A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...
Abstract: In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter ...
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Abstract: To address the challenges in network anomaly detection, such as data label imbalance and the poor performance of traditional Support Vector Machines (SVM) in fitting high-dimensional, ...
ABSTRACT: This article explores the use of Support Vector Machines (SVM) for diagnosing diabetes based on fourteen medical and behavioral variables. Following a theoretical overview of diabetes and ...
Ms. Mutcherson is a professor at Rutgers Law School. Right now in an Atlanta hospital room lies a 30-year-old nurse and mother, Adriana Smith. Ms. Smith, who is brain-dead, has been connected to life ...
1 San Juan Bautista School of Medicine, Caguas, Puerto Rico, United States 2 Independent Researcher, Monmouth County, NJ, United States Background: In many countries, patients with headache disorders ...
In the era of big data and artificial intelligence, machine learning is one of the hot issues in the field of credit rating. On the basis of combing the literature on credit rating methods at home and ...
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