Surveys

Bias Amplification to Facilitate the Systematic Evaluation of Bias Mitigation Methods.

The future of artificial intelligence (AI) safety is expected to include bias mitigation methods from development to application. The complexity and integration of these methods could grow in conjunction with advances in AI and human-AI interactions. Numerous methods are being proposed to mitigate bias, but without a structured way to compare their strengths and weaknesses. In this work, we presen...

Is more data always better? On alternative policies to mitigate bias in Artificial Intelligence health systems.

The development and implementation of Artificial Intelligence (AI) health systems represent a great power that comes with great responsibility. Their capacity to improve and transform healthcare involves inevitable risks. A major risk in this regard is the propagation of bias throughout the life cycle of the AI system, leading to harmful or discriminatory outcomes. This paper argues that the Europ...

Why do people resist AI-based autonomous cars?: Analyzing the impact of the risk perception paradigm and conditional value on public acceptance of autonomous vehicles.

This study examines the factors that lead to the acceptance of AI-based autonomous vehicles. Despite the considerable importance of AI-based autonomou...

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsaturated fats and antioxidants, lowering the risk of ca...

Demystifying Infusion Pumps: Design of a Cost-Effective Platform for Education and Innovation.

INTRODUCTION: This article presents a cost-effective, modular infusion platform to help diabetes specialists customize and understand infusion pump me...

The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe.

The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to...

Accessible halitosis diagnosis: validating the accuracy of novel AI-based compact VSC measuring instrument.

Halitosis presents a significant global health concern, necessitating the development of precise and efficient testing methodologies owing to the high...

Empowering medical students with AI writing co-pilots: design and validation of AI self-assessment toolkit.

BACKGROUND AND OBJECTIVES: Assessing and improving academic writing skills is a crucial component of higher education. To support students in this end...

On the relationship between music students' negative emotions, artificial intelligence readiness, and their engagement.

This study explored the relationship between negative emotions, engagement, and artificial intelligence (AI) readiness among 323 music students. The r...

Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.

A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospit...

Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.

BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have ...

Development and applications of a machine learning model for an in-depth analysis of pentylenetetrazol-induced seizure-like behaviors in adult zebrafish.

Epilepsy, a neurological disorder causing recurring seizures, is often studied in zebrafish by exposing animals to pentylenetetrazol (PTZ), which indu...

Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal.

This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using on...

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on indiv...

On spectral bias reduction of multi-scale neural networks for regression problems.

In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep ne...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...

The application of machine learning approaches to classify and predict fertility rate in Ethiopia.

Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This s...

Cell clone selection-impact of operation modes and medium exchange strategies on clone ranking.

Bioprocessing has been transitioning from batch to continuous processes. As a result, a considerable amount of resource was dedicated to optimising st...

CPJN: News recommendation with a content and popularity joint network.

Users may click on a news because they are interested in its content or because the news contains important information and is very popular. Modeling ...

Reducing bias in source-free unsupervised domain adaptation for regression.

Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by lever...