Deep Learning and Machine Learning

• D2R: dual regularization loss with collaborative adversarial generation for model robustness
  34th International Conference on Artificial Neural Networks (ICANN 2025)
  Z Liu, H Liang, R Ranjan, Z Zhu, V Snasel, V Ojha
• AdaGAT: Adaptive Guidance Adversarial Training for the Robustness of Deep Neural Networks
  8th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2025)
  Z Liu, H Liang, X Li, V Snasel, V Ojha
• D2Fusion: Dual-domain fusion with feature superposition for deepfake detection
  Information Fusion, Elsevier. (2025)
  Qiu X, Miao X, Wan F, Duan H, Shah T, Ojha V, Long Y, Ranjan R
• Rehearsal-free federated domain-incremental learning
  45th IEEE International Conference on Distributed Computing Systems (IEEE ICDCS 2025)
  R Sun, H Duan, J Dong, V Ojha, T Shah, R Ranjan
• Analysis of deep learning under adversarial attacks in Hierarchical Federated Learning
  High-Confidence Computing, Elsevier. (2025)
  Alqattan DS, Snasel V, Ranjan R, Ojha V
• Fragility, Robustness and Antifragility in Deep Learning
  Artificial Intelligence, Elsevier. (2024)
  Pravin C, Martino I, Nicosia G, Ojha V
• Euclidean and Poincaré Space Ensemble Xgboost
  Information Fusion, Elsevier. (2024)
  Suganthan PN, Kong L, Snasel V, Ojha V, Aly HAH
• Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
  31st International Conference on Neural Information Processing (ICONIP). (2024)
  Liu Z, Duan H, Liang H, Long Y, Snasel V, Nicosia G, Ranjan R and Ojha V
• Security Assessment of Hierarchical Federated Deep Learning
  33rd International Conference on Artificial Neural Networks (ICANN). (2024)
  Alqattan D, Sun R, Liang H, Nicosia G, Snasel V, Ranjan R, and Ojha V
• Adversarial robustness in deep learning: Attacks on fragile neurons
  30th Int. Conf. on Artificial Neural Net., ICANN (pp 16-28), Springer, LNCS, Bratislava (2021)
  Pravin C, Martino I, Nicosia G, Ojha V
• On Learnable Parameters of Optimal and Suboptimal Deep Learning Models
  31st International Conference on Neural Information Processing (ICONIP). (2024)
  Zheng Z, Liang H, Snasel V, Latora V, Pardalos P, Nicosia G, and Ojha V
• Wearable-based Behaviour Interpolation for Semi-supervised Human Activity Recognition
  Information Sciences, Elsevier. (2024)
  Duan H, Wang S, Ojha V, Wang S, Huang Y, Long Y, Ranjan R, Zheng Y
• Dual Variational Knowledge Attention for Class Incremental Vision Transformer
  International Joint Conference on Neural Networks (IJCNN). (2024)
  Duan H, Sun R, Ojha V, Shah T, Huang Z, Zizhou O, Huang Y, Long Y, Rajan R
• Modular Neural Network for Edge-based Detection of Early-stage IoT Botnet
  High-Confidence Computing. (2024)
  Alqattana D, Ojha V, Habiba F, Noord A, Morgana G, Ranjan R
• Multi-Task Deep Learning as Multi-Objective Optimization
  Encyclopedia of Optimization. (2023)
  G Di Fatta, G Nicosia, V Ojha, P Pardalos
• Large-scale Data Classification based on the Integrated Fusion of Fuzzy Learning and Graph Neural Network
  Information Fusion, Elsevier. (2023)
  Snasel V, Stepnicka M, Ojha V, Suganthan PN, Gao R, Kong L
• Low-Rank and Global-Representation-Key-based Attention for Graph Transformers
  Information Sciences, Elsevier. (2023)
  Kong L, Ojha V, Gao R, Suganthan PN, Snasel V
• Backpropagation Neural Tree (Code, Slides)
  Neural Networks, Elsevier. (2022)
  Ojha V, Nicosia G
• Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies (Code, Slides)
  Swarm and Evolutionary Computation, Elsevier (2022)
  Ojha V, Greco A, Timmis J, and Nicosia G
• CSS–A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization
  Int. Conf. on Bioinspired Optimization Methods and Their Appl., BIOMA (pp 54-68), Springer, LNCS, Maribor, Slovenia (2022)
  Kong L, Kumar A, Snášel V, Das S, Krömer P, Ojha V
• Sensitivity analysis for deep learning: Ranking hyper-parameter influence
  33rd IEEE Int. Conf. on Tools with Artificial Intelligence, ICTAI. IEEE (2021)
  Taylor R, Martino I, Nicosia G, Ojha V
• Multi-objective optimization of multi-output neural tree
  IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK (pp 1–8). IEEE. (2020)
  Ojha V, Nicosia G
• A novel ECG signal denoising filter selection algorithm based on conventional neural networks
  19th IEEE Int. Conf. on Machine Learning and Appl., ICMLA, Miami (pp 1094–1100). (2020)
  Pravin C, Ojha V
• Heuristic design of fuzzy inference systems: A review of three decades of research
  Engineering Applications in Artificial Intelligence, 85, 845–864. Elsevier. (2019)
  Ojha V Abraham A, Snášel V
• Multiobjective programming for type-2 hierarchical fuzzy trees (Code)
  IEEE Transaction on Fuzzy Systems, 26(2), 915–936. (2018)
  Ojha VK, Snášel V, Abraham A
• Metaheuristic design of feedforward neural networks: a review of two decades of research
  Engineering Applications in Artificial Intelligence, 60, 97-116. Elsevier (2017)
  Ojha VK, Snášel V, Abraham A
• Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming
  Applied Soft Computing, 52, 909-924. Elsevier (2017)
  Ojha VK, Abraham A, Snášel V
• Temporal learning using echo state network for human activity recognition
  3rd European Network Intelligence Conference, ENIC, Wroclaw (pp 217–223) IEEE (2016)
  Basterrech S, Ojha VK
• A multi-agent concurrent neurosimulated annealing algorithm: A case study on intelligent sensing of manhole gases
  International Journal of Hybrid Intelligent Systems, 12(4), 185–202. IOS Press (2016)
  Ojha VK, Dutta P, Chaudhuri A, Saha H
• Understating continuous ant colony optimization for neural network training: A case study on intelligent sensing of manhole gas components
  International Journal of Hybrid Intelligent Systems, 12(4), 203–217. IOS Press (2016)
  Ojha VK, Dutta P, Chaudhuri A, Saha H
• Metaheuristic tuning of type-II fuzzy inference systems for data mining
  IEEE Int. Conf. on Fuzzy Systems, Fuzz-IEEE, Vancouver, Canada (pp 610–617) (2016)
  Ojha VK, Abraham A, Snášel V
• Substitution of the general partial differential equation with extended polynomial networks
  IEEE Int. Jt. Conf. on Neural Netw., IEEE IJCNN, Vancouver, Canada (pp 4819–4826) (2016)
  Zjavka L, Snášel V, Ojha VK, Pedrycz W
• Neuro-fuzzy risk prediction model for computational grids
  Proc. of the 2nd Int. Afro-European Conf. Ind. Adv., Paris. (pp 127–136) Springer (2016)
  Abdelwahab S, Ojha VK, Abraham A
• Ensemble of flexible neural trees for predicting risk in a grid computing environment
  6th Int. Conf. on Innov. in Bio-Inspired Comput. Appl., Kochi (pp 151–161) Springer (2016)
  Abdelwahab S, Ojha VK, Abraham A
• An ensemble of neuro-fuzzy models for assessing risk in a cloud computing environment
  Advances in Nature and Biologically Inspired Computing (pp 27–36) Springer (2016)
  Ahmed N, Ojha VK, Abraham A
• Ensemble of adaptive neuro-fuzzy inference system using particle swarm optimization for prediction of crude oil prices
  14th Int. Conf. on Hybrid Intell. Syst., Kuwait (pp 248–253) IEEE (2014)
  Gabralla LA, Wahby TM, Ojha VK, Abraham A
• ACO for continuous function optimization: A performance analysis
  14th Int. Conf. on Intell. Syst. Design and Appl., Okinawa, Japan (pp 145–150) IEEE (2014)
  Ojha VK, Abraham A, Snášel V
• Simultaneous optimization of neural network weights and active nodes using metaheuristics
  14th Int. Conf. on Hybrid Intell. Syst., Kuwait (pp 248–253) IEEE (2014)
  Ojha VK, Abraham A, Snášel V
• A framework of secured and bio-inspired image steganography using chaotic encryption with genetic algorithm optimization
  5th Int. Conf. on Innov. in Bio-Inspired Comput. and Appl., Ostrava (pp 271–280) Springer (2014)
  Bandyopadhyay D, Dasgupta K, Mandal JK, Ojha VK, Snášel V
• Design and implementation of an improved datacenter broker policy to improve the QoS of a cloud
  5th Int. Conf. on Innov. in Bio-Inspired Comput. and Appl., Ostrava (pp 281–290) Springer (2014)
  Chatterjee T, Ojha VK, Adhikari M, Banerjee S, Biswas U, Snášel V
  

Applied Artificial Intelligence

Hydrology and Climate Science

• Automated trash screen blockage segmentation using deep learning
  British Machine Vision Conference, (2024)
  Vandaele, R., Dance, S L, Williams H., & Ojha, V
• Deep Learning for Automated Trash Screen Blockage Detection Using Cameras: Actionable Information for Flood Risk Management
  Journal of Hydroinformatics, (2024)
  Vandaele, R., Dance, S L, & Ojha, V
• Calibrated river-level estimation from river cameras using convolutional neural networks
  Environmental Data Science, Cambridge University Press (2023)
  Vandaele, R., Dance, S L, & Ojha, V
• Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras
  EGU23 (2023)
  Vandaele, R., Dance, S. L., & Ojha, V
• Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning
  Hydrology and Earth System Sciences 25(8) 4435–4453 (2021)
  Vandaele R, Dance SL, Ojha V
• Transfer learning for instance segmentation of waste bottles using Mask R-CNN algorithm
  20th Int. Conf. on Intelligent Systems Design and Applications (pp 140–149) Springer (2020)
  Jaikumar P, Vandaele R, Ojha V

• Automated water segmentation and river level detection on images using transfer learning
  42nd DAGM German Conference on Pattern Recognition, DAGM GCPR, Tubingen, Germany, Proceedings 42 (pp 232–245) Springer, LNCS (2020)
  Vandaele R, Dance SL, Ojha V

• Supervised machine learning to estimate instabilities in chaotic systems: estimation of local Lyapunov exponents
  Quarterly Journal of the Royal Meteorological Society, Wiley (2023)
  Ayers D, Lau J, Amezcua J, Carrassi A, Ojha V
• Supervised machine learning to estimate instabilities in chaotic systems: computation of local Lyapunov exponents
  EGU General Assembly 2022 Vienna, Austria, 23–27 May (2022)
  Ayers D, Lau J, Amezcua J, Carrassi A, and Ojha V
• Toward an adaptive numerical integration scheme dependent on ML-predicted accuracy
  ECMWF Workshop, Reading, UK (2020)
  Ayers D, Amezcua J , Ojha V, Carrassi A, Leeuwen PJ

Civil Engineering and Built Environment

• Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures
  (2022)
  P Deng, J Yao, C Li, S Wang, X Li, V Ojha, X He, T Matsumoto
• Adaptive search space decomposition method for pre and post buckling analyses of space truss structures
  Engineering Applications in Artificial Intelligence, Elsevier (2022)
  Ojha V, Panto, B, Nicosia G
• Design and characterization of effective solar cells
  Energy Systems, 1–28. Springer (2021)
  Ojha V, Jansen G, Patanè A, Magna AL, Romano V, Nicosia G
• Machine learning approaches to understand the influence of urban environments on human’s physiological response
  Information Sciences, 474, 154–169. Elsevier. (2019)
  Ojha VK, Griego D, Kuliga S, Bielik M, Bus P, Schaeben C, Treyer L, Standfest M, Schneider S, Konig R, Donath D, Schmitt G
• Examining trade-offs between social, psychological and energy potential of urban form
  International Journal of Geo-Information, 8(2), 1–52 (2019)
  Bielik M, Griego D, Kuliga S, Schneider S,Ojha VK Konig R, Donath D, Schmitt G
• ESUM urban sensing handbook: component, assembly and operational guide: sensor backpack & 360 videos
  Tech. Report, ETH Zurich, Switzerland (2017)
  Griego D, Kuliga S, Bielik M, Standfest M, Ojha VK, Schneider S, Konig R, Donath D, Schmitt G
• Neural tree for estimating the uniaxial compressive strength
  17th Int. Conf. Hybrid Intelligence System, New Delhi, India (pp 1–10) Springer (2018)
  Ojha VK, Mishra, DA

Medical Sciences, Systems Biology and Medicine

• Deep Learning–Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging
  The 9th Euro-China Conference on Intelligent Data Analysis and Applications
  M Bhardwaj, H Liang, A Sivaharan, S Nandhra,V Snasel, T El-Sayed, and V Ojha

• Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models
  International Conference on Machine Learning, Optimization, and Data Science (LNCS)
  Amaradio MN ,Jansen G, Ojha V, Costanza J, Di-Fatta G, Nicosia G
• Pareto Optimal Metabolic Engineering for the Growth-coupled Overproduction of Sustainable Chemicals
  Biotechnology and Bioengineering, Wiley (2022)
  Amaradio MN, Ojha V, Jansen G, Pappalardo X, Costanza J, Nicosia G

• Predictive modeling of die filling of pharmaceutical granules using the flexible neural tree
  Neural Computing Application 29(7), 467–481. Springer (2016)
  Ojha VK, Schiano S, Wu C, Abraham A, Snášel V
• Dimensionality reduction and function approximation of poly(lactic-co-glycolic acid) micro-and nano-particle dissolution rate
  International Journal of Nanomedicine, 10, 1119–1129. (2015)
  Ojha VK, Jackowski K, Abraham A, Snášel V
• Ensemble of heterogeneous flexible neural trees for the approximation and feature-selection of Poly (lactic-co-glycolic acid) micro-and nano-particle
  Proc. of the 2nd Int. Afro-European Conf. Ind. Adv., Paris (pp 155–165) Springer (2016)
  Ojha VK, Abraham A, Snášel V
• Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile
  6th World Congr. on Nature and Bio Inspired Comput. NaBIC, Porto (pp 121–126) IEEE (2014)
  Ojha VK, Jackowski K, Abraham A, Snášel V
• Dimensionality reduction and prediction of the protein macromolecule dissolution profile
  5th Int. Conf. on Innov. in Bio-Inspired Comput. Appl., Ostrava (pp 301–310) Springer (2014)
  Ojha VK, Jackowski K, Snášel V, Abraham A
• Machine learning tools for modeling of powder mixing
  Int. Congress on Particle Technology, Nurnberg, Germany (2016). German National Library.
  Ojha VK, Schiano S, Wu C, Abraham A, Snášel V

Sensors and Signal Processing

• Accent and gender recognition from English language speech and audio using signal processing and deep learning
  20th Int. Conf. on Hybrid Intelligent Systems (pp 62–72) Springer (2020)
  Shergill JS, Pravin C, Ojha V
• Classification of musical preference in generation Z through EEG signal processing and machine learning
  20th Int. Conf. on Intelligent Systems Design and Applications (pp 117-127) Springer (2020)
  Ward B, Pravin C, Chetcuti A, Hayashi Y, Ojha V
• Identifying hazardousness of sewer-pipeline gas-mixture using classification methods
  Neural Computing Application, 28(6), 1343–1354. Springer(2016)
  Ojha VK, Dutta P, Chaudhuri A
• Study of various conjugate gradient based ANN training methods for designing intelligent manhole gas detection system
  2nd Int. Symp. on Comput. and Business Intell., New Delhi, India (pp 83–87) IEEE (2013)
  Ojha VK, Dutta P, Chaudhuri A, Saha H
• Convergence analysis of backpropagation algorithm for designing an intelligent system for sensing manhole gases
  Hybrid Soft Computing Approaches (pp 215–236) Springer (2014)
  Ojha VK, Dutta P, Chaudhuri A, Saha H
• Performance comparison of different intelligent techniques applied on detecting proportion of different components in manhole gas mixture
  Handbook of Research on Comput. Intel. for Eng, and Sci. (pp 758–785) IGI Global (2013)
  Ojha VK, Dutta P
• Conjugate gradient trained neural network for intelligent sensing of manhole gases to avoid human fatality
  Adv. in Secure Comput., Internet Services, and Appl. (pp 257–280) IGI Global (2013)
  Dutta P, Ojha VK
• Detection of the proportion of different gas components present in manhole gas mixture using backpropagation neural network
  Int. Proc. of Comput. Sc. Inf. Tech. 37 (pp 11–15) (2012)
  Ojha VK, Dutta P, Saha H, Ghosh S
• A novel neuro simulated annealing algorithm for detecting proportion of component gases in manhole gas mixture
  3rd Int. Conf. on Adv. in Comput. and Commun., Kochi, India (pp 238–241) IEEE (2012)
  Ojha VK, Dutta P, Saha H, Ghosh S
• Application of real-valued neuro genetic algorithm in the detection of components present in manhole gas mixture
  Proc. 2nd Int. Conf. on Comput. Sci., Eng. and Appl., India (pp 333–340) Springer (2012)
  Ojha VK, Dutta P, Saha H, Ghosh S
• Linear regression based statistical approach for detecting proportion of component gases in manhole gas mixture
  1st Int Symp. on Physics and Technology of Sensors, Mumbai (pp 17-20) IEEE (2012)
  Ojha VK, Dutta P, Saha H, Ghosh S
• Portable sensor array system for intelligent recognizer of manhole gases
  6th Int. Conf. on Sensing Technology, Kolkata, India (pp 589-594) IEEE (2012)
  Ghosh S, Saha H, Roychaudhuri C, Ojha VK, Dutta P
• Sensor array for manhole gas analysis
  1st Int. Symp. on Physics and Technology of Sensors, Mumbai (pp 9–12) IEEE (2012)
  Ghosh S, Roy A, Singh S, Ojha VK, Dutta P

Edited Books

• 10th International Conference Machine Learning, Optimization, and Data Science, LOD 2024, Tuscuanny, Italy, Sep 22 - 26, 2024, (Springer LNCS )
  Nicosia G, Ojha V, Giesselbach S, Pardalos P, Umeton R
• 9th International Conference Machine Learning, Optimization, and Data Science, LOD 2023, Grasmere, Lake District, UK, Sep 22 - 26, 2023, (Springer LNCS )
  Nicosia G, Ojha V, Malfa EL, Jansen G, Sciacca V, Pardalos P, Giuffrida G, Umeton R
• 8th International Conference Machine Learning, Optimization, and Data Science, LOD 2022, Siena, Italy, Sep 18–22, 2022, (Springer LNCS )
  Nicosia G, Ojha V, Malfa EL, Jansen G, Sciacca V, Pardalos P, Giuffrida G, Umeton R
• 7th International Conference Machine Learning, Optimization, and Data Science, LOD 2021, Grasmere, Lake District, UK, October 4 – 8, 2021 (LNCS, volume 13163)
  Nicosia G, Ojha V, Malfa EL, Jansen G, Pardalos P, Giuffrida G, Umeton R
• 6th International Conference Machine Learning, Optimization, and Data Science, LOD 2020, Siena, Italy, July 19–23, 2020, (Springer LNCS, volume 12565)
  Nicosia G, Ojha V, Malfa EL, Jansen G, Sciacca V, Pardalos P, Giuffrida G, Umeton R

Thesis

Feature Selection and Function Approximation Using Adaptive Algorithms
Department of Computer Science, Technical University of Ostrava, Ostrava, Czech Republic (2016)
Ojha VK