Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Cognitive Robotics, Journal Year: 2023, Volume and Issue: 3, P. 54 - 70

Published: Jan. 1, 2023

Artificial Intelligence (AI), Machine Learning (ML), and Deep (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, DL are transforming robotics, making robots more intelligent, efficient, adaptable to complex tasks environments. Some applications include autonomous navigation, object recognition manipulation, natural language processing, predictive maintenance. These technologies also being used development collaborative (cobots) that can work alongside humans adapt changing environments tasks. The be transportation systems order provide safety, efficiency, convenience passengers companies . Also, playing a critical role advancement manufacturing assembly robots, enabling them efficiently, safely, intelligently. Furthermore, they wide range aviation management, helping airlines improve reduce costs, customer satisfaction. Moreover, help taxi better, safer services customers. research presents an overview current developments discusses various robot modification. Further works regarding suggested fill gaps between existing studies published papers. By reviewing systems, it is possible investigate modify performances enhance productivity robotic industries.

Language: Английский

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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

Journal of Computer Information Systems, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 32

Published: Oct. 5, 2023

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because potential to cause drastic widespread shifts in many aspects life that are comparable those the Internet smartphones. More specifically, AI utilizes machine learning, neural networks, other techniques generate new content (e.g. text, images, music) by analyzing patterns information from training data. enabled have wide range applications, creating personalized improving business operations. Despite benefits, there also significant concerns about negative implications AI. In view this, current article brings together experts variety fields expound provide multi-disciplinary insights on opportunities, challenges, research agendas specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, workplace, manufacturing, sustainable IT management).KEYWORDS: Generative intelligencemachine learninglarge language modelChatGPTBard Disclosure statementNo conflict was reported author(s).

Language: Английский

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Artificial intelligence-based solutions for climate change: a review

Environmental Chemistry Letters, Journal Year: 2023, Volume and Issue: 21(5), P. 2525 - 2557

Published: June 13, 2023

Abstract Climate change is a major threat already causing system damage to urban and natural systems, inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because integrates internet resources make prompt suggestions based on accurate climate predictions. Here we review recent research applications in mitigating the adverse effects change, with focus energy efficiency, carbon sequestration storage, weather renewable forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, resilient cities. We found that enhancing efficiency can significantly contribute impact change. Smart manufacturing reduce consumption, waste, emissions 30–50% and, particular, consumption buildings 30–50%. About 70% gas industry utilizes technologies enhance accuracy reliability forecasts. Combining smart grids optimize power thereby electricity bills 10–20%. Intelligent transportation systems dioxide approximately 60%. Moreover, management design cities through application further promote sustainability.

Language: Английский

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Deep learning: systematic review, models, challenges, and research directions

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(31), P. 23103 - 23124

Published: Sept. 7, 2023

Abstract The current development in deep learning is witnessing an exponential transition into automation applications. This can provide a promising framework for higher performance and lower complexity. ongoing undergoes several rapid changes, resulting the processing of data by studies, while it may lead to time-consuming costly models. Thus, address these challenges, studies have been conducted investigate techniques; however, they mostly focused on specific approaches, such as supervised learning. In addition, did not comprehensively other techniques, unsupervised reinforcement techniques. Moreover, majority neglect discuss some main methodologies learning, transfer federated online Therefore, motivated limitations existing this study summarizes techniques supervised, unsupervised, reinforcement, hybrid learning-based addition each category, brief description categories their models provided. Some critical topics namely, transfer, federated, models, are explored discussed detail. Finally, challenges future directions outlined wider outlooks researchers.

Language: Английский

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Adopting Artificial Intelligence for enhancing the implementation of systemic circularity in the construction industry: A critical review

Sustainable Production and Consumption, Journal Year: 2022, Volume and Issue: 35, P. 509 - 524

Published: Dec. 10, 2022

Language: Английский

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Advances in Artificial Intelligence for Image Processing

Advances in computational intelligence and robotics book series, Journal Year: 2023, Volume and Issue: unknown, P. 73 - 95

Published: June 30, 2023

AI has had a substantial influence on image processing, allowing cutting-edge methods and uses. The foundations of processing are covered in this chapter, along with representation, formats, enhancement methods, filtering. It digs into for machine learning, neural networks, optimization strategies, digital watermarking, picture security, cloud computing, augmentation, data pretreatment methods. impact computing platforms, performance, privacy, security also covered. chapter's consideration future trends applications emphasises the contributions that made to as well ethical societal ramifications technology.

Language: Английский

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BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction

Buildings, Journal Year: 2023, Volume and Issue: 13(2), P. 441 - 441

Published: Feb. 4, 2023

The construction industry is slow to adopt new technologies. implementation of digital technologies and remote operations using robots were considered farfetched affairs unbelievable approaches. However, the effect COVID-19 on clients companies put high pressure managers seek solutions justified need for operating or distant controlling This paper aims investigate state play in technology presents a roadmap developing implementing required industry. disruption methods working safely remotely coincided with advent advanced automation autonomous identify gaps 11 disruptive that may lead upheaval transformation sector, perhaps this decade. A road map can be helpful business strategies at organizational level as theoretical measure, it facilitate process practical measure. used framework policymakers set company next 10 years (2030).

Language: Английский

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

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Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis

Sustainability, Journal Year: 2022, Volume and Issue: 14(21), P. 14549 - 14549

Published: Nov. 5, 2022

As smart technology promotes the development of various industries, artificial intelligence (AI) has also become an important driving force for innovation and transformation in education. For teachers, how to skillfully apply AI teaching improve their literacy a necessary goal sustainable professional development. This research examines correlations among dimensions teachers order promote effectiveness class adoption (AIL). Our findings are based on analysis 1013 survey results, where we tested level including Knowing Understanding (KUAI), Applying (AAI), Evaluating Application (EAIA), Ethics (AIE). We find that AAI had significant, positive effect other three dimensions. Thus, analysis, government should take action cultivate teachers’ literacy. In literacy, choice curriculum, content, methods, practical resources special training be diverse committed making essential enabler future

Language: Английский

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Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(34)

Published: March 8, 2024

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between and holds potential revolutionize design production AM-printed parts. This review delves into challenges opportunities emerging intersection these two dynamic fields. It provides a comprehensive analysis publication landscape ML-related research field AM, explores common applications (such as quality control, process optimization, microstructure analysis, material formulation), concludes by presenting an outlook that underscores utilization advanced models, development sensors, AM-related Notably, garnered increased attention due its superior performance across applications. is envisioned integration processes will significantly enhance 3D printing capabilities areas.

Language: Английский

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