Multimedia-based emerging technologies and data analytics for Neuroscience as a Service (NaaS)

Shabaz, Mohammad; Sharma, Ashutosh; Ajrawi, Shams Al; Estrela, Vania V.

doi:10.1016/j.neuri.2022.100067

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…High speed railway signal fault diagnosis forms a turnout fault diagnosis model with deliverable evaluation indexes through the training and optimization of the fault diagnosis model based on deep learning integration [16]. The turnout fault phenomenon of high-speed railway is input into the fault diagnosis model, and the model automatically outputs the type and cause of the fault, so as to realize the intelligent diagnosis of turnout equipment fault [17][18][19]. The architecture of this research work is depicted in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Lian

Wang

Sharma

et al. 2022

IJCAI

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The rapid development of railway transportation towards high speed, high density and heavy load has led to even higher requirements for the safety of railway signal equipment. The safety of railway signal equipment is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. This article utilizes the deep learning algorithm of artificial intelligence for investigating the interlocking faults in the railway transportation. This paper uses ADASYN data synthesis method to synthesize few category samples, uses TF-IDF to extract features and transform vectors, and proposes a deep learning integration method based on combined weight. The results show that BiGRU has better overall classification performance when evaluated on the index of primary and secondary fault classification accuracy. The classification accuracy improvement of 5% is achieved for primary fault classification and the comprehensive evaluation index of secondary fault classification is improved by about 9%. It was revealed that when compared with ADASYN + BiLSTM neural network, the comprehensive evaluation index of primary fault classification accuracy is improved by about 6%, and the comprehensive evaluation index of secondary fault classification is improved by about 10%. It is demonstrated that deep learning integration is an effective method to improve the classification performance of turnout fault diagnosis model.Povzetek: Za železniški sistem je bila uporabljena metodologija globokih nevronskih mrež za iskanje napak v signalih.

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Section: Methodsmentioning

confidence: 99%

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Lian

Wang

Sharma

et al. 2022

IJCAI

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“…In the formula, 𝑅 is a standard constant. In combination with block area template matching method, landscape spatial environment design and distributed detection are carried out, and contour point matching model is used to extract edge features of landscape spatial environment design [21][22][23]. The maximum gray value of image analysis department of landscape spatial environment design is:…”

Section: A Landscape Space Environment Visionmentioning

confidence: 99%

Application of interactive Genetic Algorithm in landscape planning and design

Li¹,

Sharma²

2022

IJCAI

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This article aims at improving the design effect of garden landscape space environment and optimizes the structure of garden landscape space environment. An optimization design method of garden landscape space environment based on interactive genetic algorithm is proposed in this article by designing a landscape space environment design with image visual feature space distributed monitoring model and fuzzy pixel area feature fusion reconstruction model. The landscape space environment design of the image by multistage decomposition and pixel gray characteristics has been established landscape space environment design image visual feature reconstruction model. The method proposed in this article combines the block area template matching method with landscape space environment design of the image features visual reconstruction. The visual space distributed detection is done with information fusion using the similarity model reconstruction for landscape space environment design of image visual perception process of information fusion. In order to extract fuzzy characteristic landscape space environment design of the image, interactive genetic algorithm is used to realize the quality assessment of landscape art information fusion perception and visual reconstruction. The simulation results show that compared with the traditional method, the visual reconstruction quality of landscape spatial environment design image processed by this method is better along with higher image recognition accuracy, and the output signal-to-noise ratio is improved by 14.6%. The experimental results prove that the introduction of interactive genetic algorithm in landscape planning and design can effectively solve the problems of multi-level feature decomposition and pixel feature separation in the process of landscape design. The proposed method achieves better optimization of landscape spatial environment structure, and achieves good landscape spatial environment design effect.Povzetek: Za snovanje prostorskih krajinskih načrtov, npr. vrtov, je uporabljen interaktivni genetski algoritem.

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“…Their experimental results suppress side-lobe levels and present better performance. Shabaz et al [17], presents a study on multimedia-based emerging technologies and data analysis of neuroscience as a service. Zhang et al [18], discussed the application of data mining in creating an information management system for colleges and universities.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of operation vibration state of coal mine mechatronics equipment based on data mining

Jia¹,

Deb²,

Sajja³

2022

J. vibroeng.

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In order to explore the prediction of the operation vibration state of coal mine mechatronics equipment, the author proposes a method based on data mining in response to the problems of large amount of data in the operating state of coal mine electromechanical equipment, low data utilization, and slow speed of single-machine massive data mining, using Map Reduce technology, a dual Map Reduce mining prediction framework is proposed, establish a data mining prediction model for the running state of dual Map Reduce, using MapReduce1 to extract features of monitoring data, use MapReduce2 to predict and analyze feature data. Finally, by building the Hadoop platform, reveal the relationship between Hadoop cluster nodes and parallel processing speed, the efficiency of the data mining prediction framework is verified: Perform an experimental comparative analysis of the single prediction model and the proposed AGB combined prediction model, the prediction accuracy of the AGB combined prediction model is verified.

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Multimedia-based emerging technologies and data analytics for Neuroscience as a Service (NaaS)

Cited by 8 publications

References 9 publications

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Application of interactive Genetic Algorithm in landscape planning and design

Prediction of operation vibration state of coal mine mechatronics equipment based on data mining

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