Cited by Traffic forecasting using LSTM and SARIMA models: A comparative analysis

Predicting green hydrogen production using electrolyzers driven by photovoltaic panels and wind turbines based on machine learning techniques: A pathway to on-site hydrogen refuelling stations DOI

Baki Barış Urhan, Ayşe Erdoğmuş, Ahmet Şakir Dokuz

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 101, P. 1421 - 1438

Published: Jan. 8, 2025

Language: Английский

Citations

Machine learning models for solar forecasting and impact on green hydrogen production costs DOI

Celal Erbay

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 132, P. 225 - 238

Published: May 1, 2025

Language: Английский

Citations

Intelligent Manufacturing in Aerospace: Integrating Industry 4.0 Technologies for Operational Excellence and Digital Transformation DOI

Vineet Bhatia, Sumati Sidharth, Sanjeev Kumar Khare

et al.

Springer series in advanced manufacturing, Journal Year: 2024, Volume and Issue: unknown, P. 389 - 434

Published: Jan. 1, 2024

Language: Английский

Citations

Digital Twin Integration for Enhanced Control in FDM 3D Printing DOI

Sourabh Anand,

Manoj Kumar Satyarthi,

Pushpendra S. Bharti

et al.

Springer series in advanced manufacturing, Journal Year: 2024, Volume and Issue: unknown, P. 373 - 388

Published: Jan. 1, 2024

Language: Английский

Citations

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation DOI

Vimlesh Kumar Ojha,

Sanjeev Goyal, Mahesh Chand

et al.

Concurrent Engineering, Journal Year: 2024, Volume and Issue: 32(1-4), P. 58 - 77

Published: Nov. 5, 2024

Integration of sophisticated technologies such as Internet Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing (AMS). However, implementation data-driven decision making in AMS still remains challenging due to heterogeneity, real-time processing demands, integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption DDDM enhance its decision-making capabilities. consists six stages: stage, sensing knowledge application stage. The proposed leverages extract actionable insights from diverse datasets, integrates CPS create seamless interaction between digital systems, employs IoT acquisition monitoring. is validated through comprehensive case study involving CNC milling machine dataset, demonstrating significant improvements operational efficiency, accuracy, response time. involves detailed collection steps, preprocessing, analysis, showcasing framework’s practical effectiveness. results show that addresses existing challenges provides scalable solution AMS.

Language: Английский

Citations

Securing the Future: The Intersection of IoT, Cybersecurity Implementation, and AI Management DOI

Vikas Kumar, Sangeeta Rani,

Tanvi Rustagi

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

Unlocking the Power of IoT, Machine Learning, and Cloud Computing: Case Studies in Smart Homes DOI

Archna Goyal, Kamlesh Lakhwani, Bhavna Sharma

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

Design of an Iterative Model for Fault Severity Classification Using CNN-LSTM and Attention Mechanisms DOI

Shashi Rathore,

Parul Sahare, Mayur Parate

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

ARPPNet-An improved hybrid deep network for spatial domain steganalysis DOI

Ankita Gupta, Rita Chhikara,

Prabha Sharma

et al.

AIP conference proceedings, Journal Year: 2024, Volume and Issue: 3217, P. 020033 - 020033

Published: Jan. 1, 2024

Language: Английский

Citations

Advancements in self-supervised learning with deep learning DOI

Manoj Wadhwa,

Utpal Shrivastava, Suresh Kumar

et al.

AIP conference proceedings, Journal Year: 2024, Volume and Issue: 3217, P. 020020 - 020020

Published: Jan. 1, 2024

Language: Английский

Citations