Autors: Shindjalova R., Prodanova, K. S., Svestarov V.
Title: Modeling data for tilted implants in grafted with bio-oss maxillary sinuses using logistic regression
Keywords: dentology, statistics

References

  1. Shindjalova, R., Prodanova, K., Svestarov, V., 2014, Modeling data for tilted implants in grafted with bio-oss maxillary sinuses using logistic regression, AIP Conference Proceedings, Volume 1631 (1), pp. 58-62

Issue

, 2014, United States, DOI 10.1063/1.4902458

Цитирания (Citation/s):
1. Ismael, A.M. and Gomes, J.C., 2021. The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images. In Biomedical Computing for Breast Cancer Detection and Diagnosis (pp. 290-309). IGI Global. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
2. 1. Alay, N.,Al-Baity, H., Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits, 2020. Sensors 20(19), p. 5523. DOI: 10.3390/s20195523 SJR (2019)=0,65 (Q1) - 2020 - в издания, индексирани в Scopus или Web of Science
3. 2. Toğaçar, M., Ergen, B. and Cömert, Z., 2020. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), pp.23-39. https://doi.org/10.1016/j.mehy.2019.109503 SJR (2019)=0.44 (Q3) - 2020 - в издания, индексирани в Scopus или Web of Science
4. 3. Toğaçar, M., Ergen, B. and Cömert, Z., 2020. Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders. Medical hypotheses, 135, p.109503. https://doi.org/10.1016/j.mehy.2019.109503 SJR (2019)= 0.43 (Q3) - 2020 - в издания, индексирани в Scopus или Web of Science
5. 4. Başaran, E., Cömert, Z. and Çelik, Y., 2020. Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomedical Signal Processing and Control, 56, p.101734. https://doi.org/10.1016/j.bspc.2019.101734 SJR (2018)= 0.71 (Q2) - 2020 - в издания, индексирани в Scopus или Web of Science
6. 5. Togacar, M., Ergen, B. and Sertkaya, M.E., 2019. Subclass separation of white blood cell images using convolutional neural network models. Elektronika ir Elektrotechnika, 25(5), p.63-68. https://doi.org/10.1016/j.bbe.2019.11.004 SJR (2018)= 0.44 (Q3) - 2020 - в издания, индексирани в Scopus или Web of Science
7. 6. Toğaçar, M., Özkurt, K.B., Ergen, B. and Cömert, Z., 2019. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A: Statistical Mechanics and its Applications, p.123592. https://doi.org/10.1016/j.physa.2019.123592 SJR (2018)=0.70 (Q2) - 2020 - в издания, индексирани в Scopus или Web of Science
8. 7. Djongova E., Georgiev T., Dzhabalyan K., Andreeva R., Dimova-Gabrovska M., 2016. A clinical case of perforation of the maxillary sinus membrane during sinus lift surgery and a proposed methodology for the management of subsequent complications. Scripta Scientifica Medicinae Dentalis, [S.l.], 2(1), p. 70-75. doi:http://dx.doi.org/10.14748/ssmd.v1i1.1683 - 2016 - в издания, индексирани в Scopus или Web of Science
9. Cömert, Z., Akbulut, Y., Akpinar, M.H., Alçin, Ö.F., Budak, Ü., Aslan, M. and Şengür, A., 2020. Electrocardiogram beat classification using deep convolutional neural network techniques. Modelling and Analysis of Active Biopotential Signals in Healthcare, 1, pp.12-1. - 2020 - в издания, индексирани в Scopus или Web of Science
10. Schulte-Sasse, R., Budach, S., Hnisz, D. and Marsico, A., 2021. Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nature Machine Intelligence, 3(6), pp.513-526. - 2021 - в издания, индексирани в Scopus или Web of Science
11. Patel, D.K., Lopez-Benitez, M., Soni, B. and Garcia-Fernandez, A.F., 2020. Artificial neural network design for improved spectrum sensing in cognitive radio. Wireless Networks, 26(8), pp.6155-6174. SJR=0.42 (Q2) - 2020 - в издания, индексирани в Scopus или Web of Science
12. Canbalaban, E. and Efe, M.Ö., 2019, September. Facial expression classification using convolutional neural network and real time application. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 23-27). IEEE. - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
13. Augustauskas, R., Lipnickas, A. and Surgailis, T., 2021. Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network. Sensors, 21(11), p.3633. SJR(2020)=0.8 (Q1),1 - 2021 - в издания, индексирани в Scopus или Web of Science
14. Yavuzkiliç, S., Akhtar, Z., Sengür, A. and Siddique, K., 2021. DeepFake face video detection using hybrid deep residual networks and LSTM architecture. In AI and Deep Learning in Biometric Security (pp. 81-104). CRC Press. - 2021 - в издания, индексирани в Scopus или Web of Science
15. Ismael, A.M. and Gomes, J.C., 2022. The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images. In Research Anthology on Medical Informatics in Breast and Cervical Cancer (pp. 289-308). IGI Global. - 2022 - в издания, индексирани в Scopus или Web of Science
16. Thakur, P., Atway, J., Limbach, P.A. and Addepalli, B., 2022. RNA Cleavage Properties of Nucleobase-Specific RNase MC1 and Cusativin Are Determined by the Dinucleotide-Binding Interactions in the Enzyme-Active Site. International Journal of Molecular Sciences, 23(13), p.7021. ISSN: 1422-0067 ; SJR (2021)=0,92 (Q1) - 2022 - в издания, индексирани в Scopus или Web of Science
17. G. Gouarin, C. Zanato, S. Ibrahim, T. Brigaud, Fluorinated Peptide Approach for the Inhibition of Rotamase, Proceedings of 36th European Peptide Symposium, 2022, DOI: 10.17952/36EPS/36EPS.2022.295 - 2022 - в издания, индексирани в Scopus или Web of Science
18. Mishra S, et al.. Lung Cancer Detection (LCD) from Histopathological Images using Fine-Tuned Deep Neural Network, Ann Med Health Sci Res, .12, pp1-7, 2022. DOI: https://doi.org/10.54608.annalsmedical.2022.74 - 2022 - в издания, индексирани в Scopus или Web of Science
19. 1. Yanlin Liu, Shuihai Dou, Yanping Du and Zhaohua Wang, Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt, Electronics 2023, 12, 2475. https://doi.org/10.3390/electronics12112475 SJR (2022)=0.36 (Q2) - 2023 - в издания, индексирани в Scopus или Web of Science
20. Ismael, A.M. and Gomes, J.C., 2023. The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images. In Research Anthology on Medical Informatics in Breast and Cervical Cancer (pp. 289-308). IGI Global. (Im F= 0.17)9 - 2023 - в издания, индексирани в Scopus или Web of Science

Вид: публикация в международен форум, публикация в издание с импакт фактор