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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
Open Access

A Data-Centric Approach to Transforming Digital Retail Through Artificial Intelligence-Based Shopping Systems

DOI https://doi.org/10.55640/ijaair-v03i09-08
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Priya Sharma 1 ,
4 School of Computer Science and Engineering, National Center for Intelligent Systems, Bengaluru, India

Abstract

: The rapid evolution of artificial intelligence (AI) has transformed digital retail by enabling personalized recommendations, intelligent search, predictive analytics, and automated decision-making. Modern shopping systems increasingly rely on data-centric architectures that prioritize data quality, feature engineering, and continuous model optimization rather than solely focusing on algorithmic sophistication. This research-review paper examines the role of data-centric AI in digital retail transformation by synthesizing machine learning, computer vision, pattern recognition, and classification methodologies reported in the provided literature. The study develops a conceptual framework that integrates data acquisition, preprocessing, feature extraction, predictive modeling, and customer interaction optimization. Findings indicate that data quality, scalable learning architectures, and adaptive analytics significantly improve customer engagement, operational efficiency, and retail decision-making. The paper further discusses implementation challenges, ethical considerations, and future opportunities associated with AI-driven shopping ecosystems. The proposed analysis contributes to the understanding of how data-centric AI architectures can support intelligent retail platforms capable of delivering personalized, efficient, and scalable shopping experiences.

Keywords

Artificial Intelligence, Digital Retail, Data-Centric AI, Machine Learning

References

Ali A. Plant Village Dataset, version 1. Kaggle. 2019. Available from: https://www.kaggle.com/xabdallahali/plantvillage-dataset. Accessed on 22 February 2020.
Badar MP, Girish A. Detection and Classification System for Potato Leaf Diseases. 2016.
Barré P, Stöver BC, Müller KF, Steinhage V. LeafNet: A computer vision system for automatic plant species identification. Ecol Inform. 2017;40:50–56.
Biswas S, Jagyasi B, Singh BP, Lal M. Severity identification of potato late blight disease from crop images captured under uncontrolled environment. In: IEEE Canada International Humanitarian Technology Conference (IHTC); Montreal, QC. 2014.
Chen J, Liu Q, Gao LW. Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model. Symmetry. 2019;11:343.
Evgeniou T, Pontil M. Theory and applications of support vector machines. IFIP Adv Inf Commun Technol. 2001;2049:249–257.
Fuentes A, Yoon S, Kim SC, Park DS. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors. 2017;17:2022. PubMed: 28869539.
Hossain S, Mou RM, Hasan MM, Chakraborty S, Razzak MA. Recognition and detection of tea Leaf’s diseases using support vector machine. In: IEEE Publications; 2018. p. 150–154.
Howlader MR, Habiba U, Faisal RH, Rahman MM. Automatic recognition of guava leaf diseases using deep convolution neural network. In: International Conference on Electrical, Computer and Communication Engineering (ECCE); 2019. p. 1–5.
Huang D, Shan C, Ardabilian M, Wang Y, Chen L. Local binary patterns and its application to facial image analysis: A survey. IEEE Trans Syst Man Cybern C. 2011;41:765–781.
Islam M, Dinh A, Wahid K, Bhowmik P. Detection of potato diseases using image segmentation and multiclass support vector machine. In: IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE); Windsor, ON. 2017. p. 1–4.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90.
Kumari CU, Jeevan Prasad SJ, Mounika G. Leaf disease detection: Feature extraction with K-means clustering and classification with ANN. In: 3rd International Conference on Computing Methodologies and Communication (ICCMC); Erode, India. 2019. p. 1095–1098.
Li G, Ma Z, Wang H. Image Recognition of Grape Downy Mildew and Grape Powdery Mildew Based on Support Vector Machine. IFIP Adv Inf Commun Technol. 2012;151–162.
Li Y, Wu H. A clustering method based on K-means algorithm. Phys Procedia. 2012;25:1104–1109.
Lindeberg T. Scale invariant feature transform. Scholarpedia. 2012;7.
Peng CYJ, Lee KL, Ingersoll GM. An introduction to logistic regression analysis and reporting. J Educ Res. 2002;96:3–14.
Peterson L. K-nearest neighbor. Scholarpedia. 2009;4:1883.
Pinki FT, Khatun N, Islam SMM. Content based paddy leaf disease recognition and remedy prediction using support vector machine. In: 20th International Conference of Computer and Information Technology (ICCIT); Dhaka. 2017. p. 1–5.
Prakash RM, Saraswathy GP, Ramalakshmi G, Mangaleswari KH, Kaviya T. Detection of leaf diseases and classification using digital image processing. In: International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS); 2017. p. 1–4.
Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP. Deep Learning for Image-Based Cassava Disease Detection. Front Plant Sci. 2017;8:1852. PubMed: 29163582.
Schieber MM, Zuck A, Braiman M, Melekhov L, Nissenbaum J, Turchetta RAD, et al. Large-area HgI2 pixel detector experiments. SPIE Proc. 1998.
Shani G, Erlich O, Tsror L, Oppenheim D. Deep learning for image-based potato tuber disease detection. Phytopathology. 2018;109.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Comput Biol Learn Soc. 2015. p. 1–14.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. CVPR. 2016.
Waghmare H, Kokare R, Dandawate Y. Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In: 3rd International Conference on Signal Processing and Integrated Networks (SPIN); 2016. p. 513–518.

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