Machine learning: A multifaceted exploration of trends, regulations, and global impact DOI

Singh Baidwan Rishwinder,

Singh Tusharika,

Kumar Santosh

и другие.

i-manager’s Journal on Future Engineering and Technology, Год журнала: 2024, Номер 19(4), С. 33 - 33

Опубликована: Янв. 1, 2024

The field of Machine Learning (ML) demands a comprehensive exploration encompassing research advancements, industry applications, and emerging regulatory considerations. This article delves into these multifaceted aspects, identifying key trends challenges that are shaping the landscape ML. literature reveals machine learning is rapidly transforming various industries. For instance, in healthcare, ML algorithms achieve accuracy rates exceeding 90% medical image analysis, leading to earlier diagnoses improved patient outcomes. Similarly, nanotechnology, employed design optimize novel materials, enhancing properties by approximately 50% compared traditional methods. However, ethical legal implications Artificial Intelligence (AI) necessitate careful consideration. explores ongoing discussions surrounding regulations responsible development this domain. By offering perspective integrates considerations, analysis aims serve as valuable resource for academics policymakers navigating complexities opportunities associated with learning.

Язык: Английский

SLRNode: node similarity-based leading relationship representation layer in graph neural networks for node classification DOI

Fuchuan Xiang,

Yao Xiao,

Fenglin Cen

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(5)

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

0
A dynamic anchor-based online semi-supervised learning approach for fault diagnosis under variable operating conditions DOI
Wei Li, Zeyi Liu, Pengyu Han

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 130137 - 130137

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
AI-Driven Security Systems and Intelligence Threat Response Using Autonomous Cyber Defense DOI
Salam Al-E’mari, Yousef Sanjalawe,

Fuad Fataftah

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 35 - 78

Опубликована: Апрель 23, 2025

The expanding cyber threat landscape has compelled organizations to adopt AI-driven security systems for robust defense against sophisticated attacks. This chapter explores artificial intelligence in cybersecurity, emphasizing its role intelligent detection, analysis, and response. AI models, including supervised unsupervised learning, deep reinforcement have redefined cybersecurity by enabling behavior-based anomaly detection automated mitigation. Key discussions highlight autonomous making real-time decisions, leveraging adaptive control loops, employing self-healing mechanisms resilience. also examines challenges operational scalability, ethical implications of automation, the necessity human oversight decision-making. findings underscore need synergy between automation expertise foster an intelligent, ecosystem.

Язык: Английский

Процитировано

0
Challenges and potential research directions for machine learning-based cyber-attack detection in IoT networks DOI
Nguyen Quang Hieu,

Bui Duc Manh,

Dinh Thai Hoang

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 375 - 394

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Machine Learning in Information and Communications Technology: A Survey DOI Creative Commons
Ηλίας Δρίτσας, Μαρία Τρίγκα

Information, Год журнала: 2024, Номер 16(1), С. 8 - 8

Опубликована: Дек. 27, 2024

The rapid growth of data and the increasing complexity modern networks have driven demand for intelligent solutions in information communications technology (ICT) domain. Machine learning (ML) has emerged as a powerful tool, enabling more adaptive, efficient, scalable systems this field. This article presents comprehensive survey on application ML techniques ICT, covering key areas such network optimization, resource allocation, anomaly detection, security. Specifically, we review effectiveness different models across ICT subdomains assess how integration enhances crucial performance metrics, including operational efficiency, scalability, Lastly, highlight challenges future directions that are critical continued advancement ML-driven innovations ICT.

Язык: Английский

Процитировано

1
Machine learning: A multifaceted exploration of trends, regulations, and global impact DOI

Singh Baidwan Rishwinder,

Singh Tusharika,

Kumar Santosh

и другие.

i-manager’s Journal on Future Engineering and Technology, Год журнала: 2024, Номер 19(4), С. 33 - 33

Опубликована: Янв. 1, 2024

The field of Machine Learning (ML) demands a comprehensive exploration encompassing research advancements, industry applications, and emerging regulatory considerations. This article delves into these multifaceted aspects, identifying key trends challenges that are shaping the landscape ML. literature reveals machine learning is rapidly transforming various industries. For instance, in healthcare, ML algorithms achieve accuracy rates exceeding 90% medical image analysis, leading to earlier diagnoses improved patient outcomes. Similarly, nanotechnology, employed design optimize novel materials, enhancing properties by approximately 50% compared traditional methods. However, ethical legal implications Artificial Intelligence (AI) necessitate careful consideration. explores ongoing discussions surrounding regulations responsible development this domain. By offering perspective integrates considerations, analysis aims serve as valuable resource for academics policymakers navigating complexities opportunities associated with learning.

Язык: Английский

Процитировано

0