Nanomaterials in the Automobile Sector

Harun-Ur-Rashid, M.

doi:10.21741/9781644902288-6

Cited by 4 publications

(2 citation statements)

References 54 publications

(57 reference statements)

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“…The Epileptic Seizure dataset, originally from [23], includes electroencephalogram (EEG) recordings from 500 patients. A preprocessed version of the dataset available at Kaggle is applied [24]. Converting the EEG signals from time series to a tabular format results in a dataset consisting of 11, 500 rows and 178 columns.…”

Section: Methodsmentioning

confidence: 99%

Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks

Nik

Riegler

Halvorsen

et al. 2023

Lecture Notes in Computer Science

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High-quality tabular data is a crucial requirement for developing data-driven applications, especially healthcare-related ones, because most of the data nowadays collected in this context is in tabular form. However, strict data protection laws complicates the access to medical datasets. Thus, synthetic data has become an ideal alternative for data scientists and healthcare professionals to circumvent such hurdles. Although many healthcare institutions still use the classical de-identification and anonymization techniques for generating synthetic data, deep learning-based generative models such as generative adversarial networks (GANs) have shown a remarkable performance in generating tabular datasets with complex structures. This paper examines the GANs' potential and applicability within the healthcare industry, which often faces serious challenges with insufficient training data and patient records sensitivity. We investigate several state-of-the-art GAN-based models proposed for tabular synthetic data generation. Healthcare datasets with different sizes, numbers of variables, column data types, feature distributions, and inter-variable correlations are examined. Moreover, a comprehensive evaluation framework is defined to evaluate the quality of the synthetic records and the viability of each model in preserving the patients' privacy. The results indicate that the proposed models can generate synthetic datasets that maintain the statistical characteristics, model compatibility and privacy of the original data. Moreover, synthetic tabular healthcare datasets can be a viable option in many data-driven applications. However, there is still room for further improvements in designing a perfect architecture for generating synthetic tabular data.

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Section: Methodsmentioning

confidence: 99%

Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks

Nik

Riegler

Halvorsen

et al. 2023

Lecture Notes in Computer Science

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“…The potential for developing novel and influential micro/nanodevices using BINMs is anticipated to grow due to technological advancements and improved comprehension of biological systems. Through the utilization of the distinct characteristics exhibited by these nanomaterials in the form of nanocomposite gels [ 23 , 24 , 25 , 26 ] and films [ 27 , 28 , 29 ], structural colored nanomaterials [ 30 , 31 , 32 ], organo-metallic nanomaterials [ 33 ], molecular machines [ 34 ], and nanobiosensors [ 35 ] have found widespread application and have replaced mainly more conventional bulk materials in a variety of sectors [ 36 , 37 , 38 , 39 , 40 , 41 ] as well as in theoretical inquiries [ 42 ]. Researchers and practitioners can fabricate devices that imitate or draw inspiration from biological systems to execute targeted functions, frequently surpassing the efficiency and efficacy of conventional devices.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Nanomaterials for Micro/Nanodevices: A New Era in Biomedical Applications

Harun-Ur-Rashid,

Jahan,

Foyez

et al. 2023

Micromachines

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Exploring bio-inspired nanomaterials (BINMs) and incorporating them into micro/nanodevices represent a significant development in biomedical applications. Nanomaterials, engineered to imitate biological structures and processes, exhibit distinctive attributes such as exceptional biocompatibility, multifunctionality, and unparalleled versatility. The utilization of BINMs demonstrates significant potential in diverse domains of biomedical micro/nanodevices, encompassing biosensors, targeted drug delivery systems, and advanced tissue engineering constructs. This article thoroughly examines the development and distinctive attributes of various BINMs, including those originating from proteins, DNA, and biomimetic polymers. Significant attention is directed toward incorporating these entities into micro/nanodevices and the subsequent biomedical ramifications that arise. This review explores biomimicry’s structure–function correlations. Synthesis mosaics include bioprocesses, biomolecules, and natural structures. These nanomaterials’ interfaces use biomimetic functionalization and geometric adaptations, transforming drug delivery, nanobiosensing, bio-inspired organ-on-chip systems, cancer-on-chip models, wound healing dressing mats, and antimicrobial surfaces. It provides an in-depth analysis of the existing challenges and proposes prospective strategies to improve the efficiency, performance, and reliability of these devices. Furthermore, this study offers a forward-thinking viewpoint highlighting potential avenues for future exploration and advancement. The objective is to effectively utilize and maximize the application of BINMs in the progression of biomedical micro/nanodevices, thereby propelling this rapidly developing field toward its promising future.

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Emerging Trends in Engineering Polymers: A Paradigm Shift in Material Engineering

Harun-Ur-Rashid,

Imran

2024

Recent Prog Mater

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Emerging Trends in Engineering Polymers signify a pivotal transformation in material engineering, marking a departure from traditional materials towards innovative, multifunctional, and sustainable polymers. This review delineates the forefront of advancements in polymer materials, including high-performance, bio-based, biodegradable, innovative, and functional polymers. Highlighting their enhanced mechanical properties, thermal stability, and chemical resistance showcases these materials' pivotal role in driving technological progress. The exploration extends to advanced manufacturing techniques such as 3D printing, electrospinning, and the fabrication of polymer nanocomposites, underscoring their impact on customizing product properties and scaling production. Central to this discourse is the sustainability and environmental stewardship in the polymer sector, addressing recycling methodologies, the circular economy, and regulatory frameworks guiding sustainable practices. The review juxtaposes traditional and emerging recycling processes, illuminating the path toward more sustainable material cycles. Furthermore, it ventures into emerging applications across diverse sectors such as energy, electronics, healthcare, automotive, and aerospace, elucidating the transformative potential of engineering polymers in these domains. Challenges spanning technical, economic, environmental, and regulatory landscapes are critically examined, setting the stage for future directions in research and development. The review culminates in a forward-looking perspective, advocating for interdisciplinary collaboration and material science innovation to navigate modern engineering challenges' complexities. Through this comprehensive analysis, the review articulates a narrative of evolution and opportunity within engineering polymers, poised to redefine material engineering in the decades to come.

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Nanomaterials in the Automobile Sector

Cited by 4 publications

References 54 publications

Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks

Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks

Bio-Inspired Nanomaterials for Micro/Nanodevices: A New Era in Biomedical Applications

Emerging Trends in Engineering Polymers: A Paradigm Shift in Material Engineering

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