One of the biggest problems for major airline is predicting flight delay. Airlines try to reduce delays gain loyalty their customers. Hence, a prediction model that airliners can use forecast possible significant importance. In this regards, artificial neural network (ANN) techniques be beneficial application. main challenges using ANNs handling nominal variables. 1-of-N encoding widely used deal with problem, however, method known performance ANN's by introducing multicollinearity. paper,...

Purpose The global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This paper aims to integrate explainable AI into decision-making process in emergency scenarios help mitigate high levels complexity and uncertainty associated with these situations. An solution is designed extract insights opioid overdose (OD) that can government agencies improve their medical response reduce opioid-related deaths....

A robust model for power system load forecasting covering different horizons of time from short-term to long-term is an indispensable tool have a better management the system. However, as horizon in increases, it will be more challenging accurate forecast. Machine learning methods got attention efficient dealing with stochastic pattern and resulting forecasting. In this study, problem case study New England Network studied using several commonly used machine such feedforward artificial...

Demand forecasting is critical for energy systems, as difficult to store and should only be supplied needed. Researchers attempted improve forecasts of consumption. However, they assume independent factors increase at a constant growth rate, which unrealistic. Existing methods are designed determine annual consumption, whereas energy-planning organizations rely on short- or medium-term consumption values. Therefore, we propose new framework that introduces models scenarios. We apply cohort...

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...

There is an ever-increasing disparity between the number of organs needed for transplantation and available donation. As a result, thousands people die every year while waiting organ transplant. Therefore, it now more critical than ever to study factors associated with A better understanding such will help immeasurably in formulating data-driven strategies improving familial consent This research combines machine learning methods network science accurately predict donation outcomes. In this...

Terrorist groups (attackers) always strive to outmaneuver counter-terrorism agencies with different tactics and strategies for making successful attacks. Therefore, understanding unexpected attacks (outliers) is becoming more important. Studying such will help identify the from past events that be most dangerous when are not ready protection interventions. In this paper, we propose a new approach defines terrorism outliers in current location by using non-similarities among interactions. The...

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...

Abstract The issue of left against medical advice (LAMA) patients is common in today’s emergency departments (EDs). This represents a medico-legal risk and may result potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause to “leave advice” vital mitigate potentially eliminate these adverse outcomes. paper proposes framework for studying affect LAMA EDs. integrates machine learning, metaheuristic optimization, model interpretation techniques....

The forecasting of wind speed with high accuracy has been a very significant obstacle to the enhancement power quality, for volatile behavior makes difficult. In order generate more reliable and determine best model different heights, needs be predicted accurately. Recent studies show that soft computing approaches are preferred over physical methods because they can provide fast techniques forecast short-term speed. this study, multilayer perceptron neural network an adaptive fuzzy...

The issue of left before treatment complete (LBTC) patients is common in today’s emergency departments (EDs). This represents a medico-legal risk and may cause revenue loss. Thus, understanding the factors that to “leave complete” vital mitigate potentially eliminate these adverse effects. paper proposes framework for studying affect LBTC outcomes EDs. integrates machine learning, metaheuristic optimization, model interpretation techniques. Metaheuristic optimization used hyperparameter...

The growth period takes place between the ages of 4-14 and concept occupation in children begins to form this period. Vocational choice is affected by students’ interests. This study aims expand age range short-sphere inventory using supervised learning algorithms. In research, Short Sphere Inventory derived from Holland’s theory used as material. includes 8th-grade, high school, university students. a machine learning-based analytical model has been developed that allows be expanded. As...