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AI driven system for enhancing consumer electronics through maintenance personalization and security – Scientific Reports

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Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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Abstract

The rapid expansion of consumer electronics has created an urgent need for advanced solutions to critical challenges such as predictive maintenance, user personalization, and device security. Traditional models often struggle to address these issues due to their limited adaptability and performance. This paper introduces GenAI-A, an innovative AI model that integrates Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Dynamic Recommendation Algorithms (DRA), and anomaly detection techniques to provide a comprehensive solution for consumer electronics. By leveraging the strengths of generative AI and self-renewal capabilities, GenAI-A enhances predictive maintenance, optimizes user experience, and strengthens biometric security. The model’s hybrid architecture utilizes GANs for realistic data representation, VAEs for the generation of complex data distributions, and DRA for real-time user personalization. The novelty of GenAI-A lies in its cross-regularized coupling between the GAN and VAE modules, where latent features are jointly optimized through a shared loss function to achieve consistent generative–representational learning. Unlike conventional hybrids that treat these models independently, GenAI-A introduces a dynamic feedback mechanism in which the DRA and anomaly detection modules operate directly in the shared latent space, enabling self-adaptive personalization and continual refinement of generative outputs. Experimental validation across four real-world datasets, i.e., Smartphone Sensor, Labelled Faces in the Wild (LFW), Pecan Street Energy Consumption, and SECOM Manufacturing, demonstrates significant improvements in device uptime, user engagement, and biometric security, with a notable reduction in false positives. Unlike existing hybrid generative models, GenAI-A introduces a novel integration of these components in a dynamic, self-learning system that adapts in real time to evolving user behaviors and device conditions. This unique combination of techniques sets GenAI-A apart from traditional approaches, establishing a new benchmark for AI-driven solutions in consumer electronics.

Data availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Abbreviations

AE–Attn:

AutoEncoder–attention

AI:

Artificial intelligence

ANOVA:

Analysis of variance

AUC–ROC:

Area under the receiver operating characteristic curve

AUC–PR:

Area under the precision–recall curve

CNN:

Convolutional neural network

CLIP:

Contrastive language–image pretraining

DRA:

Dynamic reinforcement-based adaptation

F1:

F1-score (harmonic mean of precision and recall)

FPR:

False positive rate

GAN:

Generative adversarial network

GenAI-A:

Generative–adaptive artificial intelligence model

GTr:

GAN–transformer hybrid model

HSD:

Honestly significant difference (Tukey’s test)

IoT:

Internet of things

K–Fold:

K-fold cross-validation

LFW:

Labeled faces in the wild dataset

CPU:

Central processing unit

DNN:

Deep neural network

ViT:

Vision transformer

LSTM:

Long short-term memory

ML:

Machine learning

MLP:

Multi-layer perceptron

PCA:

Principal component analysis

RF:

Random forest

RNN:

Recurrent neural network

SECOM:

Semiconductor manufacturing dataset

SVM:

Support vector machine

TP:

True positive

TN:

True negative

ViT:

Vision transformer

VAE:

Variational autoencoder

DL:

Deep learning

HPC:

High-performance computing

PR:

Precision–recall

SD:

Standard deviation

TPR:

True positive rate

GPU:

Graphics processing unit

AE:

AutoEncoder

CLIP:

Contrastive language–image pretraining model

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Acknowledgements

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R827), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R827), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

  1. School of Computer Applications, Galgotias University, Greater Noida, UP, India

    Sarita Simaiya

  2. Sr. Vice President of IT & Strategic Planning, Palayekar Companies Inc DBA PALNAR, Cranbury, NJ, 08512, USA

    Vivek Singh

  3. Lead Application Development Engineer, FinTech, Amazon, Seattle, IL, 60654, USA

    Praveena Challa

  4. Data Architect-Data & AI Analytics, Cisco Marketing, Cisco Systems, Fuquay Varina, NC, 27526, USA

    Kapil Kumar Sharma

  5. Platform Administrator, Data & Analytics, Zurich North America, Schaumburg, IL, 60196, USA

    Sathish Kuppan Pandurangan

  6. Eritrea Institute of Technology, Mai-Nefhi College, Himbrti, Mai Nefhi, Eritrea

    Lidia Gosy Tekeste

  7. Department of Mechatronics, Faculty of Engineering, Ain Shams University, Cairo, 11566, Egypt

    Ehab Seif Ghith

  8. Electrical Engineering Department, Faculty of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Shimaa A. Hussien

  9. School of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India

    Umesh Kumar Lilhore

Authors

  1. Sarita Simaiya
  2. Vivek Singh
  3. Praveena Challa
  4. Kapil Kumar Sharma
  5. Sathish Kuppan Pandurangan
  6. Lidia Gosy Tekeste
  7. Ehab Seif Ghith
  8. Shimaa A. Hussien
  9. Umesh Kumar Lilhore

Contributions

Sarita Simaiya led the study’s design, data collection, and manuscript writing, playing a pivotal role in shaping the direction of the research. Vivek Singh contributed to the research methodology, particularly in experimental design and data analysis. Praveena Challa was instrumental in conducting experiments and interpreting key results. Kapil Kumar Sharma’s expertise was crucial in the technical aspects of the study, including the application of specialized tools and techniques. Sathish Kuppan Pandurangan supported the conceptual framework and methodology development. Lidia Gosy Tekeste contributed to the data analysis and helped refine the theoretical model, while Ehab Seif Ghith offered valuable insights into statistical analysis and result interpretation. Shimaa A. Hussien played a key role in manuscript writing and revisions, ensuring the clarity and quality of the final paper. Lastly, Umesh Kumar Lilhore contributed significantly to the literature review and provided expertise in interpreting the findings within a broader context.

Corresponding authors

Correspondence to Lidia Gosy Tekeste or Umesh Kumar Lilhore.

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Competing interests

The authors declare no competing interests.

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Cite this article

Simaiya, S., Singh, V., Challa, P. et al. AI driven system for enhancing consumer electronics through maintenance personalization and security. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37401-5

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  • DOI: https://doi.org/10.1038/s41598-026-37401-5

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