Titlebook: Data Science and Security; Proceedings of IDSCS Samiksha Shukla,Hiroki Sayama,Durgesh Kumar Mishra Conference proceedings 2024 The Editor(s

彎曲道理

,Enhancing Medical Decision Support Systems with?the?Two-Parameter Logistic Regression Model,n its potential to enhance the reliability of predictions for binary outcome variables in the medical domain. These novel estimators offer a promising solution to the multicollinearity challenge, contributing to more accurate and trustworthy results, ultimately benefiting medical practitioners and r

體貼

Homophily, Mobility and Opinion Formationholds with the highest net worth do not necessarily carry the highest debt. Additionally, households with high net worth and assets may not have high home values, while households with high home values may have relatively lower assets. The study provides actionable insights that can guide credit com

嘲笑

Marcin Jod?owiec,Marek Krótkiewicz, and evolutionary algorithm help in optimizing initial solution sets to a much more optimal solution set for generating ontologies. Overall, a highest average precision percentage of 94.09%, highest average accuracy percentage of 95.09%, highest average recall percentage of 96.09%, and highest aver

Condescending

Thu Tran Minh Nguyen,Thinh Pham Quoc Tranalyzing the workforce skill requirements of the organization. It needs a strategic plan to ensure the appropriate people are in the right roles at the right times. Talent Management is a crucial element of every business’s performance. In this process, data play a pivotal role in evaluating the exis

混合

Anecdote

俗艷

Lecture Notes in Computer Science traditional methods. Without the use of paired training data, the suggested method of Cycle-GAN is a sort of Generative Adversarial Network (GAN) that can learn to translate images from one domain to another. Cycle-GAN has demonstrated its efficacy in various image-to-image translation problems by

IRS

https://doi.org/10.1007/978-3-319-56010-6 of the standout features of the hybrid G-CNN model is its exceptional loss metrics. It achieves the lowest loss at an astonishingly low value of 0.030, and concurrently, it records the lowest validation loss at 0.031. These results clearly establish the superiority of the hybrid G-CNN model in comp

Explosive

Pudendal-Nerve