RETRACTED: Public welfare organization management system based on FPGA and deep learning

Zuo, Mingzhang

doi:10.1016/j.micpro.2020.103333

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…The numerous methods of assessing human health and the data heterogeneity have become far more complicated and vastly larger in size [195]; thus, the issue requires additional computation [196]. Furthermore, novel hardware-based parallel processing solutions such as FPGAs and GPUs [197][198][199] have been developed to solve the computation issues associated with DL. Recently, numerous techniques for compressing the DL models, designed to decrease the computational issues of the models from the starting point, have also been introduced.…”

Section: Model Compressionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Model Compressionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…In line with this, Mulia said that increase community participation in implementation and supervision would impact the community's welfare (Mulia, 2019). In the organisational system, public welfare promoting social support is one of the Government's in uences to create a conducive welfare organisation (Min, 2021). In other words, the welfare of the people is the main object of management in improving the welfare of the state.…”

Section: Public Welfarementioning

confidence: 98%

Synchronising the Implementation of Simultaneous General Elections with the National Development Planning 2025 – 2045

Dirkareshza

2022

Preprint

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Simultaneous general elections have a lousy tendency by several people but do not see a large future impact on national development planning in 2025–2045. The purpose of this article is to present a new perspective on viewing simultaneous general elections by linking national development planning as a future impact on public welfare. This research is qualitative by using a statute approach in comparing laws and regulations that change in each period of simultaneous general elections and synchronising laws and regulations with national development planning. The conceptual approach is the basis for the author to conceptualise the synchronisation of simultaneous general elections to national development planning in the RPJPN 2025–2045. The research results from this article show three outlines: First, the impact of simultaneous general elections results in harmony in the implementation of national development planning. Second, synchronising simultaneous general elections to national development planning eliminates the disparities between regions that occur in Indonesia. Third, the synchronisation results, as discussed, will produce a gradual welfare state in 2025–2045.

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“…Additional computation power is required to comply with vast sizes of heterogeneous data in healthcare. Modern hardware based parallel processing technologies have been proposed such as Field programmable gate arrays (FPGA) and Graphics processing units (GPUs) to alleviate the computational limitations associated with deep learning [181], [182]. Techniques for compressing deep learning models to reduce the model computational issues have also been designed such as parameter pruning, knowledge distillation, use of compact convolution filters and estimation of information parameters for preservation using low rank factorization [87].…”

Section: F Model Compressionmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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RETRACTED: Public welfare organization management system based on FPGA and deep learning

Cited by 14 publications

References 6 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Synchronising the Implementation of Simultaneous General Elections with the National Development Planning 2025 – 2045

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

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