

Elevating E-Commerce: The Evolution of Virtual Try-On Technology
Abstract
In recent years, virtual trial rooms have become increasingly popular and sought- after by retailers, particularly in the fashion industry, where they are viewed as transformative technology. "Virtual Try-On" represents a contemporary approach to shopping, offering customers the ability to engage in realistic dress fittings through marker-based Augmented Reality (AR) technology. Developed utilizing Lens Studio for AR effects creation, this virtual trial room is easily accessible on mobile devices. Users are provided with the opportunity to try on a diverse range of clothing and accessories, including watches, without the constraints of physical inventory. Additionally, the application enables users to effortlessly modify apparel colors and sizes to ensure the perfect fit with a simple tap. Through the scanning of a QR code, the app overlays and anchors a 3D outfit onto the user's body in real-time, affording them the freedom to move and experience a 360° view of the attire. Ultimately, this tool offers significant advantages for businesses, including enhanced accessibility, time savings, and cost reduction, thereby furnishing them with a competitive edge
References
M. Idhammad, K. Afdel, and M. Belouch, “Semi-supervised machine learning approach for DDoS detection,” Applied Intelligence, vol. 48, no. 10, pp. 3193–3208, Oct. 2018, doi: 10.1007/S10489-018-1141-2/METRICS.
M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection,” Pattern Recognit Lett, vol. 51, pp. 1–7, Jan. 2015, doi: 10.1016/J.PATREC.2014.07.019.
S. C. Lin and S. S. Tseng, “Constructing detection knowledge for DDoS intrusion tolerance,” Expert Syst Appl, vol. 27, no. 3, pp. 379–390, Oct. 2004, doi: 10.1016/J.ESWA.2004.05.016.
R. K. C. Chang, “Defending against flooding-based distributed denial-of-service attacks: A tutorial,” IEEE Communications Magazine, vol. 40, no. 10, pp. 42–51, Oct. 2002, doi: 10.1109/MCOM.2002.1039856.
S. Yu, “Distributed Denial of Service Attack and Defense,” 2014, doi: 10.1007/978-1-4614-9491-1.
K. Kalegele, K. Sasai, H. Takahashi, G. Kitagata, and T. Kinoshita, “Four Decades of Data Mining in Network and Systems Management,” IEEE Trans Knowl Data Eng, vol. 27, no. 10, pp. 2700–2716, Oct. 2015, doi: 10.1109/TKDE.2015.2426713.
J. Han, M. Kamber, and J. Pei, “C o nc epts a nd T ec hniques-C ha pter 2”.
P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn, vol. 63, no. 1, pp. 3–42, Apr. 2006, doi: 10.1007/S10994-006-6226-1/METRICS.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, Dec. 2009, doi: 10.1109/CISDA.2009.5356528.
A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani, “Toward developing a systematic approach to generate benchmark datasets for intrusion detection,” Comput Secur, vol. 31, no. 3, pp. 357–374, May 2012, doi: 10.1016/J.COSE.2011.12.012
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