Browsing by Author "Mahmud, S M Hasan"

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A New Method to Handle Facebook Users in the Distributed Database System
(Scopus, 2020) Rana, Md. Shohel; Hossin, Md Altab; Mahmud, S M Hasan; Jahan, Hosney; Hossen, Md. Anwar
The hasty growth of technology and social media has carried momentous changes to humanoid communication. Facebook, the largest online social media in the last few years has more than 200 million active users where more than 3.5 billion minutes are spent on Facebook daily. Since the competence of Facebook is subject to mostly on the processing of the massive volume of data. The volume of data is increasing day to day as well as the number of inactive and fake users. In this paper, we propose a new model using distributed database concept for management of users and their activities. This proposed mod-el helps to keep the system scalable, reliable, and faster and let the Facebook accessible from anywhere with high accessibility.
Assessing the Effect of Imbalanced Learning on Cross-project Software Defect Prediction
(10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE, 2019-07-08) Sohan, Md Fahimuzzman; Jabiullah, Md Ismail; Rahman, Sheikh Shah Mohammad Motiur; Mahmud, S M Hasan
Software Defect Prediction (SDP) identifies the defect-prone modules from software source code, which helps to serve good quality software. Mostly previous cross-project SDP models were built based on single project data, where single project was used to prepare prediction models. However, this investigation represents an empirical study of SDP where multiple projects data have been used to prepare prediction models. In this study, multiple projects data have been used to prepare a balance and an imbalance datasets. After that this datasets have been used in different prediction models with eight different classifier algorithms. The trained models have been cross-checked by one balanced and imbalanced test datasets. Five evaluation metrics have been considered for evaluating the performance of the models. The experimental results show that there was no significant changes observed between balanced and imbalanced training models. Only AUC (Area Under the Curve) scores have increased significantly in terms of balanced training model with imbalanced test datasets. In the same training model with the balanced test, accuracy and AUC score have increased significantly. However, this study covers widely by creating the classification model from multiple projects' historical data. Further, it proves that if the sufficient number of non-defective and defective data are supplied in the prediction model, it can predict balanced and imbalanced both categories dataset alike. Here, recommendation will be to consider the imbalanced learning while building the prediction model for cross-projects.
Bioinformatics and System Biology Approach to Identify the Influences of Sars-cov-2 Infections to Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease Patients
(Briefings in bioinformatics, 2021) Mahmud, S M Hasan; Al-Mustanjid, Md; Akter, Farzana; Rahman, Md Shazzadur; Ahmed, Kawsar; Rahman, Md Habibur; Chen, Wenyu; Moni, Mohammad Ali
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.
CSV-ANNOTATE: Generate annotated tables from CSV file
(IEEE, 2018-06-28) Mahmud, S M Hasan; Hossin, Md Altab; Jahan, Hosney; Noori, Sheak Rashed Haider; Bhuiyan, Touhid
The Semantic Web is a part of the current World Wide Web (WWW), which can facilitate a common mechanism to publish, share, and reuse data beyond the boundaries of web applications. It is widely believed that the majority of the datasets stored on the current web are in tabular data format (CSV, spreadsheets, SQL dumps, HTML tables etc), commonly in the comma-separated values (CSV) format. In order to prepare the CSV data semantically structured, interoperable, accessible and reusable for various web applications, they need to be extracted from the CSV files and converted into annotated table. Therefore, we propose an effective approach to generate annotated tables from CSV file. However, annotated table for CSV provides possibilities for data publishers to refer data validating, converting, displaying and inputting by following the Semantic Web standard. This research presents the conversion strategies of CSV file into annotated tables. Here, we design a parsing algorithm and development techniques to demonstrate the annotated tabular data model (column, row, and cell). An experiment is carried out to observe and compare the time efficiency of the annotation process. This method and findings provide a valuable reference for potential implementers to further operate the Semantic data.
Csv2rdf: Generating rdf data from csv file using semantic web technologies
(Journal of Theoretical and Applied Information Technology, 2018-10-31) Mahmud, S M Hasan; Hossin, M.A.; Jahan, H.; Noori, Sheak Rashed Haider
Recently, a large amount of Governments and public administrations data are stored on the Web in various file formats, mostly in the tabular data form such as Comma Separated Values (CSV) or Excel. CSV format is simple and practical, but it is difficult to express the relevant metadata such as data provenance, meaning of data fields, relationships between data fields, and user access approaches/rights, etc. In order to make the CSV data semantically structured, interoperable, accessible and reusable for various Web applications, they need to be extracted from the CSV files and converted into the Resource Description Framework (RDF) format that provides superior data assimilation and query functionality. In this paper, we focus on how the Semantic Web technologies are used to convert CSV data into RDF. Therefore, we present a method and techniques to parse the CSV file; the parsed CSV data are complemented with metadata annotations to generate the annotated tabular data model which is then converted into RDF triples. According to the conceptual correspondences between the CSV data model and RDF data model, we designed a set of algorithms to generate RDF triples from the CSV data. Our developed prototype tool, CSV2RDF, is used for evaluating the performance of the proposed method through real-world CSV datasets. The implementation and experimental outcomes demonstrate that our pro-posed method is feasible to generate RDF data from CSV datasets, with satisfactory performance on any size of data sets.
Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier
(Tech Science Press, 2023-10-08) Mahmud, S M Hasan; Ali, Md Mamun; Shahriar, Mohammad Fahim; Al-Zahrani, Fahad Ahmed; Ahmed, Kawsar; Nandi, Dip; Bui, Francis M.
Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build a predictive model, open-source data was collected where five stages of images of AD were available as Cognitive Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and AD. Every stage of AD is considered as a class, and then the dataset was divided into three parts binary class, three class, and five class. In this research, we applied different preprocessing steps with augmentation techniques to efficiently identify AD. It integrates a random oversampling technique to handle the imbalance problem from target classes, mitigating the model overfitting and biases. Then three machine learning classifiers, such as random forest (RF), K-Nearest neighbor (KNN), and support vector machine (SVM), and two deep learning methods, such as convolutional neuronal network (CNN) and artificial neural network (ANN) were applied on these datasets. After analyzing the performance of the used models and the datasets, it is found that CNN with binary class outperformed 88.20% accuracy. The result of the study indicates that the model is highly potential to detect AD in the initial phase.
Identification of Biomarkers and Pathways for the Sars-cov-2 Infections That Make Complexities in Pulmonary Arterial Hypertension Patients
(Briefings in Bioinformatics, 2021-03) Taz, Tasnimul Alam; Ahmed, Kawsar; Paul, Bikash Kumar; Al-Zahrani, Fahad Ahmed; Mahmud, S M Hasan; Moni, Mohammad Ali
This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients’ lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein–protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.
Identification of Biomarkers and Pathways for the Sars-cov-2 Infections That Make Complexities in Pulmonary Arterial Hypertension Patients
(Briefings in Bioinformatics, 2021-02-22) Taz, Tasnimul Alam; Ahmed, Kawsar; Paul, Bikash Kumar; Al-Zahrani, Fahad Ahmed; Mahmud, S M Hasan; Moni, Mohammad Ali
This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients’ lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein–protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.
PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors
(Springer Nature, 2023-06-11) Mia, Md. Rajib; Rahman, Md. Ashikur; Ali, Md. Mamun; Ahmed, Kawsar; Bui, Francis M.; Mahmud, S M Hasan
Chronic Kidney Disease (CKD) is major concern of death in recent years that can be cured by early treatment and proper supervision. But early detection of CKD and exact risk factors should be known to ensure proper treatment. The study mainly aims to address the issue by building a predictive model and discovers the most significant risk factors employing machine learning (ML) approach for CKD patients. Four individual machine learning classifiers were applied to conduct this study. It is found that GB performed very poor compare to other applied classifiers where RF and LightGBM outperformed with 99.167% accuracy. In terms of risk factors, it is found that sg, hemo, sc, pcv, al, rbcc, htn, dm, bgr, and sod are the most significant factors, which are mainly correlated with CKD. The study and its findings indicate that it will enable patients, doctors and clinicians to identify CKD patients early and ensure proper treatment for them.