Qatar University Institutional Repository: Recent submissions
Now showing items 1101-1120 of 30885
-
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 ... -
Random vector functional link network: Recent developments, applications, and future directions
( Elsevier , 2023 , Article)Neural networks have been successfully employed in various domains such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, ... -
A cultural evolution with a modified selection function and adaptive α-cognition procedure for numerical optimization
( Elsevier , 2023 , Article)In recent years, several population-based evolutionary and swarm algorithms have been developed and used in the literature. This work introduces an improved Cultural Algorithm with a modified selection function and a dynamic ... -
Online dynamic ensemble deep random vector functional link neural network for forecasting
( Elsevier , 2023 , Article)This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL's ... -
A spectral-ensemble deep random vector functional link network for passive brain-computer interface
( Elsevier , 2023 , Article)Randomized neural networks (RNNs) have shown outstanding performance in many different fields. The superiority of having fewer training parameters and closed-form solutions makes them popular in small datasets analysis. ... -
Online learning using deep random vector functional link network
( Elsevier , 2023 , Article)Deep neural networks have shown their promise in recent years with their state-of-the-art results. Yet, backpropagation-based methods may suffer from time-consuming training process and catastrophic forgetting when performing ... -
A problem-specific knowledge based artificial bee colony algorithm for scheduling distributed permutation flowshop problems with peak power consumption
( Elsevier , 2023 , Article)A distributed permutation flowshop scheduling problem (DPFSP) with peak power consumption is addressed in this work. The instantaneous energy consumption of each factory cannot exceed a threshold. First, a mathematical ... -
Problem feature based meta-heuristics with Q-learning for solving urban traffic light scheduling problems
( Elsevier , 2023 , Article)An urban traffic light scheduling problem (UTLSP) is studied by using problem feature based meta-heuristics with Q-learning. The goal is to minimize the network-wise total delay time within a time window by finding a ... -
An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification
( Science China Press , 2024 , Article)Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and ... -
Probabilistic Wind Power Forecasting Using Optimized Deep Auto-Regressive Recurrent Neural Networks
( IEEE Computer Society , 2023 , Article)Wind power forecasting is very crucial for power system planning and scheduling. Deep neural networks (DNNs) are widely used in forecasting applications due to their exceptional performance. However, the DNNs' architectural ... -
Adaptive generalized predictive voltage control of islanded ac microgrid in presence of symmetric and asymmetric faults
( Elsevier Ltd , 2024 , Article)In islanded microgrids, efficiently controlling the output voltage and frequency of voltage source inverters while maintaining stability poses a significant challenge, particularly during grid fault conditions. This paper ... -
UNIFIED DISCRETE DIFFUSION FOR SIMULTANEOUS VISION-LANGUAGE GENERATION
( International Conference on Learning Representations, ICLR , 2023 , Conference)The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present ... -
Orthogonal Experimental Design Based Binary Optimization Without Iteration for Fault Section Diagnosis of Power Systems
( IEEE Computer Society , 2024 , Article)Fault section diagnosis (FSD) is considerably indispensable for the continuous and reliable electricity supply. In general, the analytical model of FSD is solved by derivative-free intelligent metaheuristic algorithms. ... -
Support Vector Machine Based Models with Sparse Auto-encoder Based Features for Classification Problem
( Springer Science and Business Media Deutschland GmbH , 2023 , Conference)Auto-encoder is a special type of artificial neural network (ANN) that is used to learn informative features from data. In the literature, the generalization performance of several machine learning models have been improved ... -
Double Regularization-Based RVFL and edRVFL Networks for Sparse-Dataset Classification
( Springer Science and Business Media Deutschland GmbH , 2023 , Conference)In our previous work, the random vector functional link network (RVFL) and the ensemble deep RVFL network (edRVFL) have been proven to be competitive for tabular-dataset classification, and their sparse pre-trained versions ...
