Comparison of different T-norm operators in classification problems

Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting and weighting in the construction of fuzzy rule based classification systems. For calculating these merits the product has been usually used as a T-norm. In this paper different T-norms have been used for calculating the confidence and support measures. Therefore, the calculations in rule selection and rule weighting steps (in the process of constructing the fuzzy rule based classification systems) are modified by employing these T-norms. Consequently, these changes in calculation results in altering the overall accuracy of rule based classification systems. Experimental results obtained on some well-known data sets show that the best performance is produced by employing the Aczel-Alsina operator in terms of the classification accuracy, the second best operator is Dubois-Prade and the third best operator is Dombi. In experiments, we have used 12 data sets with numerical attributes from the University of California, Irvine machine learning repository (UCI).Comment: 6 pages, 1 figure, 4 tables; International Journal of Fuzzy Logic Systems (IJFLS) Vol.2, No.3, July 201

An Adaptive Image Encryption Scheme Guided by Fuzzy Models

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.Comment: Iranian Journal of Fuzzy Systems (2023

A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm

Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy similarity measures by multi objective genetic algorithm (FSFSM - MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it

SELF-VS: Self-supervised Encoding Learning For Video Summarization

Despite its wide range of applications, video summarization is still held back by the scarcity of extensive datasets, largely due to the labor-intensive and costly nature of frame-level annotations. As a result, existing video summarization methods are prone to overfitting. To mitigate this challenge, we propose a novel self-supervised video representation learning method using knowledge distillation to pre-train a transformer encoder. Our method matches its semantic video representation, which is constructed with respect to frame importance scores, to a representation derived from a CNN trained on video classification. Empirical evaluations on correlation-based metrics, such as Kendall's $\tau$ and Spearman's $\rho$ demonstrate the superiority of our approach compared to existing state-of-the-art methods in assigning relative scores to the input frames.Comment: 9 pages, 5 figure

Some factorization properties of idealization in commutative rings with zero divisors

We study some factorization properties of the idealization \idealztn{R}{M} of a module $M$ in a commutative ring $R$ which is not necessarily a domain. We show that \idealztn{R}{M} is ACCP if and only if $R$ is ACCP and $M$ satisfies ACC on its cyclic submodules, provided that $M$ is finitely generated. We give an example to show that the BF property is not necessarily preserved in idealization, and give some conditions under which \idealztn{R}{M} is a BFR. We also characterize the idealization rings which are UFRs

Investigating the classical problem of pursuit, in two modes

The pursuit problem is a historical issue of the application of mathematics in physics, which has been discussed for centuries since the time of Leonardo Da Vinci, and its applications are wide ranging from military and industrial to recreational, but its place of interest is nowhere but nature and inspiration from the way of migration of birds and hunting of archer fish. The pursuit problem involves one or more pursuers trying to catch a target that is moving in a certain direction. In this article, we delve into two modes of movement: movement on a straight line and movement on a curve. Our primary focus is on the latter. Within the context of movement on a straight line, we explore two methods and compare their respective results. Furthermore, we investigate the movement of two particles chasing each other and extend these findings to N particles that are chasing each other in pairs. By leveraging these two modes of movement, we present a novel relationship for two-particle and N-particle systems in pursuit. Lastly, we analyze the movement of moths around a lamp and evaluate their motion in relation to two-particle and N-particle systems in pursuit. The results of this analysis are carefully examined