The Future of Web Development: Exploring JavaScript's Role in Web3 and Decentralized Apps
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As the digital landscape evolves, the rise of Web3 and decentralized applications (dApps) is reshaping the future of web development. At the center of this transformation is JavaScript, a programming language that has remained a cornerstone of web development for decades. In the context of Web3, JavaScript continues to play a critical role by enabling seamless interaction with blockchain networks and smart contracts. This article explores the importance of JavaScript in the development of decentralized applications, its integration with blockchain technologies, and its continued adaptability in the ever-growing Web3 ecosystem. From popular libraries like Web3.js and Ethers.js to emerging trends such as decentralized finance (DeFi) and NFTs, JavaScript proves to be a versatile tool in the decentralized web revolution. However, the shift to Web3 also presents challenges for JavaScript, including scalability, security, and performance concerns. This article provides a comprehensive overview of JavaScript’s role in the future of web development, offering insights into both its opportunities and limitations in the decentralized era.
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1. Priya, M. M., Makutam, V., Javid, S. M. A. M., & Safwan, M. AN OVERVIEW ON CLINICAL DATA MANAGEMENT AND ROLE OF PHARM. D IN CLINICAL DATA MANAGEMENT.
2. Pei, Y., Liu, Y., Ling, N., Ren, Y., & Liu, L. (2023, May). An end-to-end deep generative network for low bitrate image coding. In 2023 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IRRELEVANT.
3. Pei, Y., Liu, Y., & Ling, N. (2023, December). MobileViT-GAN: A Generative Model for Low Bitrate Image Coding. In 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP) (pp. 1-5). IEEE.
4. Pei, Y., Liu, Y., & Ling, N. (2020, October). Deep learning for block-level compressive video sensing. In 2020 IEEE international symposium on circuits and systems (ISCAS) (pp. 1-5). IEEE.
5. Zhizhong Wu, Xueshe Wang, Shuaishuai Huang, Haowei Yang, Danqing Ma, Research on Prediction Recommendation System Based on Improved Markov Model. Advances in Computer, Signals and Systems (2024) Vol. 8: 87-97. DOI: http://dx.doi.org/10.23977/acss.2024.080510.
6. Ma, D., Wang, M., Xiang, A., Qi, Z., & Yang, Q. (2024). Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment. arXiv preprint arXiv:2404.12634.
7. Yang, H., Wang, L., Zhang, J., Cheng, Y., & Xiang, A. (2024). Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology. arXiv preprint arXiv:2406.09773.
8. Wang, L., Cheng, Y., Xiang, A., Zhang, J., & Yang, H. (2024). Application of Natural Language Processing in Financial Risk Detection. arXiv preprint arXiv:2406.09765.
9. Dave, A., & Dave, K. Dashcam-Eye: Federated Learning Based Smart Dashcam Based System for Automotives. J Artif Intell Mach Learn & Data Sci 2024, 2(1), 942-945.
10. Hossen, M. M., Ashraf, A., Hasan, M., Majid, M. E., Nashbat, M., Kashem, S. B. A., ... & Chowdhury, M. E. (2024). GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management. Waste Management, 174, 439-450.
11. Hossen, M. M., Majid, M. E., Kashem, S. B. A., Khandakar, A., Nashbat, M., Ashraf, A., ... & Chowdhury, M. E. (2024). A reliable and robust deep learning model for effective recyclable waste classification. IEEE Access.
12. Saha, P., Kunju, A. K. A., Majid, M. E., Kashem, S. B. A., Nashbat, M., Ashraf, A., ... & Chowdhury, M. E. (2024). Novel multimodal emotion detection method using Electroencephalogram and Electrocardiogram signals. Biomedical Signal Processing and Control, 92, 106002.
13. Chowdhury, A. T., Newaz, M., Saha, P., Majid, M. E., Mushtak, A., & Kabir, M. A. (2024). Application of Big Data in Infectious Disease Surveillance: Contemporary Challenges and Solutions. In Surveillance, Prevention, and Control of Infectious Diseases: An AI Perspective (pp. 51-71). Cham: Springer Nature Switzerland.
14. Chowdhury, A. T., Newaz, M., Saha, P., Majid, M. E., Mushtak, A., & Kabir, M. A. (2024). Application of Big Data in Infectious Disease Surveillance: Contemporary Challenges and Solutions. In Surveillance, Prevention, and Control of Infectious Diseases: An AI Perspective (pp. 51-71). Cham: Springer Nature Switzerland.
15. Majid, M. E., Marinova, D., Hossain, A., Chowdhury, M. E., & Rummani, F. (2024). Use of Conventional Business Intelligence (BI) Systems as the Future of Big Data Analysis. American Journal of Information Systems, 9(1), 1-10.
16. Abul, S. B., Forces, Q. A., Muhammad, E. H., Tabassum, M., Muscat, O., Molla, M. E., ... & Khandakar, A. A Comprehensive Study on Biomass Power Plant and Comparison Between Sugarcane and Palm Oil Waste.
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