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Book: Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis

Pani, Subhendu Kumar / Dash, Sujata / Flammini, Francesco / Chan Bukhari, Syed Ahmad / Dos Santos, Wellington P.

2021

Author's details	Subhendu Pani is Professor and Principal at Krupajal Computer Academy, Odisha, India. His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He has been published in more than 150 international publications, five authored books, fifteen edited and forthcoming books, and twenty book chapters. He is a fellow in SSARSC and life member in IE, ISTE, ISCA, OBA, OMS, SMIACSIT, SMUACEE, and CSI. § Sujata Dash is Associate Professor of Computer Science at North Orissa University in the Department of Computer Application, Baripada, India. She is a recipient of Titular Fellowship from Association of Commonwealth Universities, UK. She has worked as a visiting professor of Computer Science Department of University of Manitoba, Canada. She has published more than 170 technical papers. Wellington P. dos Santos is Associate Professor, Department of Biomedical Engineering, Federal University of Pernambuco (UFPE), Recif
Keywords	Computational Intelligence ; Smart sensing ; Big data analytics ; Internet of Health Things ; Cognition computing ; Predictive modeling ; COVID-19 ; computational modelling ; computational Intelligence ; pandemics ; Predictive Modeling
Language	English
Size	432 p.
Edition	1
Publisher	Springer International Publishing
Document type	Book
Note	PDA Manuell_12
Format	160 x 241 x 29
ISBN	9783030797522 ; 303079752X
Database	PDA

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Article ; Online: A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms

E. Syed Mohamed / Tawseef Ahmad Naqishbandi / Syed Ahmad Chan Bukhari / Insha Rauf / Vilas Sawrikar / Arshad Hussain

Healthcare Analytics, Vol 3, Iss , Pp 100185- (2023)

2023

Abstract: The prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian ... ...

Abstract	The prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian government to impose safety precautions such as lockdowns and communication ban. Consequently, the region of Kashmir experienced a marked rise in anxiety as a result of these lifestyle changes. Machine learning has proven useful in the early diagnosis and prognosis of certain diseases. Therefore, this study aims to classify anxiety problems early by utilising a pre-clinical mental health dataset collected after the abrogation of article 370 in Kashmir. The first part of the paper aims at developing and implementing a prediction model based on classification into one of the five pre-clinical anxiety stages, i.e., Stage 1: minimal anxiety, Stage 2: mild anxiety, Stage 3: moderate anxiety, Stage 4: severe anxiety, and Stage 5: very severe anxiety. The second part offers recommendations for those suffering from anxiety disorders. Feature selection and prediction are used to predict the correct stage of anxiety for best possible medical intervention. Three different algorithms: Support Vector Machine(SVM), Multilayer Perceptron (MLP), and Random Forest (RF), are employed for predicting anxiety stages. Among them, random forest (RF) achieved 98.13% accuracy. A forecasted likelihood condition was assessed to provide a suitable recommendation. Further, accuracy and kappa statistics are used to assess the performance of the suggested model, which offers a significant addition to predicting anxiety early, and exhibits high prediction and recommendation accuracy. This study aims to assist mental health professionals and experts in making quick and accurate choices.
Keywords	Mental health ; Machine learning ; Prediction ; Support Vector Machine ; Multilayer Perceptron ; Random Forest ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling.

Tummala, Sudhakar / Kadry, Seifedine / Bukhari, Syed Ahmad Chan / Rauf, Hafiz Tayyab

Current oncology (Toronto, Ont.)

2022 Volume 29, Issue 10, Page(s) 7498–7511

Abstract: The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain ... ...

Abstract	The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model's ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model's ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief.
MeSH term(s)	Humans ; Magnetic Resonance Imaging/methods ; Brain Neoplasms/diagnostic imaging ; Glioma ; Neural Networks, Computer
Language	English
Publishing date	2022-10-07
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1236972-x
ISSN	1718-7729 ; 1198-0052
ISSN (online)	1718-7729
ISSN	1198-0052
DOI	10.3390/curroncol29100590
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Article: Modifiable risk factors and overall cardiovascular mortality: Moderation of urbanization.

Sajid, Mirza Rizwan / Muhammad, Noryanti / Zakaria, Roslinazairimah / Bukhari, Syed Ahmad Chan

Journal of public health research

2020 Volume 9, Issue 4, Page(s) 1893

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2020-11-17
Publishing country	Italy
Document type	Journal Article
ISSN	2279-9028
ISSN	2279-9028
DOI	10.4081/jphr.2020.1893
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey.

Zafar, Mehwish / Sharif, Muhammad Imran / Sharif, Muhammad Irfan / Kadry, Seifedine / Bukhari, Syed Ahmad Chan / Rauf, Hafiz Tayyab

Life (Basel, Switzerland)

2023 Volume 13, Issue 1

Abstract: The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells ...

Abstract	The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.
Language	English
Publishing date	2023-01-04
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662250-6
ISSN	2075-1729
ISSN	2075-1729
DOI	10.3390/life13010146
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A linked data graph approach to integration of immunological data.

Bukhari, Syed Ahmad Chan / Mandell, Jeff / Kleinstein, Steven H / Cheung, Kei-Hoi

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

2020 Volume 2019, Page(s) 1742–1749

Abstract: Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL ( ... ...

Abstract	Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.
Language	English
Publishing date	2020-02-06
Publishing country	United States
Document type	Journal Article
ISSN	2156-1125
ISSN	2156-1125
DOI	10.1109/bibm47256.2019.8982986
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: LinkedImm: a linked data graph database for integrating immunological data.

Bukhari, Syed Ahmad Chan / Pawar, Shrikant / Mandell, Jeff / Kleinstein, Steven H / Cheung, Kei-Hoi

BMC bioinformatics

2021 Volume 22, Issue Suppl 9, Page(s) 105

Abstract: Background: Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are ... ...

Abstract	Background: Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Results: We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. Conclusion: We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.
MeSH term(s)	Databases, Factual ; Information Storage and Retrieval ; Language ; Semantic Web ; Systems Biology
Language	English
Publishing date	2021-08-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04031-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing.

Sadad, Tariq / Bukhari, Syed Ahmad Chan / Munir, Asim / Ghani, Anwar / El-Sherbeeny, Ahmed M / Rauf, Hafiz Tayyab

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 1672677

Abstract: Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The ... ...

Abstract	Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.
MeSH term(s)	Bayes Theorem ; COVID-19/diagnosis ; Cardiovascular Diseases/diagnosis ; Cloud Computing ; Humans ; Machine Learning ; Pandemics ; Photoplethysmography/methods
Language	English
Publishing date	2022-08-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/1672677
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Article ; Online: Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.

Sajid, Mirza Rizwan / Khan, Arshad Ali / Albar, Haitham M / Muhammad, Noryanti / Sami, Waqas / Bukhari, Syed Ahmad Chan / Wajahat, Iram

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 5475313

Abstract: Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by ... ...

Abstract	Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.
MeSH term(s)	Cardiovascular Diseases ; Humans ; Machine Learning ; Neural Networks, Computer ; Risk Factors ; Supervised Machine Learning ; Support Vector Machine
Language	English
Publishing date	2022-05-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/5475313
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: LinkedImm

Syed Ahmad Chan Bukhari / Shrikant Pawar / Jeff Mandell / Steven H. Kleinstein / Kei-Hoi Cheung

BMC Bioinformatics, Vol 22, Iss S9, Pp 1-

a linked data graph database for integrating immunological data

2021 Volume 14

Abstract: Abstract Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases ... ...

Abstract	Abstract Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Results We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. Conclusion We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.
Keywords	Ontology ; Knowledgebase ; Graph database ; Immunology ; Influenza vaccine ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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