perfectly conserved sequences ({PCS}) between human and mouse are significantly enriched for small proteins},
  volume = {11},
  doi = {10.7490/f1000research.1119288.1},
  urldate = {2023-07-26},
  booktitle = {Bioinformatics and {Computational} {Biology} {Conference} ({BBCC}), 2022},
  author = {Žifčáková, Lucia and Jahin, Md Abrar and Miller, Jonathan},
  month = dec,
  year = {2022},
}

2021

DIT4BEARs Smart Roads Internship

Md Abrar Jahin, and Andrii Krutsylo

Jul 2021

Internship Report at UiT-The Arctic University of Norway; Affiliated with DIT4BEARs project

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The research internship at UiT - The Arctic University of Norway was offered for our team being the winner of the ’Smart Roads - Winter Road Maintenance 2021’ Hackathon. The internship commenced on 3 May 2021 and ended on 21 May 2021 with meetings happening twice each week. In spite of having different nationalities and educational backgrounds, we both interns tried to collaborate as a team as much as possible. The most alluring part was working on this project made us realize the critical conditions faced by the arctic people, where it was hard to gain such a unique experience from our residence. We developed and implemented several deep learning models to classify the states (dry, moist, wet, icy, snowy, slushy). Depending upon the best model, the weather forecast app will predict the state taking the Ta, Tsurf, Height, Speed, Water, etc. into consideration. The crucial part was to define a safety metric which is the product of the accident rates based on friction and the accident rates based on states. We developed a regressor that will predict the safety metric depending upon the state obtained from the classifier and the friction obtained from the sensor data. A pathfinding algorithm has been designed using the sensor data, open street map data, weather data.
@misc{jahin_dit4bears_2021, title = {{DIT4BEARs} {Smart} {Roads} {Internship}}, doi = {10.48550/arXiv.2107.06755}, urldate = {2023-07-26}, publisher = {arXiv}, author = {Jahin, Md Abrar and Krutsylo, Andrii}, month = jul, year = {2021}, note = {Internship Report at UiT-The Arctic University of Norway; Affiliated with DIT4BEARs project}, keywords = {Computer Science - Machine Learning}, annote = {Comment: 6 pages}, }