Autism Data Classification Using AI Algorithms with Rules: Focused Review

Bioengineering, Год журнала: 2025, Номер 12(2), С. 160 - 160

Опубликована: Фев. 7, 2025

Autism Spectrum Disorder (ASD) presents challenges in early screening due to its varied nature and sophisticated signs. From a machine-learning (ML) perspective, the primary include need for large, diverse datasets, managing variability ASD symptoms, providing easy-to-understand models, ensuring predictive models that can be employed across different populations. Interpretable or explainable classification algorithms, like rule-based decision tree, play crucial role dealing with some of these issues by offering exploited clinicians. These offer transparency decision-making, allowing clinicians understand reasons behind diagnostic decisions, which is critical trust adoption medical settings. In addition, interpretable algorithms facilitate identification important behavioural features patterns associated ASD, enabling more accurate diagnoses. However, there scarcity review papers focusing on classifiers detection from perspective. Thereby this research aimed conduct recent works order provide added value consolidating current research, identifying gaps, guiding future studies. Our would enhance understanding techniques, based data used generate obtain performance trying highlight intervention ways ASD. Integrating advanced AI methods deep learning improve model interpretability, exploration, accuracy ASD-detection applications. While hybrid approach has feature selection relevant detected an efficient manner, transparent explanations decisions. This clinical applications where content as achieving high accuracy.

Язык: Английский

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Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches

Applied Data Science and Analysis, Год журнала: 2024, Номер 2024, С. 121 - 147

Опубликована: Авг. 7, 2024

There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter decision region. These are committed presenting different models way model would be wrong its classification or prediction. The field of study still relatively young and has develop strong bodies scientific research eliminate gaps current knowledge. This paper provides literature review defenses based on highly cited articles conference published Scopus database. Through assessment 128 systematic articles: 80 original papers 48 till May 15, 2024, this categorizes reviews from domains, Graph Neural Networks, Deep Learning Models for IoT Systems, others. posits findings identified metrics, citation analysis, contributions these studies while suggesting area’s further development robustness’ protection mechanisms. objective work basic background defenses, need maintaining adaptability platforms. In context, contribute building efficient sustainable mechanisms AI applications various industries

Язык: Английский

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A Novel Cognitive Rough Approach for Severity Analysis of Autistic Children Using Spherical Fuzzy Bipolar Soft Sets

Cognitive Computation, Год журнала: 2024, Номер unknown

Опубликована: Сен. 6, 2024

Язык: Английский

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Emerging Trends in Applying Artificial Intelligence to Monkeypox Disease: A Bibliometric Analysis

Applied Data Science and Analysis, Год журнала: 2024, Номер 2024, С. 148 - 164

Опубликована: Сен. 8, 2024

Monkeypox is a rather rare viral infectious disease that initially did not receive much attention but has recently become subject of concern from the point view public health. Artificial intelligence (AI) techniques are considered beneficial when it comes to diagnosis and identification through medical big data, including imaging other details patients’ information systems. Therefore, this work performs bibliometric analysis incorporate fields AI bibliometrics discuss trends future research opportunities in Monkeypox. A search over various databases was performed title abstracts articles were reviewed, resulting total 251 articles. After eliminating duplicates irrelevant papers, 108 found be suitable for study. In reviewing these studies, given on who contributed topics or fields, what new appeared time, papers most notable. The main added value outline reader process how conduct correct comprehensive by examining real case study related disease. As result, shows great potential improve diagnostics, treatment, health recommendations connected with Possibly, application can enhance responses outcomes since hasten effective interventions.

Язык: Английский

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Hybrid weights structure model based on Lagrangian principle to handle big data challenges for identification of oil well production: A case study on the North Basra oilfield, Iraq

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109465 - 109465

Опубликована: Окт. 18, 2024

Язык: Английский

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