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Researcher profile

Rabindra Lamsal

Rabindra Lamsal contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computation and LanguageSocial and Information NetworksApplicationsMachine Learning

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Published work

4 published item(s)

preprint2026arXiv

A Hybrid Framework for Natural Language Querying of IFC Models with Relational and Graph Representations

Building Information Modeling (BIM) is widely used in the Architecture, Engineering, and Construction (AEC) industry, but the complexity of Industry Foundation Classes (IFC) limits accessibility for non-expert users. To address this, we introduce IfcLLM, a hybrid framework for natural language interaction with IFC-based BIM models. It transforms IFC models into complementary representations: a relational representation for structured element properties and geometry, and a graph representation for topological relationships. These representations are integrated through iterative retry-and-refine LLM reasoning. We implement the framework using an open-weight LLM (GPT OSS 120B), supporting reproducible and deployment-oriented workflows. Evaluation on three IFC models with queries derived from 30 scenarios shows first-attempt accuracy of 93.3%-100%, with all failures recovered using a fallback LLM. The results show that combining complementary representations with iterative reasoning enables more accessible natural language querying of IFC data while supporting routine BIM analysis tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

GeoCovaxTweets: COVID-19 Vaccines and Vaccination-specific Global Geotagged Twitter Conversations

Social media platforms provide actionable information during crises and pandemic outbreaks. The COVID-19 pandemic has imposed a chronic public health crisis worldwide, with experts considering vaccines as the ultimate prevention to achieve herd immunity against the virus. A proportion of people may turn to social media platforms to oppose vaccines and vaccination, hindering government efforts to eradicate the virus. This paper presents the COVID-19 vaccines and vaccination-specific global geotagged tweets dataset, GeoCovaxTweets, that contains more than 1.8 million tweets, with location information and longer temporal coverage, originating from 233 countries and territories between January 2020 and November 2022. The paper discusses the dataset's curation method and how it can be re-created locally, and later explores the dataset through multiple tweets distributions and briefly discusses its potential use cases. We anticipate that the dataset will assist the researchers in the crisis computing domain to explore the conversational dynamics of COVID-19 vaccines and vaccination Twitter discourse through numerous spatial and temporal dimensions concerning trends, shifts in opinions, misinformation, and anti-vaccination campaigns.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Twitter conversations predict the daily confirmed COVID-19 cases

As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83--51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning

Cricket, especially the Twenty20 format, has maximum uncertainty, where a single over can completely change the momentum of the game. With millions of people following the Indian Premier League (IPL), developing a model for predicting the outcome of its matches is a real-world problem. A cricket match depends upon various factors, and in this work, the factors which significantly influence the outcome of a Twenty20 cricket match are identified. Each player's performance in the field is considered to find out the overall weight (relative strength) of the teams. A multivariate regression based solution is proposed to calculate points for each player in the league and the overall weight of a team is computed based on the past performance of the players who have appeared most for the team. Finally, a dataset is modeled based on the identified seven factors which influence the outcome of an IPL match. Six machine learning models were trained and used for predicting the outcome of each 2018 IPL match, 15 minutes before the gameplay, immediately after the toss. Three of the trained models were seen to be correctly predicting more than 40 matches, with Multilayer Perceptron outperforming all other models with an impressive accuracy of 71.66%.

0 citations0 reviewsNo code linkedOpen access