Subhabrata Roy

@nit.ac.in

Assistant Professor Department of Electronics and Communication Engineering
Narula Institute of Technology Agarpara Kolkata-700109 West Bengal

Subhabrata Roy


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https://researchid.co/subhabrata_ece

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Signal Processing, Health Informatics, Multidisciplinary
33

Scopus Publications

341

Scholar Citations

11

Scholar h-index

16

Scholar i10-index

Scopus Publications

  • Graphene–VO2 hybrid metamaterial biosensor with machine learning–assisted absorption and virus detection in the THz regime
    Shankar Vuyyala, Subhabrata Roy, Kotagiri Shashidhar
    Optical Engineering, 2026
    In this research, we propose a reconfigurable terahertz (THz) metamaterial-based biosensing structure that integrates graphene with vanadium dioxide (VO2) to achieve intelligent absorption control and virus detection. The hybrid configuration leverages the electrically tunable conductivity of graphene together with the thermally induced phase transition of VO2, resulting in dual-mode switching and improved structural flexibility. The designed device exhibits strong sensing capability, achieving a sensitivity of 3.025 THz/RIU. It also offers a high figure of merit of 44.14 RIU−1 and a quality factor (Q) of 63.41, indicating sharp resonance and efficient performance. These results confirm the sensor’s capability to detect subtle changes in the refractive index of biological samples, making it highly suitable for identifying different viruses and biochemical analytes. In addition, a machine learning framework is incorporated as a numerical surrogate modeling tool to predict absorption trends based on simulated data, thereby reducing computational effort and accelerating parametric design exploration. Compared with previously reported VO2-, MXene-, and graphene-based absorbers, the proposed biosensor exhibits improved tunability, higher detection accuracy, and adaptive functionality. This work, therefore, demonstrates a viable route toward next-generation smart THz biosensors for biomedical diagnostics and sensing technologies. The reported results are based on full-wave numerical simulations and highlight refractive index-based sensing capability, with machine learning used solely as a computational design-assistance tool.
  • SCNN: An Improved Convolutional Neural Network for Early Detection of Alzheimer's Disease
    Subhabrata Roy, Gyanendra Kumar Maurya
    2026 International Conference on Signal Analysis for Smart Systems Signass 2026, 2026
  • Q-Detect: An Attention-Guided Quantum Ensemble Framework for Robust DeepFake Audio Detection
    Angshuman Roy, Anuvab Sen, Soham Haldar, Subhabrata Roy
    Proceedings of the National Conference on Communications Ncc, 2026
    The proliferation of DeepFake audio technologies has raised significant security and ethical concerns, necessitating robust detection mechanisms. This paper proposes the novel Q-Detect framework that combines a Quantum Vision Transformer (QViT) with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a custom attention mechanism for efficient DeepFake audio detection. The QViT leverages variational quantum circuits within its attention mechanisms to capture intricate spectral patterns from Mel spectrograms, while the BiLSTM network poses temporal dependencies using extracted audio features. Comparative analysis with state-of-theart CNN architectures and conventional Vision Transformers demonstrates the superiority of our proposed approach, highlighting the potential of quantum-enhanced models in audio forensics.
  • EEMamba: A Hardware-Aware Energy-Efficient State-Space Model for Eurosat Classification
    Maneet Chatterjee, Anuvab Sen, Subhabrata Roy
    International Geoscience and Remote Sensing Symposium IGARSS, 2025
    The increasing dimensionality and spectral bandwidth of satellite imagery pose serious challenges to image classification based on remote sensing. Conventional techniques like transformers and convolutional neural networks (CNNs) have been used in the past for image classification, however, they frequently demand high computational costs and have trouble accurately simulating spatiotemporal dependencies. Using the EuroSAT dataset we present EEMamba a novel framework created especially for satellite image classification using State Space Models (SSMs). These problems are resolved by EEMambas integration of SSMs which enable effective latent pattern extraction in multispectral data while preserving a lightweight architecture. This method improves classification accuracy while also lessening the computational load. A significant benefit of our framework is its capacity to maximize feature extraction by utilizing multispectral bands guaranteeing robustness against noise and resolution fluctuations. To measure EEMambas computational efficiency we perform a thorough analysis of floating-point operations per second (FLOPs) and performance enhancements. Large Earth observation datasets are among the real-time geospatial analysis applications that our framework can handle. We prove how EEMamba is a shining example of how state-space dynamics can revolutionize the analysis of geospatial images thus paving the way for future research that employs state-of-the-art machine learning techniques to address the evolving challenges in remote sensing image classification.
  • MRI-Based Classification of Early and Late Mild Cognitive Impairment Using Correlation-Guided Machine Learning Models
    Subhabrata Roy, Harsh Raj Gupta, Prashant Kumar
    2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025, 2025
    Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and Alzheimer's Disease (AD), with subtypes Early MCI (EMCI) and Late MCI (LMCI) exhibiting varying risks of progression. Accurate classification of these substages is crucial for timely therapeutic intervention and clinical trial stratification. This work presents a machine learning-based framework leveraging volumetric features extracted from MRI scans available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Three experimental pipelines were explored: (i) Decision Tree models trained on features selected via Point Biserial correlation, (ii) Decision Trees refined through correlation thresholding combined with feature ranking, and (iii) Random Forest classifiers trained on all available features. Model performance was optimized via GridSearchCV and assessed using accuracy, precision, recall, and F1-score. Results demonstrated that Random Forest outperformed other approaches, achieving an accuracy of 95.12 % and balanced precision-recall values of 95.0 %. These findings establish the potential of ensemble learning to effectively differentiate EMCI and LMCI stages. The study highlights the integration of neuroimaging biomarkers with data-driven models as a promising direction for enhancing early-stage AD diagnosis and personalized patient management.
  • TerraMorphNet: Deep Fusion Ensemble for Mars Hirise Terrain Classification with Custom Features
    Angshuman Roy, Anuvab Sen, Subhabrata Roy, Subhrajit Deb
    International Geoscience and Remote Sensing Symposium IGARSS, 2025
    We introduce TerraMorphNet, a two-branch ensemble for classifying Martian terrains in HiRISE imagery, featuring a novel set of custom-designed morphological and textural features. The first branch utilizes these unprecedented features-such as crater rims, spider spokes, swiss cheese holes, dune patterns, and slope streak orientations-paired with Adaptive Synthetic Sampling (ADASYN) to train a Random Forest classifier that addresses class imbalance. The second branch compresses deep embeddings from ConvNeXtV2 via a conditional Variational Autoencoder, followed by an MLP enhanced by ADASYN to recognize minority classes like impact ejecta and spiders. By fusing these domain-specific features with robust deep CNN representations through a weighted ensemble, TerraMorPHNET leverages both innovative morphological analysis and state-of-the-art deep learning. Experiments on eight terrain classes demonstrate that our ensemble outperforms individual branches: the Random Forest branch achieves 0.92 average scores while the MLP branch reaches up to 0.95 precision. The final ensemble attains 94.94 % accuracy on a 2,547 -sample test set with high precision, recall, and F1-scores across classes. These results highlight how integrating newly crafted Mars-oriented features with advanced deep embeddings significantly enhances terrain classification, underscoring the impact of novel domain expertise in planetary surface exploration.
  • AeroMeldNet: Lightweight Multi-Task MambaVision Framework for Real-Time Aerial Scene and Disaster Detection
    Anuvab Sen, Angshuman Roy, Subhabrata Roy, Subhrajit Deb
    International Geoscience and Remote Sensing Symposium IGARSS, 2025
    Unmanned aerial vehicles (UAVs) and other edge devices often require real-time scene classification and disaster detection under tight resource constraints. However, most existing pipelines treat these tasks separately or artificially merge data, overlooking potential synergies while risking data imbalance. We propose AeroMeldNet, a lightweight partial-labeled multi-task pipeline built on MambaVision, featuring two heads (scene vs. disaster) and advanced optimization stages: pruning, dynamic quantization, half-precision training, and knowledge distillation. Our multi-stage approach drastically reduces FLOPs and parameter counts without sacrificing accuracy. We leverage the AID dataset for aerial scene classification and AIDER dataset for disaster detection, preserving each dataset's unique label space. Extensive experiments indicate up to 95-96% accuracy on respective test sets with significant FLOPs reduction, making it feasible for real-time edge deployment. Our method addresses the scarcity of combined aerial datasets by enabling partiallabeled multi-task training, demonstrating that synergy across tasks can yield robust feature extraction without forcing normal re-labeling. This pipeline sets a practical precedent for resourceefficient drone-based aerial detection systems.
  • On the Detection of Alzheimer's Disease and Mild Cognitive Impairment using Priority Feature Selection based Decision Tree Classifier
    Subhabrata Roy, Sukalyan Maity, Sagnik Dutta, Aditya Kumar Thakur, Anjana Banerjee
    2025 IEEE Guwahati Subsection Conference Gcon 2025, 2025
    Alzheimer’s disease (AD) is one of the most common neurodegenerative dementias affecting the elderly population. Early detection of AD is crucial for improving quality of life, which has led to significant research interest worldwide. This study introduces an innovative approach to categorize brain MRI scans into two groups: mild cognitive impairment (MCI) and AD. The classification leverages volumetric information from four key features: cerebrospinal fluid (CSF), amygdala, caudate, and grey matter (GM). Using an information gain-based feature selection approach and a decision tree classifier, the proposed method is evaluated on ADNI dataset of brain MRI images. The method’s superiority is demonstrated by performance metrics such as accuracy, sensitivity, specificity, and F1-score, which show average gains of 12.02%, 8.52%, and 17.72% in accuracy, sensitivity, and specificity, respectively, when compared to state-of-the-art techniques.
  • A Survey of Hyperparameter Selection Methods for Weather Forecasting Using State-of-the-Art Machine Learning Algorithms
    Anuvab Sen, Udayon Sen, Mayukhi Paul, Ananya Sutradhar, Taraka Nithin Vankala, Chhandak Mallick, Aakash Mallik, Angshuman Roy, Sujith Sai, Subhabrata Roy
    Studies in Computational Intelligence, 2025
  • ExoSpikeNet: A Light Curve Analysis Based Spiking Neural Network for Exoplanet Detection
    Maneet Chatterjee, Anuvab Sen, Subhabrata Roy
    Proceedings 2024 13th IEEE International Conference on Communication Systems and Network Technologies Csnt 2024, 2024
    Exoplanets are celestial bodies orbiting stars beyond our Solar System. Although historically they posed detection challenges, Kepler's data has revolutionized our understanding. By analyzing flux values from t he Kepler (K2) Mission, we investigate the intricate patterns in starlight that may indicate the presence of exoplanets. This study has investigated a novel approach for exoplanet classification using spiking Neural Networks (SNNs) applied to the data obtained from the NASA Kepler (K2) mission. SNNs offer a unique advantage by mimicking the spiking behavior of neurons in the brain, allowing for more nuanced and biologically inspired processing of temporal data. Experimental results showcase the efficacy of the proposed SNN architecture, excelling in terms of various performance metrics such as accuracy, F1 score, precision, and recall.
  • DE-ViT: State-Of-The-Art Vision Transformer Model for Early Detection of Alzheimer's Disease
    Anuvab Sen, Subhabrata Roy, Ariv Debnath, Gourav Jha, Rahul Ghosh
    2024 National Conference on Communications Ncc 2024, 2024
  • HBO-DEViT: Vision Transformer Based Attention-Guided Evolutionary Architecture for Ship-Iceberg Categorisation in Arctic Sar Images
    Anuvab Sen, Sujith Sai, Chhandak Mallick, Subhabrata Roy, Udayon Sen
    International Geoscience and Remote Sensing Symposium IGARSS, 2024
  • QGAPHnet : Quantum Genetic Algorithm Based Hybrid QLSTM Model for Soil Moisture Estimation
    Sujith Sai, Anuvab Sen, Chhandak Mallick, Aakash Mallik, Udayon Sen, Mayukhi Paul, Ananya Sutradhar, Subhabrata Roy
    International Geoscience and Remote Sensing Symposium IGARSS, 2024
  • On the Detection of Alzheimer's Disease using Naïve Bayes Classifier
    Abhijit Chandra, Subhabrata Roy
    2nd International Conference on Microwave Optical and Communication Engineering Icmoce 2023, 2023
  • A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease
    Anuvab Sen, Udayon Sen, Subhabrata Roy
    Proceedings 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2023, 2023
  • On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier
    Subhabrata Roy, Abhijit Chandra
    Multimedia Tools and Applications, 2022
  • A Deep Learning Approach for the Design of Narrow Transition-Band FIR Filter
    Subhabrata Roy, Abhijit Chandra
    Circuits Systems and Signal Processing, 2022
  • On the Design of Variable Filtered-OFDM Based LDACS for Future Generation Air-to-Ground Communication System
    Subhabrata Roy, Abhijit Chandra
    IEEE Transactions on Circuits and Systems II Express Briefs, 2022
  • Design of FIR filter ISOTA with the aid of genetic algorithm
    Abhijit Chandra, Amit Kumar, Subhabrata Roy
    Integration, 2021
  • A Survey of FIR Filter Design Techniques: Low-complexity, Narrow Transition-band and Variable Bandwidth
    Subhabrata Roy, Abhijit Chandra
    Integration, 2021
  • Difference between Alzheimer's Disease and Mild Cognitive Impairment: Z-test based Study
    Abhijit Chandra, Subhabrata Roy
    IEEE 2nd International Conference on Applied Electromagnetics Signal Processing and Communication Aespc 2021 Proceedings, 2021
  • A Study on the Optimum Selection of Interpolation Factor for the Design of Narrow Transition Band FIR Filter Using IBM
    Subhabrata Roy, Abhijit Chandra
    Lecture Notes in Networks and Systems, 2021
  • On the Detection of Alzheimer's Disease using Support Vector Machine Based Majority Voter Classifier
    Abhijit Chandra, Subhabrata Roy
    Proceedings of the 8th International Conference on Signal Processing and Integrated Networks Spin 2021, 2021
  • A Triangular Common Subexpression Elimination Algorithm with Reduced Logic Operators in FIR Filter
    Subhabrata Roy, Abhijit Chandra
    IEEE Transactions on Circuits and Systems II Express Briefs, 2020
  • Design of narrow transition band variable bandwidth digital filter
    Subhabrata Roy, Abhijit Chandra
    Iet Circuits Devices and Systems, 2020
  • On the Order Minimization of Interpolated Bandpass Method Based Narrow Transition Band FIR Filter Design
    Subhabrata Roy, Abhijit Chandra
    IEEE Transactions on Circuits and Systems I Regular Papers, 2019
  • Interpolated Band-pass Method Based Narrow-band FIR Filter : A Prospective Candidate in Filtered-OFDM Technique for the 5G Cellular Network
    Subhabrata Roy, Abhijit Chandra
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2019
  • Design of Narrow Transition Band Digital Filter: An Analytical Approach
    Subhabrata Roy, Abhijit Chandra
    Integration, 2019
  • Prefilter-equalizer based structure: A new design strategy for narrow-band FIR filters
    Arnab Ghosh, Subhabrata Roy, Abhijit Chandra
    Proceedings of 2018 IEEE Applied Signal Processing Conference Aspcon 2018, 2018
  • A New Design Strategy of Sharp Cut-off FIR Filter with Powers-of-two Coefficients
    Subhabrata Roy, Abhijit Chandra
    2018 International Conference on Wireless Communications Signal Processing and Networking Wispnet 2018, 2018
  • A new method for denoising ECG signal using sharp cut-off FIR filter
    Subhabrata Roy, Abhijit Chandra
    2018 International Symposium on Devices Circuits and Systems Isdcs 2018, 2018
  • On outage secrecy minimisation in an energy harvesting relay assisted cognitive radio networks
    Avik Banerjee, Santi P. Maity, Subhabrata Roy
    Iet Communications, 2018
  • On residual energy maximization in energy harvesting cognitive radio network
    Avik Banerjee, Santi P. Maity, Subhabrata Roy
    IEEE Wireless Communications and Networking Conference Wcnc, 2017

RECENT SCHOLAR PUBLICATIONS

  • Graphene–VO2 hybrid metamaterial biosensor with machine learning–assisted absorption and virus detection in the THz regime
    S Vuyyala, S Roy, K Shashidhar
    Optical Engineering 65 (4), 047103 , 2026
    2026
  • Q-Detect: An Attention-Guided Quantum Ensemble Framework for Robust DeepFake Audio Detection
    A Roy, A Sen, S Haldar, S Roy
    2026 National Conference on Communications (NCC), 280-285 , 2026
    2026
  • sCNN: An Improved Convolutional Neural Network for Early Detection of Alzheimer's Disease
    S Roy, GK Maurya
    2026 International Conference on Signal Analysis for Smart Systems (SIGNASS … , 2026
    2026
  • MRI-Based Classification of Early and Late Mild Cognitive Impairment Using Correlation-Guided Machine Learning Models
    S Roy, HR Gupta, P Kumar
    2025 IEEE 17th International Conference on Computational Intelligence and … , 2025
    2025
  • TerraMorphNet: Deep Fusion Ensemble for Mars Hirise Terrain Classification with Custom Features
    A Roy, A Sen, S Roy, S Deb
    IGARSS 2025-2025 IEEE International Geoscience and Remote Sensing Symposium … , 2025
    2025
  • EEMamba: A Hardware-Aware Energy-Efficient State-Space Model for Eurosat Classification
    M Chatterjee, A Sen, S Roy
    IGARSS 2025-2025 IEEE International Geoscience and Remote Sensing Symposium … , 2025
    2025
  • AeroMeldNet: Lightweight Multi-Task MambaVision Framework for Real-Time Aerial Scene and Disaster Detection
    A Sen, A Roy, S Roy, S Deb
    IGARSS 2025-2025 IEEE International Geoscience and Remote Sensing Symposium … , 2025
    2025
  • On the Detection of Alzheimer’s Disease and Mild Cognitive Impairment using Priority Feature Selection based Decision Tree Classifier
    S Roy, S Maity, S Dutta, AK Thakur, A Banerjee
    2025 IEEE Guwahati Subsection Conference (GCON), 1-6 , 2025
    2025
  • A review on early detection of Alzheimer’s disease: employing deep learning, machine learning, and statistical methods
    S Biswas, A Sen, S Roy
    CSI Transactions on ICT , 2025
    2025
    Citations: 4
  • A Survey of Hyperparameter Selection Methods for Weather Forecasting Using State-of-the-Art Machine Learning Algorithms
    TN Vankala, C Mallick, A Mallik, A Roy, S Sai, S Roy
    Computational Intelligence: 14th and 15th International Joint Conference on … , 2025
    2025
  • QGAPHnet: quantum genetic algorithm based hybrid QLSTM model for soil moisture estimation
    S Sai, A Sen, C Mallick, A Mallik, U Sen, M Paul, A Sutradhar, S Roy
    IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium … , 2024
    2024
    Citations: 4
  • Hbo-devit: Vision transformer based attention-guided evolutionary architecture for ship-iceberg categorisation in arctic sar images
    A Sen, S Sai, C Mallick, S Roy, U Sen
    IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium … , 2024
    2024
    Citations: 4
  • ExoSpikeNet: a light curve analysis based spiking neural network for exoplanet detection
    M Chatterjee, A Sen, S Roy
    2024 IEEE 13th International Conference on Communication Systems and Network … , 2024
    2024
    Citations: 3
  • DE-ViT: State-Of-The-Art Vision Transformer Model for Early Detection of Alzheimer's Disease
    A Sen, S Roy, A Debnath, G Jha, R Ghosh
    30th National Conference on Communications (NCC 2024), 1-6 , 2024
    2024
    Citations: 21
  • A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease
    A Sen, U Sen, S Roy
    2023 IEEE 15th International Conference on Computational Intelligence and … , 2023
    2023
    Citations: 11
  • A Survey of Hyperparameter Selection Methods for Weather Forecasting Using State-of-the-Art Machine Learning Algorithms
    A Sen, U Sen, M Paul, A Sutradhar, TN Vankala, C Mallick, A Mallik, ...
    International Joint Conference on Computational Intelligence, 265-293 , 2023
    2023
  • On the classification of Alzheimer’s disease, mild cognitive impairment and healthy control: maximum a posteriori probability based approach
    A Chandra, S Roy
    CSI Transactions on ICT 11 (2-3), 111-118 , 2023
    2023
    Citations: 4
  • On the Detection of Alzheimer's Disease using Naïve Bayes Classifier
    A Chandra, S Roy
    2023 International Conference on Microwave, Optical, and Communication … , 2023
    2023
    Citations: 10
  • On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier
    S Roy, A Chandra
    Multimedia Tools and Applications 81 (30), 43145-43161 , 2022
    2022
    Citations: 18
  • A Deep Learning Approach for the Design of Narrow Transition-Band FIR Filter
    S Roy, A Chandra
    Circuits, Systems, and Signal Processing 41 (10), 5578-5613 , 2022
    2022
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • A survey of fir filter design techniques: low-complexity, narrow transition-band and variable bandwidth
    S Roy, A Chandra
    Integration 77, 193-204 , 2021
    2021
    Citations: 50
  • On the order minimization of interpolated bandpass method based narrow transition band FIR filter design
    S Roy, A Chandra
    IEEE Transactions on Circuits and Systems I: Regular Papers 66 (11), 4287-4295 , 2019
    2019
    Citations: 37
  • Design of narrow transition band digital filter: An analytical approach
    S Roy, A Chandra
    Integration 68, 38-49 , 2019
    2019
    Citations: 25
  • A triangular common subexpression elimination algorithm with reduced logic operators in FIR filter
    S Roy, A Chandra
    IEEE Transactions on Circuits and Systems II: Express Briefs 67 (12), 3527-3531 , 2020
    2020
    Citations: 24
  • DE-ViT: State-Of-The-Art Vision Transformer Model for Early Detection of Alzheimer's Disease
    A Sen, S Roy, A Debnath, G Jha, R Ghosh
    30th National Conference on Communications (NCC 2024), 1-6 , 2024
    2024
    Citations: 21
  • On the detection of Alzheimer’s disease using fuzzy logic based majority voter classifier
    S Roy, A Chandra
    Multimedia Tools and Applications 81 (30), 43145-43161 , 2022
    2022
    Citations: 18
  • On residual energy maximization in energy harvesting cognitive radio network
    A Banerjee, SP Maity, S Roy
    2017 IEEE Wireless Communications and Networking Conference (WCNC), 1-6 , 2017
    2017
    Citations: 18
  • On the design of variable filtered-OFDM based LDACS for future generation air-to-ground communication system
    S Roy, A Chandra
    IEEE Transactions on Circuits and Systems II: Express Briefs 69 (2), 644-648 , 2021
    2021
    Citations: 16
  • Interpolated Band-pass Method Based Narrow-band FIR Filter : A Prospective Candidate in Filtered-OFDM Technique for the 5G Cellular Network
    S Roy, A Chandra
    2019 IEEE Region 10 Conference (TENCON 2019) , 2019
    2019
    Citations: 13
  • A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease
    A Sen, U Sen, S Roy
    2023 IEEE 15th International Conference on Computational Intelligence and … , 2023
    2023
    Citations: 11
  • On the detection of Alzheimer’s disease using support vector machine based majority voter classifier
    A Chandra, S Roy
    2021 8th International conference on signal processing and integrated … , 2021
    2021
    Citations: 11
  • A new method for denoising ECG signal using sharp cut-off FIR filter
    S Roy, A Chandra
    2018 International Symposium on Devices, Circuits and Systems (ISDCS) , 2018
    2018
    Citations: 11
  • On the Detection of Alzheimer's Disease using Naïve Bayes Classifier
    A Chandra, S Roy
    2023 International Conference on Microwave, Optical, and Communication … , 2023
    2023
    Citations: 10
  • Design of FIR filter ISOTA with the aid of genetic algorithm
    A Chandra, A Kumar, S Roy
    Integration 79, 107-115 , 2021
    2021
    Citations: 10
  • Design of narrow transition band variable bandwidth digital filter
    S Roy, A Chandra
    IET Circuits, Devices & Systems 14 (6), 750-757 , 2020
    2020
    Citations: 10
  • A new design strategy of sharp cut-off fir filter with powers-of-two coefficients
    S Roy, A Chandra
    2018 International Conference on Wireless Communications, Signal Processing … , 2018
    2018
    Citations: 10
  • On outage secrecy minimisation in an energy harvesting relay assisted cognitive radio networks
    A Banerjee, SP Maity, S Roy
    IET Communications 12 (18), 2253-2265 , 2018
    2018
    Citations: 9
  • A Deep Learning Approach for the Design of Narrow Transition-Band FIR Filter
    S Roy, A Chandra
    Circuits, Systems, and Signal Processing 41 (10), 5578-5613 , 2022
    2022
    Citations: 8
  • Difference between Alzheimer’s Disease and Mild Cognitive Impairment: Z-test based Study
    A Chandra, S Roy
    2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal … , 2022
    2022
    Citations: 8
  • A review on early detection of Alzheimer’s disease: employing deep learning, machine learning, and statistical methods
    S Biswas, A Sen, S Roy
    CSI Transactions on ICT , 2025
    2025
    Citations: 4