no code implementations • 5 May 2024 • Agni Rakshit, Gautam Bandyopadhyay, Tanujit Chakraborty
The Black-Scholes option pricing model remains a cornerstone in financial mathematics, yet its application is often challenged by the need for accurate hedging strategies, especially in dynamic market environments.
1 code implementation • 8 Apr 2024 • Jintu Borah, Tanujit Chakraborty, Md. Shahrul Md. Nadzir, Mylene G. Cayetano, Shubhankar Majumdar
Accurate and reliable air quality forecasting is essential for protecting public health, sustainable development, pollution control, and enhanced urban planning.
no code implementations • 4 Mar 2024 • Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
no code implementations • 25 Jan 2024 • Abdenour Hadid, Tanujit Chakraborty, Daniel Busby
Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities.
1 code implementation • 30 Dec 2023 • Shovon Sengupta, Tanujit Chakraborty, Sunny Kumar Singh
This study proposes a novel filtered ensemble wavelet neural network (FEWNet) that can produce reliable long-term forecasts for CPI inflation.
1 code implementation • 10 Dec 2023 • Shraddha M. Naik, Tanujit Chakraborty, Abdenour Hadid, Bibhas Chakraborty
This paper introduces an imbalanced data-oriented approach using probabilistic neural networks (PNNs) with a skew normal probability kernel to address this major challenge.
no code implementations • 24 Nov 2023 • Xueqing Liu, Nina Deliu, Tanujit Chakraborty, Lauren Bell, Bibhas Chakraborty
Mobile health (mHealth) technologies aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions.
no code implementations • 30 Aug 2023 • Tanujit Chakraborty, Ujjwal Reddy K S, Shraddha M. Naik, Madhurima Panja, Bayapureddy Manvitha
Since their inception in 2014, Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas.
1 code implementation • 9 Jun 2023 • Rahisha Thottolil, Uttam Kumar, Tanujit Chakraborty
These synthetic urban universes mimic global urban patterns and evaluating their landscape structures through spatial pattern analysis can aid in comprehending landscape dynamics, thereby enhancing sustainable urban planning.
1 code implementation • 16 Dec 2022 • Madhurima Panja, Tanujit Chakraborty, Sk Shahid Nadim, Indrajit Ghosh, Uttam Kumar, Nan Liu
In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
1 code implementation • 9 Sep 2022 • Zakaria Elabid, Tanujit Chakraborty, Abdenour Hadid
Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data directly from the dynamics and their differential equations.
1 code implementation • 8 Sep 2022 • Lena Sasal, Tanujit Chakraborty, Abdenour Hadid
Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others.
1 code implementation • 21 Jun 2022 • Madhurima Panja, Tanujit Chakraborty, Uttam Kumar, Nan Liu
Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics.
1 code implementation • 1 Apr 2022 • Madhurima Panja, Tanujit Chakraborty, Uttam Kumar, Abdenour Hadid
In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns.
1 code implementation • 9 Jun 2021 • Arnob Ray, Tanujit Chakraborty, Dibakar Ghosh
This study develops an optimized ensemble deep learning (OEDL) framework wherein these two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and reproducibility prompting a new wave of applications in the forecasting of dynamics.
no code implementations • 25 Oct 2018 • Tanujit Chakraborty, Ashis Kumar Chakraborty
Regularity conditions for universal consistency and the idea of parameter optimization of the proposed model are provided.
no code implementations • 31 May 2018 • Tanujit Chakraborty
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage.