Search Results for author:
Found 16 papers, 6 papers with code
Multimodal Emotion Recognition using Audio-Video Transformer Fusion with Cross Attention
Despite its potential, multimodal emotion recognition faces significant challenges, particularly in synchronization, feature extraction, and fusion of diverse data sources.
A Review of 315 Benchmark and Test Functions for Machine Learning Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions
In the rapidly evolving optimization and metaheuristics domains, the efficacy of algorithms is crucially determined by the benchmark (test) functions.
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems.
Text-Based Product Matching -- Semi-Supervised Clustering Approach
Matching identical products present in multiple product feeds constitutes a crucial element of many tasks of e-commerce, such as comparing product offerings, dynamic price optimization, and selecting the assortment personalized for the client.
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning
Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL.
GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization
To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics.
GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization
This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG).
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection
With the surge in data generation, both vertically (i. e., volume of data) and horizontally (i. e., dimensionality), the burden of the curse of dimensionality has become increasingly palpable.
Unveiling the frontiers of deep learning: innovations shaping diverse domains
As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training.
Evolutionary Dynamic Optimization Laboratory: A MATLAB Optimization Platform for Education and Experimentation in Dynamic Environments
In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB).
Meta-learning approaches for few-shot learning: A survey of recent advances
Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction.
A fuzzy adaptive metaheuristic algorithm for identifying sustainable, economical, lightweight, and earthquake-resistant reinforced concrete cantilever retaining walls
In earthquake-prone zones, the seismic performance of reinforced concrete cantilever (RCC) retaining walls is significant.
Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches
Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations.
Soil Erosion in the United States. Present and Future (2020-2050)
Future scenarios with current CPs indicate an increase between 8% to 21% under different combinations of SSP-RCP scenarios of climate and LULC changes.
LAB: A Leader-Advocate-Believer Based Optimization Algorithm
This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems.
Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization.
