Machine Learning Models With Hyperparameter Optimization for Voice Pathology Classification on Saarbrücken Voice Database.

Early diagnosis and referral are crucial in the treatment of voice disorders. Contemporary investigations have indicated the efficacy of voice pathology detection systems in significantly contributing to the evaluation of voice disorders, facilitating early diagnosis of such pathologies. These systems leverage machine learning methodologies, widely applied across diverse domains, and exhibit particular potential in the realm of voice pathology classification.

Significance of measuring anthropometric and atherogenic indices in patients with polycystic ovary syndrome.

Background: Polycystic ovary syndrome (PCOS) is a prevalent hormonal disorder affecting 5-15% of women of reproductive age, characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology. PCOS is associated with metabolic disturbances such as dyslipidemia, insulin resistance (IR), and an increased risk of type 2 diabetes (T2DM) and cardiovascular disease.

Objective: The aim of this study is to apply new anthropometric indices [body adiposity index (BAI), visceral adiposity Index (VAI), lipid accumulation product (LAP), body roundness index (BRI), a body shape index (ABSI)] and new atherogenic indices [Castelli index-I, Castelli index-II, atherogenic risk of plasma (AIP), atherogenic coefficient (AC), lipoprotein combined index (LCI), triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio, metabolic score for insulin resistance (METS-IR), triglyceride glucose (TyG) index, triglyceride glucose-dody mass (TyG-BMI) index, triglyceride glucose-waist circumference (TyG-WC) index] metabolic score of insulin resistance to patients with PCOS.

ARE THE FACTORS AFFECTING ADVERSE PREGNANCY OUTCOMES DIFFERENT IN OBESE GESTATIONAL DIABETES PREGNANT WOMEN RECEIVING DIET OR INSULIN TREATMENT?

Context: We aimed to examine the factors affecting adverse gestational outcome in gestational diabetes (GDM) patients, who were grouped as obese and normal- weight, having only-diet, or insulin treatments.

Subjects And Methods: The study included 373 patients, treated with diet or insulin. These patients were sub-grouped as obese and non-obese, and examined retrospectively.

A novel cloning template designing method by using an artificial bee colony algorithm for edge detection of CNN based imaging sensors.

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors.

Stable modeling based control methods using a new RBF network.

This paper presents a novel model with radial basis functions (RBFs), which is applied successively for online stable identification and control of nonlinear discrete-time systems. First, the proposed model is utilized for direct inverse modeling of the plant to generate the control input where it is assumed that inverse plant dynamics exist. Second, it is employed for system identification to generate a sliding-mode control input.