User-friendly Web Application Research Articles

Adaptive Ambulance Management System

The ability to quickly and accurately retrieve information is essential for emergency medical response. This project integrates biometric technology and web applications to offer a comprehensive solution for improving emergency medical services. Our approach starts with the use of a fingerprint scanner, which records information on patients and guardians and links it to distinctive fingerprints. A Node MCU processes and stores this data, making it possible to quickly and securely identify patients in urgent situations. The Node MCU displays patient information on an LCD panel as soon as they provide their fingerprint. Both ambulance drivers and hospital administration have access to a user-friendly web application simultaneously. The software gives users access to a database of nearby hospitals that is divided into public and private facilities. Soon, the selected hospital will be informed about the patient condition and able to make the treatment arrangements earlier. The patient's preferences and the resources at their disposal can help ambulance crew make wise selections. Ambulance crew have two selection buttons, one is government hospital selection button and another is private hospital selection button. By reducing response times, this function improves patient care. In order to safeguard sensitive patient data, the project places a strong emphasis on data security and privacy. Our goal is to maximize emergency medical response through this integrated system, ensuring that patients receive prompt and efficient care when it counts.

Fake News Detector

The proliferation of fake news in digital media presents a significant challenge to information integrity. This research explores the application of machine learning, specifically logistic regression, for automated fake news detection using a dataset sourced from Kaggle. Text preprocessing techniques, including tokenization, stemming, and TF-IDF vectorization, were applied to extract features from news articles. A logistic regression model was trained on the processed data to classify articles as real or fake. The model achieved high accuracy rates of 98.68% on the training set and 97.67% on the testing set. Additionally, a user-friendly Streamlit web application was developed for real-time prediction of fake news. This study demonstrates the efficacy of logistic regression in combatting misinformation and contributes to enhancing information credibility in digital media. Top of Form Key Words — Fake news detection, machine learning, logistic regression, TF-IDF vectorization, text preprocessing, information integrity.

Classroom Management

Efficient classroom management is crucial for fostering an environment conducive to learning and student development. This paper introduces a novel approach to classroom management through the integration of a user-friendly web application. The system aims to streamline various administrative tasks, enhance communication between educators, students, and parents, and promote student engagement. The web application offers a centralized platform for educators to manage attendance records, assignments, and grades digitally, reducing the time spent on administrative tasks. Additionally, it facilitates real-time communication through messaging features, enabling quick updates on class schedules, assignments, and announcements.

Exam Assessor Tool: An Automated System for Efficient Answer Sheet Evaluation

With Education 4.0 and four quadrant approach number of innovations have gone into academics for efficient, experiential, and outcome-based education however assessment schemes are still very much dependent on manual assessment methods which are time-consuming and cumbersome. The grading system can sometimes be irrational, with diversified schemes for the same course and can also be biased. Covid 19 pandemic caused a global economic avalanche like we’ve never experienced in our lifetime. Many countries have implemented control measures such as blockades and curfews. The education system in this chaos saw a silver lining with academics shifting to online mode, with paradigm shift in teaching, assessment techniques too need to evolve. Work done is an effort to ease the process of assessment, a machine learning assisted model is developed that automates subjective answer evaluation in the education sector. Our project involved several crucial steps, including grayscale conversion, Natural Language Processing (NLP) for data cleansing, data splitting, and training an artificial neural network (ANN) to predict scores based on extracted features. ANN-based system grades subjective responses without human intervention, reducing the workload of teachers and professors. Model constructed an ANN architecture with three layers using Rectified Linear Activation Unit (ReLU) and Sigmoid activation functions. Trained model was incorporated into a user-friendly web application using the Streamlit library. Model design gives a major boost in grading efficiency and accuracy while providing valuable feedback to students. Research surveys were conducted, and a dataset was constructed for training and testing the model. study yielded an accuracy of 83.14% after employing techniques such as text cleaning, preprocessing, and feature extraction.

The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024.

The EMBL-EBI Job Dispatcher sequence analysis tools framework (https://www.ebi.ac.uk/jdispatcher) enables the scientific community to perform a diverse range of sequence analyses using popular bioinformatics applications. Free access to the tools and required sequence datasets is provided through user-friendly web applications, as well as via RESTful and SOAP-based APIs. These are integrated into popular EMBL-EBI resources such as UniProt, InterPro, ENA and Ensembl Genomes. This paper overviews recent improvements to Job Dispatcher, including its brand new website and documentation, enhanced visualisations, improved job management, and a rising trend of user reliance on the service from low- and middle-income regions.

A data browsing application for accessing gene and module-level blood transcriptome profiles of healthy pregnant women from high- and low-resource settings.

Transcriptome profiling data, generated via RNA sequencing, are commonly deposited in public repositories. However, these data may not be easily accessible or usable by many researchers. To enhance data reuse, we present well-annotated, partially analyzed data via a user-friendly web application. This project involved transcriptome profiling of blood samples from 15 healthy pregnant women in a low-resource setting, taken at 6 consecutive time points beginning from the first trimester. Additional blood transcriptome profiles were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public repository, representing a cohort of healthy pregnant women from a high-resource setting. We analyzed these datasets using the fixed BloodGen3 module repertoire. We deployed a web application, accessible at https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/which displays the module-level analysis results from both original and public pregnancy blood transcriptome datasets. Users can create custom fingerprint grid and heatmap representations via various navigation options, useful for reports and manuscript preparation. The web application serves as a standalone resource for exploring blood transcript abundance changes during pregnancy. Alternatively, users can integrate it with similar applications developed for earlier publications to analyze transcript abundance changes of a given BloodGen3 signature across a range of disease cohorts. Database URL: https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/.

Dynamic switching of psychological test questions and design of scalable database: A study of web page data visualization and front-end interaction

As the need for efficient and user-friendly web applications in the field of psychological assessment continues to grow, traditional web development approaches are challenged to provide a dynamic and adaptive user experience. This study focuses on addressing these challenges through a comprehensive study that provides insight into the current state of psychological assessment web development, highlighting the shortcomings of traditional approaches, including the static limitations of Hyper Text Markup Language (HTML) and limited client-side scripting. The result is a user experience that lacks the responsiveness and flexibility that modern users expect. To tackle these problems, the intended solution suggested in this work combines a strong and scalable database design with a dynamic switching mechanism for psychological exam questions. Features-rich, aesthetically pleasing, and functioning client-server Web applications are created using technologies such as Servlets and Java Server Pages (JSP). The research contains practical case studies that demonstrate how Java-based user interface design can be successfully applied in real-world scenarios. The advantages of modularity, code reusability, and maintainability are demonstrated in these case studies. The study examines how selected technologies affect the performance, security, and accessibility of web UI, while also addressing load times, data integrity, and user inclusion issues. To summarize, this investigation underscores the crucial role of dynamic and scalable databases and Java-based technologies in enhancing the design of psychological assessment Web UIs.Developers can create web applications that exceed user expectations and provide superior user experience in the evolving digital landscape by using the proposed frameworks comprehensive toolkit.

234 Predictive Radiomics in Glioblastoma: A Machine Learning Approach for Personalized Survival Outcomes and a Novel Web Application

INTRODUCTION: Diverse characteristics of glioblastomas (GBMs) complicate survival predictions, requiring personalized approaches. METHODS: From The Cancer Imaging Archive (TCIA) database, 611 patients from the UPENN-GBM collection and 500 patients from the UCSF-PDGM collections were included in this study. UPENN-GBM collection was used as the training set and for 5-fold internal cross-validation, whereas the UCSF-PDGM collection was used for external validation. Survival at 3, 6, 12, and 24 months were the outcomes of interest. Necrotic tumor core, enhancing tumor, and whole tumor segmentation masks for T1 with contrast, T1, T2, and FLAIR images were obtained automatically with a pre-trained Swin UNETR model. From these regions of interest, radiomic features were extracted using the PyRadiomicsLibrary. The LASSO (least absolute shrinkage and selection operator) method was employed for radiomic feature selection. The ML algorithms TabPFN, XGBoost, LightGBM, Support Vector Machine (SVM), and Random Forest (RF) were utilized to predict the survival outcomes of interest. RESULTS: The highlight of our study, our web application is available for use via the following link: https://huggingface.co/spaces/MSHSNeurosurgeryResearch/Radiomics_Based_Glioblastoma_Survival_Prediction_Tool. CONCLUSIONS: This study not only provides a radiomics pipeline for predicting the GBM survival outcomes, but it also contributes to the clinical adoption of these models by developing a fully automatic and user-friendly web application which make them available to clinicians worldwide.

757 Machine Learning-Driven Web Application for Enhanced Clinical Decision Support in Thoracolumbar Spinal Cord Injuries

INTRODUCTION: Thoracolumbar spinal cord injuries (tlSCI) can result in negative short-term in-hospital outcomes, underscoring the importance of precise prediction tools. METHODS: This retrospective cohort study included 147,826 patients from the American College of Surgeons Trauma Quality Program database. XGBoost, LightGBM, CatBoost, and Random Forest algorithms were utilized in combination with the Optuna optimization library for hyperparameter tuning. Models were evaluated graphically with receiver operating characteristic (ROC) curves, precision-recall curves, and numerically with precision, recall, F1 score, accuracy, area under ROC (AUROC), and area under PRC. The best-performing models were integrated into an open-access web application for individualized predictions. RESULTS: The best-performing models for each outcome were as follows: for predicting mortality, Random Forest achieved the highest AUROC (0.865, 95% CI 0.848-0.879); in predicting non-home discharges, LightGBM had the highest AUROC (0.801, 95% CI 0.794-0.804); and for predicting major complications, Random Forest performed best with an AUROC of 0.733 (95% CI 0.642-0.808). Algorithms' receiver operator curves for the outcomes: (A) mortality, (B) non-home discharges, and (C) major complications. CONCLUSIONS: This study successfully developed ML models to predict critical in-hospital outcomes for patients with tlSCI. By incorporating the top-performing algorithms into a user-friendly web application, these models have the potential to improve clinical decision-making and management strategies for tlSCI patients, ultimately enhancing patient care and outcomes.

Predictive Modeling for Cardiovascular Disease Risk Assessment

Abstract: Heart disease remains one of the leading causes of mortality worldwide. Early detection and risk assessment are crucial for effective prevention and management. This research paper presents a novel approach utilizing machine learning techniques, particularly decision trees, for predicting heart disease risk. The study utilizes a dataset sourced from the UCI Machine Learning Repository, encompassing diverse features such as age, gender, height, weight, cholesterol levels, and other relevant attributes. The proposed model aims to accurately classify individuals into risk categories based on their demographic and health-related information. Additionally, a user-friendly web application is developed using Python Flask, enabling users to input their data and receive instant risk assessments. Through rigorous experimentation and evaluation, the efficacy and reliability of the predictive model are demonstrated, offering valuable insights for early intervention and personalized healthcare strategies in the fight against heart disease.

Doctor Appointment System

Abstract: In today's fast-paced world, efficient healthcare services are essential for maintaining the well-being of individuals. This abstract introduces a Doctor Appointment Booking System, designed to streamline the process of scheduling medical appointments. This system leverages technology to improve healthcare access, reduce patient waiting times, and enhance overall patient experience. The Doctor Appointment Booking System incorporates user-friendly web and mobile applications, allowing patients to conveniently browse through a list of available doctors, view their profiles, and select suitable appointment slots. It also provides a user-friendly interface for healthcare providers to manage their schedules and update their availability in real-time. Additionally, it features an integrated notification system to send appointment reminders, thus reducing no-show rates and ensuring optimal resource utilization. Furthermore, this system offers secure and confidential storage of patient data, ensuring compliance with relevant healthcare regulations, such as HIPAA. Data analytics capabilities enable the generation of valuable insights for both healthcare providers and administrators to improve resource allocation and optimize the patient experience

Abstract 4921: A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning

Abstract Understanding the genetic vulnerabilities (i.e., genetic dependencies) of cancer cells is crucial for developing novel anti-cancer treatments. The Cancer Dependency Map (DepMap) projects, conducted by the Broad Institute and Wellcome Sanger Institute, have performed comprehensive genome-wide CRISPR-Cas9 knockout screens across diverse cancer cell lines. These data resources have enabled computational biologists to glean insights into cancer genetic dependencies using sophisticated computational models. As different genomic mechanisms govern tumorigenesis and genetic vulnerability, the relationship between cancer genomics and genetic dependencies is nonlinear, making the prediction of genetic dependencies challenging. Addressing this challenge, we previously developed a deep learning model, namely DeepDEP, to predict cancer genetic dependencies using multi-omic profiles (Chiu et al. DOI:10.1126/sciadv.abh1275). DeepDEP has a transfer learning scheme that integrates genomic representations captured from unlabeled tumor samples of the Cancer Genome Atlas (TCGA) and genomic features that are predictive of genetic dependencies in cell lines. Such a design enables the application of DeepDEP to both cell lines and tumors. The model demonstrated superior performance compared to various conventional machine learning methods. Prediction results were validated using independent cell-line datasets and patient clinical data. Here, we developed an intuitive web application for DeepDEP which allows users to easily utilize the model on their in-house data. Users can simply indicate whether the query sample is derived from a cancer cell line or a tumor, upload any combination of mutation, gene expression, DNA methylation, and copy number alteration datasets, and view the predicted dependencies of 1,298 cancer-relevant genes. Along with the genetic dependency predictions, users are provided with model performance metrics, detailed gene annotations, and links to useful resources such as Ensembl and COSMIC. Our web application features a variety of interactive visualizations to guide users to interpret the results at the levels of individual genes and pathways. The tool has two supplemental modules to enhance its functionality. One allows users to search for similar cell lines to the query sample within the DepMap project or for similar tumors within the TCGA project. The other supplemental module provides data exploration tools for pre-calculated predictions across all TCGA tumors. In summary, this user-friendly web application provides researchers with access to deep learning predictions of cancer genetic dependencies with various interactive visualization and analysis tools. It is anticipated that this application will facilitate the study of cancer genetic dependencies and enhance the efficiency of anti-cancer drug discovery. Citation Format: Michael J. Kasper, Li-Ju Wang, Michael Ning, Yufei Huang, Yu-Chiao Chiu. A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4921.

Enhancing banking governance: A machine learning-based credit risk classification

<p>Risk management in the banking sector has gained heightened significance following the 2008 Global Financial Crisis. With the advent of Machine Learning (ML) techniques, financial institutions are increasingly turning to Artificial Intelligence (AI) for enhanced risk assessment and management. This paper introduces a systematic protocol for implementing a decision tree classifier tailored for credit risk classification. Additionally, we develop a user-friendly web application utilizing the Flask framework and Python Pickle library. This application offers customers an intuitive interface to input their attributes and receive predictions regarding their credit risk classification. Our empirical findings demonstrate that the Support Vector Machine (SVM) achieves a commendable accuracy of 77% in classifying customers based on their banking data. Furthermore, the web application proves to be an effective means for customers to interact with the ML model, enhancing accessibility and user engagement. These outcomes underscore the substantial benefits that ML techniques can bring to the banking industry, enabling improved risk detection and management while concurrently enhancing customer service delivery.</p>

AquMADE: A GIS-based web application to assess groundwater quality by introducing a risk-based irrigation water quality index (RB-IWQI)

Groundwater quality monitoring plays a vital role in effective water management across all groundwater basins. The provision of decision support systems can assist decision-makers in strategic planning and the conservation of groundwater resources. In this study, AquMADE—a user-friendly and comprehensive web application—is developed to integrate GIS and multi-criteria decision-making methods seamlessly for modifying the irrigation water quality index. AquMADE serves multiple functionalities. Firstly, it empowers decision-makers to import, interpolate, and visualize collected data, enabling a holistic view of the information at hand. Secondly, it offers the flexibility for decision-makers to adjust the weights of various parameters through the utilization of the analytical hierarchy process, taking into consideration their risk attitudes during the aggregation process using ordered weighted averaging method. Finally, the incorporation of sensitivity analysis methods allows for the evaluation of the impact of changes in inputs, such as criteria weights and aggregation methods, on the resulting outputs. The system underwent user testing in Khoy City, Iran, where it received positive feedback. The assessment revealed that the system performed satisfactorily in terms of functionality, interactivity, and ease of use.

PCTA, a pan-cancer cell line transcriptome atlas

A substantial volume of RNA sequencing data have been generated from cancer cell lines. However, it requires specific bioinformatics skills to compare gene expression levels across cell lines. This has hindered non-bioinformaticians from fully utilizing these valuable datasets in their research. To bridge this gap, we established a curated Pan-cancer Cell Line Transcriptome Atlas (PCTA) dataset. This resource aims to provide a user-friendly platform, allowing researchers without extensive bioinformatics expertise to access and leverage the wealth of information within the dataset for their studies. The PCTA dataset encompasses the expression matrix of 24,965 genes, featuring data from 84,385 samples derived from 5677 studies. This comprehensive compilation spans 535 cell lines, representing a spectrum of 114 cancer types originating from 30 diverse tissue types. On UMAP plots, cell lines originating from the same type of tissue tend to cluster together, illustrating the dataset's ability to capture biological relationships. Additionally, an interactive and user-friendly web application (https://pcatools.shinyapps.io/PCTA_app/) was developed for researchers to explore the PCTA dataset. This platform allows users to examine the expression of their genes of interest across a diverse array of samples.

Data Analytics: Unlocking Multivision’s Future Perspectives

This research examines the problem of making data analysis understandable to non-technical consumers in today’s world of extensive data. We present a project that combines a user-friendly web application built on the Django framework with simple data analysis using linear regression. Through the use of the simple statistical technique of linear regression, even non-technical people may obtain valuable insights from large-scale datasets. Easy-to-use online application that makes data uploading, analysis, and interpretation a breeze. Our method uses built-in linear regression methods in the Django framework to automate dataset analysis. In this paper, the user interface design and the use of linear regression are explained, together with technical architecture insights. Facilitating inclusion across several areas, the major objective is to democratize data analysis. Our goal is to empower non-technical individuals and enable a wider audience to understand and apply data for informed decision-making by providing a tool that combines the efficiency of linear regression with an easy-to-use online interface.

A Machine Learning-Based Thermobarometer for Magmatic Liquids

Abstract Experimentally calibrated models to recover pressures and temperatures of magmas are widely used in igneous petrology. However, large errors, especially in barometry, limit the capacity of these models to resolve the architecture of crustal igneous systems. Here, we apply machine learning to a large experimental database to calibrate new regression models that recover P–T of magmas based on melt composition plus associated phase assemblage. The method is applicable to compositions from basalt to rhyolite, pressures from 0.2 to 15 kbar, and temperatures of 675°C to 1400°C. Testing and optimisation of the model with a filter that removes estimates with standard deviation above the 50th percentile show that pressures can be recovered with root-mean-square-error (RMSE) of 1.1 to 1.3 kbar and errors on temperature estimates of 21°C. Our findings demonstrate that, given constraints on the coexisting mineral assemblage, melt chemistry is a reliable recorder of magmatic variables. This is a consequence of the relatively low thermodynamic variance of natural magma compositions despite their relatively large number of constituent oxide components. We apply our model to two contrasting cases with well-constrained geophysical information: Mount St. Helens volcano (USA), and Askja caldera in Iceland. Dacite whole-rocks from Mount St Helens erupted 1980 to 1986, inferred to represent liquids extracted from cpx–hbl–opx–plag–mt–ilm mush, yield melt extraction source pressures of 5.1 to 6.7 kbar in excellent agreement with geophysical constraints. Melt inclusions and matrix glasses record lower pressures (0.7–3.8 kbar), consistent with magma crystallisation within the upper reaches of the imaged geophysical anomaly and during ascent. Magma reservoir depth estimates for historical eruptions from Askja match the location of seismic wave speed anomalies. Vp/Vs anomalies at 5 to 10 km depth correspond to hot (~990°C) rhyolite source regions, while basaltic magmas (~1120°C) were stored at 7 to 17 km depth under the caldera. These examples illustrate how our model can link petrology and geophysics to better constrain the architecture of volcanic feeding systems. Our model (MagMaTaB) is accessible through a user-friendly web application (https://igdrasil.shinyapps.io/MagmaTaBv4/).

Multiple Disease Prediction System

The field of machine learning, a subset of artificial intelligence, has revolutionized numerous industries by enabling predictive analytics on vast datasets. In healthcare, predictive analytics holds great promise for enhancing patient care by providing insights for informed decision-making. Diseases like Parkinson's, diabetes, and heart conditions pose significant global challenges due to delayed detection, often caused by limited medical resources. Early recognition of these ailments is crucial, as late-stage diagnoses can be challenging to treat effectively. To address this gap, this project utilizes machine learning classification algorithms to predict these diseases. A user-friendly medical test web application has been developed to make these predictions accessible to a broad audience, aiming to facilitate early assessments and improve public health outcomes. Keywords— Disease Prediction, Artificial Intelligence, Machine Learning, Classification Algorithms.

Combining fuzzy MCDM with Kano model and FMEA: a novel 3-phase MCDM method for reliable assessment

AbstractIn the circular economy era, this study addresses sustainable business management for high-investment and long-life cycle projects, where accurate and reliable assessments are crucial to ensuring successful outcomes. The objective is to elevate the reliability of assessments by introducing a novel decision-making method that, for the first time, integrates time-based satisfaction and risk factors simultaneously. We propose a 3-phase multi-criteria decision-making (MCDM) method, which combines fuzzy MCDM comprising fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal situation (TOPSIS), Kano model, and failure mode and effects analysis (FMEA) techniques, to handle reliable assessments effectively. Our method is distinct in its incorporation of time-based satisfaction weights derived from Kano model, emphasising decision-makers’ criteria preferences in short, medium, and long terms. Furthermore, we introduce risk-discounted weights by using FMEA to tune criteria scores. The method is validated via a numerical example case, assessing and selecting the most appropriate hydrogen storage method for lightweight vehicles. The results suggest that cryo-compressed hydrogen tank with 250–350 bar and at cryogenic temperature is the most suitable storage method. Health & safety with a weight of 0.5318 emerges as the most important main criterion, and permeation & leakage with a weight of 0.4008 is the most important sub-criterion. To bridge the gap between theoretical research and practical application, we transform the new method into a user-friendly web application with graphical user interface (GUI). End-users can conduct reliable assessments and foster sustainable business management through informed decision-making.

Librarian: A quality control tool to analyse sequencing library compositions.

Robust analysis of DNA sequencing data needs to include a set of quality control steps to ensure that technical bias is kept to a minimum. A metric easily obtained is the frequency of each of the nucleobases for each position across all sequencing reads. Here, we explore the differences in nucleobase compositions of various library types produced by standard experimental methodologies. We obtained the compositions of nearly 3000 publicly available datasets and subjected them to Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction for a two-dimensional representation of their composition characteristics. We find that most library types result in a specific composition profile. We use this to give an estimate of how strongly the composition of a test library resembles the profiles of previously published libraries, and how likely the test sample is to be of a particular type. We introduce Librarian, a user-friendly web application and command line tool which enables checking base compositions of test libraries against known library types. Library preparation methods strongly influence the per position nucleobase content. By comparing test libraries to a database of previously published library types we can make predictions regarding the library preparation method. Librarian is a user-friendly tool to access this information for quality assurance purposes as discrepancies can flag potential irregularities very early on.