Published December 18, 2025 | Version v1

DASTURIY TA'MINOT ORQALI INTELLEKTUAL TIZIMNI EKSPERMENTAL TADQIQ QILISH

  • 1. Raqamli texnologiyalar va sun'iy intellekt kafedrasi professori, "Toshkent irrigatsiya va qishloq xo'jaligini mexanizatsiyalash muhandislari instituti" Milliy tadqiqot universiteti
  • 2. O'zbekiston Respublikasi Oliy ta'lim, fan va innovatsiyalar vazirligi huzuridagi Oliy attestatsiya komissiyasi bosh inspektori
  • 3. Raqamli texnologiyalar va sun'iy intellekt kafedrasi izlanuvchisi, "Toshkent irrigatsiya va qishloq xo'jaligini mexanizatsiyalash muhandislari instituti" Milliy tadqiqot universiteti
  • 4. Chirchiq davlat pedagogika universiteti, Toshkent viloyati, O'zbekiston

Description

Raqamli tasvirlash sohasidagi tasvir sifatini oshirish hamda tasvirni tiklash masalalarida shovqin darajasini aniq baholash tasvirni qayta ishlashning ko'plab vazifalarini hal etishga imkon beradi. Biroq, raqamli tasvirlarda shovqin darajasini aniqlash o'ta murakkab va hali hamon aniq yechimi ishlab chiqilmagan masala hisoblanadi. Mazkur tadqiqot ishida tasvirlarning barcha turida uchraydigan Gauss shovqini tadqiq qilingan bo'lib, bunda ushbu shovqin darajasining parametrini aniqlash vazifa sifatida qo'yilgan. Shuningdek, tasvir sifatini etalonsiz baholash ko'rsatkichlari orasida mashhur bo'lgan BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) ko'rsatkichidan foydalangan holda, shovqin parametrining qaysi oraliqqa tegishli ekanligini aniqlash mexanizmi taklif qilingan. Taklif etilgan mexanizm yangi kiruvchi tasvirning BRISQUE qiymati asosida tasvirdagi shovqin darajasini tahminan aniqlash imkonini beradi.

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References

  • Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Yang, X. (2021). Deep knowledge tracing with knowledge graph embedding for intelligent tutoring systems. Knowledge-Based Systems, 227, 107224.
  • Cranefield, S., Winikoff, M., Dignum, V., & Dignum, F. (2023). Responsible AI by design in practice. AI and Ethics, 3(1), 183-199.
  • Hagendorff, T., & Meding, K. (2021). The big picture on AI ethics. AI and Ethics, 1(1), 65-75.
  • Kautz, H. (2022). The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture. AI Magazine, 43(1), 105-126.
  • Liu, Y., Zhang, H., Wu, Q., & Chen, L. (2023). Knowledge graph representation learning: A comparative analysis of techniques and applications. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3412-3426.
  • Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501-507.
  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
  • Tran, S. N., & Le, H. T. (2021). Fractal representations for knowledge organization in neural-symbolic systems. Neural Computing and Applications, 33(16), 10201-10215.
  • Tuomi, I. (1999). Data is more than knowledge: Implications of the reversed knowledge hierarchy for knowledge management and organizational memory. Journal of Management Information Systems, 16(3), 103-117.
  • Walker, L. O., & Avant, K. C. (2011). Strategies for theory construction in nursing (5th ed.). Prentice Hall.
  • Wang, Y. (2015). Formal cognitive models of data, information, knowledge, and intelligence. WSEAS Transactions on Computers, 14, 770-781.
  • Zins, C. (2007). Conceptual approaches for defining data, information, and knowledge. Journal of the American Society for Information Science and Technology, 58(4), 479-493.
  • Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
  • Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3-9.
  • Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136.
  • Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Press.
  • Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
  • Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163-180.
  • Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Prentice Hall.
  • Baral, C., Bolander, T., van Ditmarsch, H., & McIlraith, S. (2022). A review of logical foundations of formal argumentation. Artificial Intelligence, 302, 103597.
  • Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Yang, X. (2021). Deep knowledge tracing with knowledge graph embedding for intelligent tutoring systems. Knowledge-Based Systems, 227, 107224.
  • Cranefield, S., Winikoff, M., Dignum, V., & Dignum, F. (2023). Responsible AI by design in practice. AI and Ethics, 3(1), 183-199.
  • Danks, D., & London, A. J. (2022). Algorithmic bias in autonomous systems. Proceedings of the 36th AAAI Conference on Artificial Intelligence, 4691-4697.
  • d'Avila Garcez, A. S., Gori, M., Lamb, L. C., Serafini, L., Spranger, M., & Tran, S. N. (2019). Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. Journal of Applied Logics, 6(4), 611-632.