The Fusion of Fuzzy Theories and Natural Language Processing: A State-of-the-Art Survey

M Liu, H Zhang, Z Xu, K Ding - Applied Soft Computing, 2024 - Elsevier
Recent years have witnessed a drastic surge in natural language processing (NLP), which is
a popular research orientation in artificial intelligence. In contrast to precise numbers …

[HTML][HTML] A Recommendation System Supporting the Implementation of Sustainable Risk Management Measures in Airport Operations

S Carpitella, B Brentan, A Certa, J Izquierdo - Algorithms, 2023 - mdpi.com
This paper introduces a recommendation system aimed at enhancing the sustainable
process of risk management within airport operations, with a special focus on Occupational …

Time and frequency-domain feature fusion network for multivariate time series classification

T Lei, J Li, K Yang - Expert Systems with Applications, 2024 - Elsevier
Multivariate time series classification is a significant research topic in the realm of data
mining, which encompasses a wide array of practical applications in domains such as …

A Self-organizing Deep Network Architecture Designed Based on LSTM Network via Elitism-driven Roulette-Wheel Selection for Time-Series Forecasting

K Zhou, SK Oh, W Pedrycz, J Qiu, K Seo - Knowledge-Based Systems, 2024 - Elsevier
In this study, we propose a new self-organizing deep network architecture of fuzzy
polynomial neural networks (FPNN) based on Fuzzy rule-based Polynomial Neurons (FPNs) …

GA-FCFNN: A new forecasting method combining feature selection methods and feedforward neural networks using genetic algorithms

R Zhang, X Ma, C Zhang, W Ding, J Zhan - Information Sciences, 2024 - Elsevier
In the modern landscape, the fusion of forecasting and computational intelligence empowers
organizations to extract invaluable insights from vast datasets, facilitating informed decision …

An interpretable multi-scaled agent hierarchy for time series prediction

H Rafiei, MR Akbarzadeh-T - Expert Systems with Applications, 2024 - Elsevier
The traditional time series analysis treats the time series as a dynamic system of sequential
entries, leading to complex models and a lack of interpretability. Time series, however, can …

[HTML][HTML] Backpropagation through time learning for recurrence-aware long-term cognitive networks

G Nápoles, A Jastrzebska, I Grau… - Knowledge-Based Systems, 2024 - Elsevier
Abstract Fuzzy Cognitive Mapping (FCM) and the extensive family of models derived from it
have firmly established their strong position in the landscape of machine learning …

Sparse and regression learning of large-scale fuzzy cognitive maps based on adaptive loss function

Q Zhou, Y Ma, Z Xing, X Yang - Applied Intelligence, 2024 - Springer
Fuzzy cognitive maps (FCMs) learning is a hot topic in recent years. However, as the
number of concepts increases in FCMs, it is difficult to learn the sparse and robust FCMs …

Traffic flow prediction with a multi-dimensional feature input: A new method based on attention mechanisms

S Zhang, J Ma, B Geng, H Wang - Electronic Research Archive, 2024 - aimspress.com
Accurately predicting traffic flow is an essential component of intelligent transportation
systems. The advancements in traffic data collection technology have broadened the range …

Exploiting Fuzzy Logic for Time Series Classification in Networks

S Bishnoi, AK Bhagat - 2024 3rd International Conference for …, 2024 - ieeexplore.ieee.org
Fuzzy logic has come to be a crucial tool for processing and interpreting facts in diverse
fields, which includes the evaluation and type of time collection information. This paper …