Customers confidence and trust towards privacy policy: a conceptual research of hotel revenue management

2023

Preprint

The purpose of the paper is development of a conceptual model for the representation of knowledge as an activeintellectual substance and, on this basis, study of metaphysics of knowledge transformation process being produced both individuallyand collectively in the practice of organizations. The first principle of knowledge engineering, as Edward Albert Feigenbaum noted,says that the power in solving problems that an intellectual subject (person or machine) manifests in the process of activity dependsprimarily on its knowledge base, and only secondly on the methods of inference used. Strength is hidden in knowledge. The processof producing knowledge is permanent and does not depend on whether an individual is going to use this knowledge or not.Knowledge constantly produces new knowledge regardless of the owner's desire. Besides that, knowledge can’t arise from nothing,but always – from some knowledge obtained earlier. As well as the intelligence, knowledge is an emergent instance arising from thecollective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta). Idiosyncrasy of this interaction isexpressed precisely in the creation of new knowledge.

Section: Genetic Algorithmsmentioning

confidence: 99%

Organizational Knowledge Transformation and Selforganizing Knowledge Representation Model

2023

Preprint

“…Reference [45] proposed a heart disease prediction framework called "Hybridization" that combined several machine learning algorithms into a single model. The Cleveland datasets from the online machine learning repository of the UCI consisting of 303 instances and 14 features were used in the model training and testing processes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review of Data Mining and Machine Learning Techniques for Predicting Heart Disease

2023

Preprint

: Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart diseasediagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostictools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequatepreventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors.Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures areinadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed bydifferent scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decisionsupport systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques.Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widelyemployed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in thecreation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporatemultiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

“…The earlier data sets have focused only on a limited set of distractions and many of them are not publicly available. StateFarm's distracted driver detection competition on Kaggle defined ten postures to be detected [38]. This was the first set of data to consider a wide variety of distractions and was available to the public.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Distracted Drivers Detection Using Deep Learning

2023

Preprint

In the last few years, the number of road accidents is increasing worldwide. According to the World HealthOrganization the most common cause behind these accidents is driver’s distraction and in many cases is caused by the use of a mobilephone. An attempt to develop a system for detecting distracted drivers and warn the responsible person against it was done. Thesystem is a CNN based system that detects and identifies the cause of distraction. The base architecture for the CNN is VGG-16 andis modified for this task. Various activation functions (Leaky ReLU, DReLU, SELU) were used in order to investigate performance.Also, the performance of a lightweight attention module (squeeze-and-excitation) was evaluated. Experimental results show that thesystem outperforms earlier lightweight models in literature achieving an accuracy of 95.82%.