Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limite...
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Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimisation problem. Among the two, the pattern recognition problem has been considerably explored while the optimisation problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimisation algorithms including swarm and evolutionary algorithms. However, the focus on optimisation-based methodologies, in general, swarm and evolutionary algorithm-based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm-based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.
The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Leaming(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, swarm and Ev...
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The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Leaming(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, swarm and evolutionary (SWEVO) algorithms have also shown significant performance to improve the efficiency of the IDS models. This survey covers SWEVO-based IDS approaches such as Genetic Algorithm(GA), Ant Colony Optimization(ACO), Particle swarm Optimization(PSO), Artificial Bee Colony Optimization(ABC), Firefly Algorithm(FA), Bat Algorithm(BA), and Flower Pollination Algorithm(FPA). The paper also discusses applications of the SWEVO in the field of IDS along with challenges and possible future directions.
Microarray data has evolved into an indispensable tool for scrutinizing gene expression, prompting researchers to strategically utilize a minimal set of pertinent gene expression profiles to refine the accuracy of tum...
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ISBN:
(纸本)9783031734199;9783031734205
Microarray data has evolved into an indispensable tool for scrutinizing gene expression, prompting researchers to strategically utilize a minimal set of pertinent gene expression profiles to refine the accuracy of tumor identification. The imperative task of identifying key differential genes in colon cancers, crucial for distinguishing patients from the normal population, has led to the development of various techniques and algorithms. swarm and evolutionary algorithms (SEA), renowned for their efficiency as global search agents, have emerged as particularly effective in optimizing the selection of a pertinent subset of genes related to colon cancer. This paper introduces an innovative approach that integrates swarm and evolutionary algorithms to tackle the challenge of feature selection in datasets related to colon cancer. A thorough comparative analysis is conducted, high-lighting the differences between the proposed method and an alternative feature selection approach based on swarm and evolutionary algorithms. The extensive experimental results convincingly demonstrate the favorability and effectiveness of the proposed model, showcasing superior accuracy and a notable reduction in the number of selected features.
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