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Book ; Online: Divorce Prediction with Machine Learning

Ahsan, Md Manjurul

Insights and LIME Interpretability

2023

Abstract: Divorce is one of the most common social issues in developed countries like in the United States. Almost 50% of the recent marriages turn into an involuntary divorce or separation. While it is evident that people vary to a different extent, and even over ...

Abstract	Divorce is one of the most common social issues in developed countries like in the United States. Almost 50% of the recent marriages turn into an involuntary divorce or separation. While it is evident that people vary to a different extent, and even over time, an incident like Divorce does not interrupt the individual's daily activities; still, Divorce has a severe effect on the individual's mental health, and personal life. Within the scope of this research, the divorce prediction was carried out by evaluating a dataset named by the 'divorce predictor dataset' to correctly classify between married and Divorce people using six different machine learning algorithms- Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Na\"ive Bayes (NB), and, Support Vector Machines (SVM). Preliminary computational results show that algorithms such as SVM, KNN, and LDA, can perform that task with an accuracy of 98.57%. This work's additional novel contribution is the detailed and comprehensive explanation of prediction probabilities using Local Interpretable Model-Agnostic Explanations (LIME). Utilizing LIME to analyze test results illustrates the possibility of differentiating between divorced and married couples. Finally, we have developed a divorce predictor app considering ten most important features that potentially affect couples in making decisions in their divorce, such tools can be used by any one in order to identify their relationship condition.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-10-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis.

Ahsan, Md Manjurul / Ali, Md Shahin / Siddique, Zahed

Neural networks : the official journal of the International Neural Network Society

2024 Volume 173, Page(s) 106157

Abstract: Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the performance of Machine Learning (ML) models resulting in biased predictions. Numerous techniques have been proposed to address CIP, including, but not limited ... ...

Abstract	Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the performance of Machine Learning (ML) models resulting in biased predictions. Numerous techniques have been proposed to address CIP, including, but not limited to, Oversampling, Undersampling, and cost-sensitive approaches. Due to its ability to generate synthetic data, oversampling techniques such as the Synthetic Minority Oversampling Technique (SMOTE) are the most widely used methodology by researchers. However, one of SMOTE's potential disadvantages is that newly created minor samples overlap with major samples. Therefore, the probability of ML models' biased performance toward major classes increases. Generative adversarial network (GAN) has recently garnered much attention due to their ability to create real samples. However, GAN is hard to train even though it has much potential. Considering these opportunities, this work proposes two novel techniques: GAN-based Oversampling (GBO) and Support Vector Machine-SMOTE-GAN (SSG) to overcome the limitations of the existing approaches. The preliminary results show that SSG and GBO performed better on the nine imbalanced benchmark datasets than several existing SMOTE-based approaches. Additionally, it can be observed that the proposed SSG and GBO methods can accurately classify the minor class with more than 90% accuracy when tested with 20%, 30%, and 40% of the test data. The study also revealed that the minor sample generated by SSG demonstrates Gaussian distributions, which is often difficult to achieve using original SMOTE and SVM-SMOTE.
MeSH term(s)	Algorithms ; Machine Learning ; Support Vector Machine ; Probability
Language	English
Publishing date	2024-02-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2024.106157
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Article ; Online: Machine learning-based heart disease diagnosis: A systematic literature review.

Ahsan, Md Manjurul / Siddique, Zahed

Artificial intelligence in medicine

2022 Volume 128, Page(s) 102289

Abstract: Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patients' data, ... ...

Abstract	Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patients' data, detecting heart disease during the early stage is feasible. However, both ECG and patients' data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the existing literature, this study takes a systematic literature review (SLR) approach to uncover the challenges associated with imbalanced data in heart diseases predictions. Before that, we conducted a meta-analysis using 451 reference literature acquired from the reputed journals between 2012 and November 15, 2021. For in-depth analysis, 49 referenced literature has been considered and studied, taking into account the following factors: heart disease type, algorithms, applications, and solutions. Our SLR study revealed that the current approaches encounter various open problems/issues when dealing with imbalanced data, eventually hindering their practical applicability and functionality. In the diagnosis of heart disease, machine learning approaches help to improve data-driven decision-making. A metadata analysis of 451 articles and content analysis of 49 selected articles of heart disease diagnosis. Researchers primarily concentrated on enhancing the performance of the models while disregarding other issues such as the interpretability and explainability of Machine learning algorithms.
MeSH term(s)	Algorithms ; Electrocardiography ; Heart Diseases/diagnosis ; Humans ; Machine Learning
Language	English
Publishing date	2022-03-29
Publishing country	Netherlands
Document type	Journal Article ; Meta-Analysis ; Review ; Systematic Review
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2022.102289
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Article: Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.

Ahsan, Md Manjurul / Luna, Shahana Akter / Siddique, Zahed

Healthcare (Basel, Switzerland)

2022 Volume 10, Issue 3

Abstract: Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool ... ...

Abstract	Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.
Language	English
Publishing date	2022-03-15
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare10030541
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Article ; Online: Machine-Learning-Based Disease Diagnosis

Md Manjurul Ahsan / Shahana Akter Luna / Zahed Siddique

Healthcare, Vol 10, Iss 541, p

A Comprehensive Review

2022 Volume 541

Abstract	Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.
Keywords	artificial neural networks ; convolutional neural networks ; COVID-19 ; deep learning ; deep neural networks ; diabetes ; Medicine ; R
Subject code	006
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Invariant Scattering Transform for Medical Imaging

Ahsan, Md Manjurul / Raman, Shivakumar / Siddique, Zahed

2023

Abstract: Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the ... ...

Abstract	Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners. Comment: Accepted for Springer book chapter for a book "Data-driven approaches to Medical Imaging"
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-04-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: BSGAN

Ahsan, Md Manjurul / Raman, Shivakumar / Siddique, Zahed

A Novel Oversampling Technique for Imbalanced Pattern Recognitions

2023

Abstract: Class imbalanced problems (CIP) are one of the potential challenges in developing unbiased Machine Learning (ML) models for predictions. CIP occurs when data samples are not equally distributed between the two or multiple classes. Borderline-Synthetic ... ...

Abstract	Class imbalanced problems (CIP) are one of the potential challenges in developing unbiased Machine Learning (ML) models for predictions. CIP occurs when data samples are not equally distributed between the two or multiple classes. Borderline-Synthetic Minority Oversampling Techniques (SMOTE) is one of the approaches that has been used to balance the imbalance data by oversampling the minor (limited) samples. One of the potential drawbacks of existing Borderline-SMOTE is that it focuses on the data samples that lay at the border point and gives more attention to the extreme observations, ultimately limiting the creation of more diverse data after oversampling, and that is the almost scenario for the most of the borderline-SMOTE based oversampling strategies. As an effect, marginalization occurs after oversampling. To address these issues, in this work, we propose a hybrid oversampling technique by combining the power of borderline SMOTE and Generative Adversarial Network to generate more diverse data that follow Gaussian distributions. We named it BSGAN and tested it on four highly imbalanced datasets: Ecoli, Wine quality, Yeast, and Abalone. Our preliminary computational results reveal that BSGAN outperformed existing borderline SMOTE and GAN-based oversampling techniques and created a more diverse dataset that follows normal distribution after oversampling effect.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-05-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches

Ahsan, Md Manjurul / Raman, Shivakumar / Siddique, Zahed

2023

Abstract: Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve productivity - a key challenge. This study ...

Abstract	Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve productivity - a key challenge. This study explored the effectiveness of machine learning (ML) approaches, specifically transfer learning (TL) models, for defect detection in 3D-printed cylinders. Images of cylinders were analyzed using models including VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2. Performance was compared across two datasets using accuracy, precision, recall, and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest performance, with an average F1-score of 0.32. Similarly, in the second study, MobileNetV2 correctly classified all instances, while ResNet50 struggled with more false positives and fewer true positives, resulting in an F1-score of 0.75. Overall, the findings suggest certain TL models like MobileNetV2 can deliver high accuracy for AM defect classification, although performance varies across algorithms. The results provide insights into model optimization and integration needs for reliable automated defect analysis during 3D printing. By identifying the top-performing TL techniques, this study aims to enhance AM product quality through robust image-based monitoring and inspection.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-10-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Deep transfer learning approaches for Monkeypox disease diagnosis.

Ahsan, Md Manjurul / Uddin, Muhammad Ramiz / Ali, Md Shahin / Islam, Md Khairul / Farjana, Mithila / Sakib, Ahmed Nazmus / Momin, Khondhaker Al / Luna, Shahana Akter

Expert systems with applications

2023 Volume 216, Page(s) 119483

Abstract: Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown ... ...

Abstract	Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.
Language	English
Publishing date	2023-01-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2017237-0
ISSN	0957-4174
ISSN	0957-4174
DOI	10.1016/j.eswa.2022.119483
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Book ; Online: Monkeypox Image Data collection

Ahsan, Md Manjurul / Uddin, Muhammad Ramiz / Luna, Shahana Akter

2022

Abstract: This paper explains the initial Monkeypox Open image data collection procedure. It was created by assembling images collected from websites, newspapers, and online portals and currently contains around 1905 images after data augmentation. ... Comment: This ...

Abstract	This paper explains the initial Monkeypox Open image data collection procedure. It was created by assembling images collected from websites, newspapers, and online portals and currently contains around 1905 images after data augmentation. Comment: This is the attempt of creating monkeypox image dataset collected from various sources and it will continue to update by collectiong samples from journals and other public access domains
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Publishing date	2022-06-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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