International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Symbolically representing the knowledge acquired by a neural network is a profound endeavor aimed at illuminating the latent information embedded within the network. The literature offers a multitude of algorithms dedicated to extracting symbolic classification rules from neural networks. While some excel in producing highly accurate rules, others specialize in generating rules that are easily comprehensible. Nevertheless, only a scant few algorithms manage to strike a harmonious balance between comprehensibility and accuracy. One such exemplary technique is the Rule Extraction from Neural Network Using Classified and Misclassified Data (RxNCM) algorithm, which adeptly generates straightforward and precise rules outlining input data ranges with commendable accuracy. This article endeavors to enhance the classification performance of the RxNCM algorithm by leveraging ensemble technique. Ensembles, a burgeoning field, focus on augmenting classifier performance by harnessing the strengths of individual classifiers. Extraction of rules through neural network ensembles is relatively underexplored, this paper bridges the gap by introducing the Rule extraction using Neural Network Ensembles (RENNE) algorithm. RENNE is designed to refine the classification rules derived from the RxNCM algorithm through ensemble strategy. Specifically, RENNE leverages patterns correctly predicted by an ensemble of neural networks during the rule generation process. The efficacy of the algorithm is validated using seven datasets sourced from the UCI repository. The outcomes indicate that the proposed RENNE algorithm outperforms the RxNCM algorithm in terms of performance.

The rapid growth of the internet and computing devices significantly increases the rise of the quantity of information. The major technique which is utilized to lower the limit of information quantity is ATS. By this technique, the text documents are precisely summarized within a short time as per user requirements. However, its effectiveness is limited due to various issues like subject identification, elucidation, summary generation and analysis of the generated summary. To tackle these issues and to obtain precise and concise summarized text, this proposed approach results as the efficient multi-document summarization approach by focusing on those issues. This proposed approach is incorporated with preprocessing, feature extraction, knowledge extraction and summarization processes. Initially, preprocessing is done by BERT tokenization and it results in preprocessed tokens from multiple documents. The features extracted from preprocessed tokens are the proposed aspect term feature, TF-IDF feature, Word2Vec feature and average word length feature. After, relevant features are extracted from the preprocessed token to train modified CNN in the feature extraction process. Instantaneously, the preprocessed tokens are employed in the knowledge extraction process for extracting essential knowledge to improve the training ability of modified CNN. This modified CNN is trained with the obtained weight, extracted feature set and knowledge to obtain precise and concise summarized text from multiple documents. Thus, the final summarized outcome has been from modified CNN with transfer learning (MCNN-TL). Finally, a variety of analyses are used to assess the suggested multi-document summary methodology, demonstrating its superiority over current summarization methods.

Due to the limitations associated with the use of a single type of data during the recommendation process, recent research has focused on developing new fusion-based recommenders that make use of multiple heterogeneous sources of information to provide more accurate suggestions. However, the realistic and flexible methods available to users for expressing their preferences for products and services inherently generate uncertain, imperfect, and ambiguous data that feed recommenders and thus affect their accuracy. As a result, Recommender Systems (RS) make significant use of soft mathematical tools to deal with uncertainty. Among these tools is Dempster-Shafer Theory (DST), which has been shown to be effective at dealing with the inherent uncertainty in numerous applications. This article provides a survey of the use of DST in the RS field. Thus, after a brief introduction to recommender systems and the DST, this survey discusses recent DST applications in RS. It introduces a new taxonomy that encompasses the primary application context for DST-based RS solutions, as well as a comprehensive multi-criteria analysis of the peer-reviewed papers. The resulting comparisons are analyzed to draw conclusions, identify current study limitations, and define future research directions. This survey serves as a valuable resource for the entire research community that is interested in recommender systems and DST.

In this paper, we introduce and study lacunary A-statistical convergence of complex uncertain sequences via Orlicz functions. We investigate some aspects of uncertainty such as mean, measure, almost surely, uniformly almost surely and distribution. Additionally, we make an effort to provide illustrative examples that elucidate the interrelations within the constructed sequence spaces.

In this article, a fuzzy adaptive finite time (FT) output feedback tracking fault-tolerant control (FTC) scheme is designed for stochastic non-strict feedback nonlinear systems (NSFNSs) with actuator failure, input saturation, and unknown control direction. The system studied in this article is more complex than previous results and more in line with the actual situation. The advantages of this study are as follows: (i) A fuzzy state observer of high gain is established to deal with the states unmeasured problem of the system. (ii) The Nussbaum function is employed to address unknown control direction and actuator failure problems, and the Smooth non-affine function is utilized to dispose of the input saturation. (iii) The dynamic surface technique introduces errors that are eliminated by using the error compensation technology, and fuzzy logic systems (FLSs) are applied to appropriate unknown nonlinear functions in the system. The controller developed in this study guarantees that tracking errors can converge to a small neighborhood around the origin within FT and overall signals in the stochastic system are semi-global finite-time stable in probability (SGFSP). Finally, three simulation examples are introduced to verify the effectiveness of the suggested control strategy.