Page 173 - Emerging Trends and Innovations in Web-Based Applications and Technologies
P. 173
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
VII. CONCLUSION [5] McCleery, J., et al. "The National Institute on Aging and
The proposed solution leverages an innovative machine- Alzheimer’s Association Research Framework: A
learning system to automate product classification within the Commentary from the Cochrane Dementia and
Quick Mart second-hand marketplace. With an impressive Cognitive Improvement Group." Alzheimer's &
accuracy rate of 92.14%, the system efficiently categorizes Dementia, vol. 15, no. 1, pp. 179–181, 2019.
products into three key classifications: normal, refurbished, [6] Kimura, N., et al. "Caregivers’ Perspectives of Quality
or faulty. This automation significantly reduces transaction of Life of People with Young- and Late-Onset
errors and enhances the overall user experience, providing a Alzheimer Disease." Journal of Geriatric Psychiatry
smoother process for buyers and sellers alike. By utilizing a and Neurology, vol. 31, pp. 76–83, 2018.
diverse dataset of over 10,000 product images, the system
ensures accurate identification regardless of a product's [7] Johnson, N. A., et al. "Pattern of Cerebral
condition or origin. Hypoperfusion in Alzheimer Disease and Mild
Cognitive Impairment Measured with Arterial Spin-
Advanced image processing techniques play a crucial role in
Labeling MR Imaging: Initial Experience." Radiology,
enhancing the system’s adaptability and performance
vol. 234, no. 3, pp. 851–859, 2005.
throughout both the training and testing phases. These
techniques allow the model to better understand and [8] Cedazo-Minguez, A., and B. Winblad. "Biomarkers for
categorize products in various conditions, ensuring reliable Alzheimer’s Disease and Other Forms of Dementia:
results even in complex marketplace scenarios. The system’s Clinical Needs, Limitations, and Future Aspects."
ability to classify accurately across different product types Experimental Gerontology, vol. 45, no. 1, pp. 5–14,
underscores its effectiveness in reducing errors and 2010.
improving the user experience, ultimately driving customer [9]
satisfaction and fostering increased transactions. Nanni, L., et al. "A Comparison of Methods for
Extracting Information from the Co-occurrence Matrix
Looking ahead, future improvements to the system will focus for Subcellular Classification." Expert Systems with
on incorporating larger and more authentic datasets, as well Applications, vol. 40, no. 18, pp. 7457–7467, 2013.
as employing contrast enhancement methods and developing [10]
sophisticated feature selection algorithms. These Barker, J., et al. "Automated Classification of Brain
Tumor Type in Whole-Slide Digital Pathology Images
advancements will further refine the model, allowing it to
Using Local Representative Tiles." Medical Image
generalize even more effectively across diverse product
Analysis, vol. 30, pp. 60–71, 2015.
appearances and incomplete information. By revolutionizing
product classification in the second-hand commerce sector, [11] Liu, T., et al. "A Hybrid Machine Learning Approach to
this system positions Quick Mart for continued success and Cerebral Stroke Prediction Based on Imbalanced
growth within the industry. Medical Dataset." Artificial Intelligence in Medicine,
VIII. FUTURE SCOPE vol. 101, pp. 101723, 2019.
While the proposed model has yielded significant [12] Fang, G., et al. "A Machine Learning Approach to Select
improvements in streamlining the second-hand marketplace, Features Important to Stroke Prognosis."
there remains ample potential for further development. Computational Biology and Chemistry, vol. 88, pp.
Future enhancements could involve integrating advanced 107316, 2020.
filtering techniques and exploring additional features in the [13]
platform’s algorithms, such as those used in Guerrero, R., et al. (2018). "White Matter
Hyperintensity and Stroke Lesion Segmentation and
recommendation systems, price prediction, and image
Differentiation Using Convolutional Neural
recognition. The goal is to refine the user experience,
Networks." NeuroImage: Clinical, 17: 918-934.
ensuring smarter, more efficient transactions. Additionally,
the incorporation of machine learning models for fraud [14] Diniz, P.H.B., et al. (2018). "Detection of White Matter
detection, automated negotiations, and real-time supply- Lesion Regions in MRI Using SLICO and Convolutional
demand analytics could be explored to elevate the platform's Neural Network." Computer Methods and Programs in
capabilities. Biomedicine, 167: 49-63.
REFERENCES [15] Alatas, Bilal, et al. (2022). “Identification of Novel
[1] Kadam, Ankita, Sartaj Bhuvaji, and Sujit Deshpande. Noninvasive Diagnostics Biomarkers in Parkinson’s
"Brain Tumor Classification using Deep Learning Disease and Improving the Disease Classification
Algorithms." Using Support Vector Machine,” BioMed Research
[2] Chaganti, Sai Yeshwanth, et al. "Image Classification International.
using SVM and CNN." 2020 International Conference [16] Ali, L., et al. (2022). “A Novel Sample and Feature
on Computer Science, Engineering, and Applications Dependent Ensemble Approach for Parkinson’s
(ICCSEA). IEEE, 2020. Disease Detection.” Neural Comput & Applic.
[3] Sultan, Hossam H., Nancy M. Salem, and Walid [17] Smith, J., et al. (2024). “Leveraging AI and IoT for
AlAtabany. "Multi-classification of Brain Tumor Efficient Transactions in Second-Hand Marketplaces.”
Images Using Deep Neural Networks." IEEE Access 7 Journal of Smart Retail Technology, 15(3), 245-260.
(2019): 69215-69225.
[18] Lee, M., et al. (2023). “AI-Powered Price Optimization
[4] Blank, R. H. "End-of-Life Decision Making for in Online Second-Hand Marketplaces.” International
Alzheimer’s Disease Across Cultures." Springer Journal of E-Commerce Technology, 22(4), 305-318.
Singapore, 2019, pp. 121–136.
IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 163