2024
Arnes J. I. (2024). “Toward a Collaborative Platform for Hybrid Designs Sharing a Common Cohort”. PhD Thesis. UiT The Arctic University of Norway.
Paul R., Buckchash H., Parida S., & Prasad D.K. (2024). “Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the Sámi Language”. arXiv preprint. https://arxiv.org/abs/2405.05777.
Naskar G., Mohiuddin S., Malakar S., Cuevas E., & Sarkar R. (2024). “Deepfake Detection using Deep Feature Stacking and Meta-learning”. Heliyon, 10(4), e25933. https://www.sciencedirect.com/science/article/pii/S2405844024019649
Somani A., Gupta A., Sekh A.A., Agarwal K., & Prasad D.K. (2024). “Blend & Predict: Domain-Adaptable Few-Shot Learning for Microscopy Imaging”. In 2024 IEEE International Conference on Image Processing (ICIP). IEEE. https://cmsworkshops.com/ICIP2024/papers/accepted_papers.php
Banerjee P., Akarte S.M., Kumar P., Shamsuzzaman M., Butola A., Agarwal K., Prasad D.K., Melandsø F., & Habib A. (2024). “High-resolution imaging in acoustic microscopy using deep learning”. Machine Learning: Science and Technology, 5(1), 015007. https://iopscience.iop.org/article/10.1088/2632-2153/ad1c30/pdf
Agarwal R., Das A., Horsch A., Agarwal K., & Prasad D.K. (2024). “Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis”. arXiv preprint. https://arxiv.org/abs/2404.04903
Agarwal R., Naidu K.P., Horsch A., Agarwal K., & Prasad D.K. (2024). “packetLSTM: Dynamic LSTM Framework for Streaming Data with Varying Feature Space”. arXiv preprint. https://arxiv.org/abs/2410.17394
Agarwal R., Sinha A., Vishwakarma A., Coubez X., Clausel M., Constant M., Horsch A., & Prasad D.K. (2024). “No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series”. arXiv preprint. https://arxiv.org/abs/2309.08698
Buckchash H., Biswas M., Agarwal R., & Prasad D.K. (2024). “Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs”. arXiv preprint. https://arxiv.org/abs/2409.10242
Somani A., Gupta A., Sekh A.A., Agarwal K., & Prasad D.K. (2024). “Blend & Predict: Domain-Adaptable Few-Shot Learning for Microscopy Imaging”. In 2024 IEEE International Conference on Image Processing (ICIP), Oct 27 (pp. 2095-2100). IEEE.
Agarwal R., & Prasad D.K. (2024). “Cloud Computing For Everyone”. BPB. Under Review.
Arora G., Butola A., Rajput R., Agarwal R., Agarwal K., Horsch A., Prasad D.K., & Senthilkumaran P. (2024). “Taxonomy of hybridly polarized Stokes vortex beams”. Optics Express, 32, 7404-7416. https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-5-7404&id=546969
Gupta D.K., Bamba U., Thakur A., Gupta A., Agarwal R., Sharan S., Demir E., Agarwal K., & Prasad D.K. (2024). “An UltraMNIST classification benchmark to train CNNs for very large images”. Sci Data, 11, 771. https://doi.org/10.1038/s41597-024-03587-4
Somani A., Horsch A., Bopardikar A., & Prasad D.K. (2024). “Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks”. Nordic Machine Intelligence, 4(2), 1-8.
Sekh A.A., & Prasad D.K. (2024). “Gender and Diversity Policies in AI: Strategies, Metrics, and Case Studies”. World Scientific (In-Press).
Pal R., Kar S., & Sekh A.A. (2024). “Customizable and Programmable Deep Learning”. In 27th International Conference on Pattern Recognition (ICPR 2024).
Pal R., Kar S., & Sekh A.A. (2024). “Enhancing Accessibility in Online Shopping: A Dataset and Summarization Method for Visually Impaired Individuals”. SN Computer Science, 5(8), 1010.
Pattanaik B., Mandal S., Tripathy R.M., & Sekh A.A. (2024). “Rumor Detection using Dual Embeddings and Text-based Graph Convolutional Network”. Discover Artificial Intelligence.
Pattanaik B., Mandal S., Tripathy R.M., & Sekh A.A. (2024). “Ensemble Approach to Rumor Detection with BERT, GPT, POS Features”. International Journal of Informatics and Communication Technology (IJ-ICT).
2023
Ghosal, K., Singh, A., Malakar, S., & Gupta, D. (2023). “Deep Learning based Joint Inversion of Electrical Resistivity Tomography and Radio Magnetotelluric Data”. AGU23. https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1313132
Somani A, Horsch A, Prasad D.K. “Interpretability in Deep Learning”. (pp. 1-466). Springer. 1st ed. 2023 edition (Hardcover ISBN: 978-3-031-20638-2; Published: 01 May 2023). https://doi.org/10.1007/978-3-031-20639-9
Punnakkal, A.R., Godtliebsen, G., Somani, A., Maldonado, S.A.A., Birgisdottir, Å.B., Prasad, D.K., ... & Agarwal, K. (2023). “Analyzing Mitochondrial Morphology Through Simulation Supervised Learning”. JoVE (Journal of Visualized Experiments), (193), e64880. https://www.jove.com/t/64880/analyzing-mitochondrial-morphology-through-simulation-supervised
Jadhav S. (2023). “Reconstructing 3D Geometries of Sub-Cellular Structures from SMLM Point Clouds”. Master Thesis. UiT The Arctic University of Norway. Grade: A.
Celeste A. V. (2023). “Presenting CODS (Cell Organelle Dynamic Simulation)”. Master Thesis. UiT The Arctic University of Norway. Grade: A.
Godtliebsen, G., Larsen, K.B., Bhujabal, Z., Opstad, I.S., Nager, M., Punnakkal, A.R., ... & Birgisdottir, Å.B. (2023). “High-resolution visualization and assessment of basal and OXPHOS-induced mitophagy in H9c2 cardiomyoblasts”. Autophagy, 19(10), 2769-2788. https://www.tandfonline.com/doi/full/10.1080/15548627.2023.2230837
Malakar, S., Sen, S., Romanov, S., Kaplun, D., & Sarkar, R. (2023). “Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study”. Journal of King Saud University-Computer and Information Sciences, 35(9), 101757.
Somani A., Horsch A., Bopardikar A., & Prasad D.K. (2023). “Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks”. Nordic Machine Intelligence Special Issue.
Biswas, M., Buckchash, H., & Prasad, D.K. (2023). “pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems”. Neurocomputing Journal. http://doi.org/10.48550/arXiv.2308.06983
Singh, A., Bhambhu, Y., Buckchash, H., Gupta, D.K., & Prasad, D.K. (2023). “Latent Graph Attention for Enhanced Spatial Context”. Machine Learning. http://doi.org/10.48550/arXiv.2307.04149
Agarwal, R., Gupta, D., Horsch, A., & Prasad, D.K. (2023). “Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts”. Transactions on Machine Learning Research. https://openreview.net/pdf?id=R9CgBkeZ6Z → GitHub
Jadhav, S., Kuchibhotla, R., Agarwal, K., Habib, A., & Prasad, D.K. (2023). “Deep learning-based denoising of acoustic images generated with point contact method”. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 6(3).
Bhatt, S., Butola, A., Kumar, A., Thapa, P., Joshi, A., Jadhav, S., ... & Mehta, D.S. (2023). “Single-shot multispectral quantitative phase imaging of biological samples using deep learning”. Applied Optics, 62(15), 3989-3999.
Johannessen, E., Johansson, J., Hartvigsen, G., Horsch, A., Årsand, E., & Henriksen, A. (2023). “Collecting health-related research data using consumer-based wireless smart scales”. International Journal of Medical Informatics, 173, 105043. https://www.sciencedirect.com/science/article/pii/S1386505623000618
Johannessen, E., Henriksen, A., Årsand, E., Horsch, A., Johansson, J., & Hartvigsen, G. (2023). “Health research requires efficient platforms for data collection from personal devices”. Studies in Health Technology and Informatics, 302.
Barken, T.L., Bonacina, S., Bostad, R., Gabarron, E., Garcia, B., Haddeland, K., ... & Årsand, E. (2023). “University campus as a smart technology-supported active learning arena”. Septentrio Reports, (1).
Somani, A., Banerjee, P., Rastogi, M., Habib, A., Agarwal, K., & Prasad, D.K. “Image Inpainting with Hypergraphs for Resolution Improvement in Scanning Acoustic Microscopy”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 3112-3121. https://openaccess.thecvf.com/content/CVPR2023W/DL-UIA/papers/Somani_Image_Inpainting_With_Hypergraphs_for_Resolution_Improvement_in_Scanning_Acoustic_CVPRW_2023_paper.pdf → GitHub
Banerjee, P., Mishra, S., Yadav, N., Agarwal, K., Melandsø, F., Prasad, D.K., & Habib, A. (2023). “Image inpainting in acoustic microscopy”. AIP Advances, 13(4).
Banerjee, N., Malakar, S., Gupta, D.K., Horsch, A., & Prasad, D.K. (2023, November). “Guided U-Net Aided Efficient Image Data Storing with Shape Preservation”. In Asian Conference on Pattern Recognition. Cham: Springer Nature Switzerland.
Rohit Agarwal; Gyanendra Das; Saksham Aggarwal; Alexander Horsch; Dilip K. Prasad. (2023). “Mabnet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval”. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE. https://ieeexplore.ieee.org/abstract/document/10094987 → GitHub → YouTube
Jadhav, S., Majhi, S., Chowdhury, A.S., Prasad, D.K., & Agarwal, K. “Reconstructing 3D shape from 3D ThunderSTORM Point Clouds”. Focus on Microscopy Conference 2023, Porto, Portugal.
Gupta, D., Mago, G., Chavan, A., Prasad, D., & Thomas, R.M. (2023). “Patch Gradient Descent: Training Neural Networks on Very Large Images”. In Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@NeurIPS 2023).
Aggarwal, S., Gupta, T., Sahu, P.K., Chavan, A., Tiwari, R., Prasad, D.K., & Gupta, D.K. (2023). “On Designing Light-Weight Object Trackers Through Network Pruning: Use CNNs Or Transformers?”. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1-5). IEEE.
Rishabh Tiwari, Arnav Chavan, Deepak Gupta, Gowreesh Mago, Animesh Gupta, Akash Gupta, Suraj Sharan, Yukun Yang, Shanwei Zhao, Shihao Wang, Youngjun Kwak, Seonghun Jeong, Yunseung Lee, Changick Kim, Subin Kim, Ganzorig Gankhuyag, Ho Jung, Junwhan Ryu, HaeMoon Kim, Byeong H. Kim, Tu Vo, Sheir Zaheer, Alexander Holston, Chan Park, Dheemant Dixit, Nahush Lele, Kushagra Bhushan, Debjani Bhowmick, Devanshu Arya, Sadaf Gulshad, Amirhossein Habibian, Amir Ghodrati, Babak Bejnordi, Jai Gupta, Zhuang Liu, Jiahui Yu, Dilip Prasad, Zhiqiang Shen. “RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning”. In IEEE/CVF International Conference on Computer Vision (2023), pp. 1534-1543.
Bamba, U., Anand, N., Aggarwal, S., Prasad, D.K., & Gupta, D.K. (2023). “Partial Binarization of Neural Networks for Budget-Aware Efficient Learning”. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2336-2345).
Punnakkal, A.R., Jadhav, S.S., Horsch, A., Agarwal, K., & Prasad, D.K. (2023). “MiShape: 3D Shape Modelling of Mitochondria in Microscopy”. arXiv preprint arXiv:2303.01546.
Iqra Qasim, Horsch, A., & Prasad, D.K. (2023). “Dense Video Captioning: A survey of Techniques, Datasets, and Evaluation Protocols”. Submitted to ACM Computing Surveys. https://doi.org/10.48550/arXiv.2311.02538
Rohit Agarwal, Aman Sinha, Ayan Vishwakarma, Xavier Coubez, Marianne Clausel, Mathieu Constant, Alexander Horsch, Dilip K. Prasad. (2023). “Modelling Irregularly Sampled Time Series Without Imputation”. arXiv preprint arXiv:2309.08698. https://arxiv.org/abs/2309.08698 → GitHub
2022
Dong, H., Zhou, J., Qiu, C., Prasad, D.K., & Chen, I.M. (2022). “Robotic manipulations of cylinders and ellipsoids by ellipse detection with domain randomization”. IEEE/ASME Transactions on Mechatronics, 28(1), 302-313.
Somani, A., Sekh, A.A., Opstad, I.S., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Horsch, A., Agarwal, K., & Prasad, D.K. (2022). “Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning”. Biomedical Optics Express, 13(10), 5495-5516. https://opg.optica.org/boe/fulltext.cfm?uri=boe-13-10-5495 → GitHub → Data Release
Soham Chattopadhyay, Malachowski, A., Swain, J.K., Dalmo, R.A., Horsch, A., & Prasad, D.K. (2022). “Mapping functional changes in the embryonic heart of Atlantic salmon post viral infection using AI technique”. In IEEE International Conference on Image Processing (ICIP). link
Singh, D., Somani, A., Horsch, A., & Prasad, D.K. (2022). “Counterfactual explainable gastrointestinal and colonoscopy image segmentation”. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9761664
Weitz, M., Syed, S., Hopstock, L.A., Morseth, B., Prasad, D.K., & Horsch, A. (2022). “Discrimination of sleep and wake periods from a hip-worn raw acceleration sensor using recurrent neural networks”. medRxiv. https://www.medrxiv.org/content/10.1101/2022.03.07.22270992v1.full.pdf
Rai, S., Kumari, A., & Prasad, D.K. (2022). “Client Selection in Federated Learning under Imperfections in Environment”. AI. https://www.mdpi.com/2673-2688/3/1/8
Liu, Z., Roy, M., Prasad, D.K., & Agarwal, K. (2022). “Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering”. IEEE Transactions on Computational Imaging. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9735387
Agarwal, R., Agarwal, K., Horsch, A., & Prasad, D.K. (2022). “Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs”. In International Conference on Neural Information Processing (pp. 549-561). https://arxiv.org/abs/2008.11828 → YouTube
2021
Sekh, A.A., Opstad, I.-S., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Agarwal, K., & Prasad, D.K. (2021). “Physics-based machine learning for sub-cellular segmentation in living cells”. Nature Machine Intelligence. https://www.nature.com/articles/s42256-021-00420-0
Chattopadhyay, S., Zary, L., Quek, C., & Prasad, D.K. (2021). “Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network”. Expert Systems with Applications. https://www.sciencedirect.com/science/article/pii/S0957417421009544
Singh, D., Somani, A., Prasad, D., & Horsch, A. (2021). “T-MIS: Transparency adaptation in medical image segmentation”. Nordic Machine Intelligence, 1(1), 11-13. https://journals.uio.no/NMI/article/download/9120/7742
Xue, H., Batalden, B.-M., Sharma, P., Johansen, J.A., & Prasad, D.K. (2021). “Biosignals based driving skill classification using machine learning: a case study of maritime navigation”. Applied Sciences. https://www.mdpi.com/2076-3417/11/20/9765/htm
Somani, A., Sekh, A.A., Opstad, I.S., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Agarwal, K., Prasad, D.K., & Horsch, A. (2021). “Digital staining of mitochondria in label-free live-cell microscopy”. In Bildverarbeitung für die Medizin 2021 (pp. 235-240). Springer. https://www.researchgate.net/publication/349658691_Digital_Staining_of_Mitochondria_in_Label-free_Live-cell_Microscopy
Joshi, D., Butola, A., Kanade, S., Prasad, D., Sevanthi, A.M., Singh, N., Bisht, D., & Mehta, D.S. (2021). “Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network”. Optics & Laser Technology, 137. https://www.researchgate.net/publication/348160775_Label-free_non-invasive_classification_of_rice_seeds_using_optical_coherence_tomography_assisted_with_deep_neural_network
Jadhav, S., Acuña, S.M., Opstad, I., Ahluwalia, B., Agarwal, K., & Prasad, D. (2021). “Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning”. Biomedical Optics Express, 12, 191.
Zhe, Q., Ahmed, S.A., Quek, C., & Prasad, D.K. (2021). “Recurrent Self-evolving Takagi–Sugeno–Kan Fuzzy Neural Network (RST-FNN) Based Type-2 Diabetic Modeling”. In Advances in Intelligent Systems and Computing. https://www.researchgate.net/publication/350862158_Recurrent_Self-evolving_Takagi-Sugeno-Kan_Fuzzy_Neural_Network_RST-FNN_Based_Type-2_Diabetic_Modeling
Seow, J., Ahmed, S.A., Quek, C., & Prasad, D.K. (2021). “seMLP: Self-evolving Multi-layer Perceptron in Stock Trading Decision Making”. SN Computer Science, 2. https://doi.org/10.1007/s42979-021-00524-9
Liu, F., Ahmed, S.A., Quek, C., Ng, G., & Prasad, D.K. (2021). “RS-HeRR: A rough set-based Hebbian rule reduction neuro-fuzzy system”. Neural Computing and Applications, 33. https://doi.org/10.1007/s00521-020-04997-2
2020
Ströhl, F., Jadhav, S., Ahluwalia, B.S., Agarwal, K., & Prasad, D.K. (2020). “Object detection neural network improves Fourier ptychography reconstruction”. Optics Express, 28, 37199. https://doi.org/10.1364/OE.409679
Butola, A., Kanade, S.R., Bhatt, S., Dubey, V.K., Kumar, A., Ahmad, A., Prasad, D.K., Senthilkumaran, P., Ahluwalia, B.S., & Mehta, D.S. (2020). “High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network”. Optics Express. https://doi.org/10.1364/OE.402666
Sekh, A.A., Opstad, I.S., Agarwal, R., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Agarwal, K., & Prasad, D.K. (2020). “Simulation-supervised deep learning for analysing organelles states and behaviour in living cells”. arXiv preprint arXiv:2008.12617. https://arxiv.org/pdf/2008.12617.pdf
Singh, A., Bhave, A., & Prasad, D.K. (2020). “Single image dehazing for a variety of haze scenarios using back projected pyramid network”. In European Conference on Computer Vision Workshops. https://link.springer.com/chapter/10.1007/978-3-030-66823-5_10
Sekh, A.A., Opstad, I.S., Birgisdottir, Å.B., Myrmel, T., Ahluwalia, B.S., Agarwal, K., & Prasad, D.K. (2020). “Learning nanoscale motion patterns of vesicles in living cells”. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). http://openaccess.thecvf.com/content_CVPR_2020/html/Sekh_Learning_Nanoscale_Motion_Patterns_of_Vesicles_in_Living_Cells_CVPR_2020_paper.html
Pal, R., Sekh, A.A., Kar, S., & Prasad, D.K. (2020). “Neural network based country-wise risk prediction of COVID-19”. Applied Sciences. https://doi.org/10.3390/app10186448
Butola, A., Popova, D., Prasad, D.K., Ahmad, A., Habib, A., Tinguely, J.C., Basnet, P., Acharya, G., Senthilkumaran, P., Mehta, D.S., & Ahluwalia, B.S. (2020). “High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition”. Scientific Reports. https://www.nature.com/articles/s41598-020-69857-4
Butola, A., Prasad, D.K., Ahmad, A., Dubey, V., Qaiser, D., Srivastava, A., Senthilkumaran, P., Ahluwalia, B.S., & Mehta, D.S. (2020). “Deep learning architecture ‘LightOCT’ for diagnostic decision support using optical coherence tomography images of biological samples”. Biomedical Optics Express, 11(9), 5017-5031. https://opg.optica.org/boe/fulltext.cfm?uri=boe-11-9-5017&id=434402