Publications

Fuzzy sets engineering

Proximity fuzzy clustering for web context analysis

This study extends the web classification approach through a proximity-based fuzzy clustering sensible to the influence of the page. The proximity-based fuzzy clustering works in an unsupervised manner, augmented by a certain auxiliary supervision mechanism. The supervision scheme is realized via a number of proximity “hints” (constraints) that specify an extent to which some pairs of patterns are regarded similar or different. The hints are provided externally to the clustering algorithm and improve the searching activity by customizing the user’s navigation. In this paper we focus on the feature spaces corresponding to the Web data characterizing the context analysis and we discuss how the knowledge extraction process identifies the right context.

Measuring Weak Consistency and Weak Transitivity of Pairwise Comparison Matrices

Under the assumption of rational economics, the opinions of decision makers should exhibit some transitivity properties. It is an important issue on how to measure the transitivity properties of the provided preference relations over a set of alternatives. In this study, we report the methods for measuring weak consistency (w-consistency) and weak transitivity (w-transitivity) of pairwise comparison matrices (PCMs) originating from the analytic hierarchy process (AHP). First, some interesting properties of PCMs with w-consistency and w-transitivity are studied. Second, novel methods are proposed to construct the quantification indices of w-consistency and w-transitivity of PCMs, respectively. Some comparisons with the existing methods are offered to illustrate the novelty of the proposed ones. Third, an optimization model is put forward to modify a PCM without any transitivity property to a new one with w-consistency and w-transitivity, respectively. The particle swarm optimization (PSO) algorithm is adopted to solve the nonlinear optimization problems. A novel decision-making model is established by considering the w-transitivity as the minimum requirement. Some numerical examples are carried out to illustrate the developed methods and models. It is observed that the proposed indices can be computed efficiently and reflect the inherent relations of the entries in a PCM with w-consistency and w-transitivity, respectively.

Assessing growth potential of careers with occupational mobility network and ensemble framework

Correction to: Dynamic Emotion Understanding Based on Two-Layer Fuzzy Support Vector Regression-Takagi-Sugeno Model

Knowledge Representation and Information Processing in Intelligent Controllers

12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022)

Logistics in the Sky: A Two-Phase Optimization Approach for the Drone Package Pickup and Delivery System

The application of drones in last-mile distribution has been a contentious research topic in recent years. Existing urban distribution modes mostly depend on trucks. This paper proposes a novel package pickup and delivery mode and system wherein multiple drones collaborate with automatic devices. The proposed mode uses free areas on top of residential buildings to set automatic devices as delivery and pickup points of packages, and employs drones to transport packages between buildings and depots. The integrated scheduling problem of package drop-pickup considering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> -drones, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> -depots, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> -customers is crucial for the system. Therefore, we propose a simulated-annealing-based two-phase optimization (SATO) approach to solve this problem. In the first phase, tasks are allocated to depots for serving, such that the initial problem is decomposed into multiple single-depot scheduling problems with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> -drone. In the second phase, considering the drone capability and task demand constraints, we generated a route-planning scheme for drones in each depot. Concurrently, an improved variable neighborhood descent (IVND) algorithm was designed in the first phase to reallocate tasks, and a local search (LS) algorithm was proposed to search for high-quality solutions in the second phase. Finally, extensive experiments and comparative studies were conducted to verify the effectiveness of the proposed approach.

Regular expressions with truth values in lattice-monoid and their languages

In this study, we introduce the concept of lattice-valued regular expressions. Such expressions become not only the necessary tool for the analysis and synthesis of fuzzy automata, but give rise to a recursive generation of the family of fuzzy languages accepted by fuzzy automata from certain simple fuzzy languages. The equivalence between lattice-valued regular expressions and lattice-valued finite automata is demonstrated. We also show that the family of fuzzy languages accepted by deterministic lattice-valued finite automata (abbreviated as DLA) is not closed under Kleene closure operation. Similarly, we show that in general the family of fuzzy languages accepted by (nondeterministic) lattice-valued finite automata (abbreviated as LA) is not closed under complement operation. These two findings form an essential difference between lattice-valued finite automata and classical finite automata. We also elaborate on regular expressions produced by DLA.

Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread

Enhanced Dynamic Deep Q-Network for Federated Learning Scheduling Policies on Iot Devices Using Explanation-Driven Trust

Long-Term Prediction Model for Fuzzy Granular Time Series Based on Trend Filter Decomposition and Ensemble Learning

In the realm of control theory, the complex task of long-term time series prediction has been profoundly transformed by the confluence of advancements in computer technology and machine learning. However, the application of fuzzy information granularity remains a significant challenge, primarily due to the potential for substantial data distortion. To address this limitation, we propose an innovative long-term prediction model based on granularity time series, which integrates $l_{1}$ -trend filter decomposition and integrated learning. The core of our model lies in a novel modal decomposition method that utilizes $l_{1}$ -trend filters and a validity function to meticulously extract valuable insights from the original time series, thereby enhancing the precision of data analysis while preserving the integrity of the original data. Furthermore, we introduce a groundbreaking formula to measure the similarity of fuzzy information granularity, classifying time series components into three distinct categories: trend, period, and noise. By applying distinct prediction strategies to each category, we construct an integrated learning model that leverages the strengths of each component. At the heart of our model is a multilinear information granularity prediction approach, which is based on trend time windows and utilizes the newly developed similarity measure. This method not only maintains the integrity of the original time series but also offers a more accurate representation of the similarity between information grains. Empirical results from publicly available datasets validate the superior performance of our proposed prediction model, demonstrating its potential to significantly enhance long-term time series prediction accuracy.

Using I-subgroup-based weighted generalized interval t-norms for aggregating basic uncertain information

A Deep Learning Model with Data Enrichment for Intent Detection and Slot Filling

A model for accurately solving the tasks of intent detection and slot filling requires a substantial amount of user queries annotated manually, which is time-consuming and costly. In this study, in order to circumvent this problem, we propose a method of expanding a training set for handling these two tasks. The data are augmented by applying a random mutation to the training samples, following a set of heuristic rules. For validation of the method, we construct a compound neural architecture composed of a long short-term memory layer, an attention mechanism, and conditional random fields. The experiments conducted on the Automatic Terminal Information Service (ATIS) dataset demonstrate that the models trained on the expanded datasets improve their F-score for the slot-filling task. This improvement is particularly significant for small datasets. We believe that this method allows for the expansion of a small set of previously annotated user queries into a large training set that is sufficient for correctly constructing a model of intent detection and slot filling. Therefore, our approach can be used to save time and money by reducing the amount of data required to train a natural language understanding model. Its novelty consists in expanding a training set for a deep learning model based on a random mutation of training samples, following a set of heuristic rules that utilise external lexicons and the training data itself.

A multiple attribute interval type-2 fuzzy group decision making and its application to supplier selection with extended LINMAP method

Artificial Intelligence and Soft Computing

Biomedical Spectral Classification Using Stochastic Feature Selection and Fuzzy Aggregation

Classifying magnetic resonance spectra is often difficult due to the curse of dimensionality; a high-dimensional feature space couple with a small sample size. We present an aggregation strategy that combines predicted disease states from multiple classifiers with the anticipated outcome that the aggregated predictions are superior to any individual classifier prediction. Multiple classifiers are presented with different, randomly selected, subsets of spectral features. The fuzzy integration results are compared against the best individual classifier operating on a spectral feature subset.

CMOS implementation of Conscience Mechanism in Kohonen"s Neural Network

Reference ESPLAB-CONF-2010-001 URL: http://www.gnss-signals.de/ Record created on 2010-03-08, modified on 2017-05-10