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Volume 152, Issue CFeb 2024
Publisher:
  • Elsevier Science Publishers B. V.
  • PO Box 211 1000 AE Amsterdam
  • Netherlands
ISSN:1568-4946
Reflects downloads up to 10 Feb 2025Bibliometrics
research-article
A lightweight network based on local–global feature fusion for real-time industrial invisible gas detection with infrared thermography
Abstract

The detection of industrial invisible gas plays a vital role in preventing environmental pollution and fire accidents. Optical gas imaging (OGI) with infrared thermography is widely used in the field of gas leak monitoring and treatment by ...

Highlights

  • The first thermal image dataset of industrial gas to our knowledge is available.
  • Visual gas detection achieves high accuracy by local-global feature extraction and fusion.
  • Our model realizes real-time automatic visual gas detection ...

research-article
Dynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology
Abstract

Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy ...

Highlights

  • The dynamic constraint tolerance factor ε is designed according to the feasible ratio and updated algebras.
  • A novel adaptive boundary constraint handling technology (ABCHT) is designed according to the extent of the out-of-bounds ...

research-article
Granular fuzzy rule-based model construction under the collaboration of multiple organizations
Abstract

In the real world, phenomena are often observed and recorded by multiple organizations which results in multiple sources of data. When dealing with such data, the centralized modeling approach aims to achieve collaborative modeling by fusing ...

research-article
A framework based on generational and environmental response strategies for dynamic multi-objective optimization
Abstract

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. ...

Highlights

  • The novel framework includes the generational response strategy.
  • The feed-forward center point strategy was an example run in the novel framework.
  • The statistical results showed that the proposed strategy is very competitive.
  • ...

research-article
Immune Plasma Programming: A new evolutionary computation-based automatic programming method
Abstract

Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment ...

Highlights

  • Immune Plasma Programming (IPP) is presented for symbolic regression problems.
  • IPP is inspired by the Immune Plasma Algorithm based on immune plasma therapy.
  • IPP’s performance is comparable with the prominent automatic programming ...

research-article
Tree-shaped multiobjective evolutionary CNN for hyperspectral image classification
Abstract

Convolutional neural networks (CNNs) have achieved significant performances in hyperspectral image (HSI) classification in recent years. However, designing a high-performance CNN depends on human expertise heavily, which usually takes ...

Highlights

  • A multiobjective evolutionary CNN is proposed to design a suitable CNN with high classification accuracy and high computational efficiency for hyperspectral image classification automatically.
  • A specific individual representation is ...

research-article
Bi-objective multi-mode resource-constrained multi-project scheduling using combined NSGA II and Q-learning algorithm
Abstract

Multi-mode resource-constrained multi-project scheduling problem (MRCMPSP) plays a pivotal role in project management, serving as a critical component in production management for Engineering-to-Order manufacturing companies to enhance ...

Highlights

  • A bi-objective algorithm is proposed for solving the multi-mode resource-constrained multi-project scheduling problem.
  • The novel proposed algorithm demonstrates superior performance compared to classical algorithms such as NSGA II, PSO,...

research-article
A portfolio trading system using a novel pixel graph network for stock selection and a mean-CDaR optimization for portfolio rebalancing
Abstract

Due to the complexity and dynamic nature of financial markets, portfolio management tasks require continuous adaptation with market intelligence. Furthermore, to make profitable trading decisions, it is essential to predict the future performance ...

Highlights

  • A novel PGN model for image graph classification is proposed to predict BUY/SELL signals.
  • Mean–Conditional Drawdown at Risk optimization model is developed for asset allocation.
  • An ensemble feature selection algorithm with a ...

research-article
Evolving masked low-rank transformer for long text understanding
Abstract

Long sequence text processing is time-consuming owing to the ultra-large-scale self-attention computing. Recent advances demonstrate the attention in transformer can be accelerated by redundancy removal, and there are various sparse variants for ...

Highlights

  • Proposed a novel Multi-objective Evolutionary Algorithm-based method for long text processing of transformer models.
  • Developed the DM and LoRA modules, providing an accelerated mechanism for considering input tokens and sparse patterns ...

research-article
Developing seasonal z-number regression for waste-disposal forecasting in a Taiwanese hospital
Abstract

This study develops a seasonal z-number regression (SZR) to forecast the daily generated amounts of clinical waste for recycling and related waste. The proposed SZR designs new z-number intervals based on least-squares support-vector regression ...

Highlights

  • This study develops novel seasonal z-number regression (SZR).
  • The SZR are examined to forecast the daily waste generated.
  • The SZR model demonstrates better performance compared to the other approaches.

research-article
A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare
Abstract

Artificial intelligence (AI) applications based on deep learning for diagnosing type-II diabetes are sometimes difficult to understand and communicate even as patients are eager to understand the rationale behind the diagnostic results. ...

Highlights

  • The deep neural network-random forest with modified incremental interpretation approach is proposed for diabetes diagnosis.
  • A deep neural network is constructed to predict the probability of a patient having diabetes.
  • A random ...

research-article
Def-DReL: Towards a sustainable serverless functions deployment strategy for fog-cloud environments using deep reinforcement learning
Abstract

Modern cloud applications are composed of tens of thousands of environment-agnostic serverless functions that can be deployed in either a fog or cloud environment. The key to sustaining fog computing is to offload the maximum amounts of ...

Highlights

  • DRL-based serverless function deployment mechanism in fog and cloud environments.
  • Functions’ parameters (e.g. short runtime, memory demand) are taken into account.
  • Emphasizing the end-users requests based on their distance and ...

research-article
A survey on personalized itinerary recommendation: From optimisation to deep learning
Abstract

The tourism industry is a significant contributor to the global economy, responsible for generating nearly 10% of the world’s GDP and employing around 9% of the global workforce. A crucial aspect of this industry is personalised itinerary ...

Highlights

  • We discuss deep learning techniques based POI/itinerary recommendations.
  • It focuses two research directions: user satisfaction and provider satisfaction.
  • It explores user satisfaction into non-personalised and personalised sub-...

research-article
Interactively iterative group decision-making method with interval-valued intuitionistic fuzzy preference relations based on a new additively consistent concept
Abstract

Interval-valued intuitionistic fuzzy preference relation (IVIFPR) is a powerful instrument for describing uncertain judgement of expert in preference group decision making (PGDM). Nevertheless, the extant additive consistency definitions for ...

Highlights

  • Present strongly additive and weakly additive consistency definitions of IVIFPRs.
  • Design an interactively iterative algorithm to enhance the consistency of IVIFPR.
  • Design an interactively iterative algorithm to enhance the ...

research-article
Data preprocessing to improve fairness in machine learning models: An application to the reintegration process of demobilized members of armed groups in Colombia
Abstract

The use of machine learning allows automating decision-making based on data, saving time and resources compared to traditional methods that require human intervention. This automation poses significant challenges in terms of ensuring that the ...

Highlights

  • New database, which is innovative in the literature and has the potential to added value with social impact.
  • Fairness in unbalanced learning associating optimization techniques and sensitive groups with classes.
  • Fairness notions ...

research-article
An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario
Abstract

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the ...

Highlights

  • A multi-robot task allocation problem in nuclear scenario is presented.
  • An evolutionary algorithm with two stages is proposed to solve the problem.
  • A local search mechanism combining crowding distance and 2-opt is proposed.
  • ...

research-article
An ɛ-constrained multiobjective differential evolution with adaptive gradient-based repair method for real-world constrained optimization problems
Abstract

Over the past decade, incorporating information from the objective function into the constraint-handling process has garnered considerable attention in evolutionary algorithm research. Stemming from this, multiobjective optimization has emerged ...

Highlights

  • The information from objective function has been utilized to improve the standard gradient-based repair method.
  • An adaptive scheme is specifically designed to automate our proposed repair method for ɛ-constrained multiobjective ...

review-article
Artificial neural networks applications in construction and building engineering (1991–2021): Science mapping and visualization
Abstract

Artificial neural network (ANN) has acquired noticeable interest from the research community to handle complex problems in Construction and Building engineering (CB). This interest has led to an enormous amount of scientific publications in ...

Highlights

  • Performing scientometric review for ANN Applications in Construction and Building Engineering publications.
  • Visualizing publications collaboration networks for key contributors.
  • Visualizing publications direct citation networks for ...

research-article
EEG emotion recognition based on Ordinary Differential Equation Graph Convolutional Networks and Dynamic Time Wrapping
Abstract

Graph Convolutional Network (GCN) has been extensively utilized to extract relations among electroencephalography (EEG) electrode channels for its strong ability to handle non-Euclidean data. However, GCN still has some issues when it comes to ...

Highlights

  • A method based on Dynamic Time Wrapping (DTW) algorithm to measure the similarities among signals of EEG channels on time domain is proposed.
  • A graph neural network based on Ordinary Differential Equation (ODE) is introduced to extract ...

research-article
Enhanced autoencoder-based LiDAR localization in self-driving vehicles
Abstract

The ability of self-driving vehicles to carry out navigation tasks successfully relies heavily on the implementation of a strong localization system. Global navigation satellite systems have been used to provide such information to the vehicle. ...

Highlights

  • First paper tailoring outcomes for vehicle location detection in latent space.
  • Modified auto-encoder cost function to learn vehicle positions.
  • Offers mathematical proofs for algorithm convergence.
  • Achieves excellent accuracy ...

research-article
Intelligent strategic bidding in competitive electricity markets using multi-agent simulation and deep reinforcement learning
Abstract

Aiming at the lack of comprehension of agents in Multi-Agent Simulation (MAS) based on classic Reinforcement Learning algorithms of competitive electricity markets, an intelligent strategic bidding method using Deep Reinforcement Learning (DRL) ...

Highlights

  • A theoretical framework of intelligent electricity market bidding using MAS and DRL.
  • Three MAS models of intelligent bidding are established based on three DRL Algorithms.
  • Convergence speed and response sensitivity using three DRL ...

research-article
A 2-additive Choquet integral-based multi-criterion decision-making method with complex linguistic information in drug value assessment
Abstract

With the escalating incidence and mortality rates of lung cancer, the targeted drugs assessment has emerged a key problem, serving as the foundation for incorporating targeted drugs into the national basic medical insurance. This paper executes ...

Highlights

  • We propose a method to calculate the weights of experts based on the regret theory.
  • Methods to deal with the complex linguistic information are developed.
  • A linear programming to determine the weights and interactions of criteria ...

research-article
A software trustworthiness evaluation methodology for cloud services with picture fuzzy information
Abstract

VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is one of the most commonly used decision-making technique. This work provides a software trustworthiness evaluation method based on VIKOR technique in a group decision-making (GDM) ...

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Highlights

  • An entropy-based method for determining the decision maker weights is provided.
  • A new normalization projection measure is developed in picture fuzzy environment.
  • A group regret measure is introduced.
  • A new ranking method with ...

research-article
Inverse distance weighting and radial basis function based surrogate model for high-dimensional expensive multi-objective optimization
Abstract

Radial basis function (RBF) models have attracted a lot of attention in assisting evolutionary algorithms for solving computationally expensive optimization problems. However, most RBFs cannot directly provide the uncertainty information of their ...

Highlights

  • Proposed an surrogate assisted evolutionary algorithm.
  • Builded surrogates based on the radial basis function and inverse distance weighting.
  • Proposed a modified lower confidence bound to balance exploration and exploitation.
  • ...

research-article
Systemic risk measurement: A Quantile Long Short-Term Memory network approach▪
Abstract

In finance, systemic risk is the risk that the crisis of an institution could trigger instability or bring down an entire system or market. The Delta Conditional Value-at-Risk is a market-based measure proposed by the recent literature to ...

Highlights

  • An innovative approach for measuring Systemic Risk is introduced.
  • The proposed approach is based on Long Short-Term Memory Networks.
  • The model removes the assumptions required by the bivariate CCC-GARCH approach.
  • Multiple ...

research-article
Multi-objective trajectory planning for segment assembly robots using a B-spline interpolation- and infeasible-updating non-dominated sorting-based method
Abstract

The rapid and smooth functioning of segment assembly robots, which is always conflicting, is critical to improving efficiency and ensuring safety during tunneling construction, particularly for the series-actuated robots employed in non-circular ...

Highlights

  • A multi-objective optimization model for trajectory planning for the segment assembly robot of non-circular shield machines has been established.
  • An infeasible-updating non-dominated sorting-based evolutionary algorithm (INSEA) is ...

research-article
A high-precision crown control strategy for hot-rolled electric steel using theoretical model-guided BO-CNN-BiLSTM framework
Abstract

The prediction accuracy of strip crown is low under complex industrial data environments to general machine learning models, i.e., lack of reasonable mechanism explanation and spatial dimension dependence, which will directly affect the product ...

Highlights

  • The theoretical model of hot rolling is established and verified.
  • Theoretical model and BO-CNN-BiLSTM framework are combined.
  • The crown prediction model is built based on TG-BO-CNN-BiLSTM framework.
  • A high-precision crown ...

research-article
Echo state network structure optimization algorithm based on correlation analysis
Abstract

Echo State Network (ESN) is an effective variant of Recurrent Neural Network (RNN). However, it is difficult for traditional ESN to determine the reservoir size that matches a given task. In this paper, an ESN structure optimization pruning ...

Highlights

  • Correlations between neurons are measured by the characteristic matrix.
  • The reservoir neurons are pruned according to the pruning criterion.
  • Sample information is retained by averaging the transverse propagation of the weight.
  • ...

research-article
Dynamic three-way multi-criteria decision making with basic uncertain linguistic information: A case study in product ranking
Abstract

The concept of basic uncertain linguistic information (BULI) is proposed as an extension of basic uncertain information to enhance the measurement of data quality in decision-making processes by allowing for more flexible utilization of degrees ...

Highlights

  • We establish a dynamic BULI-based outranking relation.
  • We build a BULI decision-theoretic rough set model.
  • We define the similarity measure for BULI.
  • We construct the BULI-based three-way decision model.
  • We propose the ...

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