An approach for moving object recognition based on BPR and CI

Wang, Li; Xu, Lida; Liu, Renjing; Wang, Hai Hong

doi:10.1007/s10796-008-9130-3

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…Extensions of this work include the tagging of multiple labels like headings, paragraph text, header/footer text, and table text, in a variety of digital document formats. Properly classifying table text and header/footer text will require more data features and perhaps more reliance on decision theory concepts such as Choquet integrals (Wang, Xu, Liu, & Wang, ). Our approach applies both syntactic details and semantic descriptors, which could be applied in future research to detect core document content and summary descriptors in a wider range of documents.…”

Section: Discussion and Future Workmentioning

confidence: 99%

A supervised learning approach for heading detection

Budhiraja

Mago²

2020

Expert Systems

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As the Portable Document Format (PDF) file format increases in popularity, research in analysing its structure for text extraction and analysis is necessary. Detecting headings can be a crucial component of classifying and extracting meaningful data. This research involves training a supervised learning model to detect headings with features carefully selected through recursive feature elimination. The best performing classifier had an accuracy of 96.95%, sensitivity of 0.986 and a specificity of 0.953. This research into heading detection contributes to the field of PDF based text extraction and can be applied to the automation of large scale PDF text analysis in a variety of professional and policy based contexts.

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Section: Discussion and Future Workmentioning

confidence: 99%

A supervised learning approach for heading detection

Budhiraja

Mago²

2020

Expert Systems

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Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

Alahakoon

Nawaratne

et al. 2020

Inf Syst Front

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The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.

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Heuristic algorithms for effective broker deployment

2011

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In the pervasive e-business applications covering large geographical areas and involving many RFID readers or sensors, broker deployment strategies have a direct effect on the deployment cost and collaboration efficiency. By analyzing the deployment cost and collaboration basis, this paper proposes a model for the broker deployment problem, and presents two heuristic algorithms for the multi-object optimization of broker deployment, where one is for the deployment area which contains the zones forbidden to place brokers in, and the other is for the deployment area without any forbidden zone. Experiments are conducted to demonstrate the effectiveness of the proposed algorithms. The experimental results also show the deployment algorithms have the advantage of being low cost of deployment. Moreover, the brokers deployed carry relatively balanced loads and messages are forwarded from an event source to a broker over a small number of hops.

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An approach for moving object recognition based on BPR and CI

Cited by 7 publications

References 32 publications

A supervised learning approach for heading detection

A supervised learning approach for heading detection

Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

Heuristic algorithms for effective broker deployment

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