Machine learning (ML) is transforming healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, traditional ML workflows require specialized skills, infrastructure, resources, limiting accessibility for many professionals. This paper explores how Google Cloud's BigQuery simplifies the development deployment of models using SQL, reducing technical barriers. Through a case study on diabetes prediction Diabetes Health Indicators Dataset, we...

We employ a time-dependent Hurst analysis to identify EEG signals that differentiate between healthy controls and combat-related PTSD subjects. The exponents, calculated using rescaled range analysis, demonstrate significant differential response samples which may lead diagnostic applications. To overcome the non-stationarity of data, we apply an appropriate window length wherein data displays stationary behavior. then use exponents for each channel as hypothesis test statistics differences...

Abstract Models of the stock market often focus on predicting direction market. Instead following this approach, we created a model to predict daily absolute percent change S&P 500. An accurate metric would greatly increase profitability option trading strategies such as straddles and iron condors. In project, novel features were based historical data fed machine learning algorithms Decision Trees, Rule Based Classifiers, K-mean Clusters, Kernels. our findings, Trees Kernels showed an...

<title>Abstract</title> Seismic events present a significant global threat, underscoring the need for effective models to provide insights into these natural disasters. This paper addresses critical advanced seismic event analysis by combining traditional data with cutting-edge machine learning models. The primary objective is develop that classify different types based on their geological and characteristics forecast magnitude. activities categorized groups magnitude enhance understanding...

Clusters of genes in co-expression networks are commonly used as functional units for gene set enrichment detection and increasingly features (attribute construction) statistical inference sample classification. One the practical challenges clustering these purposes is to identify an optimal partition network where individual clusters neither too large, prohibiting interpretation, nor small, precluding general inference. Newman Modularity a spectral algorithm that automatically finds number...

In this project the EEG – electroencephalogram - channel(s) will be characterized to diagnose PTSD Post-traumatic stress disorder cases. For aim, we applied boosting methods including a combination of K-mean and Support Vector Machine (SVM) models find feature weights detect We classified 32 channels 12 subjects 6 healthy controls using 6-mean classifier. The linear SVM found distinguished within each subject for cluster. It was that significant F4, F8, Pz are smaller in than subjects. This...

We introduce kernel smoothing method to extract the global trend of a time series and remove short scales variations fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that multifractality nature TEPIX returns is due both fatness probability density function long range correlations between them. MF-DFA results help us understand how genetic algorithm methods act. Then we utilize recently developed for carrying out successful forecasts in financial deriving...

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, Random Forest. The models, particularly Bayes Forest, demonstrate high effectiveness, as shown through data visualizations. research concludes that integrating these analytical methods significantly...

The periodic table of elements includes 92 with many unknown properties like melting point, boiling heat vaporization, and molar capacity some specific such as Curium, Berkelium, Californium, Einsteinium. Physicists, chemists, other scientists have done successful experiments to predict these mysterious features using the first principal methods. But still been unclear. In this project we apply Machine Learning models linear logistic regression unknow values. known values split train test...

<title>Abstract</title> This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Naïve Bayes, K-Mean Clustering, Random Forest. The models, particularly Bayes Forest, demonstrate high effectiveness, as shown through data visualizations. research concludes that integrating these analytical...

Dental health assessment is a critical component of overall well-being, and advancements in computer vision deep learning have opened new avenues for automating enhancing this process. In study, we present comprehensive approach to dental cavity analysis, spanning localization, quantification, visualization. Our methodology leveraged diverse dataset colored images that had been meticulously augmented annotated. The You Only Look Once model was employed precise providing bounding box...

In this project, the electroencephalogram (EEG) channel(s) is used to better characterize post-traumatic stress disorder (PTSD). For aim, we applied boosting methods along with a combination of k-means and Support Vector Machine (SVM) models find diagnostic channels PTSD cases healthy subjects. We grouped 32 12 subjects (6 6 controls) using k-means. Channels brain are by clustering method most similar part brain. This approach uses SVM performing classification based on cluster classes been...

The purpose of this retrospective study is to measure machine learning models' ability predict glaucoma drainage device failure based on demographic information and preoperative measurements. medical records 165 patients were used. Potential predictors included the patients' race, age, sex, intraocular pressure (IOP), visual acuity, number IOP-lowering medications, type previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in less 20% from baseline,...

Purpose: Boron deficiency can be a limiting factor for the flowering and fruit production of olive orchards. The appropriate time foliar spray needs to found in each environment. Research method: Effect (350 mg L-1 from boric acid) after harvest (Stage 1), during flower bud differentiation 2), anthesis 3) alone or combination, on set percentage, leaf inflorescence boron, zinc, iron concentration, soluble carbohydrates content leaves three commercial cultivars was investigated. Findings:...

Abstract Models of the stock market often focus on predicting direction either up or down. Instead following that approach, this paper created a model for daily absolute percent change S&amp;P 500. An accurate metric would greatly increase profitability option trading strategies such as straddles and iron condors. In publication, novel features were based historical data fed to machine learning algorithms Decision Trees, Rule Based Classifiers, K-mean Clusters, Kernels. our findings, Trees...

Heart disease is a significant global health concern, and accurate diagnosis essential for the effective treatment. In this study, we focus on utilizing Support Vector Machine (SVM) algorithm with radial basis function (RBF) kernel to develop heart classification model. The SVM model RBF achieves an accuracy of 91.85%, precision, recall, F1-score metrics supporting model's ability correctly identify positive instances. To support our results, 5-mean clustering method classified data. We...