Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management DOI Creative Commons
Maryam Bagheri, Mohsen Bagheritabar,

Sohila Alizadeh

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 296 - 296

Published: Dec. 31, 2024

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding how these models are divided analyzed, leaving gaps in normalization benchmarking. present research usually overlooks holistic for comparing ML-enabled ISs, significantly considering pivotal function criteria like precision, sensitivity, specificity. To address gaps, we conducted broad exploration 306 state-of-the-art papers to novel taxonomy IS management. We categorized studies six key areas, namely systems, treatment-planning monitoring resource allocation preventive hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is most effective parameter throughout all models. In addition, majority were published 2022 2023, with MDPI as leading publisher Python prevalent programming language. This extensive synthesis not only bridges but also proposes actionable insights ML-powered

Language: Английский

From Data to Decisions: The Power of Machine Learning in Business Recommendations DOI Creative Commons
Kapilya Gangadharan,

Anoop Purandaran,

Malathi Kanagasabai

et al.

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 17354 - 17397

Published: Jan. 1, 2025

Language: Английский

Citations

1
Nature‐Inspired Meta‐Heuristic Algorithms for Resource Allocation in the Internet of Things DOI Open Access

Fatemeh Amirghafouri,

Ali Akbar Neghabi, Hassan Shakeri

et al.

International Journal of Communication Systems, Journal Year: 2025, Volume and Issue: 38(5)

Published: Feb. 17, 2025

ABSTRACT The Internet of Things (IoT) is a paradigm‐shifting concept that helps realize an acquisition, processing, and analytical global network, digitizing tangible entities to enhance efficiency safety in various smart cities, healthcare, Industry 4.0 domains. However, whereas IoT scales, with several heterogeneous devices diverse, varied capabilities service demands, cloud resource management usually faces the challenge intricate complexity efficiently allocating resources despite demand for quality (QoS). Hence, this paper systematically reviews nature‐inspired metaheuristic algorithm applications allocation solving NP‐hard problems. We summarize recent advances methods, including comparisons against traditional methods. also discuss practical feasibility scaling issues real‐world scenarios. Further, we have highlighted few gaps current literature provided recommendations on specific topics future research, thereby indicating how develop scalable, efficient solutions meet IoT's ever‐evolving demands.

Language: Английский

Citations

0
Quantum bee-inspired algorithm using quantum circuit and gradient descent optimizer on product recommendation DOI

P. Bhaskaran,

S. Prasanna

Evolutionary Intelligence, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 1, 2025

Language: Английский

Citations

0
Towards Efficient Information Retrieval in Internet of Things Environments Via Machine Learning Approaches DOI
Qin Yuan,

Yuping Lai

Journal of The Institution of Engineers (India) Series B, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 17, 2024

Language: Английский

Citations

1
A comparative analysis of machine learning techniques for building cooling load prediction DOI

Saeideh Havaeji,

Pouya Ghanizadeh Anganeh,

Mehdi Torbat Esfahani

et al.

Journal of Building Pathology and Rehabilitation, Journal Year: 2024, Volume and Issue: 9(2)

Published: July 9, 2024

Language: Английский

Citations

1
Improving Recommendation System Accuracy Augmenting User Profile with Matrix Factorization DOI

Sanjeev Dhawan,

Kulvinder Singh,

Manoj Kumar Yadav

et al.

Published: May 9, 2024

Language: Английский

Citations

0
Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management DOI Creative Commons
Maryam Bagheri, Mohsen Bagheritabar,

Sohila Alizadeh

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 296 - 296

Published: Dec. 31, 2024

The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective management by improving patient monitoring, diagnostic accuracy, and treatment optimization. Notwithstanding its revolutionizing capacity, the area lacks a systematic understanding how these models are divided analyzed, leaving gaps in normalization benchmarking. present research usually overlooks holistic for comparing ML-enabled ISs, significantly considering pivotal function criteria like precision, sensitivity, specificity. To address gaps, we conducted broad exploration 306 state-of-the-art papers to novel taxonomy IS management. We categorized studies six key areas, namely systems, treatment-planning monitoring resource allocation preventive hybrid systems. Each category was analyzed depending on significant variables, uncovering that adaptability is most effective parameter throughout all models. In addition, majority were published 2022 2023, with MDPI as leading publisher Python prevalent programming language. This extensive synthesis not only bridges but also proposes actionable insights ML-powered

Language: Английский

Citations

0