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  1. Artikel ; Online: Machine Learning-Based Approaches for Forecasting COVID-19 Cases in Bangladesh

    Satu, Md. Shahriare / Howlader, Koushik Chandra / Islam, Sheikh Mohammed Shariful

    SSRN Electronic Journal ; ISSN 1556-5068

    2020  

    Schlagwörter covid19
    Sprache Englisch
    Verlag Elsevier BV
    Erscheinungsland us
    Dokumenttyp Artikel ; Online
    DOI 10.2139/ssrn.3614675
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel: Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.

    Howlader, Koushik Chandra / Satu, Md Shahriare / Awal, Md Abdul / Islam, Md Rabiul / Islam, Sheikh Mohammed Shariful / Quinn, Julian M W / Moni, Mohammad Ali

    Health information science and systems

    2022  Band 10, Heft 1, Seite(n) 2

    Abstract: Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish ... ...

    Abstract Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.
    Supplementary information: The online version contains supplementary material available at 10.1007/s13755-021-00168-2.
    Sprache Englisch
    Erscheinungsdatum 2022-02-09
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-021-00168-2
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases.

    Satu, Md Shahriare / Khan, Md Imran / Rahman, Md Rezanur / Howlader, Koushik Chandra / Roy, Shatabdi / Roy, Shuvo Saha / Quinn, Julian M W / Moni, Mohammad Ali

    Briefings in bioinformatics

    2021  Band 22, Heft 2, Seite(n) 1415–1429

    Abstract: With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and ... ...

    Abstract With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.
    Mesh-Begriff(e) COVID-19/complications ; COVID-19/virology ; Gene Ontology ; Humans ; Neoplasms/complications ; SARS-CoV-2/isolation & purification ; Signal Transduction ; Transcriptome
    Sprache Englisch
    Erscheinungsdatum 2021-02-04
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2068142-2
    ISSN 1477-4054 ; 1467-5463
    ISSN (online) 1477-4054
    ISSN 1467-5463
    DOI 10.1093/bib/bbab003
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

    Satu, Md Shahriare / Khan, Md Imran / Mahmud, Mufti / Uddin, Shahadat / Summers, Matthew A / Quinn, Julian M W / Moni, Mohammad Ali

    Knowledge-based systems

    2021  Band 226, Seite(n) 107126

    Abstract: COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic ... ...

    Abstract COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.
    Sprache Englisch
    Erscheinungsdatum 2021-05-06
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article
    ISSN 0950-7051
    ISSN 0950-7051
    DOI 10.1016/j.knosys.2021.107126
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches

    Ahammed, Khair / Satu, Md. Shahriare / Abedin, Mohammad Zoynul / Rahaman, Md. Auhidur / Islam, Shiekh Mohammed Shariful

    medRxiv

    Abstract: This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using ... ...

    Abstract This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity (94.03%) and specificity (97.01%) as well as the lowest fall out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-06-08
    Verlag Cold Spring Harbor Laboratory Press
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.06.07.20124594
    Datenquelle COVID19

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  6. Artikel: Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage.

    Akter, Tania / Ali, Mohammad Hanif / Khan, Md Imran / Satu, Md Shahriare / Uddin, Md Jamal / Alyami, Salem A / Ali, Sarwar / Azad, Akm / Moni, Mohammad Ali

    Brain sciences

    2021  Band 11, Heft 6

    Abstract: Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan ... ...

    Abstract Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.
    Sprache Englisch
    Erscheinungsdatum 2021-05-31
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2651993-8
    ISSN 2076-3425
    ISSN 2076-3425
    DOI 10.3390/brainsci11060734
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment inCOVID-19 Tweets

    Satu, Md. Shahriare / Khan, Md. Imran / Mahmud, Mufti / Uddin, Shahadat / Summers, Matthew A / Quinn, Julian M. W. / Moni, Mohammad Ali

    Abstract: COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected ... ...

    Abstract COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance of traditional and TClustVID classification methods. TClustVID showed higher performance compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation.
    Schlagwörter covid19
    Verlag MedRxiv; WHO
    Dokumenttyp Artikel ; Online
    Anmerkung WHO #Covidence: #20167973
    DOI 10.1101/2020.08.04.20167973
    Datenquelle COVID19

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  8. Artikel ; Online: TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment in COVID-19 Tweets

    Satu, Md. Shahriare / Khan, Md. Imran / Mahmud, Mufti / Uddin, Shahadat / Summers, Matthew A / Quinn, Julian M. W. / Moni, Mohammad Ali

    medRxiv

    Abstract: COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected ... ...

    Abstract COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance of traditional and TClustVID classification methods. TClustVID showed higher performance compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation.
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-08-04
    Verlag Cold Spring Harbor Laboratory Press
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.08.04.20167973
    Datenquelle COVID19

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