Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System

Sezai Furkan Pür; Şeref Sağıroğlu

doi:10.54287/gujsa.1953188

Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System

Abstract

Customer analytics in physical stores must operate under three simultaneous constraints that are absent or weaker in online retail: limited on-site computation, strict personal-data privacy obligations, and the lack of a natural digital record of customer behavior. This study proposes an end-to-end edge-cloud hybrid artificial intelligence (AI) system that addresses these constraints jointly. On the edge side, an NVIDIA Jetson Nano device equipped with a Sony IMX477 camera performs motion detection, face detection, person detection, person re-identification, and demographic and affective attribute extraction (age, gender, ethnicity, emotion). Faces are detected and masked on the edge device, so unmasked facial biometrics are never transmitted to or stored in the cloud; person re-identification feature vectors are held only in volatile memory and purged daily. On the cloud side, scheduled batch tasks extract clothing, style, and color using Mask R-CNN, a VGG network, and k-means clustering with nearest-neighbor color mapping. Age, gender, and ethnicity are recognized by a single multi-task ResNet50 model trained on a merged UTKFace and FairFace dataset; emotion is recognized by a compact custom convolutional network trained on FER-2013. The system was validated end-to-end in a 45-participant in-store pilot study; per-class accuracy, precision, recall, and F1 values are reported with 95% confidence intervals given the pilot's small sample. The two core pipeline capabilities, person detection and person re-identification, reached 93% and 61% respectively under in-store conditions; the seven secondary demographic, affective, and fashion attributes (age, gender, ethnicity, emotion, clothing, color, and style) ranged from 57% to 88% accuracy. Face detection and masking succeeded on 100% of pipeline detections, so the privacy safeguard held under field conditions. During the pilot's 91.4-minute motion-gated monitoring session, chip temperature peaked at 51.5°C and memory briefly neared its 4 GB ceiling before settling well below it; sustained continuous-load behavior was not tested. The architecture’s principal contribution is that the edge-cloud split itself enforces data minimization, instead of relying on a compliance layer added after the fact. Limitations, including the small validation sample, illumination sensitivity, and domain mismatch in style detection, are reported transparently.

Keywords

References

Aljami, H. M., Alrowais, N. A., AlAwajy, A. M., Alhrgan, S. O., Aldwaani, R. A., Alsawadi, M. S., Saqib, N. U., Alam, S. S., & Alsubaie, R. (2026). Benchmarking YOLOv8 variants for object detection efficiency on Jetson Orin NX for edge computing applications. Computers, 15(2), 74. https://doi.org/10.3390/computers15020074
Aouayeb, M., Hamidouche, W., Soladie, C., Kpalma, K., & Seguier, R. (2021). Learning vision transformer with squeeze and excitation for facial expression recognition. https://doi.org/10.48550/arXiv.2107.03107
Bradski, G. (2000). The OpenCV library. Dr. Dobb’s Journal of Software Tools, 25(11), 120-125.
Brazauskas, J., Jensen, C., Danish, M., Lewis, I., & Mortier, R. (2024). Cerberus: Privacy-preserving crowd counting and localisation using face detection in edge devices. In: Proceedings of the 7th International Workshop on Edge Systems, Analytics and Networking (EdgeSys’24), (pp. 25-30), 22 April 2024, Athens, Greece. https://doi.org/10.1145/3642968.3654817
Cavoukian, A. (2009). Privacy by design: The 7 foundational principles. Implementation and Mapping of Fair Information Practices. Information and Privacy Commissioner of Ontario, Canada.
Chia, P. J., Attanasio, G., Bianchi, F., Terragni, S., Magalhães, A. R., Goncalves, D., Greco, C., & Tagliabue, J. (2022). Contrastive language and vision learning of general fashion concepts. Scientific Reports, 12(1), 18958. https://doi.org/10.1038/s41598-022-23052-9
Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020). RetinaFace: Single-shot multi-level face localization in the wild. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 5202-5211), 13-19 June 2020, Seattle, WA, USA. https://doi.org/10.1109/CVPR42600.2020.00525
EUR-Lex, European Parliament & Council of the European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council (General Data Protection Regulation). Official Journal of the European Union, L119, 1-88. https://eur-lex.europa.eu/eli/reg/2016/679/oj

Goodfellow, I. J., Erhan, D., Luc Carrier, P., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., Zhou, Y., Ramaiah, C., Feng, F., Li, R., Wang, X., Athanasakis, D., Shawe-Taylor, J., Milakov, M., Park, J., … Bengio, Y. (2015). Challenges in representation learning: A report on three machine learning contests. Neural Networks, 64, 59-63. https://doi.org/10.1016/j.neunet.2014.09.005
Guo, J., Deng, J., Lattas, A., & Zafeiriou, S. (2022). Sample and computation redistribution for efficient face detection. In: Tenth International Conference on Learning Representations (ICLR), 25-29 April 2022. https://doi.org/10.48550/arXiv.2105.04714
Ha, S. H. (2007). Applying knowledge engineering techniques to customer analysis in the service industry. Advanced Engineering Informatics, 21(3), 293-301. https://doi.org/10.1016/j.aei.2006.12.001
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2980-2988), 22-29 October 2017, Venice, Italy. https://doi.org/10.1109/ICCV.2017.322
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778), 27-30 June 2016, Las Vegas, NV, USA. https://doi.org/10.1109/CVPR.2016.90
He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021). TransReID: Transformer-based object re-identification. In: Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 14993-15002), 10-17 October 2021, Montreal, QC, Canada. https://doi.org/10.1109/ICCV48922.2021.01474
Jia, M., Shi, M., Sirotenko, M., Cui, Y., Cardie, C., Hariharan, B., Adam, H., & Belongie, S. (2020). Fashionpedia: Ontology, segmentation, and an attribute localization dataset. In: A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Proceedings of the 16th European Conference on Computer Vision - ECCV 2020 (pp. 316-332), 23-28 August 2020, Glasgow, UK. https://doi.org/10.1007/978-3-030-58452-8_19
Jocher, G., Chaurasia, A., & Qiu, J. (2023). Ultralytics YOLOv8 (Version 8.0.0) [Computer Software]. https://github.com/ultralytics/ultralytics
Kaneko, Y., Miyazaki, S., & Yada, K. (2017). The influence of customer movement between sales areas on sales amount: A dynamic Bayesian model of the in-store customer movement and sales relationship. Procedia Computer Science, 112, 1845-1854. https://doi.org/10.1016/j.procs.2017.08.225
Kärkkäinen, K., & Joo, J. (2021). FairFace: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In: Proceedings of the 2021 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 1547-1557), 03-08 January 2021, Waikoloa, HI, USA. https://doi.org/10.1109/WACV48630.2021.00159
KVKK. (2016). Personal Data Protection Law No. 6698 [Kişisel Verilerin Korunması Kanunu]. https://www.kvkk.gov.tr/ Official Gazette of the Republic of Türkiye, No. 29677, 07 April 2016. https://www.resmigazete.gov.tr/eskiler/2016/04/20160407.htm
Laishram, L., Shaheryar, M., Lee, J. T., & Jung, S. K. (2025). Toward a privacy-preserving face recognition system: A survey of leakages and solutions. ACM Computing Surveys, 57(6), 147. https://doi.org/10.1145/3673224
Lee, T.-W., Chiu, Y.-L., Wu, Y.-S., & Liu, H.-S. (2014). An intelligent image-based customer analysis service. In: The 16th Asia-Pacific Network Operations and Management Symposium (pp. 1-4), 17-19 September 2014, Hsinchu, Taiwan. https://doi.org/10.1109/APNOMS.2014.6996534
Li, P., Wang, X., Huang, K., Huang, Y., Li, S., & Iqbal, M. (2022). Multi-model running latency optimization in an edge computing paradigm. Sensors, 22(16), 6097. https://doi.org/10.3390/s22166097
Li, R., Wang, W., Ghose, A., Li, B., & Xu, K. (2023). Predicting consumer in-store purchase through real-time video analytics: An advanced computer vision and deep learning approach. SSRN. https://doi.org/10.2139/ssrn.4513385
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In: D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Proceedings of the 13th European Conference on Computer Vision - ECCV 2014 (pp. 740-755), 6-12 September 2014, Zurich, Switzerland. https://doi.org/10.1007/978-3-319-10602-1_48
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In: B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision - ECCV 2016 (pp. 21-37), 11-14 October 2016, Amsterdam, The Netherlands. https://doi.org/10.1007/978-3-319-46448-0_2
Liu, X., Li, J., Wang, J., & Liu, Z. (2020). MMFashion: An open-source toolbox for visual fashion analysis. https://doi.org/10.48550/arXiv.2005.08847
Liu, Z., Luo, P., Qiu, S., Wang, X., & Tang, X. (2016). DeepFashion: Powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1096-1104), 27-30 June 2016, Las Vegas, NV, USA. https://doi.org/10.1109/CVPR.2016.124
Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. https://doi.org/10.1109/TIT.1982.1056489
Marzani, F., van Ede, T., Heijenk, G., & van Steen, M. (2026). Head count: Privacy-preserving face-based crowd monitoring. https://doi.org/10.48550/arXiv.2604.14250
Meden, B., Rot, P., Terhörst, P., Damer, N., Kuijper, A., Scheirer, W. J., Ross, A., Peer, P., & Štruc, V. (2021). Privacy-enhancing face biometrics: A comprehensive survey. IEEE Transactions on Information Forensics and Security, 16, 4147-4183. https://doi.org/10.1109/TIFS.2021.3096024
Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In: Proceedings of the 2008 IEEE Symposium on Security and Privacy (sp 2008) (pp. 111-125), 18-22 May 2008, Oakland, CA, USA. https://doi.org/10.1109/SP.2008.33
Newman, A. J., Yu, D. K. C., & Oulton, D. P. (2002). New insights into retail space and format planning from customer-tracking data. Journal of Retailing and Consumer Services, 9(5), 253-258. https://doi.org/10.1016/S0969-6989(02)00010-3
NVIDIA Corporation. (2019). Jetson Nano developer kit [Technical specification]. https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/product-development/
NVIDIA Corporation. (2024). Jetson Orin Nano series [Technical specification]. https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/
OpenZeka. (n.d.-a). NVIDIA Jetson Nano module [Product listing]. Retrieved July 10, 2026, from https://openzeka.com/urun/nvidia-jetson-nano-module/
OpenZeka. (n.d.-b). NVIDIA Jetson Orin Nano 4GB module [Product listing]. Retrieved July 10, 2026, from https://openzeka.com/urun/nvidia-jetson-orin-nano-4gb-module/
OpenZeka. (n.d.-c). NVIDIA Jetson Orin Nano 8GB module [Product listing]. Retrieved July 10, 2026, from https://openzeka.com/urun/nvidia-jetson-orin-nano-8gb-module/
Pham, H. V., Tran, T. G., Le, C. D., Le, A. D., & Vo, H. B. (2024). Benchmarking Jetson edge devices with an end-to-end video-based anomaly detection system. In: K. Arai (Ed.), Advances in Information and Communication (Proceedings of the 2024 Future of Information and Communication Conference, FICC 2024) (pp. 358-374). Springer. https://doi.org/10.1007/978-3-031-53963-3_25
Ray, P. P. (2022). A review on TinyML: State-of-the-art and prospects. Journal of King Saud University - Computer and Information Sciences, 34(4), 1595-1623. https://doi.org/10.1016/j.jksuci.2021.11.019
Ross, S. M. (2014). Introduction to probability models (11th ed.). Academic Press. https://doi.org/10.1016/C2012-0-03564-8
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4510-4520), 18-23 June 2018, Salt Lake City, UT, USA. https://doi.org/10.1109/CVPR.2018.00474
Santarcangelo, V., Farinella, G. M., Furnari, A., & Battiato, S. (2018). Market basket analysis from egocentric videos. Pattern Recognition Letters, 112, 83-90. https://doi.org/10.1016/j.patrec.2018.06.010
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 815-823), 07-12 June 2015, Boston, MA, USA. https://doi.org/10.1109/CVPR.2015.7298682
Shili, M., Jayasingh, S., & Hammedi, S. (2024). Advanced customer behavior tracking and heatmap analysis with YOLOv5 and DeepSORT in retail environment. Electronics, 13(23), 4730. https://doi.org/10.3390/electronics13234730
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR 2015), 7-9 May 2015, San Diego, California, USA. https://doi.org/10.48550/arXiv.1409.1556
Singh, R., Majumdar, P., Mittal, S., & Vatsa, M. (2022). Anatomizing bias in facial analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12351-12358. https://doi.org/10.1609/aaai.v36i11.21500
Somvanshi, S., Islam, M. M., Chhetri, G., Chakraborty, R., Mimi, M. S., Shuvo, S. A., Islam, K. S., Javed, S., Rafat, S. A., Dutta, A., & Das, S. (2026). From tiny machine learning to tiny deep learning: A survey. ACM Computing Surveys, 58(7), 168. https://doi.org/10.1145/3776588
Sturari, M., Liciotti, D., Pierdicca, R., Frontoni, E., Mancini, A., Contigiani, M., & Zingaretti, P. (2016). Robust and affordable retail customer profiling by vision and radio beacon sensor fusion. Pattern Recognition Letters, 81, 30-40. https://doi.org/10.1016/j.patrec.2016.02.010
Swaminathan, T. P., Silver, C., Akilan, T., & Kumar, J. (2025). Benchmarking deep learning models on NVIDIA Jetson Nano for real-time systems: An empirical investigation. Procedia Computer Science, 260, 906-913. https://doi.org/10.1016/j.procs.2025.03.273
Terhörst, P., Kolf, J. N., Huber, M., Kirchbuchner, F., Damer, N., Morales Moreno, A., Fierrez, J., & Kuijper, A. (2022). A comprehensive study on face recognition biases beyond demographics. IEEE Transactions on Technology and Society, 3(1), 16-30. https://doi.org/10.1109/TTS.2021.3111823
Tian, H., Cao, Y., & Mok, P. Y. (2023). DETR-based layered clothing segmentation and fine-grained attribute recognition. https://doi.org/10.48550/arXiv.2304.08107
Ullah, S., Ou, J., Xie, Y., & Tian, W. (2024). Facial expression recognition (FER) survey: A vision, architectural elements, and future directions. PeerJ Computer Science, 10, e2024. https://doi.org/10.7717/peerj-cs.2024
Vanaja, S., & Belwal, M. (2018). Aspect-level sentiment analysis on e-commerce data. In: Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1275-1279), 11-12 July 2018, Coimbatore, India. https://doi.org/10.1109/ICIRCA.2018.8597286
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 7464-7475), 17-24 June 2023, Vancouver, BC, Canada. https://doi.org/10.1109/CVPR52729.2023.00721
Wojke, N., Bewley, A., & Paulus, D. (2017). Simple online and realtime tracking with a deep association metric. In: Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP) (pp. 3645-3649), 17-20 September 2017, Beijing, China. https://doi.org/10.1109/ICIP.2017.8296962
Wu, Y.-K., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s flow analysis in physical retail store. Procedia Manufacturing, 3, 3506-3513. https://doi.org/10.1016/j.promfg.2015.07.672
Xu, S., Li, X., Wang, J., Cheng, G., Tong, Y., & Tao, D. (2022). Fashionformer: A simple, effective and unified baseline for human fashion segmentation and recognition. In: S. Avidan, G. Brostow, M. Cissé, G. Maria Farinella, & T. Hassner (Eds.), Proceedings of the 17th European Conference on Computer Vision - ECCV 2022 (pp. 545-563), 23-27 October 2022, Tel Aviv, Israel. https://doi.org/10.1007/978-3-031-19836-6_31
Yanagimoto, H. (2016). Customer behavior analysis with enthusiasm analysis. In: Proceedings of the 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 256-261), 10-14 July 2016, Kumamoto, Japan. https://doi.org/10.1109/IIAI-AAI.2016.149
Ye, M., Chen, S., Li, C., Zheng, W.-S., Crandall, D., & Du, B. (2025). Transformer for object re-identification: A survey. International Journal of Computer Vision, 133(5), 2410-2440. https://doi.org/10.1007/s11263-024-02284-4
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022). ByteTrack: Multi-object tracking by associating every detection box. In: S. Avidan, G. Brostow, M. Cissé, G. Maria Farinella, & T. Hassner (Eds.), Proceedings of the 17th European Conference on Computer Vision - ECCV 2022 (pp. 1-21), 23-27 October 2022, Tel Aviv, Israel. https://doi.org/10.1007/978-3-031-20047-2_1
Zhang, Z., Song, Y., & Qi, H. (2017). Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4352-4360), 21-26 July 2017, Honolulu, HI, USA. https://doi.org/10.1109/CVPR.2017.463
Zheng, S., Yang, F., Kiapour, M. H., & Piramuthu, R. (2018). ModaNet: A large-scale street fashion dataset with polygon annotations. In: Proceedings of the 26th ACM International Conference on Multimedia (pp. 1670-1678), 22-26 October 2018, Seoul, Republic of Korea. https://doi.org/10.1145/3240508.3240652
Zhou, K., Yang, Y., Cavallaro, A., & Xiang, T. (2019). Omni-scale feature learning for person re-identification. In: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 3701-3711), 27 October - 02 November 2019, Seoul, Korea (South). https://doi.org/10.1109/ICCV.2019.00380
Zuo, Y., Yada, K., Li, T., & Chen, P. (2018). Application of network analysis techniques for customer in-store behavior in supermarket. In: Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1861-1866), 07-10 October 2018, Miyazaki, Japan. https://doi.org/10.1109/SMC.2018.00322

Details

Primary Language

English

Subjects

Cloud Computing, Edge Computing, Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Sezai Furkan Pür ^*
0000-0002-2040-1827
Türkiye

Şeref Sağıroğlu
0000-0003-0805-5818
Türkiye

Early Pub Date

September 24, 2026

Publication Date

September 30, 2026

Submission Date

May 16, 2026

Acceptance Date

July 27, 2026

Published in Issue

Year 2026 Volume: 13 Number: 3

DOI

https://doi.org/10.54287/gujsa.1953188

IZ

https://izlik.org/JA64FK97CC

Cite

RIS / Bibtex

APA

Pür, S. F., & Sağıroğlu, Ş. (2026). Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System. Gazi University Journal of Science Part A: Engineering and Innovation, 13(3), 1133-1168. https://doi.org/10.54287/gujsa.1953188

AMA

1.Pür SF, Sağıroğlu Ş. Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System. GU J Sci, Part A. 2026;13(3):1133-1168. doi:10.54287/gujsa.1953188

Chicago

Pür, Sezai Furkan, and Şeref Sağıroğlu. 2026. “Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System”. Gazi University Journal of Science Part A: Engineering and Innovation 13 (3): 1133-68. https://doi.org/10.54287/gujsa.1953188.

EndNote

Pür SF, Sağıroğlu Ş (September 1, 2026) Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System. Gazi University Journal of Science Part A: Engineering and Innovation 13 3 1133–1168.

IEEE

[1]S. F. Pür and Ş. Sağıroğlu, “Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System”, GU J Sci, Part A, vol. 13, no. 3, pp. 1133–1168, Sept. 2026, doi: 10.54287/gujsa.1953188.

ISNAD

Pür, Sezai Furkan - Sağıroğlu, Şeref. “Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System”. Gazi University Journal of Science Part A: Engineering and Innovation 13/3 (September 1, 2026): 1133-1168. https://doi.org/10.54287/gujsa.1953188.

JAMA

1.Pür SF, Sağıroğlu Ş. Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System. GU J Sci, Part A. 2026;13:1133–1168.

MLA

Pür, Sezai Furkan, and Şeref Sağıroğlu. “Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 13, no. 3, Sept. 2026, pp. 1133-68, doi:10.54287/gujsa.1953188.

Vancouver

1.Sezai Furkan Pür, Şeref Sağıroğlu. Privacy-Aware Customer Analytics in Physical Stores Using an Edge-Cloud Hybrid Artificial Intelligence System. GU J Sci, Part A. 2026 Sep. 1;13(3):1133-68. doi:10.54287/gujsa.1953188