LACUNARY STATISTICAL CONVERGENCE OF ORDER α IN PARTIAL METRIC SPACES

The present study introduces the notions of statistical convergence of order $\alpha$ and strongly $q-$ summability of order $\alpha$ in partial metric spaces. We examinethe inclusion relations linked to these these concepts

On Statistical Convergence Of Order α{\alpha} In Partial Metric Spaces

The present study introduces the notions of statistical convergence of order $\alpha$ and strong $p-$ Ces\`{a}ro summability of order $\alpha$ in partial metric spaces. Also, we examine the inclusion relations between these concepts. In addition, we introduce the notion of $\lambda -$% statistical convergence of order $\alpha$ in partial metric spaces while providing relations linked to these sequence spaces.Comment: 12 pag

ON THE SPACES OF λm-BOUNDED AND λm-ABSOLUTELY p-SUMMABLE SEQUENCES

In this paper, we give the notion of λm-boundedness and p-absolute convergenceof type λm and using these notions we define new sequence spaces. We examinesome topological and geometric properties of these spaces. We also establish someinclusion relations concerning these spaces and characterize some matrix classes

Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion

PURPOSELymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach.METHODSSixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists. Then, a feature selection algorithm was used to further reduce the high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric.RESULTSAmong 271 texture features, 150 features had excellent reproducibility, which were included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy ranges for predicting LVI were 0.777–0.894 and 76%–81.5%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0.482–0.754 and 54%–68.2%, respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively.CONCLUSIONML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Overall, the method was more successful in predicting LVI than PNI