A Real- Time Machine learning based cloud computing Architecture for Smart Manufacturing

Kishore, Sajja Krishna; Vasukidevi, G; Prasad, E. Poorna Chandra; Patnala, Tulasi Radhika; Reddy, V. Prabhakar; Chanda, Pramit Brata

doi:10.1109/icaaic53929.2022.9792860

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lightweight and computationally inexpensive designs are two recommended criteria for SAAS application development [29] . Complex network architecture depends on multiple intermediate algorithms designed to increase accuracy and is unsuitable for cloud applications because of excessive resource consumption [30] . The state-of-the-art approaches in the literature review show that the research direction is moving towards the sophistication of CNN architecture in corporations with various image enhancement and classification algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…У випадку, коли традиційні обчислювальні методи вже не можуть проаналізувати всю необхідну інформацію, алгоритми ML працюють гнучкіше, легко масштабуються та автоматизують громіздкі обчислення. Для аналізу даних, розташованих на різних пристроях, доцільно використовувати розподілені алгоритми машинного навчання [6][7][8].…”

Section: особливості обробки даних у розподілених системахunclassified

Investigation of Distributed Matrix Factorisation Efficiency in the Industrial Systems

Hordiichuk-Bublivska

2023

ICTEE

View full text Add to dashboard Cite

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

show abstract

“…The architecture of the framework makes use of cutting-edge communication protocols, like MQTT or CoAP, to enable smooth data transfer between devices. The [5] framework's intelligence is further enhanced by machine learning algorithms, which allow for autonomous decision-making and predictive analytics. This combination of technologies enables industrial systems to anticipate possible future situations and proactively adapt to them, rather than just responding to the state of affairs as it exists now.…”

Section: Introductionmentioning

confidence: 99%

Navigating Industry 4.0 Frontiers: A Scalable and Resilient Next-Generation IoT Framework to Implement Future Advancements in Smart and Adaptive Industrial Systems

Bireshwar Ganguly,

2024

jes

View full text Add to dashboard Cite

The emergence of Industry 4.0 signifies a paradigm shift in industrial systems, characterized by the amalgamation of digital technologies with tangible operations. The goal of this study is to present a state-of-the-art, scalable, and robust Internet of Things (IoT) framework that will enable future innovations in intelligent and adaptable industrial systems to be seamlessly integrated. Our framework gives scalability first priority in response to Industry 4.0's dynamic nature, which is marked by fast technical evolution and rising connection in order to handle the expanding ecosystem of networked devices. The suggested structure places a strong emphasis on resilience and is designed to resist setbacks and guarantee the continuation of vital industrial processes. Our framework improves industrial systems' intelligence by utilizing edge computing, machine learning techniques, and improved communication protocols. This allows the systems to self-adapt to changing situations. Moreover, it adopts a modular architecture that facilitates interoperability and makes it simple to integrate various devices and technologies. Our IoT framework creates a solid, flexible, and future-proof industrial environment with this all-encompassing strategy, enabling businesses to confidently and effectively traverse Industry 4.0's frontiers.

show abstract

A Real- Time Machine learning based cloud computing Architecture for Smart Manufacturing

Cited by 5 publications

References 15 publications

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Investigation of Distributed Matrix Factorisation Efficiency in the Industrial Systems

Navigating Industry 4.0 Frontiers: A Scalable and Resilient Next-Generation IoT Framework to Implement Future Advancements in Smart and Adaptive Industrial Systems

Contact Info

Product

Resources

About