Introduction to Azure Machine Learning

Soh, Julian; Singh, Priyanshi

doi:10.1007/978-1-4842-6405-8_4

Cited by 3 publications

(3 citation statements)

References 0 publications

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“…Models were implemented using the TensorFlow Framework 2.3.0 and Python 3.6.9 on a 12 GB NVIDIA Tesla K80 GPU and an Intel(R) 2.3 GHz Xeon(R) micro-processor. To optimize network parameters, we applied Azure Machine Learning pipelines [29] . The platform allows automating hyperparameter tuning and to run experiments in parallel to efficiently optimize hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

Tran-Anh

Nguyen-Trong

et al. 2022

Pervasive and Mobile Computing

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Section: Methodsmentioning

confidence: 99%

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

Tran-Anh

Nguyen-Trong

et al. 2022

Pervasive and Mobile Computing

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“…Here, AI tools can execute codes for all kinds of machine learning algorithms after giving the proper parameter values and weights, which thus works in a similar way to machine learning algorithms made manually by writing codes. Moreover, Azure machine learning is also integrated with python and Jupyter notebook [28]. Microsoft Azure also offers a variety of services such as Power BI, virtual machines, SQL services, different app development tools and databases, IoT tools, API services, a firewall service, a gateway service, and security gateways.…”

Section: Machine Learning Algorithms For Optimizationmentioning

confidence: 99%

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

et al. 2022

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With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.

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“…The reviewed works seek to resolve the identification of needs quickly, in such a way that their results contribute to decision-making effectively and efficiently. The methods developed in these works allow identification of the current needs of the students [15,16]. In addition, it is important to consider that after the pandemic and isolation the needs of students have changed.…”

Section: Introductionmentioning

confidence: 99%

Proposal for the Analysis of the State of Learning in University Students with the Inclusion of ICT in the Classroom

2022

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The inclusion of information and communication technologies in education has become a priority for all universities. To meet this need, there are several research works that have dealt with the subject for several decades. However, for its inclusion, the analysis of each institution is necessary since the needs of the university population and the resources for its application change according to each situation. This work seeks to create a method that allows establishing the needs and doubts of students about the use of educational technologies in the classroom without affecting their performance. For this, a process has been designed that identifies learning needs, through the validation of data obtained from surveys and the monitoring of the academic efficiency and learning of a cohort of students. The follow-up includes a period of four years from 2019 to 2022. This follow-up allowed establishing three different realities, in 2019 the academic data was analyzed in a face-to-face education model, from 2020 to 2021 the follow-up was included in a remote model with the use of technologies as a communication channel and in 2022 these were included as a learning component, which marked an in-depth analysis of student performance and how technology affected their learning.

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Introduction to Azure Machine Learning

Cited by 3 publications

References 0 publications

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

Proposal for the Analysis of the State of Learning in University Students with the Inclusion of ICT in the Classroom

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