Recent developments in the field of high efficiency perovskite solar cells are based on stabilization crystal structure FAPbI3 while preserving its excellent optoelectronic properties. Compositional engineering of, for example, MA or Br mixed into results desired effects, but detailed knowledge local structural features, such as (dis)order cation interactions formamidinium (FA) and methylammoni...

The aim of detecting outliers in a multivariate sample can be pursued in di erent ways We investigate here the performance of several simultaneous multivariate outlier identi cation rules based on robust estimators of location and scale It has been shown that the use of estimators with high nite sample breakdown point in such procedures yields a good behaviour with respect to the prevention of ...

In this paper, we apply the fuzzy linear regression (FLR) with fuzzy intervals analysis into a neural network classi®cation model. The FLR works as a data handler and separates the data sample into two groups. By training two independent neural works with these two groups, we can better describe the distribution space of the corresponding data sample with two different functions, rather than us...

If a random set (binary image) is composed of randomly sized, disjoint translates arising as homothetics of a ®nite number of compact primitives and a granulometry is generated by a convex, compact set, then the granulometric moments of the random set can be expressed in terms of model parameters. This paper shows that, under mild conditions , any ®nite vector of granulometric moments possesses...

This paper presents an empirical investigation of the recently proposed k-Nearest Centroid Neighbours (k-NCN) classi®cation rule along with two heuristic modi®cations of it. These alternatives make use of both proximity and geometrical distribution of the prototypes in the training set in order to estimate the class label of a given sample. The experimental results show that both alternatives g...

Iman-Davenport's Statistic (FF ) is higher than the corresponding value of the F-distribution when statistical di erences are found. Holm's Method: The classi cation models above the double line performed signi cantly worse than the most signi cant model (marked in bold at the bottom) with a 95% signi cance level. Wilcoxon's Test: The symbol • indicates that the classi cation model in the row s...

Naive Bayes classi ers are a well-known and powerful type of classi ers that can easily be induced from a dataset of sample cases. However, the strong conditional independence and distribution assumptions underlying them can sometimes lead to poor classi cation performance. Another prominent type of classi ers are neuro-fuzzy classi cation systems, which derive (fuzzy) classi ers from data usin...

The explicit use of partial di erential equations (PDE's) in image processing became a major topic of study in the last years. In this work we present an algorithm for histogram modi cation via PDE's. We show that the histogram can be modi ed to achieve any given distribution. The modi cation can be performed while simultaneously reducing noise. This avoids the noise sharpening e ect in classic...

An adaptive on-line learning method is presented to faciliate pattern classi cation using active sampling to identify optimal decision boundary for a stochastic oracle with minimum number of training samples. The strategy of sampling at the current estimate of the decision boundary is shown to be optimal in the sense that the probability of convergence toward the true decision boundary at each ...