A machine learning‐based approach for wait‐time estimation in healthcare facilities with multi‐stage queues DOI Creative Commons
Amjed Al‐Mousa,

Hamza Al‐Zubaidi,

Mohammad Al‐Dweik

et al.

IET Smart Cities, Journal Year: 2024, Volume and Issue: unknown

Published: March 28, 2024

Abstract Digital technologies have been contributing to providing quality health care patients. One aspect of this is accurate wait times for patients waiting be serviced at healthcare facilities. This naturally a complex problem as there multitude factors that can impact the time. However, becomes even more if patient's journey requires visiting multiple stations in hospital; such having vital signs taken, doing an ultrasound, and seeing specialist. The authors aim provide method estimating time by utilising real dataset transactions collected from major hospital over year. work employs feature engineering compares several machine learning‐based algorithms predict patients' single‐stage multi‐stage services. Random Forest algorithm achieved lowest root mean squared error (RMSE) value 6.69 min among all learning algorithms. results were also compared against formula‐based system used industry, proposed model outperformed existing model, showing improvements 25.1% RMSE 18.9% MAE metrics. These findings indicate significant improvement accuracy predicting techniques.

Language: Английский

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO DOI Creative Commons
Muhammad Hussain

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 42816 - 42833

Published: Jan. 1, 2024

This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing thorough understanding structural components. Subsequently, highlights key innovations introduced in each variant, shedding light on incremental refinements. The includes benchmarked performance metrics, offering quantitative measure variant's capabilities. further presents variants across diverse range domains, manifesting their real-world impact. structured ensures comprehensive examination YOLOs journey, methodically communicating advancements and before delving into domain applications. It envisioned, incorporation concepts such as federated learning can introduce collaborative training paradigm, where models benefit from multiple edge devices, enhancing privacy, adaptability, generalisation.

Language: Английский

Citations

100
Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service DOI
Sarina Aminizadeh, Arash Heidari, Mahshid Dehghan

et al.

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 149, P. 102779 - 102779

Published: Jan. 24, 2024

Language: Английский

Citations

69
Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach DOI Creative Commons
M. A. Alsalem, A.H. Alamoodi,

O. S. Albahri

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 246, P. 123066 - 123066

Published: Jan. 21, 2024

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under new fuzzy environment. To develop such framework, decision matrix has been built, then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) q-ROF2TL-CODAS Combinative Distance-Based Assessment). In integration, utilized assigning the weights evaluation attributes AI, while employed AI applications. Findings show that method effectively attributes. transparency attribute receives highest importance weight (0.173566825), whereas human agency oversight criterion lowest (0.105741901). remaining are distributed between. Moreover, alternative_4 rank order (score 7.370410417), alternative_13 −4.759794397). evaluate validity proposed systematic ranking sensitivity analysis assessments were employed.

Language: Английский

Citations

25
Overview of Big Data Analytics in Modern Astronomy DOI Creative Commons

Muhammad Faaique

International Journal of Mathematics Statistics and Computer Science, Journal Year: 2023, Volume and Issue: 2, P. 96 - 113

Published: Dec. 9, 2023

Astronomers are increasingly compelled to chart the universe with ever greater precision. Projects like Sloan Digital Sky Survey (SDSS), Pan-STARRS, and Large Synoptic Telescope (LSST) generate approximately 100-200 Petabytes of data annually, presenting significant big challenges in terms storage, processing, transfer. The Square Kilometer Array (SKA), an ambitious project involving 130,000 antennas 200 dishes spanning two continents, is scheduled become operational 2028. It will collect 160 terabytes per second, translating 1 petabyte daily. Coping this immense volume necessitates real-time processing analysis, driving need for efficient machine learning image analysis algorithms. Astronomy stands as ideal domain analytics, pushing boundaries analysis. This review paper present intriguing applications scientists, exploring recent technological advancements analytics concerning astronomy. also critically assess strengths weaknesses various approaches, methodologies, or tools used within context astronomy, supported by relevant case studies.

Language: Английский

Citations

28
Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments DOI Creative Commons

Yue Pan,

Andia Foroughi

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Feb. 9, 2024

Abstract Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental caused by neurological problems. In schools educational environments, this may not only hinder child’s learning, but also lead to more crises mental convulsions. order teach students ASD, it essential understand the impact of their learning environment on interaction behavior. Different methods have been used diagnose in past, each own strengths weaknesses. Research into diagnostics has largely focused machine algorithms strategies rather than diagnostic methods. This article discusses many techniques literature, such as neuroimaging, speech recordings, facial features, EEG signals. led us conclude that settings, diagnosed cheaply, quickly, accurately through face analysis. To facilitate speed up processing information among children we applied AlexNet architecture designed edge computing. A fast method detecting disorders from settings using structure. While investigated variety methods, provide appropriate about disorder. addition, produce interpretive features. help who are suffering disease, key factors must considered: potential clinical therapeutic situations, efficiency, predictability, privacy protection, accuracy, cost-effectiveness, lack methodological intervention. The diseases troublesome, so they should identified treated.

Language: Английский

Citations

9
Enhanced Early Autism Screening: Assessing Domain Adaptation with Distributed Facial Image Datasets and Deep Federated Learning DOI Creative Commons
Mohammad Shafiul Alam, Muhammad Mahbubur Rashid

IIUM Engineering Journal, Journal Year: 2025, Volume and Issue: 26(1), P. 113 - 128

Published: Jan. 10, 2025

This study offers a significant advancement in the area of early autism screening by offering diverse domain facial image datasets specifically designed for detection Autism Spectrum Disorder (ASD). It stands out as pioneering effort to analyze two – Kaggle and YTUIA, using federated learning methods adapt differences successfully. The scheme effectively addresses integrity issue sensitive medical information guarantees wide range feature learning, leading improved assessment performance across datasets. By employing Xception backbone remarkable accuracy rate almost 90% is attained all test sets, representing enhancement more than 30% different sets. work contribution research due its unique novel dataset, analytical methods, focus on data confidentiality. resource comprehensive understanding challenges opportunities field ASD diagnosis, catering both professionals aspiring scholars. ABSTRAK: Kajian ini menawarkan kemajuan yang ketara dalam bidang saringan awal autisme dengan menyediakan pelbagai set imej wajah direka khusus untuk pengesanan Gangguan Spektrum Autisme menonjol sebagai usaha perintis menganalisis dua dan menggunakan kaedah pembelajaran teragih menyesuaikan perbezaan jayanya. Skim berkesan menangani isu integriti maklumat perubatan sensitif menjamin ciri meluas, membawa kepada prestasi penilaian lebih baik merentas berbeza. Dengan tunjang teragih, kadar ketepatan luar biasa hampir dicapai semua ujian, mewakili peningkatan daripada ujian Hasil kerja merupakan sumbangan penting penyelidikan kerana unik baharu, analisis digunakan, serta tumpuan kerahsiaan data. Sumber pemahaman menyeluruh mengenai cabaran peluang diagnosis ASD, sesuai para profesional sarjana berminat.

Citations

1
3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data DOI

V. Kavitha,

C. Siva Ram Murthy

Cognitive Neurodynamics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 13, 2025

Language: Английский

Citations

1
An intellectual autism spectrum disorder classification framework in healthcare industry using ViT-based adaptive deep learning model DOI

R Parvathy,

Rajesh Arunachalam,

Sukumaran Damodaran

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107737 - 107737

Published: March 3, 2025

Language: Английский

Citations

1
Digital healthcare framework for patients with disabilities based on deep federated learning schemes DOI
Abdullah Lakhan, Hassen Hamouda, Karrar Hameed Abdulkareem

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 169, P. 107845 - 107845

Published: Dec. 18, 2023

Language: Английский

Citations

16
The Application of Extended Reality in Treating Children with Autism Spectrum Disorder DOI

Weijia Zhao,

Song Xu, Yanan Zhang

et al.

Neuroscience Bulletin, Journal Year: 2024, Volume and Issue: 40(8), P. 1189 - 1204

Published: March 18, 2024

Language: Английский

Citations

6