Advancing Digital Assets: Ethereum-Based NFT Marketplace with Integrated Blockchain Wallet Solutions
DOI:
https://doi.org/10.32628/IJSRCSEITKeywords:
NFT, Implementation, Blockchain, Neural networkAbstract
In an increasingly computerized universe, the secure administration & trading of crypto files and documents has become a critical concern. In this estimate seeks to label this issue beside creating decentralized implementation (App) that uses distributed larger technology and deep neural network models to enable secure and efficient digital asset management, with an emphasis on NFTs. The App's features include secure wallet network, NFT picture production, stamp out, a sale out, and account handling. The App's backend is built on the Goerli development network with reliability intelligent contracts, while IPFS and ReactJS/Ethers are utilized for scattered storage and client development, individually. Furthermore, the Open AI Api is used to create unique NFT picture depends on customer input. This design showcases the actual application of distributed larger technology and deep neural network models for creating Apps for assured and scattered crypto asset management. Universal, the project adds to the continuing study on blockchain-based solutions for secure digital asset management, while also emphasizing the power of distributed larger technology and deep neural network models to change the way we handle and trade crypto assets.
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Mohiuddin A, Abdun NM, Jiankun H. Outlier detec- tion. In The State of the Art in Intrusion Prevention and Detection, Al-Sakib Khan Pathan (ed). Chapter: 1, Publisher: CRC Press: New York, USA, 2014. DOI: 10.1201/b16390-3
J. Kumaraswamy, Anil K C, T R Veena, G. Purushotham, Sunil Kumar K, “Investigating the Mechanical Properties of Al 7075 Alloy for Automotive Applications: Synthesis and Analysis” in Scopus indexed EVERGREEN Journal with ISSN: 2189-0420, Vol. 10, Issue 03, pp.1286-1295, September 2023.
Bilge L, Balzarotti D, Robertson W, Kirda E, Kruegel C. Disclosure: detecting botnet command and control servers through large-scale NetFlow analysis. Proceed- ings of the 28th Annual Computer Security Applica- tions Conference. 2012, 129–138
Kumaraswamy, J., Anil, K.C., Canbay, C.A., N D Shiva Kumar. Electro-Whirling Stir Casting: a Novel Approach for Fabricating Al7075/SiC MMCs with Enhanced Thermal Characteristics. Silicon, 16 (1), 295-306. https://doi.org/10.1007/s12633-023-02678-y
Münz G, Li S, Carle G. Traffic anomaly detection using k-means clustering. In Proceedings of Performance, Reliability and Dependability Evaluation of Communi- cation Networks and Distributed Systems, 4 GI / ITG Workshop MMBnet. Hamburg, Germany. 2007
Kumaraswamy Jayappa, Kyathasandra Chikkanna Anil, Zulfiqar A. Khan, Enhancing wear resistance in Al-7075 composites through conventional mixing and casting techniques, Journal of Materials Research and Technology, Volume 27, 2023, pp. 7935-7945. https://doi.org/10.1016/j.jmrt.2023.11.171.
Hofstede R, Bartos V, Sperotto A, Pras A. Towards real-time intrusion detection for NetFlow and IPFIX. In: 9th International Conference on Network and Ser- vice Management, CNSM 2013, October 2013, Zürich, Switzerland. 2013, 14–18
Kumaraswamy, J., Anil, K. C., Veena, T. R., Reddy, M., & Sunil Kumar, K. (2023). Influence of particulates on microstructure, Mechanical and Fractured behaviour on Al-7075 alloy composite by FEA. Australian Journal of Mechanical Engineering, 1–15. https://doi.org/10.1080/14484846.2023.2276987
Lazarevic A, Ertoz L, Kumar V, Ozgur A, Srivastava J. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the Third SIAM International Conference on Data Mining. 2003
J. Kumaraswamy et al., "Thermal Analysis of Ni-Cu Alloy Nanocomposites Processed by Sand Mold Casting," Advances in Materials Science and Engineering, vol. 2022, Article ID 2530707, 11 pages, 2022. https://doi.org/10.1155/2022/2530707.
Gogoi P, Bhattacharyya DK, Borah B, Kalita JK. A survey of outlier detection methods in network anom- aly identification. The Computer Journal 2011; 54(4):570–588.
J. Kumaraswamy, K.C. Anil and V. Shetty, Development of Ni-Cu based alloy hybrid composites through induction furnace casting, Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2022.09.215
Chandola V, Banerjee A, Kumar V. Anomaly detec- tion: a survey. ACM Computing Surveys (CSUR) 2009; 41(3):15–58.
Anil Kyathasandra Chikkanna, Kuchangi Venkatappa Manjunath, Kumaraswamy Jayappa, Mahadeva Reddy, Akash Biradar, Effect of Chilling & B4C content on Machining Efficiency and Surface Quality in Wire-Cut Machining of Aluminum Matrix Chilled Composites, Mechanics of Advanced Composite Structures, Volume 11, Issue 2 Pages 341-350. https://doi.org/10.22075/macs.2024.31090.1528
Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX. 2000, 93–104.
Garšva E, Paulauskas N, Gražulevičius G, Gulbinovič L. Packet inter-arrival time distribution in academic computer network. Elektronika ir elektrotechnika. Elec- tronics and Electrical Engineering 2014; 20(3):87–90
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