Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis.

In today's digitalized era, the usage of Android devices is being extensively witnessed in various sectors. Cybercriminals inevitably adapt to new security technologies and utilize these platforms to exploit vulnerabilities for nefarious purposes, such as stealing users' sensitive and personal data. This may result in financial losses, discredit, ransomware, or the spreading of infectious malware and other catastrophic cyber-attacks.

Artificial Intelligence and Patient Autonomy in Obesity Treatment Decisions: An Empirical Study of the Challenges.

Background This study aims to explore the factors associated with artificial intelligence (AI) and patient autonomy in obesity treatment decision-making. Methodology A cross-sectional, online, descriptive survey design was adopted in this study. The survey instrument incorporated the Ideal Patient Autonomy Scale (IPAS) and other factors affecting patient autonomy in the AI-patient relationship.

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis.

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks.

Graphene Nanoflake Uptake Mediated by Scavenger Receptors.

The biological interactions of graphene have been extensively investigated over the last 10 years. However, very little is known about graphene interactions with the cell surface and how the graphene internalization process is driven and mediated by specific recognition sites at the interface with the cell. In this work, we propose a methodology to investigate direct molecular correlations between the biomolecular corona of graphene and specific cell receptors, showing that key protein recognition motifs, presented on the nanomaterial surface, can engage selectively with specific cell receptors.

Identification of Receptor Binding to the Biomolecular Corona of Nanoparticles.

Biomolecules adsorbed on nanoparticles are known to confer a biological identity to nanoparticles, mediating the interactions with cells and biological barriers. However, how these molecules are presented on the particle surface in biological milieu remains unclear. The central aim of this study is to identify key protein recognition motifs and link them to specific cell-receptor interactions.