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Your search keyword '"Bukhari, Syed Ahmad Chan"' showing total 168 results
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168 results on '"Bukhari, Syed Ahmad Chan"'

1. Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment

Author

Khalid, Mutahira, Rahman, Raihana, Abbas, Asim, Kumari, Sushama, Wajahat, Iram, and Bukhari, Syed Ahmad Chan

Subjects
Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automation and completeness. Despite efforts in automation and the utilization of expert-created ontologies, gaps in connectivity remain prevalent within KGs. In response to these challenges, we propose an innovative approach termed ``Medical Knowledge Graph Automation (M-KGA)". M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, thereby enhancing the completeness of knowledge graphs through the integration of pre-trained embeddings. Our approach introduces two distinct methodologies for uncovering hidden connections within the knowledge graph: a cluster-based approach and a node-based approach. Through rigorous testing involving 100 frequently occurring medical concepts in Electronic Health Records (EHRs), our M-KGA framework demonstrates promising results, indicating its potential to address the limitations of existing knowledge graph automation techniques., Comment: 18 pages, 5 figures

Published
2024

5. A Decentralized Environment for Biomedical Semantic Content Authoring and Publishing

Author

Abbas, Asim, Mbouadeu, Steve Fonin, Keshtkar, Fazel, Khattak, Hasan Ali, Hameed, Tahir, Bukhari, Syed Ahmad Chan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Agapito, Giuseppe, editor, Bernasconi, Anna, editor, Cappiello, Cinzia, editor, Khattak, Hasan Ali, editor, Ko, InYoung, editor, Loseto, Giuseppe, editor, Mrissa, Michael, editor, Nanni, Luca, editor, Pinoli, Pietro, editor, Ragone, Azzurra, editor, Ruta, Michele, editor, Scioscia, Floriano, editor, and Srivastava, Abhishek, editor

Published
2023
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8. Frameworks for Querying Databases Using Natural Language: A Literature Review

Author

Dar, Hafsa Shareef, Lali, M. Ikramullah, Din, Moin Ul, Malik, Khalid Mahmood, and Bukhari, Syed Ahmad Chan

Subjects
Computer Science - Databases
Abstract

A Natural Language Interface (NLI) facilitates users to pose queries to retrieve information from a database without using any artificial language such as the Structured Query Language (SQL). Several applications in various domains including healthcare, customer support and search engines, require elaborating structured data having information on text. Moreover, many issues have been explored including configuration complexity, processing of intensive algorithms, and popularity of relational databases, due to which translating natural language to database query has become a secondary area of investigation. The emerging trend of querying systems and speech-enabled interfaces revived natural language to database queries research area., The last survey published on this topic was six years ago in 2013. To best of our knowledge, there is no recent study found which discusses the current state of the art translations frameworks for natural language for structured and non-structured query languages. In this paper, we have reviewed 47 frameworks from 2008 to 2018. Out of 47, 35 were closely relevant to our work. SQL based frameworks have been categorized as statistical, symbolic and connectionist approaches. Whereas, NoSQL based frameworks have been categorized as semantic matching and pattern matching. These frameworks are then reviewed based on their supporting language, scheme of their heuristic rule, interoperability support, dataset scope and their overall performance score. The findings stated that 70% of the work in natural language to database querying has been carried out for SQL, and NoSQL share 15%, 10% and 5% of languages like SPAROL, CYPHER and GREMLIN respectively. It has also been observed that most of the frameworks support English language only., Comment: 5 tables, 2 figures

Published
2019

9. Proficient Annotation Recommendation in a Biomedical Content Authoring Environment

Author

Abbas, Asim, Mbouadeu, Steve, Bisram, Avinash, Iqbal, Nadeem, Keshtkar, Fazel, Bukhari, Syed Ahmad Chan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Villazón-Terrazas, Boris, editor, Ortiz-Rodriguez, Fernando, editor, Tiwari, Sanju, editor, Sicilia, Miguel-Angel, editor, and Martín-Moncunill, David, editor

Published
2022
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41. EEG Forecasting With Univariate and Multivariate Time Series Using Windowing and Baseline Method.

Author

Bukhari, Syed Ahmad Chan

Subjects
EPILEPSY, SPASMS, ELECTROENCEPHALOGRAPHY, MULTIVARIATE analysis, TIME series analysis
Abstract

People suffering from epilepsy disorder are very much in need for precautionary measures. The only way to provide precaution to such people is to find some methods which help them to know in advance the occurrence of seizures. Using Electroencephalogram, the authors have worked on developing a forecasting method using simple LSTM with windowing technique. The window length was set to five time steps; step by step the length was increased by 1 time step. The number of correct predictions increased with the window length. When the length reached to 20 time steps, the model gave impressive results in predicting the future EEG value. Past 20 time steps are learnt by the neural network to forecast the future EEG in two stages; in univariate method, only one attribute is used as the basis to predict the future value. In multivariate method, 42 features were used to predict the future EEG. Multivariate is more powerful and provides the prediction which is almost equal to the actual target value. In case of univariate the accuracy achieved was about 70%, whereas in case of multivariate method it was 90%. [ABSTRACT FROM AUTHOR]

Published
2022
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