Ankit Gupta

@vsb.cz

Department of Biomedical Engineering and Cybernetics, Faculty of Electrical Engineering and Computer Science
VSB-Technical University of Ostrava

Ankit Gupta


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https://researchid.co/ankitgupta1001

RESEARCH, TEACHING, or OTHER INTERESTS

Signal Processing, Biomedical Engineering
23

Scopus Publications

599

Scholar Citations

13

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • SiViS: Simulated multi-patient physiological clinical states for advanced vital sign radar monitoring research
    Karla Miriam Reyes, Ankit Gupta, Arthur Grosmaire, Silvie Procházková, Petr Matouch, Ivona Závacká, Josef Škroch, Martin Cerny
    Scientific Data, 2026
    Continuous, clinically reliable non-contact monitoring of multiple patients' vital signs remains a significant technological challenge in care settings. This paper introduces a systematically constructed radar-based dataset designed to test methods for simultaneous multi-patient vital sign monitoring. Developed with Ostrava University Training Hospital, the dataset includes recordings from two-three advanced medical simulation mannequins (SimMan 3G Plus), emulating a wide range of physiological states-from healthy resting to acute emergencies (apnea, cardiac arrest, severe respiratory distress). We varied sensor geometry (top, frontal, lateral views at 1-4 m, 0°/45° angles) and radar parameters (ADC samples, chirp loops, ramp times, frame rates, gains), yielding over 100 uniquely configured sessions. Preliminary beamforming-based processing achieves mean heart-rate and breathing-rate errors near clinical thresholds (MAE ≈ 6.6 bpm for HR, 1.47 bpm for RR), demonstrating the dataset's utility for developing advanced signal processing and machine-learning pipelines. In keeping with FAIR principles, all data are fully documented and publicly accessible, supporting reproducible research toward noninvasive multi-patient monitoring systems.
  • RGB, a Surrogate of Infrared Facial Videos for Physiological Signs Estimations in Dark
    Ankit Gupta, Antonio G. Ravelo-García, Fernando Morgado Dias
    IEEE Transactions on Circuits and Systems for Video Technology, 2026
    Physiological signs are key indicators of cardiovascular health, which can be estimated using remote photoplethysmography. Their estimations in dark environments are particularly important, where infrared based methods were predominantly applied, since they are illumination resistant. However, the extracted signals have poor pulsatile strength with low signal-to-noise ratio, eventually resulting in spurious estimates. Conversely, RGB based methods exhibits stronger pulsatile strength, but hindered by poor illumination. To overcome these limitations, we propose 2E1D-Net, trained using a self-created database acquired in a dark environment with marginal illuminance ≤ 1 lux. It comprises dual encoders that take paired input images captured at different exposure levels, and project them to a latent. The decoder then, elevates the noise (darkness) component from the dark image, followed by multiscale feature fusion, to produce enhanced images. 2E1D-Net was trained using a linear combination of multiscale structured-similarity-index, L1 and L2 losses, respectively. Subsequently, RGB heart rate and oxygen saturation methods cascaded to trained 2E1D-Net, were tested on self-created and public databases. Experimental results proved the superiority of 2E1D-Net, over state-of-the-art, which ensured the extended ability of RGB methods for physiological measurements in dark, thereby proposing RGB as reliable and clinically relevant alternative to infrared methods without performance compromise.
  • MARS lander: Georeferencing landing and pop points of untethered ocean monitoring systems using fundamental physics
    Marko Radeta, Zahra Behboodi, Vladimir Zekovic, Décio Alves, David Pestana, Daniel Nunes, Margarida Freitas, Ankit Gupta, João Pestana, Dinarte Vieira, Sílvia Almeida, Morgado Dias, João Canning Clode, Rui Caldeira, Paulo Relvas, Antonio Vasiljevic
    Deep Sea Research Part I Oceanographic Research Papers, 2026
    Subsurface observations are crucial for understanding the ocean's role in Earth’s climate and for refining climate models. However, existing aquatic monitoring systems that allow such insights remain complex and costly due to their high demands for deployment, sampling, and recapture. Since low-cost, easy-to-deploy deep-sea landers are scarce, and with the aim of facilitating more subsurface observations, this study provides a simple method for georeferencing small-sized untethered landers. Their underwater trajectories are modelled with fundamental physics, dead reckoning, lander geometry, and numerical simulations. Using free fall, upthrust, and ocean current dynamics, the proposed approach estimates their underwater trajectories, including landing (at the seabed) and pop (at the sea surface) points. The method relies on the lander's physical characteristics, including its vertical and horizontal cross-sectional areas, to calculate the drag force coefficients used to determine its trajectories during descent and ascent through the water column. Ocean currents' magnitudes are modelled using Ekman’s exponential decay down to 90 m of the water column, while the depths until 900 m are modelled from prior ADCP surveys by varying ocean current headings with depth between -20 and 20 degrees. Surface ocean and wind current headings are modelled with open datasets from satellite telemetry. Lander's velocity, displacement, and dive time to the landing and pop points, including the total radial excursion and uncertainty in heading, are analytically derived, numerically calculated, and empirically assessed a-posteriori until 90 m, yielding a ∼38 m radial excursion (40% error) against the obtained GNSS coordinates in field deployment, and 33 degrees in heading uncertainty during a 138-second excursion. Additional random walk simulations are shown for full ocean depth obtaining radial excursion of 1,038 m with 278 min total dive time. This approach is generalizable to any subsurface aquatic monitoring systems targeting deployments with diverse payloads from smaller sea vessels, not requiring cranes, radio, GNSS, or acoustic telemetry. Since it accounts for key nature factors, our method provides special benefits in planning and optimizing deployments. Additional discussion focuses on the method's practicality for full ocean depth deployments. Graphical abstract highlighting the challenge in georeferencing the untethered low-cost landers without costly satellite uplink telemetry. The lander is deployed from smaller sea vessels at the desired (drop point) location, where the required depth is obtainable from the sea vessel's sonar. The ocean currents cause the lander to drift during descent and ascent with radial excursion relative to the drop point. Heading uncertainties are depicted as estimated circular areas of probabilities where the lander may touch the seabed (landing point). The same approach also applies to estimating the lander’s position at the sea surface (pop point). • Predicting landing and pop points of low-cost deep-sea landers a-posteriori. • Providing underwater trajectories based on the lander’s geometry, dead reckoning, numerical simulations, and hydrodynamics. • Optimizing planning, deployment, sampling, and recapture of untethered aquatic observatories. • Obtained 40% radial excursion error with in-situ deployment at 90 m depth. • Obtained radial excursion of 1,038 m with 278 min total dive time, including 95 degrees heading uncertainty using random walk simulation for the case of full ocean depth (11,000 m).
  • Radar Placement Effects on Multi-patient Heart and Respiration Monitoring, SiViS Dataset Validation
    Karla Miriam Reyes Leiva, Ankit Gupta, Martin Cerny
    Lecture Notes in Computer Science, 2026
    Noncontact radar sensing offers a compelling solution for estimation and continuous monitoring of vital signs. Despite significant progress in single patient vital sign detection, estimations for multi patient scenarios using a single radar sensor remains challenging due to the need of disentangling the overlapping physiological signals from a single radar data cube. To address this challenge, this paper presents a technical validation of the SiViS dataset. Specifically, we validate the influence of radar placement on vital sign estimation and benchmark a reference two-stage signal processing pipeline (adaptive Minimum Variance Distortionless Response, MVDR, beamforming followed by phase-based estimation). The focus of this work is on dataset validation and characterization of placement effects, rather than proposing a novel algorithm for heart and respiration rate estimation. Our preliminary evaluations test the optimal radar position for estimation and demonstrated the feasibility of concurrently estimating these vital signs in a multi patient scenario, benchmarking our basic pipeline. In conclusion, this work advances the capability of single sensor radar systems from single patient to multi patient noncontact monitoring, thereby highlighting future directions for robust multi patient monitoring.
  • Multi-Objective Undercomplete Independent Component Analysis for Radar Signals based Heart Rate and Interbeat Interval Estimations
    Ankit Gupta, Karla Reyes, Martin Cerny
    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, 2025
    This study, assuming the heart rate extraction problem as an undercomplete problem (one independent component from two mixed signals), presents a multi-objective undercomplete independent component analysis (Multi-Objective Undercomplete Independent Component Analysis (MO-UICA)) for reconstructing a cardiac signal from in-phase and quadrature signals. The proposed method comprises following components 1) multi-objective cost function which is the weighted combination of entropy of the cumulative density function (cdf), and autcorrelation, 2) Levenberg Marquardt algorithm for optimization the cost function, based on first-order and fourth-order gradient of cdf, autocorrelation (calculated between the heart rate signal and its time-shifted variant based on the cardiac cycle), respectively, and 3) undercomplete independent component analysis incorporating both. The quantitative and qualitative results presented in this work showed the efficacy and robustness of the proposed method for the estimation of the average heart rate and the interbeat intervals for different processing windows (1-60 seconds). Furthermore, its inability to provide instantaneous estimates lays down a foundation to future directions of this work.
  • Recent advancements in deep learning-based remote photoplethysmography methods
    Ankit Gupta, Antonio G. Ravelo-García, Fernando Morgado-Dias
    Data Fusion Techniques and Applications for Smart Healthcare, 2024
  • Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants
    Shyam Singh Chandel, Ankit Gupta, Rahul Chandel, Salwan Tajjour
    Solar Compass, 2023
    Varying power generation by industrial solar photovoltaic plants impacts the steadiness of the electric grid which necessitates the prediction of solar power generation accurately. In this study, a comprehensive updated review of standalone and hybrid machine learning techniques for PV power forecasting is presented. Forecasting solar generation is of importance for the sustainability of grid power and also to achieve the UN sustainable development targets by 2030. The comparison of techniques shows that grouping datasets based on input feature similarity, results in higher accuracy. Long-Short Term Memory (LSTM) is found to perform better than other deep learning networks for all time horizons. The Gate Recurrent Unit (GRU), with few trainings, is found to be better for small datasets than LSTM. Based on the more complicated data patterns, a novel architecture of the Deep Learning Network model, with the capability to analyze and forecast is presented considering factors influencing industrial solar power generation. The study is of importance to researchers, solar industry, and electricity distribution companies for sustainable development worldwide.
  • Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms
    Ankit Gupta, Fábio Mendonça, Sheikh Shanawaz Mostafa, Antonio G. Ravelo-García, Fernando Morgado-Dias
    Electronics Switzerland, 2023
    Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the amplitude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low accuracy of the traditional approach and the black box approach of the machine learning approach, the proposed systems remain untrusted by the physician. This study contributes to accurately estimating CAP in a Frequency-Time domain by A-phase and its subtypes prediction by transforming the monopolar deviated electroencephalogram signals into corresponding scalograms. Subsequently, various computer vision classifiers were tested for the A-phase using scalogram images. It was found that MobileNetV2 outperformed all other tested classifiers by achieving the average accuracy, sensitivity, and specificity values of 0.80, 0.75, and 0.81, respectively. The MobileNetV2 trained model was further fine-tuned for A-phase subtypes prediction. To further verify the visual ability of the trained models, Gradcam++ was employed to identify the targeted regions by the trained network. It was verified that the areas identified by the model match the regions focused on by the sleep experts for A-phase predictions, thereby proving its clinical viability and robustness. This motivates the development of novel deep learning based methods for CAP patterns predictions.
  • Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review
    Preety Baglat, Ahatsham Hayat, Fábio Mendonça, Ankit Gupta, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias
    Sensors, 2023
    The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.
  • A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern
    Fábio Mendonça, Sheikh Shanawaz Mostafa, Ankit Gupta, Erna Sif Arnardottir, Timo Leppänen, Fernando Morgado-Dias, Antonio G Ravelo-García
    Sleep, 2023
    Study Objectives Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night’s sleep. Methods Two ensemble classifiers were developed to automatically score the signals, one for “A-phase” and the other for “non-rapid eye movement” estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles’ classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers’ structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate. Results Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation’s accuracy, sensitivity, and specificity range was 82%–87%, 72%–80%, and 82%–88%, respectively. A similar performance was attained for the A-phase subtype’s assessments, with an accuracy range of 82%–88%. Furthermore, in the examined dataset’s variations, the API metric’s average error varied from 0.15 to 0.25 (with a median range of 0.11–0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm. Conclusions Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.
  • A Motion and Illumination Resistant Non-Contact Method Using Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm
    Ankit Gupta, Antonio G. Ravelo-Garcia, Fernando Morgado Dias
    IEEE Journal of Biomedical and Health Informatics, 2022
  • An empirical investigation of market risk, dependence structure, and portfolio management between green bonds and international financial markets
    Rimsha Ejaz, Sumaira Ashraf, Arshad Hassan, Ankit Gupta
    Journal of Cleaner Production, 2022
  • Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review
    Ankit Gupta, Antonio G. Ravelo-García, Fernando Morgado Dias
    Computer Methods and Programs in Biomedicine, 2022
  • Computational approaches for malaria diagnosis using machine learning: A review
    Mobile Health Advances in Research and Applications, 2021
  • Solving Image Processing Critical Problems Using Machine Learning
    Ajay Sharma, Ankit Gupta, Varun Jaiswal
    Studies in Big Data, 2021
  • Prediction of Alzheimer associated proteins (PAAP): A perspective to understand Alzheimer disease for therapeutic design
    Gaurav Gupta, Neha Gupta, Ankit Gupta, Pankaj Vaidya, Girish Kumar Singh, Varun Jaiswal
    International Journal of Bioinformatics Research and Applications, 2021
  • Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset
    Preety Baglat, Ahmad Waleed Salehi, Ankit Gupta, Gaurav Gupta
    IFIP Advances in Information and Communication Technology, 2020
  • Application and Analysis of Hyperspectal Imaging
    Ajay Sharma, Deep Kaur, Ankit Gupta, Varun Jaiswal
    Proceedings of IEEE International Conference on Signal Processing Computing and Control, 2019
  • Machine learning based performance evaluation system based on multi-categorial factors
    Kapil Sethi, Ankit Gupta, Varun Jaiswal
    Pdgc 2018 2018 5th International Conference on Parallel Distributed and Grid Computing, 2018
  • A review and analysis of mobile health applications for alzheimer patients and caregivers
    Gaurav Gupta, Ankit Gupta, Varun Jaiswal, Mohd Dilshad Ansari
    Pdgc 2018 2018 5th International Conference on Parallel Distributed and Grid Computing, 2018
  • Machine Learning based Prediction of Anatomical Therapeutic Chemical (ATC) Class of Drug Like Molecule
    Pankaj Vaidya, Ankit Gupta, Varun Jaiswal
    2018 International Conference on Recent Innovations in Electrical Electronics and Communication Engineering Icrieece 2018, 2018
  • Hyperspectral imaging: A brief introduction for beginners
    Smart Materials for Smart Living, 2017
  • Jenner-predict server: Prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions
    Varun Jaiswal, Sree Krishna Chanumolu, Ankit Gupta, Rajinder S Chauhan, Chittaranjan Rout
    BMC Bioinformatics, 2013

RECENT SCHOLAR PUBLICATIONS

  • MARS Lander: Georeferencing Landing and Pop Points of Untethered Ocean Monitoring Systems using Fundamental Physics
    M Radeta, Z Behboodi, V Zekovic, D Alves, D Pestana, D Nunes, ...
    Deep Sea Research Part I: Oceanographic Research Papers, 104650 , 2026
    2026
  • RGB, a Surrogate of Infrared Facial Videos for Physiological Signs Estimations in Dark
    A Gupta, AG Ravelo-García, FM Dias
    IEEE Transactions on Circuits and Systems for Video Technology , 2026
    2026
  • SiViS: Simulated multi-patient physiological clinical states for advanced vital sign radar monitoring research
    KM Reyes, A Gupta, A Grosmaire, S Procházková, P Matouch, I Závacká, ...
    Scientific Data , 2026
    2026
  • Radar Placement Effects on Multi-patient Heart and Respiration Monitoring, SiViS Dataset Validation
    KM Reyes Leiva, A Gupta, M Cerny
    International Workshop on Sensor-Based Activity Recognition and Artificial … , 2025
    2025
  • Multi-Objective Undercomplete Independent Component Analysis for Radar Signals based Heart Rate and Interbeat Interval Estimations
    A Gupta, K Reyes, M Cerny
    2025 47th Annual International Conference of the IEEE Engineering in … , 2025
    2025
  • Facial video based physiological variables estimation in dark environments
    A Gupta
    2024
  • System and Method for Estimating at least One Physiological Sign of a Subject in Dark Environments
    A Gupta, AG Ravelo-García, F Morgado-Dias
    WO Patent WO/2024/246,626 , 2024
    2024
  • Recent advancements in deep learning-based remote photoplethysmography methods
    A Gupta, AG Ravelo-García, F Morgado-Dias
    Data Fusion Techniques and Applications for Smart Healthcare, 127-155 , 2024
    2024
    Citations: 4
  • Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants
    SS Chandel, A Gupta, R Chandel, S Tajjour
    Solar Compass 8, 100061 , 2023
    2023
    Citations: 77
  • Visual explanations of deep learning architectures in predicting cyclic alternating patterns using wavelet transforms
    A Gupta, F Mendonça, SS Mostafa, AG Ravelo-García, F Morgado-Dias
    Electronics 12 (13), 2954 , 2023
    2023
    Citations: 2
  • Non-destructive banana ripeness detection using shallow and deep learning: A systematic review
    P Baglat, A Hayat, F Mendonca, A Gupta, SS Mostafa, F Morgado-Dias
    Sensors 23 (2), 738 , 2023
    2023
    Citations: 46
  • A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern
    F Mendonça, SS Mostafa, A Gupta, ES Arnardottir, T Leppänen, ...
    Sleep 46 (1), zsac217 , 2023
    2023
    Citations: 12
  • An empirical investigation of market risk, dependence structure, and portfolio management between green bonds and international financial markets
    R Ejaz, S Ashraf, A Hassan, A Gupta
    Journal of Cleaner Production 365, 132666 , 2022
    2022
    Citations: 55
  • Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review
    A Gupta, AG Ravelo-Garcia, FM Dias
    Computer methods and programs in biomedicine 219, 106771 , 2022
    2022
    Citations: 40
  • A motion and illumination resistant non-contact method using undercomplete independent component analysis and Levenberg-Marquardt algorithm
    A Gupta, AG Ravelo-García, FM Dias
    IEEE Journal of Biomedical and Health Informatics 26 (10), 4837-4848 , 2022
    2022
    Citations: 18
  • Solving image processing critical problems using machine learning
    A Sharma, A Gupta, V Jaiswal
    Machine Learning for Intelligent Multimedia Analytics: Techniques and … , 2021
    2021
    Citations: 19
  • Prediction of Alzheimer associated proteins (PAAP): A perspective to understand Alzheimer disease for therapeutic design
    G Gupta, N Gupta, A Gupta, P Vaidya, GK Singh, V Jaiswal
    International Journal of Bioinformatics Research and Applications 17 (4 … , 2021
    2021
    Citations: 5
  • Multiple machine learning models for detection of Alzheimer’s disease using OASIS dataset
    P Baglat, AW Salehi, A Gupta, G Gupta
    International Working Conference on Transfer and Diffusion of IT, 614-622 , 2020
    2020
    Citations: 66
  • Application and analysis of hyperspectal imaging
    A Sharma, D Kaur, A Gupta, V Jaiswal
    2019 5th International Conference on Signal Processing, Computing and … , 2019
    2019
    Citations: 19
  • Comparative analysis of machine learning algorithms on different datasets
    K Sethi, A Gupta, G Gupta, V Jaiswal
    Circulation in computer science international conference on innovations in … , 2019
    2019
    Citations: 32

MOST CITED SCHOLAR PUBLICATIONS

  • Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions
    V Jaiswal, SK Chanumolu, A Gupta, RS Chauhan, C Rout
    BMC bioinformatics 14 (1), 211 , 2013
    2013
    Citations: 97
  • Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants
    SS Chandel, A Gupta, R Chandel, S Tajjour
    Solar Compass 8, 100061 , 2023
    2023
    Citations: 77
  • Multiple machine learning models for detection of Alzheimer’s disease using OASIS dataset
    P Baglat, AW Salehi, A Gupta, G Gupta
    International Working Conference on Transfer and Diffusion of IT, 614-622 , 2020
    2020
    Citations: 66
  • An empirical investigation of market risk, dependence structure, and portfolio management between green bonds and international financial markets
    R Ejaz, S Ashraf, A Hassan, A Gupta
    Journal of Cleaner Production 365, 132666 , 2022
    2022
    Citations: 55
  • Non-destructive banana ripeness detection using shallow and deep learning: A systematic review
    P Baglat, A Hayat, F Mendonca, A Gupta, SS Mostafa, F Morgado-Dias
    Sensors 23 (2), 738 , 2023
    2023
    Citations: 46
  • Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review
    A Gupta, AG Ravelo-Garcia, FM Dias
    Computer methods and programs in biomedicine 219, 106771 , 2022
    2022
    Citations: 40
  • A review and analysis of mobile health applications for Alzheimer patients and caregivers
    G Gupta, A Gupta, V Jaiswal, MD Ansari
    2018 Fifth International Conference on Parallel, Distributed and Grid … , 2018
    2018
    Citations: 39
  • Comparative analysis of machine learning algorithms on different datasets
    K Sethi, A Gupta, G Gupta, V Jaiswal
    Circulation in computer science international conference on innovations in … , 2019
    2019
    Citations: 32
  • Machine learning based performance evaluation system based on multi-categorial factors
    K Sethi, A Gupta, V Jaiswal
    2018 Fifth international conference on parallel, distributed and grid … , 2018
    2018
    Citations: 25
  • Solving image processing critical problems using machine learning
    A Sharma, A Gupta, V Jaiswal
    Machine Learning for Intelligent Multimedia Analytics: Techniques and … , 2021
    2021
    Citations: 19
  • Application and analysis of hyperspectal imaging
    A Sharma, D Kaur, A Gupta, V Jaiswal
    2019 5th International Conference on Signal Processing, Computing and … , 2019
    2019
    Citations: 19
  • Mobile health applications and android toolkit for alzheimer patients, caregivers and doctors
    G Gupta, A Gupta, P Barura, V Jaiswal
    Biological Forum–An International Journal 11 (1), 199-205 , 2019
    2019
    Citations: 19
  • A motion and illumination resistant non-contact method using undercomplete independent component analysis and Levenberg-Marquardt algorithm
    A Gupta, AG Ravelo-García, FM Dias
    IEEE Journal of Biomedical and Health Informatics 26 (10), 4837-4848 , 2022
    2022
    Citations: 18
  • A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern
    F Mendonça, SS Mostafa, A Gupta, ES Arnardottir, T Leppänen, ...
    Sleep 46 (1), zsac217 , 2023
    2023
    Citations: 12
  • Machine learning based prediction of anatomical therapeutic chemical (ATC) class of drug like molecule
    P Vaidya, A Gupta, V Jaiswal
    2018 International Conference on Recent Innovations in Electrical … , 2018
    2018
    Citations: 10
  • Conserved HIV wide spectrum antipeptides-a hope for HIV treatment
    BS Rao, KK Gupta, S Kumari, A Gupta, K Pujitha
    Adv Tech Biol Med 1 (102), 2379-1764.1000102 , 2013
    2013
    Citations: 8
  • Prediction of Alzheimer associated proteins (PAAP): A perspective to understand Alzheimer disease for therapeutic design
    G Gupta, N Gupta, A Gupta, P Vaidya, GK Singh, V Jaiswal
    International Journal of Bioinformatics Research and Applications 17 (4 … , 2021
    2021
    Citations: 5
  • Recent advancements in deep learning-based remote photoplethysmography methods
    A Gupta, AG Ravelo-García, F Morgado-Dias
    Data Fusion Techniques and Applications for Smart Healthcare, 127-155 , 2024
    2024
    Citations: 4
  • A comparative analysis of tensor decomposition models using hyper spectral image
    A Gupta, A Oberoi
    arXiv preprint arXiv:1503.06561 , 2015
    2015
    Citations: 4
  • Visual explanations of deep learning architectures in predicting cyclic alternating patterns using wavelet transforms
    A Gupta, F Mendonça, SS Mostafa, AG Ravelo-García, F Morgado-Dias
    Electronics 12 (13), 2954 , 2023
    2023
    Citations: 2