Optimizing feature selection in video-based recognition using Max–Min Ant System for the online video contextual advertisement user-oriented system

Bao, Le Nguyen; Le, Dac‐Nhuong; Nguyen, Gia Nhu; Bhateja, Vikrant; Satapathy, Suresh Chandra

doi:10.1016/j.jocs.2016.10.016

Cited by 28 publications

(4 citation statements)

References 23 publications

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“…Min-max normalization method -Helps in data normalization or standardization -Ensures values of features are on a similar scale to prevent certain features from dominating the learning process -Helps to speed up the training process -Utilized after feature selection and dimensionality reduction -Helps to prevent convergence difficulty [86], [87], [108], [110], [144] Z-score normalization (standardization)…”

Section: A Addressing Imbalance Datamentioning

confidence: 99%

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Adekunle,

Fofana,

Picher

et al. 2024

IEEE Access

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In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment's safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.INDEX TERMS Insulating paper, ageing, degree of polymerization, prognostics and health management, machine learning model.

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Section: A Addressing Imbalance Datamentioning

confidence: 99%

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Adekunle,

Fofana,

Picher

et al. 2024

IEEE Access

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“…So, there is a need to optimize such spread by formulating nonlinear optimization problems via ordinary arithmetic operations rather than fuzzy operations. PSO was utilized to construct their membership functions [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. From all the above, it can be seen that no one metaheuristics algorithm can solve all kinds of problem domains; therefore, there is always a need for new algorithms that can address many types of problem domains.…”

Section: Literature Reviewmentioning

confidence: 99%

The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm

AL-kubaisy¹,

Yousif²,

Al‐Khateeb³

et al. 2021

IJCIS

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The presented study suggests a new nature-inspired metaheuristic optimization algorithm referred to as Red Colobuses Monkey (RCM) that can be used for optimization problems; this algorithm mimics the behavior related to red monkeys in nature. In preparation for proving the suggested algorithm's advantages, a set of standard unconstrained and constrained test functions is employed, sixty-four of identified test functions utilized in optimization were applied as benchmarks for checking the RCM performance. The solutions have also been upgrading their positions based on the optimal solution, which was reached thus far. Also, RCM can replace the worst red monkey by the best child found so far to give an extra enhancement to the solutions. Also, comparative performance checks with Biogeography-Based Optimizer (BBO), Artificial-Bee-Colony (ABC), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) were done. The acquired results showed that RCM is competitive in comparison to the chosen metaheuristic algorithms.

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“…PSO [20,[55][56][57] is an advanced computational technique which has been used previously, similar to GA [18], differential evolution (DE) [19], and ant colony optimization (ACO) [25,58,59]. The authors which proposed the algorithm were inspired by the idea of swarm intelligence, as typically seen in animal groups, such as birds, fish, and insects.…”

Section: A Novel Pso Algorithm Applied For Multi-round Procurement Problemmentioning

confidence: 99%

A Novel Particle Swarm Optimization Approach to Support Decision-Making in the Multi-Round of an Auction by Game Theory

Bao¹,

Huynh²,

Nguyen³

et al. 2020

IJCIS

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In this paper, game-theoretic optimization by particle swarm optimization (PSO) is used to determine the Nash equilibrium value, in order to resolve the confusion in choosing appropriate bidders in multi-round procurement. To this end, we introduce an approach that proposes (i) a game-theoretic model of the multi-round procurement problem; (ii) a Nash equilibrium strategy corresponding to the multi-round strategy bid; and (iii) an application of PSO for the determination of the global Nash equilibrium point. The balance point in Nash equilibrium can help to maintain a sustainable structure, not only in terms of project management but also in terms of future cooperation. As an alternative to procuring entities subjectively, a methodology using Nash equilibrium to support decision-making is developed to create a balance point that benefits procurement in which buyers and suppliers need multiple rounds of bidding. To solve complex optimization problems like this, PSO has been found to be one of the most effective meta-heuristic algorithms. These results propose a sustainable optimization procedure for the question of how to choose bidders and ensure a win-win relationship for all participants involved in the multi-round procurement process.

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Optimizing feature selection in video-based recognition using Max–Min Ant System for the online video contextual advertisement user-oriented system

Cited by 28 publications

References 23 publications

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

Analyzing Transformer Insulation Paper Prognostics and Health Management: A Modeling Framework Perspective

The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm

A Novel Particle Swarm Optimization Approach to Support Decision-Making in the Multi-Round of an Auction by Game Theory

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