Bakiya Ambikapathy

@veltech.edu.in

Associate Professor, Department of ECE, School of Electrical and Communication
Vel Tech Rangarajan and Dr, Sagunthala R&D Institute of Science and technology

Bakiya Ambikapathy


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https://researchid.co/bakiyaambikapathy

RESEARCH INTERESTS

Biomedical Signal/Image Processing, Fractional Calculus, Computational Intelligence
25

Scopus Publications

307

Scholar Citations

6

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Segmentation of Plasmodium Falciparum Infected Cell in Blood Smear Images Using Optimized U-Net
    R Saranya, A. Bakiya
    Proceedings of the 12th International Conference on Biosignals Images and Instrumentation Icbsii 2026, 2026
    Malaria is a major global health concern, contributing to significant illness and mortality across many regions of the world. Every year a millions of cases reported, early detection and accurate diagnosis are critical for effective disease control and timely treatment. Malaria diagnosis has been regarded as the gold standard of the manual microscopic analysis of the blood smears. The manual microscopic examination of blood smears has been considered as the gold standard for malaria diagnosis. The identification of infected cell using microscope is highly challenging during mass screening in rural areas. Hence, the identification of infected cells in blood smear images requires the efficient computer assisted system. In this study, the camera was used to capture the images of the thin blood smear of Plasmodium Falciparum in microscope after staining the slides. The quality of collected blood smear images was enhanced by Histogram Equalization (HE) techniques. Further, the Honey Badger Optimization based U Net (HBA-UNet) was developed to segment the infected cells in thin blood smear images of Plasmodium Falciparum species. The performance of the developed segmentation model is compared with existing deep learning models such as Mobilenet, Efficientnet, and UNet. The results demonstrated that the dice coefficient of HBA based U Net model attains higher (96.07%) when compared to the deep learning models. Similarly, recall, precision, accuracy and IOU of HBA based UNet model achieves 90.93%, 91.26%, 98.77% and 91.24 % respectively, which are higher when compared to the other deep learning models. This finding reveals that the HBA-UNet model is efficient and accurate segmentation of Plasmodium Falciparum infected cell in the thin blood smear images.
  • Segmentation Of Neutrophil Nucleus in White Blood Cell Images Using Kapur's entropy based Improved Grey Wolf Optimization
    R Saranya, A Bakiya
    Proceedings of the 11th International Conference on Bio Signals Images and Instrumentation Icbsii 2025, 2025
    Qualitative analysis of blood disorders is the most challenging job in current medical image. Diagnosing disorders through qualitative analysis relies heavily on approximation. Disorders related to white blood cells (WBC) are notably prevalent in medical practice. Identifying blood disorders leads to classification of various blood related conditions. Hence, the automated identification of blood related disorder allows to bypass the existing complex environment and focus on the complex image insight provided by the images. In this study, the automated segmentation of neutrophil nucleus in white blood cells microscopic through Kapur’s entropy multilevel thresholding technique. In the process of an efficient segmentation through Kapur’s entropy, the threshold optimal values are required to change the class variance or entropy criteria. To achieve these optimizations, Improved Grey Wolf (IGWO) nature-inspired metaheuristic algorithms are employed for this study. The experimental test has conducted on standard images using various levels of threshold (2, 3, 4, 5 and 6). The efficacy of Kapur’s entropy based improved grey wolf optimization approach has been evaluated and performed with established optimization methods contains Genetic Algorithm(GA), Moth Fly Optimization (MFO), Firefly Optimization, Fire Hawk Optimization and Particle Swarm Optimization (PSO). Results demonstrated that Kapur’s entropy based IGWO outperformed for neutrophil nucleus segmentation in microscopic images when compared to other optimization techniques in both quality and consistency.
  • AI-Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness
    R. Naren Shankar, S. Irish Angelin, Bakiya Ambikapathy, K. Sathish Kumar, Parvathy Rajendran
    Artificial Intelligence Applications in Aeronautical and Aerospace Engineering, 2025
  • Enhancing Jet Noise Reduction: AI-Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles
    R. Naren Shankar, S. Irish Angelin, Bakiya Ambikapathy, K. Sathish Kumar, Parvathy Rajendran
    Artificial Intelligence Applications in Aeronautical and Aerospace Engineering, 2025
  • A High Sensitive Nanomaterial Coated Side Polished Fiber Sensor for Detection of Cardiac Troponin I Antibody
    M. Valliammai, J. Mohanraj, Balasubramanian Esakki, Lung-Jieh Yang, Chua-Chin Wang, A. Bakiya
    IEEE Transactions on Nanobioscience, 2025
    The advent of evanescent field based fiber optic biosensor and advancements in nanotechnology has create an excellent opportunity in label-free detection of biomarkers which plays vital role in the early, rapid and accurate diagnosis of acute diseases. In this work, we demonstrate a high sensitive Molybdenum Tungsten Disulfide (MoWS2) coated side polished fiber (SPF) biosensor for accurate and early diagnosis of cardio vascular disease (CVD). The Cardiac Troponins I (cTnI) is identified as a biomarker of interest for early and rapid diagnonis of CVD. The proposed SPF biosensor exhibits surface plasmonic resonance (SPR) detection due to the evanescent field interaction between MoWS2 nano coated side polished region and anti-CTnI. The proposed SPF biosensor possess the high sensitivity of 82% to detect the cTnI antibody with a limit of detection (LOD) about 17.5 pg/mL. The peak SPR shift have been calculated as 61 nm for analyte concentrations of 500 pg/mL Moreover, the proposed SPF biosensor possess the high degree of selectivity and environmental stability to CTnI among three analytes such as CTnI, Estrogen and Glucose. The hydrophobic interactions of MoWS2 and cTnI antibody leads to chemical free biofunctionalization of antibody in the sensing region. Hence, the simulation results shows the surface interaction strength calculated as 1.29 KJ mol-1/nm2 in order to evaluate the hydrophobic interactions. Thus, the proposed optical biosensor is a promising candidate for "point-of-care" testing of CVD disorders and preclinical assessments.
  • Analysis of electromyograms recorded using invasive and noninvasive electrodes: a study based on entropy and Lyapunov exponents estimated using artificial neural networks
    Bakiya Ambikapathy, Kamalanand Krishnamurthy
    Journal of Ambient Intelligence and Humanized Computing, 2024
  • Mathematical Modeling of the Impact of a 5 Day Interim Relaxation at 21 Days in 42 days Lockdown Strategy on COVID-19 Transmission in India
    Flavia Immaculyne, BakiyaAmbikapathy, Kamalanand Krishnamurthy, Lourduraj De Britto
    Proceedings of the 2024 10th International Conference on Biosignals Images and Instrumentation Icbsii 2024, 2024
    As of the first week of May 2020, the COVID-19 pandemic is present in 212 nations globally. Many countries have enforced lockdown measures strategically to slow down the epidemic's spread, with the objective of relieving pressure on healthcare systems. Mathematical modelling serves as a useful tool for the prediction of the future numbers of the infected population and for analyzing the effects of various intervention strategies. In this work, a mathematical model based on ordinary differential equations has been developed for analyzing the impact of an interim relaxation of 5 days at 21 days in a 42 days lockdown period. Results of the simulation demonstrate that the relaxation of 5 days during the 42 days lockdown does not result in any adverse effects if the transmission rate remains the same. However, the model-based analysis suggests that an increase in the transmission rate by a factor of 1.2 times during the relaxation period due to the possible aggregation of the susceptible with the infected, it could shift the growth pattern of infected cases from sub-exponential to exponential.
  • Rational Quadratic Gaussian Process Regression Approaches for Identification of Active Pharmaceutical Ingredient in Biotin Tablets Using Hyperspectral Sensor
    A Bakiya, A Anitha, M Valliammai, K Lohith Kumar, S Tarun Kumar, K Jay Sri Ram
    5th International Conference on Electronics and Sustainable Communication Systems Icesc 2024 Proceedings, 2024
    Hyperspectral imaging is a technique that offers detailed chemical or compositional information that is generally not accessible through standard imaging methods like intensity or color imaging based on light reflection, transmission, or emission. Machine-learning techniques are often used to interpret hyperspectral imaging data. On the other hand., determining the amount of active pharmaceutical ingredient (API) present in biotin tablets by sample destructive techniques such as high-performance liquid chromatography (HPLC). An effective, computer-aided model is necessary to accurately determine the API content of biotin tablets. The study used a non-invasive method called hyperspectral imaging to examine the unique properties of the biotin capsules. The Rational Quadratic Gaussian Process Regression (RQ-GPR) analysed the spectral parameters obtained from the hyperspectral image. The major performance metrics derived by RQ-GPR are being contrasted with those generated from advanced regression techniques. The results demonstrated that RQ-GPR exhibited a superior performance with a R2 value of 0.99., root mean squared error (RMSE) of 1.5184., mean absolute error (MAE) of 1.1636 and mean squared error (MSE) of 2.3056. in predicting the API content from the hyperspectral imaging data of biotin tablet. The findings indicate that hyperspectral imaging is a rapid., non-invasive., and precise method for estimating the active pharmaceutical ingredient (API) content of pharmaceutical medications.
  • Deep Neural Network for Predicting Supercontinuum Broadening in Chalcogenide Photonic Quasi crystal Fiber
    M. Valliammai, A. Bakiya, J. Mohanraj, Hiran Kumar Singh, Sathis Addanki, Rajan Rishav
    Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices Nusod, 2024
    Generation SuperContinuum (SC) is a complex nonlinear process caused by chaotic and unstable behaviour, especially simulated by short duration pump pulses when the optical fiber work on the anomalous dispersion region. Understanding the spectral broadening behavior is difficult due to variations in fiber length and input pulse parameters. To address this challenge, we introduce a Deep Neural Network (DNN) to predict the SC spectrum broadening. Remarkably, the DNN provides accurate predictions across different lengths of Chalcogenide (ChG) Photonic quasi crystal Fiber (PQF), achieving a root mean square error (RMSE) of 1.3677 and an R-Squared value of 0.99. Additionally, this DNN significantly reduces the computational time compared to MATLAB and COMSOL Multiphysics software.
  • Artificial Bee-Optimized CNN for Osteoporosis Detection using Leg X-ray Images
    Bakiya A, Vetrivel V, Kamalanand K, Anitha A, Aswath S, Valliammai M, Sridevi S
    Proceedings of the 2024 10th International Conference on Biosignals Images and Instrumentation Icbsii 2024, 2024
    Osteoporosis is increasingly recognized as a worldwide health concern as people live longer. Although, Dual-Energy-X-Ray Absorptiometry (DXA) is one of the primary diagnosis techniques for diagnosing Osteoporosis, its widespread use as a screening tool is limited. This study uses an evolutionary-based deep learning algorithm to forecast osteoporosis using leg X-ray images. Further, the Convolutional Neural Network was enhanced using the artificial bee optimization algorithm to classify normal and osteoporosis cases using leg X-ray images. The effectiveness of the developed Bee CNN was compared to the conventional CNN. The results indicate that the CNN combined with bee optimization achieved a high accuracy rate of 92.32% in accurately classifying normal and osteoporosis X-ray leg images. Integrating artificial bee optimization into the CNN demonstrated superior performance compared to the conventional CNN. Based on these results, the Artificial Bee CNN model proves to be a valuable tool for straightforward osteoporosis prediction in practical clinical environments.
  • An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India
    Ganesh Ram Arumugam, Bakiya Ambikapathy, Kamalanand Krishnamurthy, Ashwani Kumar, Lourduraj De Britto
    Virusdisease, 2023
  • Identification of Nutrient Content of Psidium Guajava and Syzygium Cumini Leaves Using Hyperspectral Imaging
    A. Bakiya, Venkatesh Neeli, Dhupam Mohana Lakshmi Priya, Chapala Rama Pavan
    2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2023, 2023
  • Hybrid Classical Quantum Neural Network based classification of Photonic Band Gap crystal structure defects
    Paayas P, Sridevi S, Kanimozhi T, Valliammai M, Mohanraj J, Vinodh Kumar N, Bakiya A, Prasanna Kumar R, Rithani M
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks
    A. Bakiya, A. Anitha, T. Sridevi, K. Kamalanand
    Behavioural Neurology, 2022
  • Numerical Modeling and Simulation of the Droplet Transmission of SARS-CoV-2 in the Ambient Environment and Its Relevance to Social Distancing
    S. Thanigaiarasu, G. Balamani, A. Bakiya, Flavia Immaculyne, K. Kamalanand, R. L. J. De Britto
    Mathematical Problems in Engineering, 2022
  • Automated diagnosis of amyotrophic lateral sclerosis using electromyograms and firefly algorithm based neural networks with fractional position update
    A. Bakiya, K. Kamalanand, V. Rajinikanth
    Physical and Engineering Sciences in Medicine, 2021
  • Assessment of electromyograms using genetic algorithm and artificial neural networks
    Bakiya Ambikapathy, Kamalanand Kirshnamurthy, Rajinikanth Venkatesan
    Evolutionary Intelligence, 2021
  • Analysis of EMG Signals using Extreme Learning Machine with Nature Inspired Feature Selection Techniques
    A. Anitha, A. Bakiya
    Handbook of Machine Learning for Computational Optimization Applications and Case Studies, 2021
  • Frequency Domain Modelling of Interrelation between Dielectric and Viscoelastic Properties of Soft Tissues
    A. Bakiya, K. Kamalanand, S. Arunmozhi, V. Rajinikanth
    2020 International Conference on System Computation Automation and Networking Icscan 2020, 2020
  • Prediction of the transition from subexponential to the exponential transmission of SARS-CoV-2 in Chennai, India: Epidemic nowcasting
    Kamalanand Krishnamurthy, Bakiya Ambikapathy, Ashwani Kumar, Lourduraj De Britto
    Jmir Public Health and Surveillance, 2020
  • Mathematical modelling to assess the impact of lockdown on COVID-19 transmission in India: Model development and validation
    Bakiya Ambikapathy, Kamalanand Krishnamurthy
    Jmir Public Health and Surveillance, 2020
  • Deep neural network assisted diagnosis of time-frequency transformed electromyograms
    A. Bakiya, K. Kamalanand, V. Rajinikanth, Ramesh Sunder Nayak, Seifedine Kadry
    Multimedia Tools and Applications, 2020
  • Analysis on the effect of ECG signals while listening to different genres of music
    D Najumnissa, Paramasivam Alagumariappan, A Bakiya, Mohamed Syed Ali
    2019 2nd International Conference on Advanced Computational and Communication Paradigms Icaccp 2019, 2019
  • Information analysis on electromyograms acquired using monopolar needle, concentric needle and surface electrodes
    A Bakiya, K Kamalanand
    Proceedings of the IEEE International Conference on Recent Trends in Electrical Control and Communication Rtecc 2018, 2018
  • Design of M-PETFF using low power clock distribution element
    Arpn Journal of Engineering and Applied Sciences, 2017

RECENT SCHOLAR PUBLICATIONS

  • Segmentation of Plasmodium Falciparum Infected Cell in Blood Smear Images Using Optimized U-Net
    R Saranya, A Bakiya
    2026 Twelfth International Conference on Bio Signals, Images, and … , 2026
    2026
  • Enhancing Jet Noise Reduction: AI‐Powered Predictions of Core Length and Total Pressure Variations in Coaxial Nozzles
    RN Shankar, S Irish Angelin, B Ambikapathy, KS Kumar, P Rajendran
    Artificial Intelligence Applications in Aeronautical and Aerospace … , 2025
    2025
  • AI‐Powered Prediction of Centerline Total Pressure Variations in Coaxial Nozzles by Varying the Lip Thickness
    RN Shankar, S Irish Angelin, B Ambikapathy, KS Kumar, P Rajendran
    Artificial Intelligence Applications in Aeronautical and Aerospace … , 2025
    2025
  • Segmentation Of Neutrophil Nucleus in White Blood Cell Images Using Kapur’s entropy based Improved Grey Wolf Optimization
    R Saranya, A Bakiya
    2025 Eleventh International Conference on Bio Signals, Images, and … , 2025
    2025
  • A High Sensitive Nanomaterial Coated Side Polished Fiber Sensor for Detection of Cardiac Troponin I Antibody
    M Valliammai, J Mohanraj, B Esakki, LJ Yang, CC Wang, A Bakiya
    IEEE Transactions on NanoBioscience , 2025
    2025
    Citations: 2
  • Enhancing EMG signal classification using convolution neural network optimized with fractional order bat algorithm
    A Bakiya, V Vetrivel, K Kamalanand, A Anitha
    International Journal of Advances in Engineering Sciences and Applied … , 2024
    2024
    Citations: 5
  • Deep Neural Network for Predicting Supercontinuum Broadening in Chalcogenide Photonic Quasi crystal Fiber
    M Valliammai, A Bakiya, J Mohanraj, HK Singh, S Addanki, R Rishav
    2024 International Conference on Numerical Simulation of Optoelectronic … , 2024
    2024
    Citations: 2
  • Rational Quadratic Gaussian Process Regression Approaches for Identification of Active Pharmaceutical Ingredient in Biotin Tablets Using Hyperspectral Sensor
    A Bakiya, A Anitha, M Valliammai, KL Kumar, ST Kumar, KJS Ram
    2024 5th International Conference on Electronics and Sustainable … , 2024
    2024
    Citations: 2
  • Mathematical Modeling of the Impact of a 5 Day Interim Relaxation at 21 Days in 42 days Lockdown Strategy on COVID-19 Transmission in India
    F Immaculyne, K Krishnamurthy, L De Britto
    2024 Tenth International Conference on Bio Signals, Images, and … , 2024
    2024
  • Artificial Bee-Optimized CNN for Osteoporosis Detection using Leg X-ray Images
    A Bakiya, V Vetrivel, K Kamalanand, A Anitha, S Aswath, M Valliammai, ...
    2024 Tenth International Conference on Bio Signals, Images, and … , 2024
    2024
    Citations: 3
  • Analysis of electromyograms recorded using invasive and noninvasive electrodes: a study based on entropy and Lyapunov exponents estimated using artificial neural networks
    B Ambikapathy, K Krishnamurthy
    Journal of Ambient Intelligence and Humanized Computing 15 (1), 1115-1123 , 2024
    2024
    Citations: 24
  • Risk Assessment and Portfolio Optimization in Agriculture Investments
    CLRDP Sharma, ABA Sudarshanam
    Journal of Advanced Zoology 44 (S2), 3090-3098 , 2023
    2023
  • Hybrid Classical Quantum Neural Network based classification of Photonic Band Gap crystal structure defects
    P Paayas, S Sridevi, T Kanimozhi, M Valliammai, J Mohanraj, ...
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 2
  • Identification of Nutrient Content of Psidium Guajava and Syzygium Cumini Leaves Using Hyperspectral Imaging
    A Bakiya, V Neeli, DML Priya, CR Pavan
    2023 2nd International Conference on Smart Technologies and Systems for Next … , 2023
    2023
    Citations: 2
  • An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India
    GR Arumugam, B Ambikapathy, K Krishnamurthy, A Kumar, L De Britto
    Virusdisease 34 (1), 39-49 , 2023
    2023
    Citations: 2
  • Numerical Modeling and Simulation of the Droplet Transmission of SARS‐CoV‐2 in the Ambient Environment and Its Relevance to Social Distancing
    S Thanigaiarasu, G Balamani, A Bakiya, F Immaculyne, K Kamalanand, ...
    Mathematical Problems in Engineering 2022 (1), 6881712 , 2022
    2022
    Citations: 4
  • Automated diagnosis of amyotrophic lateral sclerosis using electromyograms and firefly algorithm based neural networks with fractional position update
    A Bakiya, K Kamalanand, V Rajinikanth
    Physical and Engineering Sciences in Medicine 44 (4), 1095-1105 , 2021
    2021
    Citations: 11
  • Analysis of EMG signals using extreme learning machine with nature inspired feature selection techniques
    A Anitha, A Bakiya
    Handbook of Machine Learning for Computational Optimization, 27-49 , 2021
    2021
    Citations: 2
  • Assessment of electromyograms using genetic algorithm and artificial neural networks
    B Ambikapathy, K Kirshnamurthy, R Venkatesan
    Evolutionary Intelligence 14 (2), 261-271 , 2021
    2021
    Citations: 23
  • Mechano-electric correlations in the human physiological system
    A Bakiya, K Kamalanand, RLJ De Britto
    CRC Press , 2021
    2021
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • Mathematical modelling to assess the impact of lockdown on COVID-19 transmission in India: Model development and validation
    B Ambikapathy, K Krishnamurthy
    JMIR public health and surveillance 6 (2), e19368 , 2020
    2020
    Citations: 129
  • Deep neural network assisted diagnosis of time-frequency transformed electromyograms
    A Bakiya, K Kamalanand, V Rajinikanth, RS Nayak, S Kadry
    Multimedia Tools and Applications 79 (15), 11051-11067 , 2020
    2020
    Citations: 63
  • Analysis of electromyograms recorded using invasive and noninvasive electrodes: a study based on entropy and Lyapunov exponents estimated using artificial neural networks
    B Ambikapathy, K Krishnamurthy
    Journal of Ambient Intelligence and Humanized Computing 15 (1), 1115-1123 , 2024
    2024
    Citations: 24
  • Assessment of electromyograms using genetic algorithm and artificial neural networks
    B Ambikapathy, K Kirshnamurthy, R Venkatesan
    Evolutionary Intelligence 14 (2), 261-271 , 2021
    2021
    Citations: 23
  • Prediction of the transition from subexponential to the exponential transmission of SARS-CoV-2 in Chennai, India: epidemic nowcasting
    K Krishnamurthy, B Ambikapathy, A Kumar, L De Britto
    JMIR public health and surveillance 6 (3), e21152 , 2020
    2020
    Citations: 12
  • Automated diagnosis of amyotrophic lateral sclerosis using electromyograms and firefly algorithm based neural networks with fractional position update
    A Bakiya, K Kamalanand, V Rajinikanth
    Physical and Engineering Sciences in Medicine 44 (4), 1095-1105 , 2021
    2021
    Citations: 11
  • Information analysis on electromyograms acquired using monopolar needle, concentric needle and surface electrodes
    A Bakiya, K Kamalanand
    2018 International Conference on Recent Trends in Electrical, Control and … , 2018
    2018
    Citations: 6
  • Enhancing EMG signal classification using convolution neural network optimized with fractional order bat algorithm
    A Bakiya, V Vetrivel, K Kamalanand, A Anitha
    International Journal of Advances in Engineering Sciences and Applied … , 2024
    2024
    Citations: 5
  • Computational Techniques for Dental Image Analysis
    K Kamalanand, B Thayumanavan, PM Jawahar
    IGI Global , 2018
    2018
    Citations: 5
  • Numerical Modeling and Simulation of the Droplet Transmission of SARS‐CoV‐2 in the Ambient Environment and Its Relevance to Social Distancing
    S Thanigaiarasu, G Balamani, A Bakiya, F Immaculyne, K Kamalanand, ...
    Mathematical Problems in Engineering 2022 (1), 6881712 , 2022
    2022
    Citations: 4
  • Mechano-electric correlations in the human physiological system
    A Bakiya, K Kamalanand, RLJ De Britto
    CRC Press , 2021
    2021
    Citations: 4
  • Analysis on the effect of ECG signals while listening to different genres of music
    D Najumnissa, P Alagumariappan, A Bakiya, MS Ali
    2019 Second International Conference on Advanced Computational and … , 2019
    2019
    Citations: 4
  • Artificial Bee-Optimized CNN for Osteoporosis Detection using Leg X-ray Images
    A Bakiya, V Vetrivel, K Kamalanand, A Anitha, S Aswath, M Valliammai, ...
    2024 Tenth International Conference on Bio Signals, Images, and … , 2024
    2024
    Citations: 3
  • A High Sensitive Nanomaterial Coated Side Polished Fiber Sensor for Detection of Cardiac Troponin I Antibody
    M Valliammai, J Mohanraj, B Esakki, LJ Yang, CC Wang, A Bakiya
    IEEE Transactions on NanoBioscience , 2025
    2025
    Citations: 2
  • Deep Neural Network for Predicting Supercontinuum Broadening in Chalcogenide Photonic Quasi crystal Fiber
    M Valliammai, A Bakiya, J Mohanraj, HK Singh, S Addanki, R Rishav
    2024 International Conference on Numerical Simulation of Optoelectronic … , 2024
    2024
    Citations: 2
  • Rational Quadratic Gaussian Process Regression Approaches for Identification of Active Pharmaceutical Ingredient in Biotin Tablets Using Hyperspectral Sensor
    A Bakiya, A Anitha, M Valliammai, KL Kumar, ST Kumar, KJS Ram
    2024 5th International Conference on Electronics and Sustainable … , 2024
    2024
    Citations: 2
  • Hybrid Classical Quantum Neural Network based classification of Photonic Band Gap crystal structure defects
    P Paayas, S Sridevi, T Kanimozhi, M Valliammai, J Mohanraj, ...
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 2
  • Identification of Nutrient Content of Psidium Guajava and Syzygium Cumini Leaves Using Hyperspectral Imaging
    A Bakiya, V Neeli, DML Priya, CR Pavan
    2023 2nd International Conference on Smart Technologies and Systems for Next … , 2023
    2023
    Citations: 2
  • An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India
    GR Arumugam, B Ambikapathy, K Krishnamurthy, A Kumar, L De Britto
    Virusdisease 34 (1), 39-49 , 2023
    2023
    Citations: 2
  • Analysis of EMG signals using extreme learning machine with nature inspired feature selection techniques
    A Anitha, A Bakiya
    Handbook of Machine Learning for Computational Optimization, 27-49 , 2021
    2021
    Citations: 2