8 citations · 18 across the 10 of their papers we have counts for
11 papers
Data-driven discovery and rapid, direct synthesis of MXenes
Ali Saffar Shamshirgar, Guilherme Ribeiro Portugal, Soheil Ershadrad +9
MXenes, two-dimensional transition-metal carbides and nitrides, are typically obtained from MAX phases, yet historical reports suggest a broader, largely unexplored chemical space.…
Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks
Sanjay Chakraborty
Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN, MEGNet, ALIGNN, and SchNet h…
A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations
Sanjay Chakraborty, Fredrik Heintz
This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and…
Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?
Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik Heintz
Large pre-trained models have demonstrated remarkable capabilities across domains, but their effectiveness in time series forecasting remains understudied. This work empirically ex…
FANTF: Fuzzy attention network based transformers for time series analysis
Sanjay Chakraborty, Fredrik Heintz
This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detection. FANTF combines fuzzy logic…
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting
Sanjay Chakraborty, Fredrik Heintz
QCAAPatchTF is a quantum attention network integrated with an advanced patch-based transformer, designed for multivariate time series forecasting, classification, and anomaly detec…
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz
Time series forecasting is an important challenge with significant applications in areas such as weather prediction, stock market analysis, scientific simulations and industrial pr…
EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik Heintz
Time series forecasting is a crucial challenge with significant applications in areas such as weather prediction, stock market analysis, and scientific simulations. This paper intr…
Generative artificial intelligence in fifth-generation education systems: A systematic review
Sanjay Chakraborty
Generative artificial intelligence is increasingly transforming education by enabling adaptive learning environments, personalized instructional support, and collaborative knowledg…
A Study on Quantum Neural Networks in Healthcare 5.0
Sanjay Chakraborty
The working environment in healthcare analytics is transforming with the emergence of healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing a com…
Gamified AI Approch for Early Detection of Dementia
Paramita Kundu Maji, Soubhik Acharya, Priti Paul +2
This paper aims to develop a new deep learning-inspired gaming approach for early detection of dementia. This research integrates a robust convolutional neural network (CNN)-based…