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Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection
Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems.
Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks
We have conducted a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information.
A Novel IoT-based Framework for Non-Invasive Human Hygiene Monitoring using Machine Learning Techniques
People's personal hygiene habits speak volumes about the condition of taking care of their bodies and health in daily lifestyle.
Pragmatic Clinical Trials in the Rubric of Structural Causal Models
Explanatory studies, such as randomized controlled trials, are targeted to extract the true causal effect of interventions on outcomes and are by design adjusted for covariates through randomization.
CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors
Using CKH, we present a methodological framework for encoding causal priors from various information sources and combining them to derive an SCM.
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset
To explore safety outcomes associated with APD, we aim to build a causal model for delirium in the ICU using large observational data sets connecting various covariates correlated with delirium.
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model
Randomized controlled trials (RCTs) are considered the gold standard for identifying causal relationships.
