Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

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Variants of history models used for training the general model using data of 40 patients. by Tomasz Gutowski (17200042)

Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms. by Aref Andishgar (18155608)

Simulated trajectories from real data, optimal schedule, and nearest optimal schedule. by Samuel Christensen (9922554)

Event-driven data flow processing. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>. by Karl W. Schulz (14309461)

Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf by Luuk A. van Duuren (12001607)

The proposed framework model–HMLFSM. by Murad Al-Rajab (10661360)

“…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …”

Comparison of the proposed model, with others in the literature. by Murad Al-Rajab (10661360)

The number of relevant genes (features) selected by the proposed model on each phase. by Murad Al-Rajab (10661360)

Predictive model-building process. by Mukhtar Ijaiya (18935122)

“…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …”

Comparison of models performance metrics. by Mukhtar Ijaiya (18935122)

“…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …”

Schematic architecture of the proposed hybrid SVM-PSO-AI framework for cardiovascular risk prediction: It has four input vectors V1 (Medical Imaging), V2 (Clinical Data), V3 (Genom... by Medhat A. Tawfeek (22522087)

Consort diagram for the study. by Nicola Lazzarini (3528197)

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Performance confidence intervals. by Nicola Lazzarini (3528197)

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Baseline characteristics of patients in the study cohort. by Nicola Lazzarini (3528197)

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Input features. by Nicola Lazzarini (3528197)

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Most important predictive features. by Nicola Lazzarini (3528197)

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Age feature importance. by Nicola Lazzarini (3528197)

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Machine learning vs. clinicians. by Nicola Lazzarini (3528197)

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