РП9 – Етап 2 – AQI пилотна система за контрол

Планирани дейности през етап 2

Постигнати резултати през етап 2

• Реализирана е стабилна архитектура на AQI пилотна система, осигуряваща надеждно взаимодействие между отделните ѝ елементи.
• Създадена е стабилна и надеждно функционираща AQI пилотна система със съответно софтуерно приложение за планиране на динамични маршрути за придвижване на пациенти с различни здравословни проблеми с цел избягване на замърсени райони, представляващи риск за тяхното здраве.
• Разработени са множество успешно функциониращи AI модели с машинно обучение за използване от създадената по проекта платформа EMULSION при предоставяне на uHealth услуги на потребители в IoT областта „умно здравеопазване“.
• Постигната е успешна работа на разработената AQI пилотна система при използването ѝ в различни сценарии и случаи на употреба.
• Постигнатите резултати по този работен пакет са представени в следните научни публикации:
• В списания с импакт фактор / импакт ранг – 21 бр.

  [9.1*]	L. Pendov, Z. Ji, I. Ganchev. 2024. “Healthy Route Generation and Recommendation”. WSEAS Transactions on Information Science and Applications, Vol. 21, December, Pp. 558-567. ISSN: 1790-0832. eISSN: 2224-3402. DOI: 10.37394/23209.2024.21.52. Scopus, SJR2023=0.126 / Q4 https://wseas.com/journals/isa/2024/b045109-033(2024).pdf
  [9.2*]	Z. Ji, Z. Yu, C. Liu, Z. Wang, S. Hao, I. Ganchev. 2024. “AFCF-Net: a novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation”. PLoS One, 19(11): e0314000, Pp. 1-26. November. (e)ISSN: 1932-6203. DOI: 10.1371/journal.pone.0314000. WoS, IF2023=2.9 / Q1; Scopus, SJR2023=0.839 / Q1 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0314000
  [9.3*]	S. Hao, X. Li, W. Peng, Z. Fan, Z. Ji, I. Ganchev. 2024. “YOLO-CXR: A novel detection network for locating multiple small lesions in chest X-ray images”. IEEE Access, Vol. 12, October, Pp. 156003-156019. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2024.3482102. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10720017
  [9.4*]	Z. Ji, X. Wang, C. Liu, Z. Wang, N. Yuan, I. Ganchev. 2024. “EFAM-Net: A Multi-Class Skin Lesion Classification Model Utilizing Enhanced Feature Fusion and Attention Mechanisms”. IEEE Access, Vol. 12, September, Pp. 143029-143041. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2024.3468612. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10695064
  [9.5*]	S. Hao, Z. Yu, B. Zhang, C. Dai, Z. Fan, Z. Ji, I. Ganchev. 2024. “MEFP-Net: A Dual-Encoding Multi-Scale Edge Feature Perception Network for Skin Lesion Segmentation”. IEEE Access, Vol. 12, September, Pp. 140039-140052. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2024.3467678. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10693416
  [9.6*]	Z. Ji, X. Li, Z. Wang, H. Zhang, N. Yuan, X. Zhang, I. Ganchev. 2024. “CafeNet: A Novel Multi-scale Context Aggregation and Multi-level Foreground Enhancement Network for Polyp Segmentation”. International Journal of Imaging Systems and Technology, Wiley. 34:e23183, September, Pp. 1-15. ISSN: 0899-9457. eISSN: 1098-1098. DOI: 10.1002/ima.23183. WoS, IF2023=3.0 / Q2; Scopus, SJR2023=0.706 / Q2 https://onlinelibrary.wiley.com/doi/10.1002/ima.23183
  [9.7*]	J. Liu, J. Mu, H. Sun, C. Dai, Z. Ji, I. Ganchev. 2024. “DLGRAFE-Net: A Double Loss Guided Residual Attention and Feature Enhancement Network for Polyp Segmentation”. PLoS One, 19(9): e0308237, Pp. 1-18. September. (e)ISSN: 1932-6203. DOI: 10.1371/journal.pone.0308237. WoS, IF2023=2.9 / Q1; Scopus, SJR2023=0.839 / Q1 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308237
  [9.8*]	S. Hao, Y. Jia, J. Liu, Z. Wang, C. Liu, Z. Ji, I. Ganchev. 2024. “ST-Double-Net: A Two-Stage Breast Tumor Classification Model Based on Swin Transformer and Weakly Supervised Target Localization”. IEEE Access, Vol. 12, September, Pp. 117921-117933. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2024.3445954. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10639378
  [9.9*]	L. Shi, W. Zhou, Y. Wu, N. Yuan, X. Zang, Z. Ji, I. Ganchev. 2024. “DCM-CNER: A Dual-Channel Model for Clinical Named Entity Recognition Based on Embedded ConvNet and Gated Dilated CNN”. IEEE Access, Vol. 12, July, Pp. 97726-97738. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2024.3422677. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10583858
  [9.10*]	J. Liu, J. Mu, H. Sun, C. Dai, Z. Ji, I. Ganchev. 2024. “BFG&MSF-Net: Boundary Feature Guidance and Multi-Scale Fusion Network for Thyroid Nodule Segmentation”. IEEE Access, Vol. 12, May, Pp. 78701-78713. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2024.3407795. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10543017
  [9.11*]	Z. Ji, J. Liu, J. Mu, H. Zhang, C. Dai, N. Yuan, I. Ganchev. 2024. “ResDAC-Net: A Novel Pancreas Segmentation Model Utilizing Residual Double Asymmetric Spatial Kernels”. Medical & Biological Engineering & Computing, Springer. March, Pp. x1-x14. ISSN: 0140-0118. eISSN: 1741-0444. DOI: 10.1007/s11517-024-03052-9. WoS, IF2023=2.6 / Q2; Scopus, SJR2023=0.641 / Q2 https://link.springer.com/article/10.1007/s11517-024-03052-9
  [9.12*]	Z. Ji, H. Sun, N. Yuan, H. Zhang, J. Sheng, X. Zhang, I. Ganchev. 2024. “BGRD-TransUNet: A Novel TransUNet-based Model for Ultrasound Breast Lesion Segmentation”. IEEE Access, Vol. 12, February, Pp. 31182-31196. (e)ISSN: 2169-3536. DOI: 0.1109/ACCESS.2024.3368170. WoS, IF2023=3.4 / Q2; Scopus, SJR2023=0.960 / Q1 https://ieeexplore.ieee.org/document/10442999
  [9.13*]	Z. Ji, J. Mu, J. Liu, H. Zhang, C. Dai, X. Zhang, I. Ganchev. 2024. “ASD-Net: A novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation”. Medical & Biological Engineering & Computing, Springer. February, Pp. x1-x15. ISSN: 0140-0118. eISSN: 1741-0444. DOI: 10.1007/s11517-024-03025-y. WoS, IF2023=2.6 / Q2; Scopus, SJR2023=0.641 / Q2 https://link.springer.com/article/10.1007/s11517-024-03025-y
  [9.14*]	S. Hao, L. Zhang, Y. Jiang, J. Wang, Z. Ji, L. Zhao, I. Ganchev. 2023. “ConvNeXt-ST-AFF: A Novel Skin Disease Classification Model Based on Fusion of ConvNeXt and Swin Transformer”. IEEE Access, Vol. 11, October, Pp. 117460-117473. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2023.3324042. WoS, IF=3.4 / Q2; Scopus, SJR=0.960 / Q1 https://ieeexplore.ieee.org/document/10283846
  [9.15*]	S. Hao, H. Wu, Y. Jiang, Z. Ji, L. Zhao, L. Liu, I. Ganchev. 2023. “GSCEU-Net: An End-to-End Lightweight Skin Lesion Segmentation Model with Feature Fusion Based on U-Net Enhancements”. Information, Vol. 14, No. 9: 486, September, Pp. 1-18. (e)ISSN: 2078-2489. DOI: 10.3390/info14090486. WoS, IF=2.4 / Q3; Scopus, SJR=0.703 / Q2 https://www.mdpi.com/2078-2489/14/9/486
  [9.16*]	Z. Ji, Z. Zhao, X. Zeng, J. Wang, L. Zhao, X. Zhang, I. Ganchev. 2023. “ResDSda_U-Net: A novel U-Net based residual network for segmentation of pulmonary nodules in lung CT images”. IEEE Access, Vol. 11, August, Pp. 87775-87789. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2023.3305270. WoS, IF=3.4 / Q2; Scopus, SJR=0.960 / Q1 https://ieeexplore.ieee.org/document/10216982
  [9.17*]	S. Hao, H. Wu, C. Du, X. Zeng, Z. Ji, X. Zhang, I. Ganchev. 2023. “CACDU-Net: A novel DoubleU-Net based semantic segmentation model for skin lesions detection in images”. IEEE Access, Vol. 11, August, Pp. 82449-82463. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2023.3300895. WoS, IF=3.4 / Q2; Scopus, SJR=0.960 / Q1 https://ieeexplore.ieee.org/document/10198429
  [9.18*]	Z. Ji, Y. Wu, X. Zeng, Y. An, L. Zhao, Z. Wang, I. Ganchev. 2023. “Lung Nodule Detection in Medical Images Based on Improved YOLOv5s”. IEEE Access, Vol. 11, July, Pp. 76371-76387. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2023.3296530. WoS, IF=3.4 / Q2; Scopus, SJR=0.960 / Q1 https://ieeexplore.ieee.org/document/10185440
  [9.19*]	Z. Ji, D. Yao, R. Chen, T. Lyu, Q. Liao, L. Zhao, I. Ganchev. 2023. “U-Net_dc: A novel U-Net-based model for endometrial cancer cell image segmentation”. Information, Vol. 14, No. 7: 366, June, Pp. 1-19. (e)ISSN: 2078-2489. DOI: 10.3390/info14070366. WoS, IF=2.4 / Q3; Scopus, SJR=0.703 / Q2 https://www.mdpi.com/2078-2489/14/7/366
  [9.20*]	Z. Ji, J. Zhao, J. Liu, X. Zeng, H. Zhang, X. Zhang, I. Ganchev. 2023. “ELCT-YOLO: An Efficient One-Stage Model for Automatic Lung Tumor Detection based on CT Images”. Mathematics, Vol. 11, No. 10: 2344, May, Pp. 1-22. (e)ISSN: 2227-7390. DOI: 10.3390/math11102344. WoS, IF=2.3 / Q1 (top 5%); Scopus, SJR=0.475 / Q2 https://www.mdpi.com/2227-7390/11/10/2344
  [9.21*]	S. Hao, H. Xu, H. Ji, Z. Wang, L. Zhao, Z. Ji, I. Ganchev. 2023. “G2-ResNeXt: A Novel Model for ECG Signal Classification”. IEEE Access, Vol. 11, April, Pp. 34808-34820. (e)ISSN: 2169-3536. DOI: 10.1109/ACCESS.2023.3265305. WoS, IF=3.4 / Q2; Scopus, SJR=0.960 / Q1 https://ieeexplore.ieee.org/document/10093803

• В трудове на конференции, реферирани в WoS/Scopus – 4 бр.

  [9.22*]	L. Pendov and I. Ganchev. 2024. “From raw data to 'best' travel route computation”. Proc. of the 2024 International Conference on Applied Mathematics and Computer Simulation (AMCS'24), Pp. 1-5, 1-3 December, Chamonix, France. (приета и изпратена за печат; ще се реферира в Scopus)
  [9.23*]	Z. Ji, S. Hao, J. Pang, I. Ganchev. 2024. “Novel SSD-Based Models for Detection of Multidimensional Pulmonary Nodules in CT Images”. Proc. of the 7th International Conference in Signal Processing and Information Security (ICSPIS 2024), Pp. 1-6, 12-14 November, Dubai, UAE. eISSN: 2831-3844. eISBN: 979-8-3503-6867-3. DOI: 10.1109/ICSPIS63676.2024.10812640. (ще се реферира в Scopus) https://ieeexplore.ieee.org/abstract/document/10812640
  [9.24*]	Z. Ji and I. Ganchev. 2024. “Impact of Channel Numbers and Training Epochs on U-Net’s Polyp Segmentation Performance”. Proc. of the 7th International Conference in Signal Processing and Information Security (ICSPIS 2024), Pp. 1-4, 12-14 November, Dubai, UAE. eISSN: 2831-3844. eISBN: 979-8-3503-6867-3. DOI: 10.1109/ICSPIS63676.2024.10812647. (ще се реферира в Scopus) https://ieeexplore.ieee.org/abstract/document/10812647
  [9.25*]	I. Ganchev and Z. Ji. 2024. “IoT system for AQI monitoring and control”. Proc. of the 2024 IEEE International Conference "Automatics and Informatics" (ICAI'24), pp. x1-x6. 10-12 October. Varna, Bulgaria. (приета и изпратена за печат; ще се реферира в Scopus) http://www.tu-varna.bg/icai/images/programa/icai24_program_20241010.pdf