An artificial neural network tool to support the decision making of designers for environmentally conscious product development2022

– Special issue of International Journal of Pattern Recognition and Artificial Intelligence
– Expands upon papers from FLAIRS conference, covers various AI techniques and applications

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10.1016/j.eswa.2022.118679

In this article , an Artificial Neural Network (ANN) model is used to estimate the trade-off between environmental load and cost effectiveness of a product’s life cycle design, which can assist the designers in their decision-making to choose environmentally benign design parameters of products.

– AI and computational technology are revolutionizing interior design graphics and modeling.
– These technologies offer benefits such as design iterations, material visualization, and time optimization.

– AI-based tools are transforming oncology clinical applications for personalized care.
– Challenges in applying AI-based tools in cancer care are discussed.

– The paper explores healthcare staff perceptions on the benefits and challenges of using AI predictive tools in clinical decision-making.
– The study identifies opportunities for the application of AI predictive tools in clinical practice.

The paper discusses the development of an AI tool based on an Artificial Neural Network (ANN) model to assist designers in making decisions for environmentally conscious product development.

– The AI tool can assist designers in choosing environmentally friendly design parameters.
– The tool enables companies to select more sustainable designs.

– Digital technology and tools are being used in the design industry.
– Artificial Intelligence (AI) is one of the latest computational technologies being utilized.

– The paper reviews existing AI technology and its potential applications in manufacturing systems.
– The paper discusses tools and techniques of AI relevant to the manufacturing environment.

– The paper discusses the application of stored programs in Artificial Intelligence.
– It focuses on production systems and their role in rule-based expert systems.

 

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