Catalysis of neural activation functions: Adaptive feed-forward training for big data applications

Sarkar, Sagnik; Agrawal, Shaashwat; Baker, Thar; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy

doi:10.1007/s10489-021-03082-y

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…The batch normalization reduces the internal co-variant shift and also regularizes the model. A rectified linear unit (ReLU) [ 24 , 25 , 26 , 27 ] activation function is applied. Two advantages accompany the ReLU activation function: (1) It realizes the sparse representation of the network.…”

Section: Methodsmentioning

confidence: 99%

FECC-Net: A Novel Feature Enhancement and Context Capture Network Based on Brain MRI Images for Lesion Segmentation

Huang

Zhang

Song³

et al. 2022

Brain Sciences

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In recent years, the increasing incidence of morbidity of brain stroke has made fast and accurate segmentation of lesion areas from brain MRI images important. With the development of deep learning, segmentation methods based on the computer have become a solution to assist clinicians in early diagnosis and treatment planning. Nevertheless, the variety of lesion sizes in brain MRI images and the roughness of the boundary of the lesion pose challenges to the accuracy of the segmentation algorithm. Current mainstream medical segmentation models are not able to solve these challenges due to their insufficient use of image features and context information. This paper proposes a novel feature enhancement and context capture network (FECC-Net), which is mainly composed of an atrous spatial pyramid pooling (ASPP) module and an enhanced encoder. In particular, the ASPP model uses parallel convolution operations with different sampling rates to enrich multi-scale features and fully capture image context information in order to process lesions of different sizes. The enhanced encoder obtains deep semantic features and shallow boundary features in the feature extraction process to achieve image feature enhancement, which is helpful for restoration of the lesion boundaries. We divide the pathological image into three levels according to the number of pixels in the real mask area and evaluate FECC-Net on an open dataset called Anatomical Tracings of Lesions After Stroke (ATLAS). The experimental results show that our FECC-Net outperforms mainstream methods, such as DoubleU-Net and TransUNet. Especially in small target tasks, FECC-Net is 4.09% ahead of DoubleU-Net on the main indicator DSC. Therefore, FECC-Net is encouraging and can be relied upon for brain MRI image applications.

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Section: Methodsmentioning

confidence: 99%

FECC-Net: A Novel Feature Enhancement and Context Capture Network Based on Brain MRI Images for Lesion Segmentation

Huang

Zhang

Song³

et al. 2022

Brain Sciences

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“…In general, big data is currently characterized by seven Vs as follows: volume (a large amount of data), variety (including structured, semi-structured, and unstructured data with different formats), velocity (high rates of data inflow and real-time processing), veracity (detailed data accumulation), value (in-depth and meaningful information), variability (offering extensionality and scalability), and valence (data interconnection) [49]. About the big data workflow, a four-step process should be followed with big data generation (generated from different sources: internal data from hospitals and medical centers, IoT data, data from Internet, and biomedical data), big data acquisition (including data collection, transmission, and pre-processing), big data storage, and big data analysis [50], [51], [52].…”

Section: B Big Datamentioning

confidence: 99%

“…Big data is defined as the ''Information asset characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value [145]''. Analyzing such enormous amount of data collected from various sources at high velocities enable generating value out of the same [52]. A detailed review on the various underlying concepts of big data have been studied by the authors in [146].…”

Section: G Big Datamentioning

confidence: 99%

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

et al. 2023

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The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.INDEX TERMS Metaverse, healthcare, virtual reality, digital twin, cybersecurity.

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“…Big data is a collection of structured, unstructured and semi-structured data, information and knowledge [76][77][78][79]. Integrating AI and big data-enabled systems collect, interpret, process and store large amounts of data.…”

Section: Xai For Big Datamentioning

confidence: 99%

A Survey of Explainable Artificial Intelligence for Smart Cities

et al. 2023

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The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.

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Catalysis of neural activation functions: Adaptive feed-forward training for big data applications

Cited by 9 publications

References 28 publications

FECC-Net: A Novel Feature Enhancement and Context Capture Network Based on Brain MRI Images for Lesion Segmentation

FECC-Net: A Novel Feature Enhancement and Context Capture Network Based on Brain MRI Images for Lesion Segmentation

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

A Survey of Explainable Artificial Intelligence for Smart Cities

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