KINDI Center for Computing Research: Recent submissions
Now showing items 101-120 of 297
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Trial-based dominance for comparing both the speed and accuracy of stochastic optimizers with standard non-parametric tests
( Elsevier , 2023 , Article)Non-parametric tests can determine the better of two stochastic optimization algorithms when benchmarking results are ordinal-like the final fitness values of multiple trials-but for many benchmarks, a trial can also ... -
Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm
( Elsevier , 2024 , Article)The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In ... -
Ensemble meta-heuristics and Q-learning for staff dissatisfaction constrained surgery scheduling and rescheduling
( Elsevier , 2024 , Article)In this study, we investigate the multi-objective surgery scheduling and rescheduling problems with considering medical staff dissatisfaction and fuzzy surgery time. Rescheduling is activated when emergency patients arrive. ... -
Low-rank and global-representation-key-based attention for graph transformer
( Elsevier , 2023 , Article)Transformer architectures have been applied to graph-specific data such as protein structure and shopper lists, and they perform accurately on graph/node classification and prediction tasks. Researchers have proved that ... -
Integrated scheduling of multi-constraint open shop and vehicle routing: Mathematical model and learning-driven brain storm optimization algorithm
( Elsevier , 2024 , Article)Recent years have witnessed a surge of interest in integrated production and distribution scheduling problems which can achieve an overall optimization of the production and distribution activities. However, integrated ... -
Ensemble artificial bee colony algorithm with Q-learning for scheduling Bi-objective disassembly line
( Elsevier , 2024 , Article)This study addresses a bi-objective disassembly line scheduling problem (Bi-DLSP), considering interference relationships among tasks. The objectives are to optimize the total disassembly time and the smoothing index ... -
TFormer: A time-frequency Transformer with batch normalization for driver fatigue recognition
( Elsevier , 2024 , Article)Within the framework of the advanced human-cybernetic interfaces (HCI), Cross-subject electroencephalogram (EEG)-based driver fatigue recognition is emerging as a pivotal application in the paradigm of Industry 5.0. ... -
Boosted multilayer feedforward neural network with multiple output layers
( Elsevier , 2024 , Article)This research introduces the Boosted Ensemble deep Multi-Layer Layer Perceptron (EdMLP) architecture with multiple output layers, a novel enhancement for the traditional Multi-Layer Perceptron (MLP). By adopting a layer-wise ... -
Recurrent ensemble random vector functional link neural network for financial time series forecasting
( Elsevier , 2024 , Article)Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear ... -
Dual population approximate constrained Pareto front for constrained multiobjective optimization
( Elsevier , 2023 , Article)For constrained multiobjective optimization problems (CMOPs), the ultimate goal is to obtain a set of well-converged and well-distributed feasible solutions to approximate the constrained Pareto front (CPF). Various ... -
Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities
( Elsevier , 2024 , Article)Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. ... -
Large-scale power system multi-area economic dispatch considering valve point effects with comprehensive learning differential evolution
( Elsevier , 2024 , Article)The role of multi-area economic dispatch (MAED) in power system operation is increasingly significant. It is a non-linear and multi-constraint problem with many local extremes when considering the valve point effects, ... -
Benchmark problems for large-scale constrained multi-objective optimization with baseline results
( Elsevier , 2024 , Article)The interests in evolutionary constrained multiobjective optimization are rapidly increasing during the past two decades. However, most related studies are limited to small-scale problems, despite the fact that many practical ... -
AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks
( Elsevier , 2024 , Article)The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, ... -
Large-scale data classification based on the integrated fusion of fuzzy learning and graph neural network
( Elsevier , 2024 , Article)Deep learning and fuzzy models provide powerful and practical techniques for solving large-scale deep-learning tasks. The fusion technique on deep learning and fuzzy system are generally classified into ensemble and ... -
Deep reinforcement learning as multiobjective optimization benchmarks: Problem formulation and performance assessment
( Elsevier , 2024 , Article)The successful deployment of Deep learning in several challenging tasks has been translated into complex control problems from different domains through Deep Reinforcement Learning (DRL). Although DRL has been extensively ... -
UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
( Elsevier , 2024 , Article)Feature selection (FS) is a crucial technique in machine learning and data mining, serving a variety of purposes such as simplifying model construction, facilitating knowledge discovery, improving computational efficiency, ... -
Parallel fractional dominance MOEAs for feature subset selection in big data
( Elsevier , 2024 , Article)In this paper, we solve the feature subset selection (FSS) problem with three objective functions namely, cardinality, area under receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC) ... -
Assessing the Effect of Model Poisoning Attacks on Federated Learning in Android Malware Detection
( Association for Computing Machinery , 2024 , Conference)Android devices are central to our daily lives, which leads to an increase in mobile security threats. Attackers try to exploit vulnerabilities and steal personal information from the installed applications on these devices. ... -
Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring
( IEEE , 2024 , Article)The traditional healthcare system is increasingly challenged by its dependence on inperson consultations and manual monitoring, struggling with issues of scalability, the immediacy of care, and efficient resource allocation. ...
