This study focuses on the application of machine learning methods for predicting cardiac diseases, aiming to develop accurate and interpretable diagnostic models. The primary objective is to enhance prediction accuracy and identify key risk factors. Various classification algorithms, including Random Forest, Gradient Boosting, Support Vector Machines (SVM), and Neural ...

The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The ...

Automating real-time sign language translation through deep learning and machine learning techniques can greatly enhance communication between the deaf community and the wider public. This research investigates how these technologies can change the way individuals with speech impairments communicate. Despite advancements, developing accurate models for recognizing ...

The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem ...

This article presents the implementation of a machine learning-based face anti-spoofing method to enhance the security of an educational information portal for university students. The study addresses the challenge of preventing academic dishonesty by ensuring that only authorized individuals can complete intermediate and final assessment tasks. The proposed method leverages ...