Introduction to Artificial Intelligence and Machine Learning

Groom, Frank M.

doi:10.1201/9780367821654-1

Cited by 3 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 3 gives an overview of the sub-areas of the term artificial intelligence. In order to perform stress or geometry optimization, machine learning can be divided into unsupervised, supervised, and reinforcement learning (Womack, 2020). Unsupervised learning is often used to identify relationships in existing data sets.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

“…Figure 5. Data extraction and machine learning training (Womack, 2020) Many alternative ML techniques could be used to fit such training data for various objectives. A range of ML methods, including deep learning, is available in powerful open-source libraries and paid software that can be employed in the above-described ML models for product creation.…”

Section: Structure and Functions Of Artificial Intelligence Approachmentioning

confidence: 99%

“…Figure 6. Schematic structure of an artificial neural network (Womack, 2020;Falter et al, 2022) By training the network, optimization of the artificial neural network in and of itself, the quality of the outputs, and eventually, the performance of the optimization strategy is substantially enhanced (Womack, 2020).…”

Section: Structure and Functions Of Artificial Intelligence Approachmentioning

confidence: 99%

“…With the feedforward approach, networking is only possible at the next higher layer. However, feedback to lower layers could also be made possible with feedback loops (Womack, 2020).…”

Section: Structure and Functions Of Artificial Intelligence Approachmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

et al. 2023

View full text Add to dashboard Cite

Due to the continuous progress in information technology, complex problems of machine elements can be investigated using numerical methods. The focus of these investigations and optimizations often aims to reduce the stresses that occur or to increase the forces and torques that can be transmitted. Interference fit connections are an essential machine element for drive technology applications and are characterized by their economical fabrication. The transmission of external loads over a large contact surface between the shaft and hub makes it less vulnerable to impact loads. These advantages contrast with disadvantages such as the limited transmittable power, the risk of friction fatigue, and stress peaks at the hub edges, which can lead to undesirable and sudden failure, especially in the case of brittle hub materials. Analytical approaches already exist for optimizing these connections, which are expensive, time-consuming, and complex, so a high degree of expert knowledge is required to apply these methods in practice successfully. This paper presents a novel method using the example of optimizing the pressure distribution in the interface of a shrink-fit connection.

show abstract

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Structure and Functions Of Artificial Intelligence Approachmentioning

confidence: 99%

Section: Structure and Functions Of Artificial Intelligence Approachmentioning

confidence: 99%

“…With the feedforward approach, networking is only possible at the next higher layer. However, feedback to lower layers could also be made possible with feedback loops (Womack, 2020).…”

Section: Structure and Functions Of Artificial Intelligence Approachmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For instance, in April 2019, the US FDA began considering AI-based software as a medical device (SaMD) by adopting the International Medical Device Regulators Forum (IMDRF) definition. 66 US-based regulatory guidance and the European Artificial Intelligence Act already account for regulatory compliant reporting of change protocols, a change that accounts for potential problems identified during and after deployment of continuously learning AI models. 67 Additional regulatory standards and legislation are underway to address potential liability issues that may arise from the use of DL in clinical applications.…”

Section: Lack Of Societal Acceptance and Regulatory Standardsmentioning

confidence: 99%

Deep learning in optical coherence tomography: Where are the gaps?

Ran

Cheung

et al. 2023

Clinical Exper Ophthalmology

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is a non‐invasive optical imaging modality, which provides rapid, high‐resolution and cross‐sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post‐processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL‐based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL‐based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real‐world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.

show abstract

Artificial Intelligence, Consumers, and the Experience Economy

Chang

Mukherjee

2022

Encyclopedia of Data Science and Machine Learning

View full text Add to dashboard Cite

A fundamental challenge for businesses catering to the modern experience economy is to understand what consumers want in experiential products and services. The article discusses the nascent knowledge base on artificial intelligence (AI) and consumers in the context of experiential products and the emerging experience economy, which are key applications for marketing practice. Furthermore, it draws upon the formal academic literature and leading practitioner press to synthesize empirical evidence, with suggestions and recommendations for marketing practitioners and policymakers tasked with driving the deployment and success of AI in the experience economy.

show abstract

Introduction to Artificial Intelligence and Machine Learning

Cited by 3 publications

References 1 publication

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

Deep learning in optical coherence tomography: Where are the gaps?

Artificial Intelligence, Consumers, and the Experience Economy

Contact Info

Product

Resources

About