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Armin Soltan and Peter Washington
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Maryam Badar and Marco Fisichella
Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, et...
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Pin-Hung Juan and Ja-Ling Wu
In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non...
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Hamed Taherdoost and Mitra Madanchian
In recent years, artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. This comparative review investigates the evolving landscape of AI advancements, providing a thorough e...
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Shiva Raj Pokhrel, Jonathan Kua, Deol Satish, Sebnem Ozer, Jeff Howe and Anwar Walid
We introduce a novel multipath data transport approach at the transport layer referred to as ?Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control? (DDPG-MPCC), which leverages deep reinforcement learning to enhance co...
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Elham Albaroudi, Taha Mansouri and Ali Alameer
The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is...
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Tiago P. Pagano, Rafael B. Loureiro, Fernanda V. N. Lisboa, Gustavo O. R. Cruz, Rodrigo M. Peixoto, Guilherme A. de Sousa Guimarães, Ewerton L. S. Oliveira, Ingrid Winkler and Erick G. Sperandio Nascimento
The majority of current approaches for bias and fairness identification or mitigation in machine learning models are applications for a particular issue that fails to account for the connection between the application context and its associated sensitive...
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Ishaani Priyadarshini
Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual?s educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to max...
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Lu Han, Xiaohong Huang, Dandan Li and Yong Zhang
In the ring-architecture-based federated learning framework, security and fairness are severely compromised when dishonest clients abort the training process after obtaining useful information. To solve the problem, we propose a Ring- architecture-based ...
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Tiago P. Pagano, Rafael B. Loureiro, Fernanda V. N. Lisboa, Rodrigo M. Peixoto, Guilherme A. S. Guimarães, Gustavo O. R. Cruz, Maira M. Araujo, Lucas L. Santos, Marco A. S. Cruz, Ewerton L. S. Oliveira, Ingrid Winkler and Erick G. S. Nascimento
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study examines ...
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