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A NEW TYPE OF THE ADDITIVE FUNCTIONAL EQUATIONS ON INTUITIONISTIC FUZZY NORMED SPACES
Arunkumar, Mohan,Bodaghi, Abasalt,Namachivayam, Thirumal,Sathya, Elumalai Korean Mathematical Society 2017 대한수학회논문집 Vol.32 No.4
In this paper, we introduce a new type of additive functional equations and establish the generalized Ulam-Hyers stability for it in intuitionistic fuzzy normed space by using direct and fixed point methods.
Akhilanand Chaurasia,Arunkumar Namachivayam,Revan Birke Koca-Ünsal,이재홍 대한치주과학회 2024 Journal of Periodontal & Implant Science Vol.54 No.1
Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/ PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%–75.9%) and no higher than 98.19 (95% CI, 97.8%–98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%–93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.