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544 results on '"Lu, Joan"'

1. Toward Extensible Low-Code Development Platforms

Author

Popov, Gregory, Lu, Joan, Vishnyakov, Vladimir, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, Shaikh, Asadullah, editor, Alghamdi, Abdullah, editor, Tan, Qing, editor, and El Emary, Ibrahiem M. M., editor

Published
2024
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2. The Effect of the Multi-Layer Text Summarization Model on the Efficiency and Relevancy of the Vector Space-based Information Retrieval

Author

Ababneh, Ahmad Hussein, Lu, Joan, and Xu, Qiang

Subjects
Computer Science - Information Retrieval
Abstract

The massive upload of text on the internet creates a huge inverted index in information retrieval systems, which hurts their efficiency. The purpose of this research is to measure the effect of the Multi-Layer Similarity model of the automatic text summarization on building an informative and condensed invert index in the IR systems. To achieve this purpose, we summarized a considerable number of documents using the Multi-Layer Similarity model, and we built the inverted index from the automatic summaries that were generated from this model. A series of experiments were held to test the performance in terms of efficiency and relevancy. The experiments include comparisons with three existing text summarization models; the Jaccard Coefficient Model, the Vector Space Model, and the Latent Semantic Analysis model. The experiments examined three groups of queries with manual and automatic relevancy assessment. The positive effect of the Multi-Layer Similarity in the efficiency of the IR system was clear without noticeable loss in the relevancy results. However, the evaluation showed that the traditional statistical models without semantic investigation failed to improve the information retrieval efficiency. Comparing with the previous publications that addressed the use of summaries as a source of the index, the relevancy assessment of our work was higher, and the Multi-Layer Similarity retrieval constructed an inverted index that was 58% smaller than the main corpus inverted index.

Published
2020

3. A Study and Design of Flexible Planar Antenna with Different Substrates for Long-Range Applications

Author

Sneha, Malik, Praveen Kumar, Lu, Joan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Devendra Kumar, editor, Peng, Sheng-Lung, editor, Sharma, Rohit, editor, and Jeon, Gwanggil, editor

Published
2023
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6. Application of Machine Learning to Predict CO 2 Emissions in Light-Duty Vehicles.

Author

Udoh, Jeffrey, Lu, Joan, and Xu, Qiang

Subjects
*CARBON emissions, *GREENHOUSE gases, *REGRESSION trees, *REGRESSION analysis, ENVIRONMENTAL compliance
Abstract

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards. One of the primary methods employed by the VCA to measure vehicle emissions for light-duty vehicles is the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). The WLTP is a global standard for testing vehicle emissions and fuel consumption, and sensors are crucial in ensuring accurate, real-time data collection in laboratories. Using the data collected by the VCA, regression machine learning models were trained to predict CO2 emissions in light-duty vehicles. Among six regression models tested, the Decision Tree Regression model achieved the highest accuracy, with a Mean Absolute Error (MAE) of 2.20 and a Mean Absolute Percentage Error (MAPE) of 1.69%. It was then deployed as a web application that provides users with accurate CO2 emission estimates for vehicles, enabling informed decisions to reduce GHG emissions. This research demonstrates the efficacy of machine learning and AI-driven approaches in fostering sustainability within the transportation sector. [ABSTRACT FROM AUTHOR]

Published
2024
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10. XML Data Integrity Based on Concatenated Hash Function

Author

Liu, Baolong, Lu, Joan, and Yip, Jim

Subjects
Computer Science - Software Engineering, Computer Science - Programming Languages
Abstract

Data integrity is the fundamental for data authentication. A major problem for XML data authentication is that signed XML data can be copied to another document but still keep signature valid. This is caused by XML data integrity protecting. Through investigation, the paper discovered that besides data content integrity, XML data integrity should also protect element location information, and context referential integrity under fine-grained security situation. The aim of this paper is to propose a model for XML data integrity considering XML data features. The paper presents an XML data integrity model named as CSR (content integrity, structure integrity, context referential integrity) based on a concatenated hash function. XML data content integrity is ensured using an iterative hash process, structure integrity is protected by hashing an absolute path string from root node, and context referential integrity is ensured by protecting context-related elements. Presented XML data integrity model can satisfy integrity requirements under situation of fine-grained security, and compatible with XML signature. Through evaluation, the integrity model presented has a higher efficiency on digest value-generation than the Merkle hash tree-based integrity model for XML data., Comment: 10 pages, International Journal of Computer Science and Information Security (IJCSIS)

Published
2009

13. Technologies in medical information processing

Author

Mahmud, Hoger, primary, Mohammadi, Mokhtar, additional, Khan, Nabeel Ali, additional, Rashid, Tarik Ahmed, additional, Al-Salihi, Nawzad K., additional, Dler Omer, Rebaz Mohammed, additional, and Lu, Joan, additional

Published
2020
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