A hybrid meta-heuristic ensemble based classification technique speech emotion recognition

Darekar, R. V.; Chavand, Meena Suhas; Sharanyaa, S.; Ranjan, Nihar M.

doi:10.1016/j.advengsoft.2023.103412

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…In this article, we introduced a new algorithm that was useful in a broad range of applications to detect and identify edge components in block levels [7], [8], [14], [16]. This scheme, derived systematically from a pixel domain algorithm, performs with minimal arithmetical operations on the DCT coefficient domain (especially multiplications).…”

Section: ) Hyun Sung Chang Member Ieee and Kyeongok Kang (2005) Et Almentioning

confidence: 99%

A Brief Survey on Data Preprocessing Techniques based on Image Represented by a Fuzzy Function using Neural Network

Ranjan

2023

IJRASET

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Deep neural network recently created offers insight on how high-level picture representation may be automatically learnedin raw pixels. Deep learning with Convolutional Neural Networks has showed significant potential in the classification and improvement of picture, but is frequently unfit for predictive modelling without spatial connections utilizing data. In order to organize high dimensional vectors in a compact way for CNN- based profound learning, we offer a method to representation. This study shows the use of deep neural networks to create a system thatcan identify different texture characteristics.

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Section: ) Hyun Sung Chang Member Ieee and Kyeongok Kang (2005) Et Almentioning

confidence: 99%

A Brief Survey on Data Preprocessing Techniques based on Image Represented by a Fuzzy Function using Neural Network

Ranjan

2023

IJRASET

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“…Multi-solution NNs, on the other hand, are initiated with multiple random solutions and evolve each solution unless the stopping criteria is met. These criteria include Genetic algorithm (GA) [20], Ant colony optimization (ACO) [21], Artificial Bee colony (ABC) [22,23], Particle swarm optimization (PSO) [24,25], Differential evolution (DE) [26], Teacher-learning based optimization (TLBO) [27], Invasive weed algorithm (IWO) [28], ensemble techniques [29], Grey Wolf optimization (GWO) [30], and others. These algorithms have high performance in terms of finding approximate global optimum solutions.…”

Section: Introductionmentioning

confidence: 99%

A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron

Salgotra

Mittal

2023

Mathematics

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This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Evolutionary Search (ES), Ant Colony Optimization (ACO), and Population-based Incremental Learning (PBIL). Statistical tests are conducted to validate the superiority of the CFS algorithm in finding global optimum solutions. The results indicate that CFS achieves significantly better outcomes with a higher convergence rate when compared to the other algorithms tested. This highlights the effectiveness of CFS in solving MLP optimization problems and its potential as a competitive algorithm in the field.

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Metaheuristic-based ensemble learning: an extensive review of methods and applications

Rezk,

Selim

2024

Neural Comput & Applic

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Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.

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A hybrid meta-heuristic ensemble based classification technique speech emotion recognition

Cited by 8 publications

References 38 publications

A Brief Survey on Data Preprocessing Techniques based on Image Represented by a Fuzzy Function using Neural Network

A Brief Survey on Data Preprocessing Techniques based on Image Represented by a Fuzzy Function using Neural Network

A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron

Metaheuristic-based ensemble learning: an extensive review of methods and applications

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