Publication
Journals
A. K. Sahoo, P. Parida, M. K. Panda, K. Muralibabu, and A. S. Mohanty, “MultiTumor Analyzer (MTA-20–55): A network for efficient classification of detected brain tumors from MRI images,” Biocybern. Biomed. Eng., Jun. 2024. https://doi.org/10.1016/j.bbe.2024.06.003 . (SCIE / Scopus) (Elsevier) (Impact Factor-6.4, Q1)
V. Keerthi Kiran, S. Dash, and P. Parida, “Vehicle detection in varied weather conditions using enhanced deep Yolo with complex wavelet,” Engineering Research Express, vol. 6, no. 2, p. 025224, Jun. 2024. https://doi.org/10.1088/2631-8695/ad507d . (SCIE / Scopus) (IOP Science) (Impact Factor-1.7, Q2)
M. K. Panda, P. Parida, and D. K. Rout, “A weight induced contrast map for infrared and visible image fusion,” Comput. Electr. Eng., vol. 117, p. 109256, Jul. 2024. https://doi.org/10.1016/j.compeleceng.2024.109256 . (SCIE / Scopus) (Elsevier) (Impact Factor-4.3, Q2)
B. Mallick, P. Parida, C. Nayak, B. Prasad, G. Palai, A. K. Goyal and Y. Massoud , “Long distance QKD propagation using optical single sideband scheme,” Opt. Contin., vol. 3, no. 3, pp. 427–440, Mar. 2024. https://doi.org/10.1364/OPTCON.507484 . (SCIE / Scopus) (Optica Publishing Group) (Impact Factor-1.6, Q2)
P. Parida, “Editorial: Physiological signal processing for wellness,” Front. Signal Process., vol. 4, no. March, pp. 1–3, Mar. 2024. https://doi.org/10.3389/frsip.2024.1391335 . (ESCI) (Frontiers)
A. K. Sahoo, P. Parida, and K. Muralibabu, “Hybrid deep neural network with clustering algorithms for effective gliomas segmentation,” International Journal of System Assurance Engineering and Management, vol. 15, no. 3, pp. 964–980, 2024. https://doi.org/10.1007/s13198-023-02183-w . (SCIE / Scopus) (Springer) (Impact Factor-2.0, Q2)
A. Kumar Sahoo, P. Parida, K. Muralibabu, and S. Dash, “Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning,” Biocybernetics and Biomedical Engineering, vol. 43, pp. 616–633, Sep. 2023. https://doi.org/10.1016/j.bbe.2023.08.003 . (SCIE / Scopus) (Elsevier) (Impact Factor-6.4, Q1)
R. Rout, P. Parida, and S. Dash, “Automatic Skin Lesion Segmentation using a Hybrid Deep Learning Network,” International Journal of Computer Information Systems and Industrial Management Application, vol. 15, pp. 238–249, Jun. 2023. Link (Scopus)
P. S. Rao, P. Parida, G. Sahu, and S. Dash, “A multi-view human gait recognition using hybrid whale and gray wolf optimization algorithm with a random forest classifier,” Image and Vision Computing, vol. 136, p. 104721, 2023. https://doi.org/10.1016/j.imavis.2023.104721 . (SCIE / Scopus) (Elsevier) (Impact Factor-4.7, Q1)
A. K. Sahoo, P. Parida, K. Muralibabu, and S. Dash, “An improved DNN with FFCM method for Multimodal Brain Tumor Segmentation,” Intelligent Systems with Applications, vol. 18, p. 200245, 2023. https://doi.org/10.1016/j.iswa.2023.200245 . (Scopus) (Elsevier)
B. Mallick, P. Parida, C. Nayak, P. K. Sahoo, and G. Palai, “Quantum key distribution over FSO channel using error reconciliation protocol,” Wireless Networks, vol. 29, no. 5, pp. 2161–2169, 2023. https://doi.org/10.1007/s11276-023-03289-6 . (SCIE / Scopus) (Springer) (Impact Factor-3, Q2)
S. Dash, P. Parida, and J. R. Mohanty, “Illumination robust deep convolutional neural network for medical image classification,” Soft Computing, Feb. 2023. https://doi.org/10.1007/s00500-023-07918-2 . (SCIE / Scopus) (Springer) (Impact Factor-4.1, Q2)
A. S. Mohanty, P. Parida, and K. C. Patra, “ASD detection using an advanced deep neural network,” Journal of Information and Optimization Sciences, vol. 43, no. 8, pp. 2143–2152, 2022. https://doi.org/10.1080/02522667.2022.2133220 . (SCIE) (Taylor & Francis) (Impact Factor-1.4, Q2)
B. Acharya, P. Parida, R. N. Panda, and P. K. Mohapatra, “A novel approach for Boa trained ann for Channel Equalization Problems,” Journal of Information and Optimization Sciences, vol. 43, no. 8, pp. 2121–2130, 2022. https://doi.org/10.1080/02522667.2022.2153996 . (SCIE) (Taylor & Francis) (Impact Factor-1.4, Q2)
V.K. Kiran, S. Dash, and P. Parida, "Edge preserving noise robust deep learning networks for vehicle classification", Concurrency and Computation: Practice and Experience, pp. 1-14, 2022. http://dx.doi.org/10.1002/cpe.7214 . (Scopus / SCIE) (Wiley) (Impact Factor-2, Q3)
S. R. Palla, G. Sahu, and P. Parida, “Human gait recognition using Firefly template segmentation,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 10, no. 5, pp. 565–575, 2021. https://doi.org/10.1080/21681163.2021.2012829 . (Scopus / SCIE) (Impact Factor-1.6, Q3) (Taylor & Francis)
R. Rout, P. Parida, Y. Alotaibi, S. Alghamdi, and O. I. Khalaf, “Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering,” Symmetry (Basel)., vol. 13, no. 11, p. 2085, Nov. 2021. https://doi.org/10.3390/sym13112085 . (Scopus / SCIE) (MDPI) (Impact Factor-2.940, Q2)
V. K. Kiran, S. Dash, and P. Parida, “Improvement on deep features through various enhancement techniques for vehicles classification,” Sensing and Imaging, vol. 22, no. 1, 2021. https://doi.org/10.1007/s11220-021-00363-1 . (Scopus/ SCIE) (Springer) (Impact Factor-2.2, Q3)
A. S. Mohanty, K. C. Patra, and P. Parida, “Toddler ASD classification using Machine Learning Techniques,” International Journal of Online and Biomedical Engineering (iJOE), vol. 17, no. 07, p. 156, 2021. https://doi.org/10.3991/ijoe.v17i07.23497 . (Scopus / SCIE) (Impact Factor-1.3, Q2)
P. Parida, J. Dash, and N. Bhoi, “Retinal blood vessel extraction from fundus images using enhancement filtering and clustering,” ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol. 19, no. 1, pp. 38–52, 2020. https://doi.org/10.5565/rev/elcvia.1239. (Scopus)
V. Keerthi Kiran, P. Parida, and S. Dash, “Vehicle Detection and Classification: A Review,” Journal of Information Assurance and Security, vol. 15, pp. 076–093, 2020. doi:http://www.mirlabs.org/jias/secured/Volume15-Issue2/Paper8.pdf . (ESCI) Link
S. R. Palla, G. Sahu, and P. Parida, “A contemporary Survey on Human Gait Recognition,” Journal of Information Assurance and Security, vol. 15, pp. 094–106, 2020. doi:http://www.mirlabs.org/jias/secured/Volume15-Issue3/Paper9.pdf . (ESCI) Link
P. Parida and R. Rout, “Transition region based approach for skin lesion segmentation,” ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol. 19, no. 1, pp. 28–39, 2020. https://doi.org/10.5565/rev/elcvia.1177. (Scopus)
R. Rout and P. Parida, “A novel method for melanocytic skin lesion extraction and analysis,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no. 2, pp. 461–473, 2020. https://doi.org/10.1080/09720529.2020.1728900. (Scopus / SCIE) (Taylor & Francis) (Impact Factor-1.4, Q2)
P. Parida, “Development of transition region based methods for image segmentation,” ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol. 18, no. 2, pp. 1–3, 2020. https://doi.org/10.5565/rev/elcvia.1176 . (Scopus)
P. Parida and N. Bhoi, “Fuzzy clustering based transition region extraction for image segmentation,” Engineering Science and Technology, an International Journal, vol. 21, no. 4, pp. 547–563, 2018. https://doi.org/10.1016/j.jestch.2018.05.012. (Scopus / SCIE) (Elsevier) (Impact Factor-5.7, Q1)
P. Parida and N. Bhoi, “Feature based transition region extraction for image segmentation: Application to worm separation from leaves,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 262–274, 2018. https://doi.org/10.1016/j.fcij.2018.08.001 . (Elsevier)
P. Parida, “Fuzzy clustering based transition region extraction for image segmentation,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 321–333, 2018. https://doi.org/10.1016/j.fcij.2018.10.002 . (Elsevier)
P. Parida and N. Bhoi, “Dual Transition Region Extraction based Colour Image Segmentation: Application to Fish Image Segmentation,” Global Journal of Computer Science and Technology: F Graphics & Vision, vol. 17, no. 3, pp. 21–29, 2017. doi:https://computerresearch.org/index.php/computer/issue/view/100235/236 .
P. Parida and N. Bhoi, “Wavelet based transition region extraction for image segmentation,” Future Computing and Informatics Journal, vol. 2, no. 2, pp. 65–78, 2017. https://doi.org/10.1016/j.fcij.2017.10.005 . (Elsevier)
P. Parida and N. Bhoi, “2-D Gabor filter based transition region extraction and morphological operation for image segmentation,” Computers & Electrical Engineering, vol. 62, pp. 119–134, 2017. https://doi.org/10.1016/j.compeleceng.2016.10.019. (Scopus / SCIE) (Elsevier) (Impact Factor-4.3, Q2)
S. K. Behera, P. Parida, N. Bhoi, and A. K. Athghara, “An Analytical Review on Various Image Segmentation Methods Based on Transition Region-Based Thresholding,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, no. 3, pp. 3845–3852, 2016. https://doi.org/10.15662/IJAREEIE.2016.0505056.
P. Parida and N. Bhoi, “Transition region based single and multiple object segmentation of gray scale images,” Engineering Science and Technology, an International Journal, vol. 19, no. 3, pp. 1206–1215, 2016. https://doi.org/10.1016/j.jestch.2015.12.009. (Scopus / SCIE) (Elsevier) (Impact Factor-5.7, Q1)
Conferences
R. Ray, S. Jena, and P. Parida, “An Effective Threshold Based Technique for Retinal Image Blood Vessel Segmentation on Fundus Image Using Average and Gaussian Filters,” 2024, pp. 175–188. https://doi.org/10.1007/978-3-031-56998-2_15 . (Scopus) (Springer)
A. K. Sahoo, P. Parida, and K. Muralibabu, “Effective Use of Clustering Techniques for Brain Tumor Segmentation,” in 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), IEEE, Nov. 2023, pp. 1–5. http://dx.doi.org/10.1109/aespc59761.2023.10390467 . (Scopus) (IEEE)
R. Rout, P. Parida, and S. Dash, “A hybrid deep learning network for skin lesion extraction,” Lecture Notes in Networks and Systems, pp. 682–689, 2023. https://doi.org/10.1007/978-3-031-27524-1_66 . (Springer)
P. S. Rao, G. Sahu, P. Parida, and S. Patnaik, “An adaptive firefly optimization algorithm for human gait recognition,” Advances in Sustainability Science and Technology, pp. 305–316, 2022. https://doi.org/10.1007/978-981-19-2277-0_28. (Springer)
R. Rout, P. Parida, and S. Patnaik, “Melanocytic skin lesion extraction using mean shift clustering,” 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA), 2021. https://doi.org/10.1109/ICEITSA54226.2021.00112 . (Scopus) (IEEE)
A. S. Mohanty, P. Parida, and K. C. Patra, “ASD classification in adolescent and adult utilizing Deep Neural Network,” Atlantis Highlights in Computer Sciences, 2021. https://doi.org/10.2991/ahis.k.210913.025. (Atlantis Press (Springer))
A. S. Mohanty, P. Parida, and K. C. Patra, “ASD classification for children using Deep Neural Network,” Global Transitions Proceedings, vol. 2, no. 2, pp. 461–466, 2021. https://doi.org/10.1016/j.gltp.2021.08.042 . (Elsevier)
V. Keerthi Kiran, S. Dash, and P. Parida, “Vehicle recognition using extensions of pattern descriptors,” IOP Conference Series: Materials Science and Engineering, vol. 1166, no. 1, p. 012046, 2021. https://doi.org/10.1088/1757-899X/1166/1/012046. (Scopus) (IOP Science)
B. Mallick, P. Parida, and G. Palai, “Simulation platform of a free-space optical network under Multipath Fading Channel,” Journal of Physics: Conference Series, vol. 1921, no. 1, p. 012016, 2021. https://doi.org/10.1088/1742-6596/1921/1/012016. (Scopus) (IOP Science)
A. S. Mohanty, P. Parida, and K. C. Patra, “Identification of Autism Spectrum Disorder using Deep Neural Network,” J. Phys. Conf. Ser., vol. 1921, p. 012006, May 2021. https://doi.org/10.1088/1742-6596/1921/1/012006. (Scopus) (IOP Science)
A. K. Sahoo and P. Parida, “Automatic clustering based approach for brain tumor extraction,” Journal of Physics: Conference Series, vol. 1921, no. 1, p. 012007, 2021. https://doi.org/10.1088/1742-6596/1921/1/012007. (Scopus) (IOP Science)
A. K. Sahoo and P. Parida, “A clustering based approach for meningioma tumors extraction from brain MRI images,” 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC), 2020. https://doi.org/10.1109/iSSSC50941.2020.9358849. (Scopus) (IEEE)
P. S. Rao, G. Sahu, and P. Parida, “Methods for automatic gait recognition: A Review,” Advances in Intelligent Systems and Computing, pp. 57–65, 2020. https://doi.org/10.1007/978-3-030-49339-4_7. (Scopus) (Springer)
V. Keerthi Kiran, P. Parida, and S. Dash, “Vehicle detection and classification: A Review,” Advances in Intelligent Systems and Computing, pp. 45–56, 2020. https://doi.org/10.1007/978-3-030-49339-4_6. (Scopus) (Springer)
R. Rout and P. Parida, “Transition region based approach for skin lesion segmentation,” Procedia Computer Science, vol. 171, pp. 379–388, 2020. https://doi.org/10.1016/j.procs.2020.04.039. (Scopus) (Elsevier)
R. Rout and P. Parida, “A review on leaf disease detection using computer vision approach,” Learning and Analytics in Intelligent Systems, pp. 863–871, 2020. https://doi.org/10.1007/978-3-030-42363-6_99. (Springer)
P. Parida, N. Bhoi, and P. Dewangan, “Colour image segmentation based on transition region and morphological operation,” 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2017. https://doi.org/10.1109/WiSPNET.2017.8299972. (Scopus / Web of Science) (IEEE)
P. Parida and N. Bhoi, “Comparative study on Graph cuts for image segmentation,” in Proceedings of 6th ISERD International Conference, Bali, Indonesia, 2014, pp. 40–46.
Book Chapters
S. Dash, P. Parida, A. S. Mohanty, and G. Sahu, “Text recognition using CRNN models based on temporal classification and interpolation methods,” in Machine Learning in Medical Imaging and Computer Vision, Institution of Engineering and Technology, 2023, pp. 157–181. doi: https://doi.org/10.1049/PBHE049E_ch7 . (IET)
R. Rout, P. Parida, S. Dash, and S. Mallik, “Techniques for removing hair from dermoscopic images,” Privacy Preservation of Genomic and Medical Data, pp. 263–281, 2023. doi: https://doi.org/10.1002/9781394213726.ch12 . (Wiley) (Scopus)
P. Parida, S. Dash, and R. Rout, “Machine Learning Algorithms for Pregnant Women,” Advances in computational intelligence and robotics book series, pp. 327–350, Sep. 2023. doi: https://doi.org/10.4018/979-8-3693-1718-1.ch019. (IGI Global) (Scopus)
S. Dash, P. Parida, V. K. Nassa, A. S. George, A. Sahaul and A. S. H. George “Glaucoma assessment using Super Pixel Classification,” Handbook of Research on Thrust Technologies’ Effect on Image Processing, pp. 310–331, Jul. 2023. https://doi.org/10.4018/978-1-6684-8618-4.ch019 . (IGI Global) (Scopus)
S. Dash, P. Parida, G. Sahu, and O. I. Khalaf, “Artificial Intelligence Models for Blockchain-Based Intelligent Networks Systems,” in Handbook of Research on Quantum Computing for Smart Environments, 2023, pp. 343–363. https://doi.org/10.4018/978-1-6684-6697-1.ch019 . (IGI Global) (Scopus)
S. Dash, P. Parida, and G. Sahu, “An Enhanced Gabor Filter Based on Heat-Diffused Top Hat Transform for Retinal Blood Vessel Segmentation,” in Advancements in Bio-Medical Image Processing and Authentication in Telemedicine, 2023, pp. 247–281. http://dx.doi.org/10.4018/978-1-6684-6957-6.ch013 . (IGI Global) (Scopus)
A. Mohanty, P. Parida and K. Patra, "Usage of ML Techniques for ASD Detection", in Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications, 1st ed., O. Jena, B. Bhushan and U. Kose, Ed. Boca Raton: CRC Press, 2022, p. 91-112. http://dx.doi.org/10.1201/9781003226147-5 . (Taylor & Francis) (Scopus)
A. S. Mohanty, P. Parida, and K. C. Patra, “Effect of COVID-19 on Autism Spectrum Disorder: Prognosis, Diagnosis, and Therapeutics Based on AI,” in Medical Virology: From Pathogenesis to Disease Control, Springer Singapore, 2021, pp. 345–387. http://dx.doi.org/10.1007/978-981-15-7317-0_18 . (Springer)