You can find all lab software/simulations/analysis code repositories at Github following the link: Git

Research Students GitHub Projects: Student-GitHub-Projects

Important & Interesting project mentions:

• Android utility app: This project was focused on development of android application with several use case utilities ####https://github.com/spawar2/Android-Utility-App-#### [Java: android.content.Context, android.support.test.InstrumentationRegistry, android.support.test.runner.AndroidJUnit4, org.junit.Test, org.junit.runner.RunWith] https://csds.gsu.edu/^^^selected method(actionPerformed, actionPerformed, addOutput, addInput, mouseClicked, evaluateBoolExpr). Date created/updated: December, 9, 2024.

• A PHP, MySQL based online shopping portal: This project was focused on development of online shopping portal similar to Amazon and ebay Demo ####https://github.com/spawar2/Final_Project_PHP_SQL####[PHP, CSS, HTML, JavaScript: mysql_import.sql]^^^Cascading Style Sheets (CSS), JavaScript, Structured Query Language (SQL) database, Hypertext Preprocessor (PHP) application. selected function(ObjectProperty, reset, login, loginBtn, createBtn, analysis, logOut, invalidLogin, invalidForm, cv2.VideoCapture, imwrite, imread, str, imshow, load, get_user, validate, add_user). Date created/updated: December, 9, 2024.

• Machine learning for selection and classification of HIV PR resistance: [*2018:1] [**2018:9, 10,11]This project was focused on applying neural network architecture on HIV deep sequencing data BioMed Central, IF=3.1 BMC Bioinformatics Paper. ####, collaboration with Dr. Harrison and Dr. Weber, Georgia State University, Atlanta, USA. https://csds.gsu.edu/profile/robert-harrison/ https://cas.gsu.edu/profile/irene-weber/ [MATHLAB, R, Python: numpy, sklearn.model, pylab, sklearn.decomposition], Plot_Clustering (Autosaved).Rd: HIV-1 protease deep Ribonucleic acid sequencing data read, K Means, Hierarchial, AGNES: Agglomerative Nesting TRY DIVISIVE Clustering, Selecting optimum number of clusters using elbow method, clusGap, clustering vectors to export. SVM.py: Support vector machine (SVM), principal component analysis (PCA). selected function(genfromtxt, train_test_split, svm.SVC, PCA, svmClassifier_2d.predict, np.meshgrid, Z.reshape, knn, kmeans, clusGap, Mclust, NbClust, dist, cutree, agnes, sapply). 1, 2, 3, DOI: 10.1186/s12859-018-2331-y, Issue: 11, Volume: 19, Pages: 262.  4, https://github.com/spawar2/HIV_Machine_Learning_Techniques#### PUBLICATION: Analysis of drug resistance in HIV protease Poster Link, Poster Link^^^(The research was supported in part by the National Institutes of Health (NIH) grant GM062920 (ITW & RWH)). Testing: Inhibitor, Accuracy, Postive Prediction Value (PPV), Recall, F, Indinavir (IDV): 0.979, 0.974, 0.985, 0.979. Date created/updated: December, 9, 2024.

• Java-platform-to-create-logic-circuit to evaluate-bolean-expression: This project was focused on developing a java platform to evaluate bolean expressions from computer architecture ####https://github.com/spawar2/Computer-Architecture–Java-platform-to-create-logic-circuit–evaluate-bolean-expression#### https://csds.gsu.edu/^^^DOI: 10.4155/fsoa-2018-0054, Issue: 4, Volume: 9, Pages: FSO330. Date created/updated: December, 9, 2024.

• Java_SQL_MiceExperiment_Record_Tracking_Software: This project was focused on development of a java gui with record tracking features Demo, collaboration with Dr. Batra, Georgia State University, Atlanta, USA  https://www.linkedin.com/in/harshul-batra/####https://github.com/spawar2/Java_SQL_MiceExperiment_Record_Tracking_Software                     App, Record_DB.sql, Record_System_tabs.java: Record System classes, Structured Query Language (Mysql) connection. selected method (Record_System_tabs, getconnection, show_users_in_jtable, jButton5ActionPerformed, jComboBox1ActionPerformed, jTable1MouseClicked, users). #### [Java: javax.swing.JFrame, javax.swing.JOptionPane, javax.swing.table.DefaultTableModel, javax.swing.table.TableModel, java.sql., javax.swing., net.proteanit.sql.DbUtils, java.sql.Connection, java.sql.Statement, java.sql.SQLException, java.util.ArrayList] Future Science OA PUBLICATION: RodentSQL: a software suite for colony management of animal protocols^^^ Date created/updated: December, 9, 2024.

• A PHP, MySQL based electronic record keeping cloud system: This project was focused on developing electronic record keeping cloud system Demo ####https://github.com/spawar2/Electronic-Cloud-Notebook–PHP–MySQL–Apache####, collaboration with Dr. Batra, Georgia State University, Atlanta, USA. https://www.linkedin.com/in/harshul-batra/ Journal of Engineering and Technology ResearchPUBLICATION: Online electronic laboratory notebook: A secured cloud storage system scripted in Hypertext Pre-processor (PHP) programming language^^^DOI: https://doi.org/10.5897/JETR2018.0638, Issue: 10, Volume: 1, Pages: 1-6. Date created/updated: December, 9, 2024.

• Glycan-to-DNA-Mapping-Web-Server: This project was focused on developing a python based web platform to handle Glycan-to-DNA-Mapping quiries Demo, [Python], collaboration with Dr. Shukkor, Georgia State University, Atlanta, United States of America (USA).  ####https://github.com/spawar2/Glycan-to-DNA-Mapping-Web-Server#### bioRxiv PUBLICATION: DNA Encoded Glycan Libraries as a next-generation tool for the study of glycan-protein interactions^^^Digital Objecy Identifier (DOI): https://doi.org/10.1101/2020.03.30.017012, Issue, Volume, Pages. DOI: https://doi.org/10.1007/978-3-030-17935-9_1, Issue, Volume, Pages: pp 3–14. Date created/updated: December, 9, 2024.

• Patient flow simulation in EMS department: This project was focused on developing a devs-java based EMS simulation to address ED over crowding Demo, [Java: DEVSJAVALab, simView., java.awt., java.io., genDevs.modeling., genDevs.simulation., GenCol.]####https://github.com/spawar2/Devs_Java_Patient_Flow_SimulationinEMS####, collaboration with Dr. Stanam, University of Iowa, Iowa City, USA. https://www.linkedin.com/in/aditya-stanam-07bbb819/ Springer Bioinformatics and Biomedical Engineering, selected method(actionPerformed, actionPerformed, addOutput, addInput, mouseClicked, evaluateBoolExpr). PUBLICATION: Developing a DEVS-JAVA Model to Simulate and Pre-test Changes to Emergency Care Delivery in a Safe and Efficient Manner^^^ Date created/updated: December, 9, 2024.

• Head-Neck-Cancer–ROC–SVM–KM–Expression-Analysis: This project was focused on support vector machine analysis of HNC patients ####https://github.com/spawar2/Head-Neck-Cancer–ROC–SVM–KM–Expression-Analysis####, collaboration with Dr. Stanam, University of Iowa, Iowa City, USA. https://www.linkedin.com/in/aditya-stanam-07bbb819/ Springer Journal of Maxillofacial and Oral Surgery PUBLICATION: A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients^^^DOI: 10.1007/s12663-019-01187-z, Issue: 2, Volume: 18, Pages: 320-327. Testing: Prediction_train alive dead alive 34 0 dead 1 1 ((34+1)/(nrow(training)))*100 [1] 97.22222. Date created/updated: December, 9, 2024.

• Interleukin_Pathway_Survival_Gene_Expression_Analysis: [*2019:10]. [**2019: 8].This project was focused on gene expression analysis of Interleukin_Pathway genes [R: Biobase, GEOquery, survminer, survival], Abstract data.R: Pancreatic cancer, Head and Neck Cancer, Ovarian patients Microarray data read, Microarray data read, robust multi array (RMA) Normalization, survival Kaplan Meir (KM) Analysis. selected function(getGEO, normalize.quantiles, merge, cluster_analysis, hclust, Kmeans, mas5, rowMeans, randomForest, survfit, chisq.test, pData, rep, colnames, factor, eBayes, decideTests, topTable, read.tree, plot, str, write.tree, library, setwd, ReadAffy, exprs, read.csv, read.delim, write.table, roundPhylogram, unroot, str, write.tree, RMA, read.table, BGmix, ccParams, TailPP, ccTrace, histTailPP, FDRplotTailPP, histccPred, plotFDR, plotPredChecks, exprSet). Collaboration with Dr. Stanam, University of Iowa, Iowa City, USA.DOI: https://doi.org/10.1016/S2665-9913(23)00098-X, Issue: 5, Volume: 6, Pages: e316-e329. https://www.linkedin.com/in/aditya-stanam-07bbb819/ https://sites.gsu.edu/bgsa/ ####https://github.com/spawar2/Interleukin_Pathway_Survival_Gene_Expression_Analysis#### American Association of Cancer Research AACR PUBLICATION: Predicting the prognosis for cancer patients with interleukins gene expression level Poster Link^^^ Date created/updated: December, 9, 2024.

• Face-Recognition-Python: This project was focused on utilizing open CV python for facial recognition [Python: cv2, sys]. ####https://github.com/spawar2/Face-Recognition-Python####^webcam.py: Data read: OpenCV video capture. selected function(cv2.VideoCapture, cv2.CascadeClassifier, faceCascade.detectMultiScale, imwrite, imread, str, imshow, print, draw_bbox, build, MaskRCNN, model.load_weights, VideoCapturemodel.detect, cap.release, cv2.destroyAllWindows). Date created/updated: December, 9, 2024.

• Forcasting-pipelines: This project was focused on developing 3 Forcasting-pipelines with time series data [R: ForeCA] ####https://github.com/spawar2/Forcasting-pipelines#### Springer Proceedings of Sixth International Congress on Information and Communication Technology PUBLICATION: Techniques of Time Series Modeling in Complex Systems^^^Forecasting.R: Data read, apply ForeCA, Correlation analysis. selected function(re.search,getROC_AUC, plot, as.numeric, sapply, as.numeric, unlist, svm, predict, Surv, survfit, surv_pvalue, foreca,ts).DOI: https://doi.org/10.1016/S2665-9913(23)00098-X, Issue: 5, Volume: 6, Pages: e316-e329. Date created/updated: December, 9, 2024.

• Random-Forest-for-Ovarian-Breast-Cancer-Patients: [**2023: 1].This project was focused on developing a biomarker utilizing random forest technique [R: GEOquery, Biobase, preprocessCore, multiClust], README.md: Breast, Ovarian, Colon, Lung cancer Microarray data read, quantile Normalization, data Test-Train Split, Neural, cluster_analysis function for KMEANS ANALYSIS, Hierarchial, Random Forest, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function(getGEO, normalize.quantiles, merge, cluster_analysis, hclust, Kmeans, mas5, rowMeans, randomForest, survfit, chisq.test, pData, rep, colnames, factor, eBayes, decideTests, topTable, read.tree, plot, str, write.tree, library, setwd, ReadAffy, exprs, read.csv, read.delim, write.table, roundPhylogram, unroot, str, write.tree, RMA, read.table).  ####https://github.com/spawar2/Random-Forest-on-Ovarian-Breast-Cancer-Patients####Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Poster Link, Presentation video, PPT^ Date created/updated: December, 9, 2024.

• GeneSearch: This project was focused on developing a java gui to search genes from public repositories Java ####https://github.com/spawar2/GeneSearch####, collaboration with Dr. Chung-dar Lu, Georgia State University, Atlanta, USA. https://www.researchgate.net/profile/Chung-Dar-Lu^^^ Date created/updated: December, 9, 2024.

• Clustering_Analysis_Cancer_Techniques [*2020:5], [**2020:6]: This project was focused on developing a biomarker tool utilizing clustering techniques [R: GEOquery, Biobase, preprocessCore, multiClust, ctc, gplots, dendextend, graphics, grDevices, amap] ####https://github.com/spawar2/Clustering_Analysis_Cancer#### 1, 2, 3,  Springer Bioinformatics and Biomedical Engineering PUBLICATION: Clustering Reveals Common Check-Point and Growth Factor Receptor Genes Expressed in Six Different Cancer Types, Poster Link, Presentation video, PPT^^^Cancer_Clustering.R: Breast, Colon, Lung, Oesophageal, Multiple Myeloma, Ovarian Microarray data read, robust multi array (RMA) Normalization, Kmeans analysis, Hierarchal clustering, Plotting. selected function(merge, cluster_analysis, hclust, cutree, rbind, heatmap.2, setwd, read.csv, library, set.seed, sample.split, subset, na.omit, scale, svm, predict, table, plot). DOI: https://doi.org/10.1016/S2665-9913(23)00098-X, Issue: 5, Volume: 6, Pages: e316-e329. Date created/updated: December, 9, 2024.

• Electric scooter for overcoming transportation and pollution problems: [*2021:4], ####https://devpost.com/software/ebird####. Prototype selected for Collegiate Cup Competition and TYE University Competition Qualifying Round 2019 and Entrepreneurship and Innovation Institute (ENI) Mentor Program PUBLICATION: Springer Sustainable Intelligent Systems, Poster Link^^^ Date created/updated: December, 9, 2024.

• Random_Forest_Classfication_Renal_Cell_Carcinoma: This project was focused on developing a biomarker tool utilizing random forest [R: forcats, randomForest] ####https://github.com/spawar2/Random_Forest_Renal_Cell_Carcinoma####, collaboration with Dr. Lahiri, Sunway University, Malaysia.  https://scholar.google.co.in/citations?user=cZaBPOoAAAAJ&hl=en https://github.com/rushikeshchopaderc https://in.linkedin.com/in/rushikesh-chopade-88470615b. This project in collaboration with Rushikesh Chopade Undergraduate student: Indian Institute of Technology (IIT), Kharagpur, India. Presentation video, PPT, Association for Computing Machinery ACM PUBLICATION: Machine learning for identification and characterization of molecular gene signatures in progression of benign tumors^Grant_Analysis.R: Renal Cell cancer Microarray data read, Microarray data read, robust multi array (RMA) Normalization, data Test-Train Split, Neural, randomForest, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function(neuralnet, colMedians, do.call, compute, randomForest, predict, confusionMatrix, svm, ggplot, predict, table, factor, glm, wald.test, as.numeric, sum, maxmindf, na.omit, rbind, mean). DOI: https://doi.org/10.1145/3469213.3469214, Issue, Volume, Pages: 1-3. Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Date created/updated: December, 9, 2024.

• COVID19-Temperature-Dashboard: This project was focused on understanding relationship between COVID19-Temperature medRxiv Paper, Demo, [R:shiny, highcharter, dplyr, UI, Server] ####https://github.com/spawar2/COVID19-Temperature-Dashboard####https://yalegenomics.shinyapps.io/deployment/?_ga=2.91441946.1803750112.1677552931-1693957918.1677552931^app.R: Shiny user interface for coronavirus-19 incidence, prevalence, deaths cases & Temperature. selected function(hcmap, fluidPage, renderHighchart, shinyApp). DOI: https://doi.org/10.1016/S2214-109X(20)30489-7, Issue: 2, Volume: 9, Pages: e144-e160. Date created/updated: December, 9, 2024.

• Regression-Analysis-Alzheimers-Disease: This project was focused on utilizing machine learning tools for feature classification in Alzheimers, [R: MASS, car, glm.predict] ####https://github.com/spawar2/Regression-Alzheimers-Disease####^Regression-Alzheimers-Disease.R: Read variables Subject (PTID) Participant ID RID Participant roster ID Image.Data.ID MRI ID Modality Image type Visit 1=screening scan Acq.Date MRI date DX.bl Diagnosis at baseline EXAMDATE Examination Date AGE Age at baseline PTGENDER Sex PTEDUCAT Years of Education PTETHCAT Ethnicity PTRACCAT Race APOE4 APOE4 genotype MMSE MMSE score for Alzheimers Disease, FIT ORDINAL REGRESSION, perform predictions. selected function(factor, glm.predict, ordinal.fit). Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Date created/updated: December, 9, 2024.

• Image augmentations: [Python: keras.datasets, mnist, models, layers, tensorflow, Sequential, Dense, Conv2D, Dropout, Flatten, MaxPooling2D]. #####https://github.com/spawar2/NLP-Challenge#####^^^Problem_3.py, Problem_4.py: MNIST database of handwritten digits has a training set of 60,000 examples data read, Horizontal and Vertical Flip Augmentation to generate mirror images, Reshaping and Normalizing the Images, Creating a Sequential Model and adding the layers, Convolution neural network training, evaluation: accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, K-means, evaluation: Homogeneity, Completeness, V-measure, Silhouette Coefficient. selected function(pd.read_csv, ImageDataGenerator, tf.keras.Sequential , model.evaluate_generator, cv2.imread, cv2.cvtColor, cv2.resize, tf.keras.models.load_model, np.argmax, cv2.ellipse). Date created/updated: December, 9, 2024.

• Machine Learning Classification Pulmonary-Embolism-Master: This project was focused on utilizing machine learning tools for feature classification in Embolism [Python: numpy, sklearn, pandas, os, MATPLOTLIB]. ####https://github.com/spawar2/Pulmonary-Embolism-Master####^^^Pulmonary-Embolism-Master.py: Pulmonary Embolism Variable read Subject (PTID) Participant ID RID Participant roster ID Image.Data.ID MRI ID Modality Image type Visit 1=screening scan Acq.Date MRI date DX.bl Diagnosis at baseline EXAMDATE Examination Date AGE Age at baseline PTGENDER Sex PTEDUCAT Years of Education PTETHCAT Ethnicity PTRACCAT Race Wells Score, 1-hot encoding, Train/Test Split, Logistic Regression, Random Forest, K nearest neighbors KNN, neural nets, prediction Evaluation Metrics: accuracy, precision, sensitivity, specificity, fscore. selected function(LogisticRegression, RandomForestClassifier, KNeighborsClassifier, MLPClassifier, log_clf.fit, log_clf_preds). Date created/updated: December, 9, 2024.

• DARTO: Program to identify orthologs for genes:  [Python: os, Bio.Blast, ssl, tkinter, askopenfilename, filedialog]. ####https://github.com/spawar2/DARTO####, collaboration with Dr. Lahiri, Sunway University, Malaysia. https://scholar.google.co.in/citations?user=cZaBPOoAAAAJ&hl=en^^^DARTO.py: Read the fasta file. Delta-BLAST 100 hits, BLASTp 1000 hits, RefSeq, tBLASTn, BLASTx, BLASTn as Database. selected function(UploadAction, NCBIWWW.qblast, NCBIXML.parse). Date created/updated: December, 9, 2024.

• Neural-Networks-for-Ovarian-Carcinomas: [*2023:1], [**26]. This project was focused on utilizing machine learning tools for feature classification in Ovarian-Carcinomas:  1, 2, 3, [R: curatedOvarianData, tidyverse, boot, plyr, e1071].  ####https://github.com/spawar2/Neural-Networks-for-Ovarian-Carcinomas####, collaboration with Dr. Lahiri, Sunway University, Malaysia. https://scholar.google.co.in/citations?user=cZaBPOoAAAAJ&hl=en Wileys Chemical Biology and Drug Design, PUBLICATION: Common cancer biomarkers of breast and ovarian types identified through artificial intelligence                             Presentation video, PPT^^ML-Ovarian-Carcinoma.R: Ovarian Microarray data read, robust multi array (RMA) Normalization, neuralnet, Support vector Machine classification, evaluation. selected function(neuralnet, colMedians, do.call, compute). DOI: http://dx.doi.org/10.1007/978-981-19-7660-5_7, Issue, Volume, Pages. Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Date created/updated: December, 9, 2024.

• Chest-X-ray-Neural-Nets: This project was focused on utilizing CNN’s for X-ray image classification: https://github.com/spawar2/Chest-X-ray-Neural-Nets [*2024:16;2023:4, 9; 2021: 17, 23], [**23, 29, 30, 37, 42], Demo, Poster Link, [https://github.com/rushikeshchopaderc https://in.linkedin.com/in/rushikesh-chopade-88470615b https://github.com/SurajK7/ https://in.linkedin.com/in/surajkumar1004 This project in collaboration with **Rushikesh Chopade, Presently with ChestAi, https://www.chestai.org/ **Suraj Kumar Undergraduate student: Indian Institute of Technology (IIT), Kharagpur, India. Project: CHEST-AI: AI tool for detection of lung diseases from chest X- ray data (Spring 2021). Poster Springer Computational Vision and Bio-Inspired Computing, Springer Intelligent Sustainable Systems, bioRxiv Paper Paper Paper Paper Paper, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,  https://link.springer.com/chapter/10.1007/978-981-19-9819-5_49 https://link.springer.com/chapter/10.1007/978-981-19-7660-5_7 https://www.researchsquare.com/article/rs-1129014/latest.pdf 1,2,3,4,5,6,7,8,9,1,2,3,4,5,6,7,8, 9, 10, 11, 12,13., https://www.biorxiv.org/content/10.1101/2021.09.21.461307v1.abstract https://dl.acm.org/doi/abs/10.1145/3469213.3469214 http://20.169.253.49:5001/login https://aws.amazon.com/marketplace/seller-profile?id=seller-b6otd3wry7lkk https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/3679/files/2023/10/ChestAi-300×167.png] (National Science Foundation (NSF) South Carolina Established Program for Stem Cooperative Research (SC EPSCoR), AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina (ADAPT), RII Track-1 award funded project Role: Co-PI/co-PI (Principal Investigator), NSF Identifier: 000879633, Award Number: 2242812, (Direct costs, Effort=100%)) [Python: fastai.vision, torchvision.models, pandas, Path], collaboration with Dr. Narayanan, Johnson & Johnson, Pennsylvania, USA, Presentation video, PPT, PPT^^All_conditions.ipynb:'Cardiomegaly','Emphysema','Effusion','Hernia','Nodule','Pneumothorax','Atelectasis','Pleural_Thickening','Mass','Edema','Consolidation','Infiltration','Fibrosis','Pneumonia' X-ray imaging data read, Test-Train Split, Neural, plotting, noise removal, Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. function(cv2.VideoCapture, imwrite, imread, str, imshow, print, draw_bbox, build, MaskRCNN, model.load_weights, VideoCapturemodel.detect, cap.release, cv2.destroyAllWindows) function(pd.read_csv, ImageDataGenerator, tf.keras.Sequential , model.evaluate_generator, cv2.imread, cv2.cvtColor, cv2.resize, tf.keras.models.load_model, np.argmax, cv2.ellipse). selected function(pd.read_csv, plt.figure, plt.Circle, plt.pie,plot_hist, add_gaussian_noise, Model, model.fit, evaluate_model, network, confusion_matrix, model.predict, model.load_weights, model.compile, model.add, model.summary, plot_confusion_matrix, Sequential, binary_accuracy, feature_string, print_val_score). DOI: https://doi.org/10.1101/2021.09.21.461307, Issue, Volume, Pages. Testing:  Score(160px, FE): 0.878; score(160px, FT): 0.879; score(320px, FE): 0.887. https://github.com/spawar2/CNN-X-ray-images/ Date created/updated: December, 9, 2024.

• BASELINe-Data: This project was focused on utilizing Bayesian estimation techniques for B and T cell profiling: Demo https://github.com/spawar2/BASELINe-Data######## [R Shiny: Tidyverse, UI, Server] https://yalegenomics.shinyapps.io/myapp/?_ga=2.91441946.1803750112.1677552931-1693957918.1677552931, collaboration with Dr. Kleinstein, Yale University, New Haven, Connecticut, USA.  https://medicine.yale.edu/lab/kleinstein/^ Date created/updated: December, 9, 2024.

• Single-Cell-RNA-Analysis: This project was focused on single cell analysis techniques: https://github.com/spawar2/Single-Cell-RNA-Analysis & https://github.com/spawar2/Single-Cell-Analysis [Python: pandas, numpy, matplotlib.pyplot, string, anndata, defaultdict, OrderedDict, sklearn.preprocessing, TruncatedSVD], collaboration with Dr. Kozma, Georgia State University, Atlanta, USA. https://loop.frontiersin.org/people/2548907/overview, IF=5.1^^^00_loading_and_preprocessing_scrnaseq_d: Selecting highly variable genes, PHATE plot dimensionality reduction for visualization. makeadata.py: Preprocessing FASTQ files into a sample by gene matrix, Align reads to the reference, Process the BUS file, BUS file into matrix, Normalize per well (CPM), Log1p, Scale columns. selected function(nd, yex, pmap, adata.obs.eval). Date created/updated: December, 9, 2024.

• COVID-SwabSeq-Testing-Run: This project was focused on testing Swab-Seq technique on COVID swab tests: https://github.com/spawar2/COVID-SwabSeq-Testing-Run [R: ggbeeswarm, MASS, speedglm, furrr, readxl, magrittr, tidyverse]^ Date created/updated: December, 9, 2024.

• Object-Recognition-Python: This project was focused on object recognition using OpenCV: https://github.com/spawar2/Object-Recognition-Python [Python: numpy, os, urllib, sys, tarfile, tensorflow, zipfile, cv2, csv, time, collections, defaultdict, StringIO, matplotlib, PIL] Paper Demo^Spotting_Program.py: Data read: OpenCV video capture, Test-Train Split, Neural, plotting, noise removal, Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. open_parking_alert.py, vehicle_detection_main.py: Kivy application development, Mask-RCNN. selected function(cv2.VideoCapture, imwrite, imread, str, imshow, print, draw_bbox, build, MaskRCNN, model.load_weights, VideoCapturemodel.detect, cap.release, cv2.destroyAllWindows). DOI: https://doi.org/10.1101/2021.09.21.461307, Issue, Volume, Pages. Date created/updated: December, 9, 2024.

• Quasi-quantum model for potentization: This project was focused on potentization analysis: https://github.com/spawar2/Shrodinger-Equation [R], Git, PPT^^^ Date created/updated: December, 9, 2024.

• System-Level-Programming: https://github.com/spawar2/System-Level-Programming, https://csds.gsu.edu/^^^test.c, test2.c, stat.c: number of white spaces, Uppercase Letters, Lowercase Letters, Number of words, Number of spaces, Number of vowels, Number of digits, Number of special characters. selected method(count). Date created/updated: December, 9, 2024.

• Data-Structures: https://github.com/spawar2/Data-Structures, https://csds.gsu.edu/^^^[java.util., java.io.]. Wordcount.java: Counts the amount of words in the file. A word can end with a --- space/tab, EOLN character or a punctuation mark (which will be part of the word). Count the amount of lines in the file. Count the amount of alphanumeric characters in the file. Count the number of sentences in the file. Count the amount of vowels in the file - only a, e, i, o, u (upper & lower case) are vowels. Count the amount of punctuations in the file. it outputs a output file with all the above information. Selected method (Record_System_tabs, getconnection, show_users_in_jtable, jButton5ActionPerformed, jComboBox1ActionPerformed, jTable1MouseClicked, users). Date created/updated: December, 9, 2024.

• CS-Introduction: https://github.com/spawar2/CS-Introduction, https://csds.gsu.edu/^^^selected method(GradesAverage). Date created/updated: December, 9, 2024.

• Advanced-Graphic-Algorithms: https://github.com/spawar2/Advanced-Graphic-Algorithms, https://csds.gsu.edu/^^^selected method(processUsingCpu, rgbaToGreyscaleCpu,clSetKernelArg). Date created/updated: December, 9, 2024.

• Fundamentals of Bioinformatics: https://github.com/spawar2/Bioinformatics-scripts, https://csds.gsu.edu/^^^selected function(dna_to_protein, palin, multiply_by_words, codons.dna_to_protein). Date created/updated: December, 9, 2024.

• CellLine Dependency Analysis: https://github.com/spawar2/CellLine-Dependency, https://csds.gsu.edu/^^^Pseudocode.Rd: Read cell lines H1299 and HCT116 dependency single cell headcount data, DESeq differential expression analysis, Mean-Average MA Plotting the expression, Kegg enrichment analysis. selected function(DESeqDataSetFromMatrix, results, DESeq, mapIds, sort, enrichKEGG).

• Anovos feature engineering process to increase efficiency tool contributor:  https://github.com/spawar2/Anovos-Contributor-User^^^

• Gene set enrichment analysis for RNA expression data: https://github.com/spawar2/Fold-Change-Comparisons [R: GenomicFeatures, clusterProfiler, enrichplot, ggplot2]^^^ Date created/updated: December, 9, 2024.

• Kivy app interface development in R: https://github.com/spawar2/Kivy-GUI-R [Python: kivy.app, kivy.lang, Builder, Screen, ObjectProperty, Popup, Label, DataBase]^^^main.py: Kivy User Interface. database.py: backend. user.py: credentials.selected function(ObjectProperty, reset, login, loginBtn, createBtn, analysis, logOut, invalidLogin, invalidForm, cv2.VideoCapture, imwrite, imread, str, imshow, load, get_user, validate, add_user). Date created/updated: December, 9, 2024.

• Structural-variant-Detection tool: https://github.com/spawar2/Structural-variant-Detection, [R]^Plot_SVs.R: Plotting different structural variants SV's for different coverages of Pacbio data, Creating objects with different coverages, Finding the list of common SV's from all uniques throughout. selected function(merge, cluster_analysis, hclust, cutree, rbind, heatmap.2, setwd, read.csv, library, set.seed, sample.split, subset, na.omit, scale, svm, predict, table, plot). Date created/updated: December, 9, 2024.

• Program to Count-relative-frequency-of-amino-acids: https://github.com/spawar2/Count-relative-frequency-of-amino-acids [Python: itertools, product, combinations, Counter]^^^Pawar-assignment.py.txt: get codon for amino acids, create translation_table, prepare the dictionary of translation_table codons, query_codons, prepare dictionary of counts, calculation of frequency, print results. selected function(expanded_code, truncate_list_of_amino_acid, defaultdict, get_codon_for_amino_acids, truncate_list_of_amino_acids).

• Text to voice conversion application: https://github.com/spawar2/Text-to-Voice-Application [Python: os, numpy, shlex, subprocess, sys, wave, deepspeech, Model, printVersions, timeit]^^^ Date created/updated: December, 9, 2024.

• CT brain hemorrhage training: https://github.com/spawar2/CNN_Training [Python: matplotlib.pyplot, torch, time, numpy, collections, OrderedDict, torch.autograd, PIL, lr_scheduler, copy, json, os]^^ct_scan_brain_hemorrhage.py: Computerized Tomography (CT) scan data read, Test-Train Split, Neural, plotting, noise removal, Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function(pd.read_csv, plt.figure, plt.Circle, plt.pie,plot_hist, add_gaussian_noise, Model, model.fit, evaluate_model, network, confusion_matrix, model.predict, model.load_weights, model.compile, model.add, model.summary, plot_confusion_matrix, Sequential). Testing: Score(160px, FE): 0.878; score(160px, FT): 0.879; score(320px, FE): 0.887. A hemorrhage is bleeding from a damaged blood vessel. Many things can cause bleeding inside and outside of your body. Types of hemorrhages range from minor (like a bruise) to major (like bleeding in your brain). Date created/updated: December, 9, 2024.

• Evaluating performance of regression and classification models with prognostic markers in lung carcinomas: https://github.com/spawar2/Regression-Lung-Carcinoma [R: randomForest, caret], 1, 2, 3, 4, collaboration with Dr. Lahiri, Sunway University, Malaysia. https://scholar.google.co.in/citations?user=cZaBPOoAAAAJ&hl=en Presentation video, PPT, Springer Bioinformatics and Biomedical^^ Engineering, Paper, Regression.R: Lung cancer Microarray data read, robust multi array (RMA) Normalization, LOGISTIC REGRESSION, Support vector machine, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC. selected function(neuralnet, colMedians, do.call, compute, randomForest, predict, confusionMatrix, svm, ggplot, predict, table, factor, glm, wald.test, as.numeric, sum). DOI: https://doi.org/10.1007/978-3-031-07802-6_35, Issue, Volume, Pages: 13347.  Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Date created/updated: December, 9, 2024.

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• Hemorhage-Detection-CT-Scan: Poster, [**33], https://github.com/spawar2/Hemorhage-Detection-CT-Scan [https://github.com/CoolSubash https://np.linkedin.com/in/subash-neupane-aa07ba228?trk=public_profile_browsemap This project in collaboration with **Subash Neupane+π, [Python: numpy, pandas, pydicom, matplotlib.pyplot, math, cv2, tensorflow, keras] (Claflin University,  Orangeburg, South Carolina, USA.  Seed award funded project Role: PI, (Direct costs, Effort=100%)) Undergraduate student: Claflin University, Orangeburg, South Carolina, USA. (Fall 2023). Utilization of Machine Learning Techniques for Aiding Detection of Ischemic Stroke Lesion, Infarct Volumes, and Small-artery Occlusion https://www.claflin-computation.com/_files/ugd/81dd80_e12daf8e87c348c5a9347af693993739.pdf]^^inceptionv3-keras-pawar.ipynb, intracranial-hemorrhage-pawar.ipynb, keras-efficientnet-pawar.ipynb: Computerized Tomography (CT) Brain Hemorrhage scan Data read, Test-Train Split, Neural, plotting, noise removal, ImageNet Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. function(cv2.VideoCapture, imwrite, imread, str, imshow, print, draw_bbox, build, MaskRCNN, model.load_weights, VideoCapturemodel.detect, cap.release, cv2.destroyAllWindows) selected function(pd.read_csv, ImageDataGenerator, tf.keras.Sequential , model.evaluate_generator, cv2.imread, cv2.cvtColor, cv2.resize, tf.keras.models.load_model, np.argmax, cv2.ellipse). Testing Accuracy: 0.98. Date created/updated: December, 9, 2024.

• Techniques of brain segmentation from CT scan: https://github.com/spawar2/Image_Segmentation [Python: pydicom, numpy, cv2, os, math, pylab, matplotlib, scipy, ndimage, skimage, morphology]^^ct_scan_brain_segmentation.py: Computerized Tomography (CT) brain hemorrhage scan Data read, Test-Train Split, Neural, plotting, noise removal, image transformation: Padding, Cropping, Masking. inceptionv3-keras-segmented-pawar.ipynb, inceptionv3-keras-unsegmented-pawar.ipynb: Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function(cv2.VideoCapture, imwrite, imread, str, imshow, print, draw_bbox, build, MaskRCNN, model.load_weights, VideoCapturemodel.detect, cap.release, cv2.destroyAllWindows) selected function(pd.read_csv, ImageDataGenerator, tf.keras.Sequential , model.evaluate_generator, cv2.imread, cv2.cvtColor, cv2.resize, tf.keras.models.load_model, np.argmax, cv2.ellipse). Date created/updated: December, 9, 2024.

• Stroke risk stratification with neural nets: [**2023:36], [R: neuralnet] (Claflin University, Orangeburg, South Carolina, USA seed funded project) Role: PI, (Direct costs, Effort=100%), 1, 2, 3, 4, 5, https://github.com/spawar2/Neural-Networks-Stroke [*2024:9][**2024:36] Springer Intelligent Sustainable Systems Paper, Presentation video, PPT^^Neural-Networks-Stroke.R: Stroke variables data read, Transform the data using a max-min normalization technique, Data Test-Train Split, Neural, neuralnet training, Evaluation Metrics: accuracy, precision, sensitivity, specificity, fscore for Hemorrhagic, Ischemic, One sided face, One sided arm, One sided leg, Asymmetry, Not ambulatory, Not able to speak, Visual disturbances, Abnormal sensation, Mental change, and Not able to grasp outcome, Visualization. selected function(neuralnet, colMedians, do.call, compute, randomForest, predict, confusionMatrix, svm, ggplot, predict, table, factor, glm, wald.test, as.numeric, sum, maxmindf). DOI: https://doi.org/10.1007/978-981-99-8031-4_3, Issue, Volume: 812, Pages: pp 21–31. Testing:  Sensitivity = 0.946564885496183 Specificity = 0.745901639344262 fscore = 0.934673366834171 Precision = 0.923076923076923 Accuracy = 0.899029126213592. A stroke happens when there is a loss of blood flow to part of the brain. Your brain cells cannot get the oxygen and nutrients they need from blood, and they start to die within a few minutes. This can cause lasting brain damage, long-term disability, or even death. Date created/updated: December, 9, 2024.

• Nano-particles property prediction with random forests: [R Shiny: MissForest, randomForest, caret, UI, Server] Demo https://github.com/spawar2/RF-ENP#####, collaboration with Dr. Swinton, Kean University, Union, New Jersey, USA https://www.kean.edu/directory/derrick-swintonhttps://yalegenomics.shinyapps.io/appenp/?_ga=2.91441946.1803750112.1677552931-1693957918.1677552931^^ENP-RF-Pawar.Rd: Engineered Nano Particles (ENP) properties, Data read, miss forest imputation for missing values, random forest, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. server.R, UI.R: User interface and backend for R Shiny application. selected function(hcmap, fluidPage, renderHighchart, shinyApp, missForest, randomForest, predict, confusionMatrix). Testing Accuracy : 1, 95% CI : (0.5407, 1), No Information Rate : 0.6667, P-Value [Acc > NIR] : 0.08779, Kappa : 1, Sensitivity : 1.0000, Specificity : 1.0000, Pos Pred Value : 1.0000, Neg Pred Value : 1.0000, Prevalence : 0.3333, Detection Rate : 0.3333, Detection Prevalence : 0.3333, Balanced Accuracy : 1.0000, Date created/updated: December, 9, 2024.

• Blood cancer expression analysis: https://github.com/spawar2/Blood-Cancer-Analysis [This project was in collaboration with Ms. A Agrawal, University of Connecticut, Special Program in Medicine (BS/MD), Experiential Learning Program Project: Identifying biomarkers between lymphoma and leukemia cancer patients (Fall 2021). CT STEM and Norwalk Science Fair (Poster)] [R: Affy, Limma] Title III and Special Initiative Office SEED Funding, Claflin University, Orangeburg, South Carolina, USA: Biomarker Identification for the Diagnosis of Chronic Lymphocytic Leukemia (CLL) (2024): (Direct and indirect costs) Github, Students involved **Lierra Rivera+π Presently doctoral student at Clemson University, Bioengineering Program, **Germari Cull+π, **Mr. Adrian Lockwood+π  (URISE mentor), and https://github.com/WKalynn, https://github.com/lerivera2, https://github.com/AdrianL769, https://github.com/germari, **Kalyn Wesby+π. Role: PI, (Direct costs, Effort=100%), GitHub^^ Date created/updated: December, 9, 2024.

• Claflin University SMART-HOME: https://github.com/spawar2/SMART-HOME-CLAFLIN [**39], Poster ,Poster

​        Poster Poster https://github.com/Claflin-SMART-HOME [R: shiny, DT]######https://yalegenomics.shinyapps.io/app-smart-home/^^

• [https://github.com/eniolla https://www.linkedin.com/in/priscilla-fatokun-35007119a https://github.com/mayor90/thesis https://campuspress.yale.edu/shrikantpawar/files/2023/12/SCICU-research-poster.pptx This project in collaboration with **Mr. Owalabi Oluwamayowa+π (South Carolina Independent Colleges & Universities SCICU award funded project Role: PI, (Direct costs, Effort=100%)),  **Priscilla E. Fatokun+π (South Carolina Independent Colleges & Universities SCICU award funded project), Undergraduate student: Claflin University, South Carolina, USA. (Fall 2023). Project: SMART-HOME proposal “collection and processing of health-care data for African-American subjects from wrist wearable devices. https://campuspress.yale.edu/shrikantpawar/files/2023/12/SCICU-research-poster.pptx https://campuspress.yale.edu/shrikantpawar/files/2024/02/Priscilla-Research-Presentation-0b920a0c624e2982.pptx], collaboration with Dr. Liles, Claflin University, Orangeburg, South Carolina, USA. SMART-HOME.Rd: Vairbale Data read from smart watch, R shiny application for visualization. selected function(hcmap, fluidPage, renderHighchart, shinyApp). Date created/updated: December, 9, 2024. http://www.drkliles.com/STARlab/ https://github.com/Mayor90 https://github.com/eniolla^^

• ChatGPT Testing:  [Python: os, time, datetime, openai], https://github.com/spawar2/ChatGPT-Testing [https://github.com/bpritish https://www.linkedin.com/in/pritishbhave This project in collaboration with P Bhave, Senior Software Engineer, Walmart. Analyze ChatGPT’s codex to determine it’s viability to identify basic issues with provided Python code using OpenAI’s code model, current `code-cushman-001`], [**44], 1, 2, 3, 4, 5, 6, 7, Overview PPT, Presentation video^^chatGPT-testing.py: Initialize the API key for OpenAI, read question, get chatgpt response. selected function(get_chatgpt_response, read_py_files_in_folder). Identification Sensitivity (100%). Date created/updated: December, 9, 2024.

• Prostrate-Cancer-Biomarker-Analysis: https://github.com/spawar2/Prostrate-Cancer-Biomarker-Analysis https://www.linkedin.com/in/zion-ratchford-07514b1b9 This project in collaboration with **Zion Ratchford+π (Thesis Advisor), Undergraduate student: Claflin University, Orangeburg, South Carolina, USA. (Fall 2023). https://campuspress.yale.edu/shrikantpawar/files/2024/04/Zion-Presentation-80fd6d81d5b4f75b-rotated-e1712021318501-225x300.jp, https://campuspress.yale.edu/shrikantpawar/files/2024/01/Final-Prospectus-Edited-7e17c09b3e8c4813.pdf,^^ https://campuspress.yale.edu/shrikantpawar/files/2024/04/ROLE-CIRCULATION-BIOMARKERS-IN-PROSTATE-CANCER-DIAGNOSIS-4dbb44b57f793d6a.pptx [R: edgeR, hgu133plus2.db, tidyverse, Affy]. Prostrate-Cancer.R: Affymetrix microarray data read, robust multi array (RMA) Normalization , Box plotting. selected function(chisq.test, pData, rep, colnames, factor, eBayes, decideTests, topTable, read.tree, plot, str, write.tree, library, setwd, ReadAffy, exprs, read.csv, read.delim, write.table, roundPhylogram, unroot, str, write.tree, RMA, read.table). Date created/updated: December, 9, 2024.

• Linkedimm: BioMed Central BMC Bioinformatics, IF=5.1 Paper https://github.com/spawar2/Linkedimm [Python: plotly, tidyverse, htmlwidgets, plotly, tidyverse, htmlwidgets], collaboration with Dr. Kleinstein, Yale University, New Haven, Connecticut, USA. https://medicine.yale.edu/lab/kleinstein/ Dr. Kei cheung, https://medicine.yale.edu/profile/kei_cheung/?tab=bio^^Linkedimm_Plot.R: Immunne B cells scatterPlot. selected function(ggplot, ggplotly, geom_boxplot, facet_wrap). DOI: https://doi.org/10.1186/s12859-021-04031-9, Issue: 22, Volume: 9, Pages: 105. Date created/updated: December, 9, 2024.

• Institutional Development Award (IDeA) Networks of Biomedical Research Excellence (INBRE) Research Education of Teachers National Science Foundation South Carolina Established Program for Stem Cooperative Research (SC EPSCoR)-RET,: Machine learning applications for studying check-point genes expressed in ten different human cancer types. https://github.com/spawar2/NSF-INBRE-EPSCoR-RET [ https://github.com/ethaharikanaidu This project in collaboration with ******Harika Baidu, teacher at Bamberg Ehrhardt High School (Summer 2022). Machine learning applications for studying check-point genes expressed in ten different human cancer types. Machine learning applications for studying check-point genes expressed in human Ovarian cancers. https://www.claflin-computation.com/_files/ugd/81dd80_1ad94ed63c6a49c887177419bdb46567.docx?dn=Etha%20project%20report.docx] [R: edgeR, hgu133plus2.db, tidyverse]^^selected function(pData, rep, colnames, factor, eBayes, decideTests, topTable, read.tree, plot, str, write.tree, library, setwd, ReadAffy, exprs, read.csv, read.delim, write.table, roundPhylogram, unroot, str, write.tree, RMA, read.table). Date created/updated: December, 9, 2024.

• TF–IDF-term frequency–inverse document frequency: [Python] https://github.com/spawar2/term-frequency-inverse-document-frequency^^^selected function(document.translate, split, Counter, calculate_tf). Date created/updated: December, 9, 2024.

• National Science Foundation SCICU: Natural Language Processing (NLP) for optimizing career advancement prospects: https://github.com/spawar2/NSF-SCICU-2022. Claflin University, Orangeburg, South Carolina, USA student Joshua Kiprano+π (Thesis Advisor), Presently software engineer at Atlassian. (South Carolina Independent Colleges & Universities SCICU award funded project) App Deploy: https://joshuakiplimo-resparse-resumematch-6a8pxp.streamlit.app/ Poster Link^^ Date created/updated: December, 9, 2024.

• Single-Cell-MA-plots-Crustacean-Species: Public Library of Science, IF=5.1 PLoS ONE Paper, [R: Deseq2, ggplots]. https://github.com/spawar2/Single-Cell-MA-plots-Crustacean-Species, collaboration with Dr. Kozma, Georgia State University, Atlanta, USA. https://loop.frontiersin.org/people/2548907/overview^^^MA-Plot.Rd: Next generation sequencing count dataset Mean average MA Plots for different species. selected function(rowMeans, read.delim, complete.cases). DOI: 10.1371/journal.pone.0230266, Issue: 3, Volume: 15, Pages: e0230266. Date created/updated: December, 9, 2024.

• miRNA-analysis: Elsevier Data in Brief Paper https://github.com/spawar2/miRNA-analysis [R: DESeq2, tidyverse], collaboration with Dr. Christain, University of Barcelona, Spain^^^deseq.R: MiRNAMicro ribonucleic acid data sequencing headcount read, DESeq differential expression analysis, Mean-Average MA Plotting the expression, Kegg enrichment analysis. selected function(as.data.frame, DESeqDataSetFromMatrix, results, tbl_df, sizeFactors, dispersions, names, DESeq). DOI: https://doi.org/10.1016/j.dib.2021.107114, Issue, Volume: 36, Pages: 107114. Date created/updated: December, 9, 2024. https://www.ub.edu/farmaco/en/pharmacognosy/research/neuropharmacology_in_aging_and_neurodegeneration/6/

• Internship: https://github.com/spawar2/INTERNSHIP [2024:**41], [This project in collaboration with Yash Singh, Sai and Mohit, Georgia Tech, Atlanta, USA. Biomarker discovery in prostrate, lung and breast cancers (Spring 2024). https://github.com/yashsingh43 https://github.com/AbhayR43 https://github.com/Msai32005 https://github.com/rrchada https://github.com/arnavmadderla] [R: Tidyverse, ggplots]. Poster Link ^^ Date created/updated: December, 9, 2024.

• Traffic-Counter-ML: [Python: os, numpy, cv2, mrcnn.config, mrcnn.model, MaskRCNN, pathlib, twilio.rest, Client, kivy, App, Label, GridLayout, TextInput, Widget, BoxLayout, random], https://github.com/spawar2/Traffic-Counter-ML^^open_parking_alert.py: Data read: OpenCV video capture, split, plotting, noise removal, MASK Convolution neural network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation, KIVY application. selected function(cv2.VideoCapture, imwrite, imread, str, imshow, print, draw_bbox, build, MaskRCNN, model.load_weights, VideoCapturemodel.detect, cap.release, cv2.destroyAllWindows). Date created/updated: December, 9, 2024.

• Calcium-Interactomes: [R: CytoHubba, Cytoscape, String, GSEA] Nature Publishing Group NPG Scientific Reports Paper https://github.com/spawar2/Calcium-Interactomes, collaboration with Dr. Yang, Georgia State University, Atlanta, USA https://cas.gsu.edu/profile/jenny-j-yang/ Dr. Rakshya-Gorkhali https://www.researchgate.net/profile/Rakshya-Gorkhali^^^DOI: https://doi.org/10.1038/s41598-021-00067-2, Issue: 11, Volume, Pages: 20576. Date created/updated: December, 9, 2024.

• Harr-Fisc-Transformations-Microarray  [R] : [*2013:4], [**16, 17, 18] BioMed Central BMC Bioinformatics, IF=4.1 Paper, https://github.com/spawar2/Harr-Fisc-Transformations-Microarray, collaboration with Dr. Rinehart, Western Kentucky University, Bowling Green, Kentucky, USA, 1, 2, 3,  https://www.wku.edu/artp/webpages/bisc_dir.php^^^^ Date created/updated: December, 9, 2024.

• 2nd CMI-PB Prediction challenge: https://github.com/spawar2/2nd-CMI-PB-Prediction-challenge/tree/main^^ Date created/updated: December, 9, 2024.

• Binary-Search-Tree-In-Java-Implementation  [Java]: https://github.com/spawar2/Binary-Search-Tree-In-Java-Implementation^^^ Date created/updated: December, 9, 2024.

• HSET-Ovarian-Cancer-Biomarker: BioMed Central BMC Journal of Ovarian Research, IF=5.1 Paper,  [R: BGmix],  [*2017:1, 2016:1], [**12]. https://github.com/spawar2/HSET-Ovarian-Cancer-Biomarker, collaboration with Dr. Aneja, Georgia State University, Atlanta, USA https://biomedical.gsu.edu/ritu/ https://sites.gsu.edu/bgsa/^^^Dr. Nathan J. Bowen, The Center for Cancer Research and Therapeutic Development (CCRTD) at Clark Atlanta University, Georgia, USA.1, 2, mas.5 script: Ovarian Microarray data read, mas5 multi array Normalization, Fold change analysis. selected function(getGEO, normalize.quantiles, merge, cluster_analysis, hclust, Kmeans, mas5, rowMeans, randomForest, survfit, chisq.test, pData, rep, colnames, factor, eBayes, decideTests, topTable, read.tree, plot, str, write.tree, library, setwd, ReadAffy, exprs, read.csv, read.delim, write.table, roundPhylogram, unroot, str, write.tree, RMA, read.table, BGmix, ccParams, TailPP, ccTrace, histTailPP, FDRplotTailPP, histccPred, plotFDR, plotPredChecks, exprSet). DOI: 10.1186/s13048-016-0224-0, Issue: 9, Volume: 17, Pages. Date created/updated: December, 9, 2024.

• Comparative-analysis-endemic-Coronaviruses: [R: ggplots] Taylor and Francis Libyan Journal of Medicine, IF=3.1 Paper https://github.com/spawar2/Comparative-analysis-endemic-Coronaviruses, collaboration with Dr. Bagasra, Claflin University, Orangeburg, South Carolina, USA  https://www.claflin.edu/academics-research/faculty-research/meet-our-faculty/omar-bagasra^^DOI: https://doi.org/10.1080/19932820.2023.2209949, Issue: 1, Volume: 18, Pages. Date created/updated: December, 9, 2024.

• National Longitudinal Study of Adolescent to Adult Health analysis shows correlations between fast food consumption with physical and mental health of adults facing food insecurities This project was in collaboration with *Mr. Madhu Gottti, Undergraduate student, Stanford University, California, USA. Amity Public School, Connecticut, USA. Experiential Learning Program Project: Identifying biomarkers between lymphoma and leukemia cancer patients (Fall 2021). Los Osos High School, California. https://github.com/spawar2/National-Longitudinal-Study [R: Tidyverse, ggplots], Github ^^ Date created/updated: December, 9, 2024.

• Acid Ceramidase in colon cancer, with Dr. Harikumar, RGCB [R: Deseq2, ggplots], Github ^^ Date created/updated: December, 9, 2024.

• Speech to text conversion using BytesIO: [Python: BytesIO, b64decode, google.colab, IPython.display] https://github.com/spawar2/Speech2Text ^^selected function(record, display). Date created/updated: December, 9, 2024.

• Diabetic Retinopathy Detection [Python: tensorflow, keras, layers, numpy, pandas, random, os, shutil, matplotlib.pyplot, imread, keras.preprocessing.image, ImageDataGenerator, categorical_accuracy, sklearn.model]: https://github.com/spawar2/Diabetic-Retinopathy-Neural-Networks/tree/main^^diabetic-retinopathy-detection.ipynb: Imaging data read, Test-Train Split, Neural, plotting, noise removal, Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function(pd.read_csv, ImageDataGenerator, tf.keras.Sequential , model.evaluate_generator, cv2.imread, cv2.cvtColor, cv2.resize, tf.keras.models.load_model, np.argmax). Testing: Loss: 0.0134 - accuracy: 0.9957 - validation_loss: 0.4649 - validation_accuracy: 0.9218. Testing: Loss: 0.0134 - accuracy: 0.9957 - validation_loss: 0.4649 - validation_accuracy: 0.9218. Diabetic retinopathy is caused by high blood sugar due to diabetes. Over time, having too much sugar in your blood can damage your retina — the part of your eye that detects light and sends signals to your brain through a nerve in the back of your eye (optic nerve). Diabetes damages blood vessels all over the body. Date created/updated: December, 9, 2024.

• Atrial Fibrillation Detection [Python: numpy, pandas, seaborn, matplotlib.pyplot, sklearn.metrics, classification_report, sklearn.model_selection, f1_score, confusion_matrix, to_categorical, class_weight, OS, warnings]: https://github.com/spawar2/Atrial-Fibrillation-Neural-Networks^^Atrial-Fibrillation-Neural-Networks: Electrocardiography/ECG data read, Test-Train Split, Neural, plotting, noise removal, Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function(pd.read_csv, plt.figure, plt.Circle, plt.pie,plot_hist, add_gaussian_noise, Model, model.fit, evaluate_model, network, confusion_matrix, model.predict, model.load_weights, model.compile). Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Atrial fibrillation (Afib) is an irregular heart rhythm that begins in your heart's upper chambers (atria). Symptoms include fatigue, heart palpitations, trouble breathing and dizziness. Afib is one of the most common arrhythmias. Risk factors include high blood pressure, coronary artery disease and having obesity. Date created/updated: December, 9, 2024.

• Murmur-Neural-Network: [Python: warnings, matplotlib, pylab inline, tensorflow, Keras, os, pandas, librosa, glob, matplotlib.pyplot] https://github.com/spawar2/Murmur-Neural-Network^^Murmur-Neural-Network.ipynb: Echocardiogram data read, Test-Train Split, Neural, plotting, noise removal, Convolution 2D network training, confusion matrix, accuracy, sensitivity, specificity, precision, recall, confusion matrix, log-loss, and area under curve and receiver operating characteristic, AUC-ROC evaluation. selected function (plt.Circle, plt.pie,plot_hist, add_gaussian_noise, Model, model.fit, evaluate_model, network, confusion_matrix, model.predict, model.load_weights, model.compile, model.add, model.summary, plot_confusion_matrix, Sequential). Testing: table(testing$V2,pred_test) Prediction_test alive dead alive 214 5 dead 31 11 ((214+11)/(nrow(testing)))*100 [1] 86.2069. Date created/updated: December, 9, 2024.

• Artificial Intelligence Based Framework for Predicting the Spread of Climate Change Related Infectious Diseases: (Scired: Scientific Research & Development) https://github.com/spawar2/SCIRED-Analysis, collaboration with Dr. Desowky, Claflin University, Orangeburg, South Carolina, USA https://desoky.com/^^ Date created/updated: December, 9, 2024.

• Neural networks on CTA in peripheral arterial diseases (PAD) [Python: pytorch, tensorflow]: https://github.com/spawar2/CTA-PAD-Neural-Nets (National Science Foundation South Carolina Established Program for Stem Cooperative Research (SC EPSCoR), AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina (ADAPT), RII Track-1 award funded project Role: Co-PI, NSF Identifier: 000879633, (Direct costs, Effort=100%)): https://scepscor.org/adapt-in-sc-thrust-2/. This project in collaboration with https://github.com/Caliese **Caliese J. Beckford+π (Thesis Advisor), **Mr. Oluwademiladeayo Ashade+π (Thesis Advisor),  https://www.linkedin.com/in/caliese-beckford-25851821b/?originalSubdomain=jm (Graduate student), Undergraduate student: **Mr. Sabb, Dinari+π (Thesis Advisor), (National Science Foundation South Carolina Established Program for Stem Cooperative Research (SC EPSCoR), AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina (ADAPT), RII Track-1 award funded project Role: Co-PI, (Direct costs, Effort=100%)), NSF Identifier: 000879633, Award Number: 2242812,  Undergraduate student: Claflin University, Orangeburg, South Carolina, USA (Fall 2023). [**2024:47], GitHub, Poster 1, Poster 2. Machine learning application for biomedical device development. https://www.linkedin.com/in/dinari-sabb-0b62b51ba https://github.com/DinariSabb^^ Date created/updated: December, 9, 2024.

• Generative AI ChatBot using gpt-3.5-turbo engine [Python, HTML: numpy, pandas, pydicom, matplotlib.pyplot, math, cv2, tensorflow, keras, OPENAI, model: gpt-4-turbo]: https://github.com/spawar2/Generative-AI-ChatBot-using-gpt-3.5-turbo-engine^^ Date created/updated: December, 9, 2024. DOI https://doi.org/10.1109/ICECET61485.2024.10698683, Issue, Volume, Pages: pp. 1-4. ​