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

Soori, Mohsen; Arezoo, Behrooz; Dastres, Roza

doi:10.1016/j.cogr.2023.04.001

Cited by 238 publications

(75 citation statements)

References 91 publications

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“…Artificial intelligence (AI) is a broad field categorized into multiple subfields, such as machine learning, computer vision, and natural language processing. , In this research, we feed the 4D Stingray software with several thousand raw holograms of nano- and microplastic particles and nonplastic river-borne (or lake-borne) particles. The objects are then found automatically by flexible analysis criteria, and more than 20 morphological parameters (e.g., particle size, shape, optical phase, perimeter, area, surface roughness, and edge gradient) of each particle are quantified and stored in our database with an assigned taxon .…”

Section: Methodsmentioning

confidence: 99%

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection

Wang,

Pal,

Pilechi

et al. 2024

Environ. Sci. Technol.

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For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano-and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/ microplastics in Lake Ontario and the Saint Lawrence River, respectively. Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient. It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics. Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data. Complementary modeling demonstrates nano-and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.

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Section: Methodsmentioning

confidence: 99%

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection

Wang,

Pal,

Pilechi

et al. 2024

Environ. Sci. Technol.

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“…ML techniques are increasingly being integrated into sensor development to enhance performance, accuracy, and efficiency. ML can help in various aspects of sensor operation such as data analysis and pattern recognition, sensor calibration, feature extraction, dynamic response prediction, fault detection and quality control, sensor array fusion, optimal material design, and adaptive sensing [73]. Incorporating ML in sensor development offers the potential to create smarter, more adaptable sensors with improved detection capabilities, reduced false alarms, and optimized performance across various applications.…”

Section: Advances In Technologymentioning

confidence: 99%

Printable metal oxide nanostructures based chemiresistive non-biological analyte sensors

Kumar,

Kim,

Kim

et al. 2023

Nano Ex.

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Non-biological analyte sensing refers to the ability to detect and quantify various chemical and physical parameters present in the environment or biological samples that are not directly associated with biological entities such as cells, tissues, or organisms. The field of non-biological analyte sensing has its roots in the early detection of any analytes, and over the years, it has expanded to include a wide range of applications such as environmental monitoring, food safety, and medical diagnostics. This perspective focuses on current status, challenges and future prospects of metal oxide nanostructures based non-biological analyte sensors. In this context, the present review aims to delve into the intricate mechanisms, fabrication techniques, and applications of printable chemical sensors for non-biological analytes. Through a comprehensive exploration of the scientific advancements and technological breakthroughs in this domain, this review seeks to provide a comprehensive understanding of the evolving landscape of printable chemical sensors and their pivotal role in modern analytical endeavours.

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“…Additionally, this can assist in predicting and identifying connections between different materials and process configurations that have not been done yet. When 3D printing is combined with AI and ML algorithms, it can help one make decisions during the design phase that reduce defects, predictive vascularization in repair mechanisms, and establish a correlation between in vitro performance and physicochemical characteristics. , Since deep convolutional neural networks (CNNs) or 3D CNNs use medical images (computed tomography (CT)) as an input for diagnosis and have been proven effective, − materials science and engineering could benefit from applying similar ML approaches and strategies. Using feedback-control systems to account for complexity and unpredictability in the target shape and motion, artificial intelligence (AI) is used in printing processes through both open-loop and closed-loop configurations …”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

Elsen,

Nayak

2024

ACS Biomater. Sci. Eng.

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3D printing has become increasingly popular in the field of bone tissue engineering. However, the mechanical properties, biocompatibility, and porosity of the 3D printed bone scaffolds are major requirements for tissue regeneration and implantation as well. Designing the scaffold architecture in accordance with the need to create better mechanical and biological stimuli is necessary to achieve unique scaffold properties. To accomplish this, different 3D designing strategies can be utilized with the help of the scaffold design library and artificial intelligence (AI). The implementation of AI to assist the 3D printing process can enable it to predict, adapt, and control the parameters on its own, which lowers the risk of errors. This Review emphasizes 3D design and fabrication of bone scaffold using different materials and the use of AI-aided 3D printing strategies. Also, the adaption of AI to 3D printing helps to develop patient-specific scaffolds based on different requirements, thus providing feedback and adequate data for reproducibility, which can be improvised in the future. These printed scaffolds can also serve as an alternative to preclinical animal test models to cut costs and prevent immunological interference.

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

Cited by 238 publications

References 91 publications

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection

Printable metal oxide nanostructures based chemiresistive non-biological analyte sensors

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

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