Human gender estimation from CT images of skull using deep feature selection and feature fusion.

This research endeavors to prognosticate gender by harnessing the potential of skull computed tomography (CT) images, given the seminal role of gender identification in the realm of identification. The study encompasses a corpus of CT images of cranial structures derived from 218 male and 203 female subjects, constituting a total cohort of 421 individuals within the age bracket of 25 to 65 years. Employing deep learning, a prominent subset of machine learning algorithms, the study deploys convolutional neural network (CNN) models to excavate profound attributes inherent in the skull CT images.

The role of morphometric characteristics in predicting 20-meter sprint performance through machine learning.

The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined.

Analysis of cyber security knowledge gaps based on cyber security body of knowledge.

Due to the increasing number of cyber incidents and overwhelming skills shortage, it is required to evaluate the knowledge gap between cyber security education and industrial needs. As such, the objective of this study is to identify the knowledge gaps in cyber security graduates who join the cyber security workforce. We designed and performed an opinion survey by using the Cyber Security Knowledge Areas (KAs) specified in the Cyber Security Body of Knowledge (CyBOK) that comprises 19 KAs.

A hybrid DNN-LSTM model for detecting phishing URLs.

Phishing is an attack targeting to imitate the official websites of corporations such as banks, e-commerce, financial institutions, and governmental institutions. Phishing websites aim to access and retrieve users' important information such as personal identification, social security number, password, e-mail, credit card, and other account information. Several anti-phishing techniques have been developed to cope with the increasing number of phishing attacks so far.