Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

Zhang, Jing; Yan, Qichen; Zhu, Xiaogang; Yu, Keping

doi:10.1016/j.dcan.2022.08.002

Cited by 53 publications

(14 citation statements)

References 36 publications

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“…Object recognition: computer vision can identify and classify objects in images and videos, making it useful for applications such as face recognition, security, 12 and autonomous vehicles.…”

Section: Related Workmentioning

confidence: 99%

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

2023

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Section: Related Workmentioning

confidence: 99%

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

2023

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“…Political education is essentially the work of nurturing people, so all work is student-oriented, around students, attention to students, service to students [16]. We will further explore the law, update the concept, innovate the model and build the ecology, and promote the tasks of university network political education of university network political education [17]. It is a major political task to promote and deepen the research of college network political education [18].…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Educational Evaluation System Using Deep Learning

Yan

Lu²

2023

IEEE Access

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Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

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“…In the modern economy, the core role of finance affects the healthy development of a country to a certain extent, and the significant negative effects of financial risks will endanger the country's financial and economic security [4]. As a result, it has been more and more important to develop effective financial auditing methods for relevant administration departments, in order to improve such supervision ability [5]. Especially in era of big data, the increasing data volume has brought more challenges to human expertise.…”

Section: Introductionmentioning

confidence: 99%

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

Zhao¹,

Wang

2023

IEEE Access

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In the big data era, traditional auditing methods are facing challenge such as limited audit scope, uneven distribution of audit power, and insufficient audit analysis. To pursue high efficiency, the utilization of big data analysis technique in financial auditing has been a novel tendency in this area. The deep learning has been popular in many areas due to its high freedom degree. Thus, this paper employs a typical deep learning model convolution neural network (CNN), and proposes a big data-driven financial auditing method using CNN. Specifically, the strong ability of feature abstraction of CNN is leveraged to extract multi-level features in materials, such as visual features, textual features, etc. Then, the multi-source features from auditing materials can be well fused for final discrimination. Some simulation experiments are conducted on real-world financial auditing scenes for assessment. And the results show that the designed the proposed financial auditing method possesses relatively high auditing accuracy.

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Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

Cited by 53 publications

References 36 publications

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

Design of an Intelligent Educational Evaluation System Using Deep Learning

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

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