Web-based Machine Learning Research Articles

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.

User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business

User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business

Real-Time Human Pose Estimation Using Machine Learning

Human pose estimation is a pivotal domain within computer vision, underpinning applications from motion capture in cinematic production to sophisticated user interfaces in desktop devices. This research delineates the implementation of real-time human pose estimation within web browsers utilizing TensorFlow.js and the PoseNet model. PoseNet, an advanced machine learning model optimized for browser-based execution, facilitates precise pose detection sans specialized hardware. The primary aim of this study is to integrate PoseNet with TensorFlow.js, achieving efficient real-time pose estimation directly in the browser by leveraging JavaScript, thereby ensuring seamless user interaction and broad accessibility. A modular system architecture is designed, focusing on optimization strategies such as model quantization, asynchronous processing, and on-device computation to enhance performance and privacy preservation. In conclusion, this research establishes a robust framework for deploying PoseNet in web environments, underscoring its potential to revolutionize human-computer interaction within browser-based applications. Our findings contribute significantly to the field of computer vision and machine learning, offering insights into the practical deployment of pose estimation models on widely accessible platforms. Keywords — ReaReal-time Pose Estimation, TensorFlow.js, PoseNet, Machine Learning, Computer Vision, Browser-based Pose Detection, Human-Computer Interaction, Multi-person Tracking, On-device Computation, Asynchronous Processing, Cross-browser Compatibility, Performance Optimization, Privacy-preserving AI, Web-based Machine Learning, Motion Capture, Fitness Tracking, Interactive , Virtual Reality Interfaces, Deep LearningLearning

Engineering G protein-coupled receptors for stabilization.

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

HRpredict: Introducing a Web-Based Application for Heart Rate Prediction and Lifestyle Recommendations

Background: Monitoring heart health requires early detection of deviations in HR, which makes it easier to detect and address heart irregularities at an early stage. Health remote systems when combined with artificial intelligence (AI) can assist in better health outcomes through early detection of heart problems. Aims: Our main goal is to create a website application (Web-App) for web browser access, aiming to utilize a Random Forest (RF) machine learning (ML) model trained to predict the average heart rate (HR) over 10 days for different periods, and to enable lifestyle and activity recommendations. Methods: The Web-App is created using Laravel, an open-source Personal Home Page (PHP) web framework that follows the model-view-controller (MVC) architectural pattern. Results: This research resulted in a web-based ML model that can be used to predict future heart rates over a 10-day period which are utilized to establish average HR values, considering baseline and three distinct periods: morning, noon, and evening across the 10-day duration. Through this Web-App lifestyle, habit, activity, and 10-day reassessment recommendations are also provided. Conclusion: The Web-App was designed to be accessed and used through a web browser, to provide lifestyle recommendations based on predicted HR readings. To determine the impact of users adhering to recommendations, further research is required.

Prediction of COVID-19 in-hospital mortality in older patients using artificial intelligence: a multicenter study.

Once the pandemic ended, SARS-CoV-2 became endemic, with flare-up phases. COVID-19 disease can still have a significant clinical impact, especially in older patients with multimorbidity and frailty. This study aims at evaluating the main characteristics associated to in-hospital mortality among data routinely collected upon admission to identify older patients at higher risk of death. The present study used data from Gerocovid-acute wards, an observational multicenter retrospective-prospective study conducted in geriatric and internal medicine wards in subjects ≥60years old during the COVID-19 pandemic. Seventy-one routinely collected variables, including demographic data, living arrangements, smoking habits, pre-COVID-19 mobility, chronic diseases, and clinical and laboratory parameters were integrated into a web-based machine learning platform (Just Add Data Bio) to identify factors with the highest prognostic relevance. The use of artificial intelligence allowed us to avoid variable selection bias, to test a large number of models and to perform an internal validation. The dataset was split into training and test sets, based on a 70:30 ratio and matching on age, sex, and proportion of events; 3,520 models were set out to train. The three predictive algorithms (optimized for performance, interpretability, or aggressive feature selection) converged on the same model, including 12 variables: pre-COVID-19 mobility, World Health Organization disease severity, age, heart rate, arterial blood gases bicarbonate and oxygen saturation, serum potassium, systolic blood pressure, blood glucose, aspartate aminotransferase, PaO2/FiO2 ratio and derived neutrophil-to-lymphocyte ratio. Beyond variables reflecting the severity of COVID-19 disease failure, pre-morbid mobility level was the strongest factor associated with in-hospital mortality reflecting the importance of functional status as a synthetic measure of health in older adults, while the association between derived neutrophil-to-lymphocyte ratio and mortality, confirms the fundamental role played by neutrophils in SARS-CoV-2 disease.

Empowering early detection: A web-based machine learning approach for PCOS prediction

Nowadays, Polycystic Ovary Syndrome (PCOS) affects many women, making it a prevalent concern. It is a hormonal disorder that causes irregular, delayed, or absent menstrual cycles in the female body. This condition can lead to the development of type 2 diabetes, gestational diabetes, weight gain, unwanted body hair, and various other complications. In severe cases, PCOS can result in infertility, posing a challenge for patients trying to conceive. Statistics show that the incidence rate of PCOS has significantly increased in recent years, which is alarming. If PCOS is identified early, people may follow their doctor's recommendations and live a better life. The dataset used for this research contains records for 541 patients. The aim of this study is to employ machine learning models to identify patterns in this disorder. The information learned is then inputted into various algorithms to assess accuracy, specificity, sensitivity, and precision using different ML models, such as Logistic Regression (LR), Decision Tree (DT), AdaBoost (AB), Random Forest (RF), and Support Vector Machine (SVM) among others. The research utilized the Mutual Information model for feature selection and compared the models to determine the most accurate one. Employing the Mutual Information model for feature engineering, AB and RF achieved the highest accuracy of 94 %.

Machine Learning based Integrated Hospital Appointment Booking and Queue Management System

Hospitals have a growing challenge of long queues and consequently more waiting time for patients. Hospitals can better manage resources and anticipate more inflow of patients through estimations on patient count. In this paper, we propose a web-based Machine Learning based Integrated Hospital Appointment Booking and Queue Management System that aims to reduce the queue time of patients and forecast the number of patients present at a given hour of the day. Queue time reduction is done by generating a unique token number for every patient which is used to lead him directly to the respective department and patient forecasting is done using Demand Forecasting which is a Machine Learning Regression Algorithm to predict future patient count at a particular hour of the day. The results can help patients make a better decision of choosing a less crowded time. This will also help the hospital to manage the patient crowd, logistics and resources more efficiently. Keywords — Hospital, queue, patient, integration, Integrated, doctor, web-based, demand forecasting, machine learning, token.

PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM

The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, and Si, the model is able to provide predictions for two output data, Yield Strength (YS) and Tensile Strength (TS). The research aims to understand the relationship between chemical composition and mechanical properties of aluminum, and to develop a tool that can be used to predict these properties with a high level of accuracy. Overall, the goal of this study is to enhance the understanding of the properties of aluminum and how it can be utilized in various applications. This study designs a web-based machine learning modeling to predict the mechanical properties of aluminum in the percentage of chemical composition, where the input data in the modeling consists of 9 variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and has 2 output data consisting of Yield Strength (YS) and Tensile Strength (TS). The modeling machine learning is designed using the Python programming language and additional libraries such as Pandas, Numpy, Scikit-learn, and Streamlit. The modeling in this study uses three algorithms consisting of Decision Trees (DT), Random Forest (RF), and Artificial Neural Network (ANN). Each algorithm is optimized with the best search parameters, and where the RF algorithm has better performance than DT and JST. The best modeling uses the RF algorithm with optimal parameters of number of trees at 20 and maximum depth of 10, with MAE values of 11.44, RMSE of 14.282, and R of 0.93 for Yield Strength (YS) predictions, and for Tensile Strength (TS) predictions, MAE values are obtained. 21,669, RMSE 27,301, and R 0.871.

Web-Based Anomaly Detection for Smart Urban Living: Drone Photography and Videography

Smart cities aim to enhance the quality of life for urban dwellers through technological advancements. Machine Learning (ML) plays a crucial role in various domains of Smart X, including education, transportation, healthcare, environment, and living. However, integrating ML into daily life poses challenges. This paper presents a web-based ML application prototype that effectively augments the daily quality of life for communities. It specifically explores the advantages of web-based photography-videography-enabled drones for citizen needs and city inspections. The application utilizes ML to detect anomalies and identify normal objects, addressing the common challenge of distinguishing normalcy from abnormality. Examples include assessing the structural integrity of house components, analyzing medical images, and evaluating the quality of fruits or hydroponic plants. The study employs exploratory and experimental methods, utilizing teachable machine learning and the Python-based Streamlit application. Experimental results demonstrate that web-based photo and video analysis expedites the detection of normal and abnormal images and videos, surpassing the limitations of visual examination with the naked eye. This research contributes to advancing ML applications in smart living for urban communities.

Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis

BackgroundHigh-dose methotrexate (HD-MTX) is a potent chemotherapeutic agent used to treat pediatric acute lymphoblastic leukemia (ALL). HD-MTX is known for cause delayed elimination and drug-related adverse events. Therefore, close monitoring of delayed MTX elimination in ALL patients is essential.ObjectiveThis study aimed to identify the risk factors associated with delayed MTX elimination and to develop a predictive tool for its occurrence.MethodsPatients who received MTX chemotherapy during hospitalization were selected for inclusion in our study. Univariate and least absolute shrinkage and selection operator (LASSO) methods were used to screen for relevant features. Then four machine learning (ML) algorithms were used to construct prediction model in different sampling method. Furthermore, the performance of the model was evaluated using several indicators. Finally, the optimal model was deployed on a web page to create a visual prediction tool.ResultsThe study included 329 patients with delayed MTX elimination and 1400 patients without delayed MTX elimination who met the inclusion criteria. Univariate and LASSO regression analysis identified eleven predictors, including age, weight, creatinine, uric acid, total bilirubin, albumin, white blood cell count, hemoglobin, prothrombin time, immunological classification, and co-medication with omeprazole. The XGBoost algorithm with SMOTE exhibited AUROC of 0.897, AUPR of 0.729, sensitivity of 0.808, specificity of 0.847, outperforming the other models. And had AUROC of 0.788 in external validation.ConclusionThe XGBoost algorithm provides superior performance in predicting the delayed elimination of MTX. We have created a prediction tool to assist medical professionals in predicting MTX metabolic delay.

HERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.

Development of a machine learning regression model for accurate sugarcane crop yield prediction, Jinja – Uganda

Sugarcane is one of the key crops grown worldwide and used for sugar processing, food, alcohol, biogas, fertilizer, and other products. There is a problem with Sugarcane yield prediction, yields aren’t accurately predicted, and this creates an impact on yields. This research looks at identifying methods used for the prediction, design, development, and evaluation of the three machine learning regression models used for predicting sugarcane yields in Uganda. This research was implemented using Data Science methodology, several machine learning algorithms for prediction of yields on dataset have been analyzed. The collected and analyzed dataset in this research had one output/ dependent variable and eight independent variables. The algorithms used to develop the prediction models are the Multiple Linear Regression algorithm, Decision Tree Regression algorithm, and Random Forest Regression algorithm to predict the output. The dataset of 3 years, 2019, 2020, and 2022 was considered and merged to train and test the model at a ratio of 80% to 20%. The accuracies of the individual models were compared after training, testing the dataset, and evaluation. The multiple Linear regression model results indicate that out of 100%, the model accuracy was 76.5%, the Decision Tree Regression Model scored 89.2%, Random Forest Regression Model was 94.6%. The random forest model came out as the best model. The Random Forest model has a percentage improvement of 60.4%. In future research, researchers can work on, A web-based machine learning model, Deep learning methods used to improve the model and more data can be used to improve the accuracy

Exploring an Affective and Responsive Virtual Environment to Improve Remote Learning

Online classes are typically conducted by using video conferencing software such as Zoom, Microsoft Teams, and Google Meet. Research has identified drawbacks of online learning, such as “Zoom fatigue”, characterized by distractions and lack of engagement. This study presents the CUNY Affective and Responsive Virtual Environment (CARVE) Hub, a novel virtual reality hub that uses a facial emotion classification model to generate emojis for affective and informal responsive interaction in a 3D virtual classroom setting. A web-based machine learning model is employed for facial emotion classification, enabling students to communicate four basic emotions live through automated web camera capture in a virtual classroom without activating their cameras. The experiment is conducted in undergraduate classes on both Zoom and CARVE, and the results of a survey indicate that students have a positive perception of interactions in the proposed virtual classroom compared with Zoom. Correlations between automated emojis and interactions are also observed. This study discusses potential explanations for the improved interactions, including a decrease in pressure on students when they are not showing faces. In addition, video panels in traditional remote classrooms may be useful for communication but not for interaction. Students favor features in virtual reality, such as spatial audio and the ability to move around, with collaboration being identified as the most helpful feature.

An AI-augmented multimodal application for sketching out conceptual design

The goal of this paper is to develop an interactive web-based machine learning application to assist architects with multimodal inputs (sketches and textual information) for conceptual design. With different textual inputs, the application generates the architectural stylistic variations of a user’s initial sketch input as a design inspiration. A novel machine learning model for the multimodal input application is introduced and compared to others. The machine learning model is performed through procedural training with the content curation of training data (1) to control the fidelity of generated designs from the input and (2) to manage their diversity. The web-based interface is at its work in progress as a frontend of the proposed application for better user experience and future data collection. In this paper, the framework of the proposed interactive application is explained. Furthermore, the implementation of its prototype is demonstrated with various examples.

Web-Based Machine Learning Algorithms for Personalized Cardiovascular Disease Risk Assessment

Web-Based Machine Learning Algorithms for Personalized Cardiovascular Disease Risk Assessment

VPI-Mlogs: A web-based machine learning solution for applications in petrophysics

Machine learning is an important part of the data science field. In petrophysics, machine learning algorithms and applications have been widely approached. In this context, Vietnam Petroleum Institute (VPI) has researched and deployed several effective prediction models, namely missing log prediction, fracture zone and fracture density forecast, etc. As one of our solutions, VPI-MLogs is a web-based deployment platform which integrates data preprocessing, exploratory data analysis, visualisation and model execution. Using the most popular data analysis programming language, Python, this approach gives users a powerful tool to deal with the petrophysical logs section. The solution helps to narrow the gap between common knowledge and petrophysics insights. This article will focus on the web-based application which integrates many solutions to grasp petrophysical data.

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer.

ObjectiveTo develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients.MethodsClinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians.ResultsA total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/.ConclusionThe results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.

Application of machine learning in cement price prediction through a web-based system

Cement is one of the most common building materials in the construction industry. Simultaneously, its price fluctuation can affect the success or failure of the construction project's performance. The study aimed to develop a web-based platform that uses machine learning algorithms on historical data of cement prices, petrol prices, diesel prices, interest rate, and exchange rate to predict future prices of cement products. The web-based learning platform was developed using hypertext markup language (HTML), cascading style sheet (CSS), MySQL, and hypertext preprocessor (PHP). For building a reliable machine learning model, python language was used to train the system. The front end, the back end, and the machine learning model were integrated with a flask python framework. A system block diagram was designed to show the web-based learning platform's interfaces. The web-based learning platform's system implementation led to the login page, the home page, database page, and cement price analytics interface. In training the machine learning model to make reliable cement price predictions, the study obtained an 80% fitted model in the linear regression. The web-based machine learning platform was able to predict the prices of cement. The rationale behind the machine learning prediction shown by the scatter plot diagram revealed that the cement increases by 250 naira biannually.

Analisis Data Mining Klasifikasi Berita Hoax COVID 19 Menggunakan Algoritma Naive Bayes

The rapid dissemination of information along with the rapid development of technology along with the massive speed of electronic media and the internet. But the rapid spread of news cannot guarantee that the information and news that we get can be validated from valid sources. Based on data released by Kominfo at the end of 2021, there were 1773 hoax news that were successfully clarified from the hoax news. Then during the Covid-19 pandemic itself, there were various hoaxes circulating in the community. Throughout 2021, the Ministry of Communications and Informatics discovered as many as 723 hoaxes about Covid-19. Based on the background above, the researchers and previous studies have discussed hoax detection in various fields. Such as, fraud detection in online writing style [1], classification of hoax news based on machine learning [3] and the application of nave Bayes and PSO algorithms for classification of hoax news on social media [4]. From here the researchers tried to carry out experiments on the nave Bayes classification algorithm to classify hoax covid 19 news. Based on the results of research that has been done, the nave Bayes model and cross validation can classify hoax news well, the resulting accuracy is 86.3% where 80-90% included in the good classification criteria. The data that is predicted to be incorrect is also not too much from a total of 300 datasets, only 41 are declared incorrect in labeling less than 2% of the total dataset, so it can be concluded that this model can be used as a reference if you want to proceed to a more complex prediction model, for example the model prediction using web-based machine learning.