[PDF][PDF] Emerging cybersecurity and privacy threats of chatgpt, gemini, and copilot: Current trends, challenges, and future directions

M Arsal, B Saleem, S Jalil, M Ali, M Zahra, AU Rehman… - 2024 - preprints.org
Generative AI chatbots have emerged as a significant scientific contribution. They can
produce text, images, audio, and video, and their applications are vast and varied in every …

[HTML][HTML] Analyzing Tor Browser Artifacts for Enhanced Web Forensics, Anonymity, Cybersecurity, and Privacy in Windows-Based Systems

MS Javed, SM Sajjad, D Mehmood, K Mansoor, Z Iqbal… - Information, 2024 - mdpi.com
The Tor browser is widely used for anonymity, providing layered encryption for enhanced
privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities …

A survey of cybersecurity laws, regulations, and policies in technologically advanced nations: A case study of Pakistan to bridge the gap

B Saleem, M Ahmed, M Zahra, F Hassan… - International …, 2024 - Springer
In the rapidly evolving digital landscape, technology permeates every aspect of global
society. As revolutionary developments continue to unfold, we witness the seamless …

Wrapper methods for multi-objective feature selection

UF Njoku, A Abelló Gamazo, B Bilalli… - … , Greece, March 28 …, 2023 - upcommons.upc.edu
The ongoing data boom has democratized the use of data for improved decision-making.
Beyond gathering voluminous data, preprocessing the data is crucial to ensure that their …

Fairness optimisation with multi-objective swarms for explainable classifiers on data streams

D Pham, B Tran, S Nguyen, D Alahakoon… - Complex & Intelligent …, 2024 - Springer
Recently, advanced AI systems equipped with sophisticated learning algorithms have
emerged, enabling the processing of extensive streaming data for online decision-making in …

Evolutionary Algorithms for Fair Machine Learning

A Freitas, J Brookhouse - Handbook of Evolutionary Machine Learning, 2023 - Springer
At present, supervised machine learning algorithms are ubiquitously used to learn predictive
models that have a major impact on people's lives. However, the vast majority of such …

Optimizing fairness tradeoffs in machine learning with multiobjective meta-models

WG La Cava - Proceedings of the Genetic and Evolutionary …, 2023 - dl.acm.org
Improving the fairness of machine learning models is a nuanced task that requires decision
makers to reason about multiple, conflicting criteria. The majority of fair machine learning …

Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams

P Amarasinghe, D Pham, B Tran, S Nguyen… - Proceedings of the …, 2024 - dl.acm.org
This paper introduces a novel approach, evolutionary multi-objective optimisation for
fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine …

An Explainable Feature Selection Approach for Fair Machine Learning

Z Yang, Z Wang, C Huang, X Yao - International Conference on Artificial …, 2023 - Springer
As machine learning (ML) algorithms are extensively adopted in various fields to make
decisions of importance to human beings and our society, the fairness issue in algorithm …

[PDF][PDF] Fairness-aware genetic-algorithm-based few-shot classification

D Wang, L Cheng, T Wang - Mathematical Biosciences and …, 2023 - aimspress.com
Artificial-intelligence-assisted decision-making is appearing increasingly more frequently in
our daily lives; however, it has been shown that biased data can cause unfairness in …