Hum Nath Bhandari

Assistant Professor of Mathematics

Roger Williams University

Hum Nath Bhandari is currently serving as a College Of Arts And Sciences Assistant Professor of Mathematics at Roger Williams University.

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Education

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Hum Nath Bhandari

Roger Williams University

Assistant Professor of Mathematics

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data science neural network (machine learning) machine learning research recurrent neural networks machine learning predictive analytics forecasting deep learning computational intelligence intelligent forecasting finance stock market prediction financial forecast computer science sequence modelling stock market deep learning models chemistry capital markets artificial intelligence data modeling numerical simulations numerical algorithms quantum chemistry chemical sciences quantitative management financial time series analysis prediction modelling volatility modeling convolutional neural networks nonlinear time series temporal pattern recognition sequential learning particle methods ab-initio methods mathematical chemistry potential theory numerical method for partial differential equation predictive learning business cycle analysis multilayer lstm models nepse index price esg index volatility lstm implementation sequential data modeling integrated framework pso method convolution neural network unified computational framework deep-sdm framework

Notable Scholarly Works

Notable Federal Grants

Collaborators

Predicting Business Cycles Using Deep Learning Models. Journal of Data Science (2026)

business cyclescomputational intelligencebusiness analyticsdata sciencequantitative managementpredictive analyticsintelligent forecastingforecastingmachine learning researchrecurrent neural networksbusiness cycle analysisneural network (machine learning)

Deep-Sdm: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models. Software (2024)

Nawa Raj PokhrelKeshab Raj DahalRamchandra RimalHum Nath BhandariBinod Rimal

deep learningcomputer sciencecomputational intelligencedeep-sdm frameworkunified computational frameworkmachine learningsequence modellingdata sciencemachine learning researchrecurrent neural networksneural network (machine learning)convolution neural network

Implementation of Deep Learning Models in Predicting Esg Index Volatility. Financial Innovation (2024)

Hum Nath BhandariNawa Raj PokhrelRamchandra RimalKeshab R. DahalBinod Rimal

stock market predictiondeep learningfinancevolatility modelingmachine learningdata sciencepredictive analyticsforecastingdeep learning modelsesg index volatilitymachine learning researchrecurrent neural networksconvolutional neural networksneural network (machine learning)financial forecast

Real Estate Market Prediction Using Deep Learning Models. Annals of Data Science (2024)

Ramchandra RimalBinod RimalHum Nath BhandariNawa Raj PokhrelKeshab R. Dahal

deep learningcomputational intelligencemachine learningpredictive learningdata sciencepredictive analyticsintelligent forecastingforecastingmachine learning researchrecurrent neural networksneural network (machine learning)

LSTM-Sdm: An Integrated Framework of LSTM Implementation for Sequential Data Modeling. Software Impacts (2022)

Hum Nath BhandariBinod RimalNawa Raj PokhrelRamchandra RimalKeshab R. Dahal

computer sciencemachine learningtime seriessequence modellingtemporal pattern recognitionintegrated frameworkdata sciencesequential learningdata modelingmachine learning researchrecurrent neural networkssequential data modelinglstm implementationnonlinear time seriesneural network (machine learning)

Predicting Nepse Index Price Using Deep Learning Models. Machine Learning with Applications (2022)

Nawa Raj PokhrelKeshab Raj DahalRamchandra RimalHum Nath BhandariRajendra K.C. KhatriBinod RimalWilliam Edward Hahn

stock market predictiondeep learningcomputational intelligencefinanceprediction modellingmachine learningdata sciencepredictive analyticsintelligent forecastingforecastingdeep learning modelsmachine learning researchrecurrent neural networksnepse index pricestock marketneural network (machine learning)financial forecast

Predicting Stock Market Index Using LSTM. Machine Learning with Applications (2022)

Hum Nath BhandariBinod RimalNawa Raj PokhrelRamchandra RimalKeshab R. DahalRajendra K.C. Khatri

stock market predictionfinancemultilayer lstm modelsmachine learningstock marketfinancial time series analysisdata sciencepredictive analyticsintelligent forecastingforecastingartificial intelligencemachine learning researchrecurrent neural networkscapital marketsneural network (machine learning)financial forecast

Pso Method for Fitting Analytic Potential Energy Functions. Application to I – (H 2 O). Journal of Chemical Theory and Computation (2018)

H. N. BhandariX. MaA. K. PaulP. SmithW. L. Hase

computational chemistrynumerical simulationsmathematical chemistrychemical engineeringapproximation theoryparticle methodsmolecular modellingquantum chemistrynumerical algorithmspso methodapplied physicschemical sciencesab-initio methodspotential theorynumerical analysisnumerical method for partial differential equationtheoretical chemistrychemistry