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Article ; Online: A blockchain based medicine production and distribution framework to prevent medicine counterfeit

Iyolita Islam / Muhammad Nazrul Islam

Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 1, Pp 101851- (2024)

1481

Abstract: Medicine counterfeiting has raised significant concern in recent years. Falsified and counterfeit drug production and distribution are illegal and a public health concern. The intensity of this problem varies significantly among different countries, ... ...

Abstract	Medicine counterfeiting has raised significant concern in recent years. Falsified and counterfeit drug production and distribution are illegal and a public health concern. The intensity of this problem varies significantly among different countries, depending on how strongly a country’s laws and procedures are followed. Preventing counterfeit medicines has thus become a critical concern, especially in developing and underdeveloped countries. This research aims, firstly, to outline the possible factors of medicine counterfeiting; secondly, to propose a blockchain-based framework to prevent medicine counterfeiting; and thirdly, to evaluate the proposed framework. A content analysis and a semi-structured interview with the key personnel related to the medicine manufacturing and distribution system in Bangladesh were carried out to derive the current scenario of medicine counterfeit. Based on the interviews and content analysis, a set of use cases for preventing medicine counterfeiting in the context of Bangladesh was explored. The required features for developing a digital solution were extracted considering the derived use cases. A blockchain-based framework for preventing medicine counterfeit was proposed by adopting the extracted features. A prototype was developed based on the proposed framework, and an evaluation study was performed to evaluate the prototype. The evaluation study showed that the average block execution time is 201 ms, and the average block time is 482 ms. Again, the proposed framework was found secure, scalable, customer-oriented, and practical compared to other systems.
Keywords	Medicine counterfeit ; Falsified drugs ; Blockchain-based framework ; Digital solution ; Medicine production and distribution ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	001
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques.

Suha, Sayma Alam / Islam, Muhammad Nazrul

Heliyon

2023 Volume 9, Issue 10, Page(s) e20524

Abstract: Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of ... ...

Abstract	Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment. This review study's objective is to analyze the relevant research works on computer-assisted methods for automatically identifying PCOS through a systematic literature review (SLR) methodology as well as investigate the research limitations and explore potential future research scopes in this domain. 28 articles have been selected using the PRISMA approach based on a set of inclusion-exclusion criteria for conducting the review. The data synthesis of the selected articles has been conducted using six data exploration themes. As outcomes, the SLR explored the topical association between the studies; their research profiles; objectives; data size, type, and sources; methodologies applied for the detection of PCOS; and lastly the research outcomes along with their evaluation measures and performances. The study also highlights areas for future research directions examining the study gaps to enhance the current efforts for autonomous PCOS identification; such as integrating advanced techniques with the current methods; developing interactive software systems; exploring deep learning and unsupervised machine learning techniques; enhancing datasets and country context; and investigating more unknown factors behind PCOS. Thus, this SLR provides a state-of-the-art paradigm of autonomous PCOS detection which will support significantly efficient clinical assessment, diagnosis and treatment of PCOS.
Language	English
Publishing date	2023-10-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e20524
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image.

Suha, Sayma Alam / Islam, Muhammad Nazrul

Scientific reports

2022 Volume 12, Issue 1, Page(s) 17123

Abstract: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most ... ...

Abstract	Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the "VGGNet16" pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being "XGBoost" model as image classifier with an accuracy of 99.89% for classification.
MeSH term(s)	Female ; Humans ; Infertility, Female ; Machine Learning ; Neural Networks, Computer ; Ovary/diagnostic imaging ; Polycystic Ovary Syndrome/diagnostic imaging ; Polycystic Ovary Syndrome/therapy
Language	English
Publishing date	2022-10-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-21724-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques

Sayma Alam Suha / Muhammad Nazrul Islam

Heliyon, Vol 9, Iss 10, Pp e20524- (2023)

2023

Abstract	Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment. This review study's objective is to analyze the relevant research works on computer-assisted methods for automatically identifying PCOS through a systematic literature review (SLR) methodology as well as investigate the research limitations and explore potential future research scopes in this domain. 28 articles have been selected using the PRISMA approach based on a set of inclusion-exclusion criteria for conducting the review. The data synthesis of the selected articles has been conducted using six data exploration themes. As outcomes, the SLR explored the topical association between the studies; their research profiles; objectives; data size, type, and sources; methodologies applied for the detection of PCOS; and lastly the research outcomes along with their evaluation measures and performances. The study also highlights areas for future research directions examining the study gaps to enhance the current efforts for autonomous PCOS identification; such as integrating advanced techniques with the current methods; developing interactive software systems; exploring deep learning and unsupervised machine learning techniques; enhancing datasets and country context; and investigating more unknown factors behind PCOS. Thus, this SLR provides a state-of-the-art paradigm of autonomous PCOS detection which will support significantly efficient clinical assessment, diagnosis and treatment of PCOS.
Keywords	Polycystic ovary syndrome (PCOS) ; Computer-assisted methods ; Systematic literature review (SLR) ; Data synthesis ; Future research scopes ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	306
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Sayma Alam Suha / Muhammad Nazrul Islam

Heliyon, Vol 9, Iss 3, Pp e14518- (2023)

2023

Abstract: Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But ...

Abstract	Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with ‘Gradient Boosting’ classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
Keywords	Polycystic ovary syndrome (PCOS) ; Dominant features ; Machine learning classification ; Stacking ensemble technique ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	006
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique.

Alam Suha, Sayma / Islam, Muhammad Nazrul

Heliyon

2023 Volume 9, Issue 3, Page(s) e14518

Abstract	Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with 'Gradient Boosting' classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
Language	English
Publishing date	2023-03-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e14518
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-19 and black fungus: Analysis of the public perceptions through machine learning.

Imtiaz Khan, Nafiz / Mahmud, Tahasin / Nazrul Islam, Muhammad

Engineering reports : open access

2021 Volume 4, Issue 4, Page(s) e12475

Abstract: While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to ... ...

Abstract	While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID-19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this SVM model was used to predict the class labels of the public tweets (
Language	English
Publishing date	2021-11-14
Publishing country	United States
Document type	Journal Article
ISSN	2577-8196
ISSN (online)	2577-8196
DOI	10.1002/eng2.12475
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Digital health interventions for cervical cancer care

Md Abdur Razzak / Muhammad Nazrul Islam / Md Shadman Aadeeb / Tasfia Tasnim

PLoS ONE, Vol 18, Iss

A systematic review and future research opportunities

2023 Volume 12

Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

Islam, Muhammad Nazrul / Islam, Md Shofiqul / Shourav, Nahid Hasan / Rahman, Iftiaqur / Faisal, Faiz Al / Islam, Md Motaharul / Sarker, Iqbal H

Scientific reports

2024 Volume 14, Issue 1, Page(s) 9884

Abstract: COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, ... ...

Abstract	COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.
MeSH term(s)	Humans ; COVID-19/epidemiology ; COVID-19/psychology ; COVID-19/virology ; Machine Learning ; Male ; Female ; Adult ; Middle Aged ; Bangladesh/epidemiology ; SARS-CoV-2/isolation & purification ; Young Adult ; Anxiety ; Aged ; Adolescent
Language	English
Publishing date	2024-04-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-60504-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Alam Suha, Sayma / Islam, Muhammad Nazrul

Heliyon. 2023 Mar., v. 9, no. 3 p.e14518-

2023

Abstract	Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with 'Gradient Boosting' classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
Keywords	diagnostic techniques ; models ; polycystic ovary syndrome ; principal component analysis ; secretion ; Polycystic ovary syndrome (PCOS) ; Dominant features ; Machine learning classification ; Stacking ensemble technique
Language	English
Dates of publication	2023-03
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e14518
Database	NAL-Catalogue (AGRICOLA)

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