Hybrid Deep Learning Framework for Facial Image Synthesis and Reconstruction Using StyleGAN2 and Autoencoders
DOI:
https://doi.org/10.51699/cajmns.v7i3.3275Keywords:
Stylegan2, Lightweight Models, Using Self-Generated Datasets, Traditional Paired Datasets, Hybrid Deep LearningAbstract
Realistic human face synthesis and reconstruction are important tasks in computer vision, entertainment, biometrics, identity verification and so on. In this work, a hybrid deep learning approach is studied that combines the generative ability of StyleGAN2 with the feature learning and reconstruction ability of a convolutional autoencoder. The main aim is to analyse the performance of an autoencoder to compress and reconstruct high quality synthetic facial images while preserving the main structural and perceptual features.Instead of using traditional real-world paired datasets, this system takes the synthetic face images generated by StyleGAN2 as the training input. The generated images provide a large and controlled dataset, allowing the model to learn facial representations without privacy concerns or data collection limitations. The synthetic images are then inputted to a convolutional autoencoder, which encodes the images into a small latent space and then reconstructs the images. The project shows how generative models and representation learning can be combined in one pipeline. It demonstrates effective utilisation of self-generated datasets for training deep-learning models, reducing the demand for large annotated datasets. The results show the model’s ability to keep the facial structure and image quality through the reconstruction process. This shows the possibility of combining the GAN-based data generation and the autoencoder-based learning for efficient image synthesis and reconstruction applications.
References
T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” in Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, United States of America, 2019.
S. Wang et al., “StegaStyleGAN: Towards Generic and Practical Generative Image Steganography,” in Proc. AAAI Conf. on Artificial Intelligence, vol. 37, no. 2, pp. 2389–2397, 2023.
R. Boina, “Assessing the Increasing Rate of Parkinson’s Disease in the US and its Prevention Techniques”,” International Journal of Biotechnology Research and Development, vol. 3, no. 1, pp. 1–18, 2022.
S. K. Sehrawat, "Transforming Clinical Trials: Harnessing the Power of Generative AI for Innovation and Efficiency," Transactions on Recent Developments in Health Sectors, vol. 6, no. 6, pp. 1-20, 2023.
S. K. Sehrawat, "Empowering the Patient Journey: The Role of Generative AI in Healthcare," International Journal of Sustainable Development Through AI, ML and IoT, vol. 2, no. 2, pp. 1-18, 2023.
S. K. Sehrawat, "The Role of Artificial Intelligence in ERP Automation: State-of-the-Art and Future Directions," Transactions on Latest Trends in Artificial Intelligence, vol. 4, no. 4, 2023.
P. P. Anand, U. K. Kanike, P. Paramasivan, S. S. Rajest, R. Regin, and S. S. Priscila, “Embracing Industry 5.0: Pioneering Next-Generation Technology for a Flourishing Human Experience and Societal Advancement,” FMDB Transactions on Sustainable Social Sciences Letters, vol.1, no. 1, pp. 43–55, 2023.
G. Gnanaguru, S. S. Priscila, M. Sakthivanitha, S. Radhakrishnan, S. S. Rajest, and S. Singh, “Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images,” in Advances in Medical Technologies and Clinical Practice, IGI Global, pp. 46–65, 2024.
G. Gowthami and S. S. Priscila, “Tuna swarm optimisation-based feature selection and deep multimodal-sequential-hierarchical progressive network for network intrusion detection approach,” Int. J. Crit. Comput.-based Syst., vol. 10, no. 4, pp. 355–374, 2023.
J. Obaid, S. Suman Rajest, S. Silvia Priscila, T. Shynu, and S. A. Ettyem, “Dense convolution neural network for lung cancer classification and staging of the diseases using NSCLC images,” in Proceedings of Data Analytics and Management, Singapore; Singapore: Springer Nature, pp. 361–372, 2023.
S. S. Priscila and A. Jayanthiladevi, “A study on different hybrid deep learning approaches to forecast air pollution concentration of particulate matter,” in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023.
S. S. Priscila, S. S. Rajest, R. Regin, and T. Shynu, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
S. S. Priscila and S. S. Rajest, “An Improvised Virtual Queue Algorithm to Manipulate the Congestion in High-Speed Network”,” Central Asian Journal of Medical and Natural Science, vol. 3, no. 6, pp. 343–360, 2022.
S. S. Priscila, S. S. Rajest, S. N. Tadiboina, R. Regin, and S. András, “Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 1, pp. 1–11, 2023.
R. Regin, Shynu, S. R. George, M. Bhattacharya, D. Datta, and S. S. Priscila, “Development of predictive model of diabetic using supervised machine learning classification algorithm of ensemble voting,” Int. J. Bioinform. Res. Appl., vol. 19, no. 3, 2023.
S. Silvia Priscila, S. Rajest, R. Regin, T. Shynu, and R. Steffi, “Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 6, pp. 53–71, 2023.
S. S. Rajest, S. Silvia Priscila, R. Regin, T. Shynu, and R. Steffi, “Application of Machine Learning to the Process of Crop Selection Based on Land Dataset,” International Journal on Orange Technologies, vol. 5, no. 6, pp. 91–112, 2023.
T. Shynu, A. J. Singh, B. Rajest, S. S. Regin, and R. Priscila, “Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology,” International Journal of Intelligent Engineering Informatics, vol. 10, no. 6, pp.484-503, 2022.
S. S. Priscila, D. Celin Pappa, M. S. Banu, E. S. Soji, A. T. A. Christus, and V. S. Kumar, “Technological frontier on hybrid deep learning paradigm for global air quality intelligence,” in Cross-Industry AI Applications, IGI Global, pp. 144–162, 2024.
S. S. Priscila, E. S. Soji, N. Hossó, P. Paramasivan, and S. Suman Rajest, “Digital Realms and Mental Health: Examining the Influence of Online Learning Systems on Students,” FMDB Transactions on Sustainable Techno Learning, vol. 1, no. 3, pp. 156–164, 2023.
S. R. S. Steffi, R. Rajest, T. Shynu, and S. S. Priscila, “Analysis of an Interview Based on Emotion Detection Using Convolutional Neural Networks,” Central Asian Journal of Theoretical and Applied Science, vol. 4, no. 6, pp. 78–102, 2023.
Agussalim, Rusli, A. Rasjid, M. Nur, T. Erawan, Iwan, and Zaenab, "Caffeine in student learning activities," J. Drug Alcohol Res., vol. 12, no. 9, Ashdin Publishing, 2023.
S. Temara, “Maximizing Penetration Testing Success with Effective Reconnaissance Techniques Using ChatGPT”, Asian Journal of Research in Computer Science, vol. 17, no. 5, pp. 19–29, 2024.
S. Temara, “The Ransomware Epidemic: Recent Cybersecurity Incidents Demystified”, Asian Journal of Advanced Research and Reports, vol. 18, no. 3, pp. 1–16, Feb. 2024.
S. Temara, “Harnessing the power of artificial intelligence to enhance next-generation cybersecurity,” World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 797–811,2024.
M. A. Yassin et al., “Advancing SDGs : Predicting Future Shifts in Saudi Arabia ’ s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data,” 2024.
M. A. Yassin, A. G. Usman, S. I. Abba, D. U. Ozsahin, and I. H. Aljundi, “Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia,” Results Eng., vol. 20, p. 101434, 2023.
S. I. Abba, A. G. Usman, and S. IŞIK, “Simulation for response surface in the HPLC optimization method development using artificial intelligence models: A data-driven approach,” Chemom. Intell. Lab. Syst., vol. 201, no. April, 2020.
G. Usman et al., “Environmental modelling of CO concentration using AI-based approach supported with filters feature extraction: A direct and inverse chemometrics-based simulation,” Sustain. Chem. Environ., vol. 2, p. 100011, 2023.
Gbadamosi et al., “New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system,” Int. J. Hydrogen Energy, vol. 50, pp. 1326–1337, 2024.
Abdulazeez, S. I. Abba, J. Usman, A. G. Usman, and I. H. Aljundi, “Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions,” ACS Appl. Nano Mater., 2023.
S. Alotaibi et al., “Sustainable Green Building Awareness: A Case Study of Kano Integrated with a Representative Comparison of Saudi Arabian Green Construction,” Buildings, vol. 13, no. 9, 2023.
S. I. Abba et al., “Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm,” Water (Switzerland), vol. 15, no. 19, 2023.
S. I. Abba, J. Usman, and I. Abdulazeez, “Enhancing Li + recovery in brine mining : integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials,” pp. 15129–15142, 2024.
Usman, S. I. Abba, N. Baig, N. Abu-Zahra, S. W. Hasan, and I. H. Aljundi, “Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater,” ACS Appl. Mater. Interfaces, Mar. 2024.
Agussalim, S. N. Fajriah, A. Adam, M. Asikin, T. Podding, and Zaenab, "Stimulant drink of the long driver lorry in Sulawesi Island, Indonesia," J. Drug Alcohol Res., vol. 13, no. 3, Ashdin Publishing, 2024.
S. Venkatasubramanian, “G-MAPQR: GNN-assisted multi-agent predictive quality of service routing for high-mobility MANETs,” IETE Journal of Research, pp. 1–15, 2026.
S. Venkatasubramanian, S. Raja, V. Sumanth, J. N. Dwivedi, J. Sathiaparkavi, S. Modak, and M. L. Kejela, “Fault diagnosis using data fusion with ensemble deep learning technique in IIoT,” Mathematical Problems in Engineering, vol. 2022, Art. no. 1682874, pp. 1–8, 2022.
S. Raja, J. Logeshwaran, S. Venkatasubramanian, M. Jayalakshmi, N. Rajeswari, N. G. Olaiya, and W. D. Mammo, “OCHSA: Designing energy-efficient lifetime-aware leisure degree adaptive routing protocol with optimal cluster head selection for 5G communication network disaster management,” Scientific Programming, vol. 2022, Art. no. 5424356, pp. 1–11, 2022.
S. Venkatasubramanian, J. N. Dwivedi, S. Raja, N. Rajeswari, J. Logeshwaran, and A. Praveen Kumar, “Prediction of Alzheimer’s disease using DHO-based pretrained CNN model,” Mathematical Problems in Engineering, vol. 2023, Art. no. 1110500, pp. 1–11, 2023.
S. Venkatasubramanian, V. Mohan, A. Subasri, S. H. Prasath, A. Thenmozi, and M. A. D. Thirumanraj, “Decentralized IoT frameworks: Blockchain-enabled trust in smart ecosystems,” in Edge Computing and Applications, N. Ramalingam, Y. El Allioui, and S. Bhattacharyya, Eds., Cham, Switzerland: Springer, 2026.
S. Venkatasubramanian, S. K. B. Pradeep Kumar CH, and M. Aurna Devi, “Optimization of IoT communication using low-power wide-area networks,” in Proc. 6th Int. Conf. Advances in Electrical, Computing, Communications and Sustainable Technologies (ICAECT), Bhilai, India, 2026, pp. 1–7.
S. Venkatasubramanian, “Real-time intrusion detection in IoT networks: A novel approach combining modified salp swarm feature selection and GLIRU model,” in Improving Threat Detection, Network Security, and Incident Response with AI, Hershey, PA, USA: IGI Global, 2025, pp. 307–332.
H. Apuri and C. Yepuri, “Design of multi-agent autonomous workflow systems using agentic AI frameworks,” in Proc. IEEE Int. Conf. Advances in Urban Computing (ICAUC), 2026.
H. Apuri, M. Aurangabadkar, S. Goel, M. M. R. Chinthala, and C. Yepuri, “Advancing infrastructure-as-code resilience through generative AI agents for predictive remediation and autonomous security enforcement,” Int. J. Engineering and Advanced Technology (IJEAT), vol. 15, no. 4, Mar. 2026.
H. Apuri et al., “Self-healing infrastructure: Autonomous LLM agents for real-time remediation of configuration drift and security misconfigurations in IaC deployments,” Int. J. Innovative Technology and Exploring Engineering (IJITEE), vol. 15, no. 4, Mar. 2026.
L. K. K. Rajendran, “Impact of treatment modalities on fertility, sexual function, and psychological outcomes in testicular cancer survivors: A comprehensive review,” International Journal of Drug Delivery Technology (IJDDT), vol. 16, no. 30s, pp. 447–453, 2026.
L. K. K. Rajendran, “From prediction to practice: A machine learning–based clinical decision support tool for bevacizumab risk stratification in oncology,” International Journal of Drug Delivery Technology (IJDDT), vol. 16, no. 30s, pp. 414–429, 2026.
L. K. K. Rajendran, “From prediction to precision: An externally validated deep learning–based survival and adjuvant therapy recommendation system for resected stage III non–small cell lung cancer,” International Journal of Drug Delivery Technology (IJDDT), vol. 16, no. 30s, pp. 430–438, 2026.
H. Pandian, “AI-based capacity forecasting models for elastic cloud and hybrid enterprise systems,” Journal of Information Systems Engineering and Management, vol. 10, no. 63s, pp. 1648–1658, Dec. 2025.
H. Pandian, “AI-driven predictive performance bottleneck detection in mission-critical financial systems,” Journal of Computational Analysis and Applications, vol. 30, no. 2, pp. 1019–1033, Mar. 2022.
H. Pandian, “Architectural optimization techniques for high-volume batch processing in Hadoop ecosystems,” International Journal of Intelligent Systems and Applications in Engineering, vol. 8, no. 4, pp. 428–439, Dec. 2020.
H. Pandian, “Embedding performance engineering into CI/CD pipelines for regulated financial systems,” Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 7892–7910, Dec. 2024.
V. T. Manne, “Post-quantum cryptography migration framework for real-time payment gateways,” in 2026 Second International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS), 2026, pp. 1–7.
V. T. Manne, “Adaptive graph-based risk scoring for real-time instant payment systems,” in 2026 Second International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS), 2026, pp. 1–7.
S. B. Venkata, “Risk-aware rework prevention in personalized hearing aid manufacturing,” Journal of Computer Science and Technology Studies, vol. 4, no. 2, pp. 215–230, 2022.
S. B. Venkata, “Predictive infrastructure orchestration in azure using terraform and dynatrace for medical systems,” in 2025 International Conference on Data, Energy and Communication Networks (DECoN), 2025, pp. 1–6.
G. V. Vasudeva, B. Gururaj, P. Y. Mallikarjun, N. M. Lutimath, and T. R. Kulkarni, “CMOS comparator design using 90 nm technology,” [Journal Name], vol. 6, pp. 291–297, 2024.
G. Vasudeva, P. Y. Mallikarjun, M. Jatkar, T. R. Kulkarni, R. R. Kulkarni, B. Gururaj, and M. P. Tejas, “Design of OPAMP based on 22 nm FinFET technology,” in Proc. Fifth Int. Conf. Trends in Computational and Cognitive Engineering, Springer, 2025, pp. 371–382.
G. Vasudeva, P. Y. Mallikarjun, S. Mahadev, T. R. Kulkarni, B. Gururaj, M. H. K. M., and J. Abhishek, “Design and development of UART communication over RSA encryption,” in Proc. 2025 Int. Conf. Electrical and Computer Engineering Researches (ICECER), 2025, pp. 1–6.
G. Vasudeva, P. Y. Mallikarjun, T. R. Kulkarni, M. Bhuvan, K. C. Gowthami, and N. Nidhi, “Design and optimisation of 3-bit flash ADC with low power double tail comparator,” in Engineering Science and Technology: Innovations for the Future, 1st ed., CRC Press, 2025.
J. D. Lakshmi and P. Y. Mallikarjun, “Design of virtual-real software-defined network for cloud,” in Intelligent Computing and Networking, Springer, 2023, pp. 367–376.
M. Jatkar and P. Y. Mallikarjun, “Optimized Cu/Fe doped boron nitride nanoribbons as nanoscale interconnect: DFT investigation,” Materials Science in Semiconductor Processing, vol. 186, 2025, Art. no. 109050.
P. Y. Mallikarjun and A. Takele, “Parallel transmission strategy for channel sensing mechanism in cognitive radio networks,” Authorea, 2023.
P. Y. Mallikarjun and Y. S. Kumarswamy, “Analysis of power transfer modeling for three-dimensional silicon chips using ThSV technologies,” in Proc. 2018 IEEE Int. Conf. System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2018, pp. 1–4.
L. Rajesh and H. S. Mohan, “EPO Based Clustering and Secure Trust-Based Enhanced LEACH Routing in WSN,” in Sustainable Communication Networks and Application, P. Karrupusamy, V. E. Balas, and Y. Shi, Eds., Lecture Notes on Data Engineering and Communications Technologies, vol. 93, Singapore: Springer, 2022.
L. Rajesh, H. S. Mohan, and M. K. Bindiya, “Hybrid Adam Coati Optimization with DLSTM for Biomedical Parameter Prediction Using Sensor,” in Proc. 2024 3rd Int. Conf. Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballari, India, 2024, pp. 1–7.
L. Rajesh and H. S. Mohan, “A multilevel efficient energy clustering protocol with secure routing (MEECSR) in WSNs,” Int. J. Applied Science and Engineering, vol. 18, 2021, Art. no. 2020344.
E. Varun, L. Rajesh, M. Lokeshwari, S. Ashwini, D. Bhat, and S. C. Kumar, “Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications,” in Advanced Interdisciplinary Applications of Deep Learning for Data Science, S. M. Biju and F. Oroumchian, Eds., Hershey, PA, USA: IGI Global, 2025, pp. 79–110.
L. Rajesh, K. R. Surabhi, K. A. Shenoy, M. Omar, M. Zain, and N. Kabra, “Browsafe: Approach to Enhance Trustworthiness for Safe and Secure Web Browsing,” ITM Web Conf., vol. 79, p. 01013, 2025.
L. Rajesh, D. Girish Kumar, P. R. P. Pani, S. R. Siddanna, and C. N. Abhilash, “Systematic Analysis of Artificial Intelligence-Driven Trust-based Authentication Schemes for Wireless Sensor Networks,” ITM Web Conf., vol. 79, p. 01015, 2025.
L. Rajesh, H. S. Mohan, and M. K. Bindiya, “Data aggregation in wireless sensor networks using Bayesian-based data encryption and fragmentation modelling,” Int. J. Intelligent Information and Database Systems, vol. 18, no. 2, pp. 201–230, 2026.
R. Dhumpati, R. Rawat, L. Rajesh, M. Dutta, D. Nimavat, and S. R. Maranan, “Secure Routing and Attack Detection in MANETs: A Hybrid Optimization Approach Using Depthwise Separable Convolutional Neural Networks,” in Proc. 2025 6th Int. Conf. Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India, 2025, pp. 1131–1137.
N. C. Kundur, H. R. Divakar, S. Khaiyum, K. P. Rakshitha, P. M. Dhulavvagol, and A. S. Meti, “Deep neural networks for precise brain tumor delineation: A U-Net and TensorFlow approach,” Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23686–23691, 2025.
M. B. Vibha, M. Chandrika, S. Khaiyum, and P. R. Kiran, “Predicting sedentary behavior in adults using stacked LSTM modeling,” International Journal of System Assurance Engineering and Management, vol. 16, no. 1, pp. 346–355, 2025.
B. C. Anil, J. S. Rajkumar, T. L. Divya, S. Khaiyum, P. R. Kiran, and B. Ramadoss, “A radiomics-based framework for liver cancer analysis using explainable artificial intelligence (XAI) methods,” Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 24098–24103, 2025.
B. C. Anil, J. S. Rajkumar, A. K. Gowdru, K. P. Rakshitha, S. Khaiyum, B. Lathamani, and B. Ramadoss, “Interpretable AI for liver cancer detection: Cascaded CNN & GLCM integration,” Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21243–21248, 2025.
L. M. Lobo, K. G. H. B., and R. K. P., “Generative ‘medical time travel’ simulators for disease progression forecasting,” in Proc. 2025 1st Int. Conf. Intelligent Computing and Communication Systems (CICCS), Bengaluru, India, 2025, pp. 1–6.
B. C. Anil, J. S. R., S. Khaiyum, T. L. Divya, K. P. Rakshitha, and C. Vishal, “Advanced AI for liver cancer detection: Vision transformers, XAI and contrastive learning,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 16, no. 7, 2025.
C. M., R. Kiran, P. Vaidya, and R. Kodnad, “Evaluation of machine learning models for cardiovascular risk assessment,” in Proc. 2024 2nd Int. Conf. Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2024, pp. 993–996.
M. R. Aishwarya, L. Jalwadi, and R. K. P., “A multimodal learning approach for stress classification using BERT embeddings and Instagram metadata,” in Proc. 2025 IEEE Int. Conf. Intelligent Signal Processing and Effective Communication Technologies (INSPECT), Gwalior, India, 2025, pp. 1–6.
M. B. Vibha, P. R. Kiran, R. R. Gondkar, and P. Nataraja, “Load balancing streamed healthcare data using MR technique,” in Proc. 2017 Int. Conf. Innovative Mechanisms for Industry Applications (ICIMIA), Bengaluru, India, 2017, pp. 641–645.
R. K. P. Rakshitha and N. N. C., “Op-RMSprop (optimized-root mean square propagation) classification for prediction of polycystic ovary syndrome (PCOS) using hybrid machine learning technique,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 13, no. 6, 2022.
P. Kotla, “Bridging legacy systems with modern automation: UiPath RPA in financial institutions,” PowerTech Journal, vol. 48, no. 4, pp. 8493–8507, Dec. 2024.
P. Kotla, “Combining document understanding and action center in UiPath for human-in-the-loop claims processing,” Journal of Information Systems Engineering and Management, vol. 8, no. 3, 2023.
P. Kotla, “Enhancing attended automation with the help of UiPath apps and data services,” International Journal of Information and Electronics Engineering, vol. 13, no. 4, pp. 39–50, Nov. 2023.
P. Kotla, “From process discovery to deployment: A UiPath-based framework for end-to-end automation in customer service,” PowerTech Journal, vol. 45, no. 3, pp. 32–43, Aug. 2021.
B. Kumar, “Blockchain-based authentication model for education data storage,” International Journal of Industrial and Systems Engineering, vol. 51, no. 4, pp. 498–515, 2025.
V. Ramalingam, B. Kumar, S. K. Gupta, D. M. Alsekait, and D. S. AbdElminaam, “A hybrid federated learning framework with generative AI for privacy-preserving and sustainable security in IoT-enabled smart environments,” Scientific Reports, vol. 16, Art. no. 3071, 2026.
B. Kumar, W. Shamas, J. Sandeep, and D. Albalushi, “Developing an advanced cybersecurity framework and blueprint: A contemporary approach to counter hacking through reverse engineering techniques,” in Smart Cyber Physical Systems, Proceedings of ICSCPS 2024, Singapore: Springer, 2024, pp. 27–37.
B. Kumar, N. B. Najmusseher, P. K. Nizar Banu, and R. Dwivedi, “Epileptic seizure detection contribution in healthcare sustainability,” in AI and IoT: Driving Business Success and Sustainability in the Digital Age, B. Awwad, Ed., Studies in Systems, Decision and Control, vol. 601, Cham, Switzerland: Springer, 2025, pp. 225–235.
B. Kumar and O. Al Falhi, “Digital transformation through APIs,” in Proc. Int. Conf. on Communication, Information Technology and Internet of Things (COM-IT-CON), 2022.
K. Al Aufi and B. Kumar, “Security testing of Android application using Drozer,” in Proc. Int. Conf. on Computational Sciences and Sustainable Technologies, Springer CCIS, 2024, pp. 8–18.
B. Al Barwani, E. Al Maani, and B. Kumar, “IoT-enabled smart cities: A review of security frameworks, privacy, risks, and key technologies,” in Proc. 1st Int. Conf. on Innovation in Information Technology and Business (ICIITB 2022), Advances in Computer Science Research, vol. 104, Springer, 2022, pp. 169–181.
P. Y. Mallikarjun and Y. S. Kumarswamy, “ThSV-based three-dimensional device to optimize low energy and reliable timer using MNM technique,” in Proc. 2018 3rd IEEE Int. Conf. Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2018, pp. 2072–2077.
P. Y. Mallikarjun, D. N. Rame Gowda, J. K. Trisha, M. Varshini, P. S. Shetty et al., “Atomistic insights into aluminium–boron nitride nanolayered interconnects for high-performance VLSI systems,” Computers, Materials & Continua, vol. 86, no. 3, 2026.
P. Y. Mallikarjun, K. H. Dekate, G. N. Rama Devi, A. K. Koshariya, R. Sagar, and Nikhilesh, “Factors influencing economic growth of online food ordering and delivery applications during COVID-19,” in Proc. Int. Virtual Conf. Newer Trends and Innovation in Nanotechnology Materials Science and Technology, vol. 12, no. 1, 2021, pp. 242–249.
L. K. K. Rajendran, “Evaluating the association of cancer-related risk factors with multisystem health: Insights into fertility, cardiovascular, and renal indicators,” Scientific Culture, vol. 12, no. 4, pp. 7520–7527, 2026.
L. K. K. Rajendran, “Integrative pharmacogenomic analysis of drug response heterogeneity across cancer cell lines: Insights from large-scale GDSC data,” Scientific Culture, vol. 12, no. 4, pp. 7537–7546, 2026.
K. Adivigneshwaran, J. Arun, M.S. O. Akash, R. Regin, R.L. Shyja, and R. Balku, “CampusConnect: A Data-Driven Web Framework for Smart Campus Operations Management,” Ale Journal of Sustainable Intelligent System Applications, vol.1, no.1, pp. 1–15, 2026.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 T. Shynu, R. Regin, S. Suman Rajest
This work is licensed under a Creative Commons Attribution 4.0 International License.
