The Future of Automation: Integrating AI and Quality Assurance for Unparalleled Performance

Venu Madhav Aragani

Vol 10, Issue 2 (Special Issue) 2024

Page Number: 19 - 27

Abstract:

Probabilistic knowledge is fundamental for present-day innovation and executives. At the point when a supervisor or specialist gives related difficulties impartial measurable information, persuading readers is simpler. The appraisal of the actual status is upheld by measurable proof, and circumstances and logical results can be laid out. The reasoning is supported by deductive reasoning, statistical data verification, and induction. Practitioners of quality should develop statistical thinking skills to fully comprehend the three quality concepts of "psychology," "essence of substance," and "process of business." Conventional quality data are gathered through data collection, data processing, statistical analysis, root cause analysis, and other methods. They contain factors, credits, absconds, interior and outside disappointment costs, etc. Good practitioners used to rely on these supposed professional qualities to get a job. If quality practitioners keep up with current practices, collecting, organising, analysing, and monitoring quality data will be easier. Precision tool machines are being integrated into many IoTs to collect data on machine operation, component diagnostics and life estimation, consumables and usage monitoring, and other data analysis. Data science, which is gradually combining data mining and forecasting, is the future of high-quality fields.

References

• Adusumalli, H. P. (2016). How Big Data is Driving Digital Transformation? ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616
• Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Investigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58
• Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Research, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618
• Adusumalli, H. P. (2018). Digitization in Agriculture: A Timely Challenge for Ecological Perspectives. Asia Pacific Journal of Energy and Environment, 5(2), 97-102. https://doi.org/10.18034/apjee.v5i2.619
• Adusumalli, H. P. (2019). Expansion of Machine Learning Employment in Engineering Learning: A Review of Selected Literature. International Journal of Reciprocal Symmetry and Physical Sciences, 6, 15–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/65
• Adusumalli, H. P., & Pasupuleti, M. B. (2017). Applications and Practices of Big Data for Development. Asian Business Review, 7(3), 111-116. https://doi.org/10.18034/abr.v7i3.597
• Ahmed, A.A.A. (2021). Event Ticketing Accounting Information System using RFID within the COVID-19 Fitness Etiquettes. Academia Letters, Article 1379. https://doi.org/10.20935/AL1379
• Azam, M. A., Mittelmann, H. D., & Ragi, S. (2021). UAV Formation Shape Control via Decentralized Markov Decision Processes. Algorithms, 14(3), 91. https://doi.org/10.3390/a14030091
• Fadziso, T., Adusumalli, H. P., & Pasupuleti, M. B. (2018). Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews. Asian Journal of Applied Science and Engineering, 7, 85–92. Retrieved from https://upright.pub/index.php/ajase/article/view/63 Chisty and Adusumalli: Applications of Artificial Intelligence in Quality Assurance and Assurance of Productivity (23-32) Page 32 Volume 11, No 1/2022 | ABCJAR
• Hossen, M. A., Diwakar, P. K. & Ragi, S. (2021). Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS. Scientific Reports, 11, 12693. https://doi.org/10.1038/s41598-021-90624-6
• Hossen, M. A., Zahir, E., Ata-E-Rabbi, H. M., Azam, M. A., and Rahman, M. H. (2021). Developing a Mobile Automated Medical Assistant for Hospitals in Bangladesh. 2021 IEEE World AI IoT Congress (AIIoT), 0366-0372, https://doi.org/10.1109/AIIoT52608.2021.9454236
• Kuan, S. P. and Perng, H. L. (2019). Knowledge should be owned by quality practitioners in the IT age. J Traffic Transportation Engg. 7.
• Madding, C., Ansari, A., Ballenger, C., Thota, A. (2020). Topic Modeling to Understand Technology Talent. SMU Data Science Review, 3(2), 1-18.
• Pasupuleti, M. B. (2016a). Data Scientist Careers: Applied Orientation for the Beginners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617
• Pasupuleti, M. B. (2016b). The Use of Big Data Analytics in Medical Applications. Malaysian Journal of Medical and Biological Research, 3(2), 111-116. https://doi.org/10.18034/mjmbr.v3i2.615
• Pasupuleti, M. B. (2017). AMI Data for Decision Makers and the Use of Data Analytics Approach. Asia Pacific Journal of Energy and Environment, 4(2), 65-70. https://doi.org/10.18034/apjee.v4i2.623
• Pasupuleti, M. B. (2020). Artificial Intelligence and Traditional Machine Learning to Deep Neural Networks: A Study for Social Implications. Asian Journal of Humanity, Art and Literature, 7(2), 137-146. https://doi.org/10.18034/ajhal.v7i2.622
• Pasupuleti, M. B., & Adusumalli, H. P. (2018). Digital Transformation of the High-Technology Manufacturing: An Overview of Main Blockades. American Journal of Trade and Policy, 5(3), 139-142. https://doi.org/10.18034/ajtp.v5i3.599
• Pasupuleti, M. B., & Amin, R. (2018). Word Embedding with ConvNet-Bi Directional LSTM Techniques: A Review of Related Literature. International Journal of Reciprocal Symmetry and Physical Sciences, 5, 9–13. Retrieved from https://upright.pub/index.php/ijrsps/article/view/64
• Pasupuleti, M. B., Miah, M. S., & Adusumalli, H. P. (2019). IoT for Future Technology Augmentation: A Radical Approach. Engineering International, 7(2), 105-116. https://doi.org/10.18034/ei.v7i2.601
• Ragi, S., Rahman, M. H., Duckworth, J., Kalimuthu, J., Chundi P. and Gadhamshetty, V. (2021). Artificial Intelligence-driven Image Analysis of Bacterial Cells and Biofilms. ACM Transactions on Computational Biology and Bioinformatics, https://doi.org/10.1109/TCBB.2021.3138304
• Rahman, M. M., Pasupuleti, M. B., & Adusumalli, H. P. (2019). Advanced Metering Infrastructure Data: Overviews for the Big Data Framework. ABC Research Alert, 7(3), 159-168. https://doi.org/10.18034/abcra.v7i3.602
• Yannan, D., Ahmed, A. A. A., Kuo, T., Malik, H. A., Nassani, A. A., Haffar, M., Suksatan, W., & Iramofu, D. P. F. (2021). Impact of CSR, innovation, and green investment on sales growth: new evidence from manufacturing industries of China and Saudi Arabia, Economic Research-Ekonomska Istraživanja, https://doi.org/10.1080/1331677X.2021.2015610

Back Download