Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Arivalagan, S.; Venkatachalapathy, K.

doi:10.5120/8846-3040

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…The service deployment has been peak due to development edge computing in research field to reduce the delay, storage process. The [15] introduced the algorithm for optimizing the interactions between edge components in big data management and cloud. By combining the edge computing with cloud computing, became new technology for networks and communication.…”

Section: Literature Surveymentioning

confidence: 99%

“…Several cloud computing servers communicate with the edge computing components in transferring the data. The main problem in previous edge-cloud hybrid server is communication bandwidth in the network between the cloud computing servers and edge computing servers increases the complexity of the system [15]. To overcome this in our architecture we have only one cloud servers and few edge devices.…”

Section: Fig 2: Service Dependency Modelmentioning

confidence: 99%

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A Survey on Service Level Components in Big-Cloud-IoT Systems with Hybrid Meta-heuristic Techniques

Yin¹,

Chen

2020

International Journal of Advanced Information and Communication Technology

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Big data is one such technology. When we receive huge volume of data, there will be high demand in processing the huge data. It can also be said challenging task in big data processing. The increases in IoT devices in the network system collect more data to be processed in centralized devices called cloud storage. Every big data is processed and stored in the cloud. To overcome the performance and latency issues in large data computation, big cloud processing system uses edge computing in it. One of the key components of IoT is edge computing. We combine big data with cloud and edge computing in this paper as hybrid edge computing system. In the edge computing system, huge number of IoT devices computes services in its nearby network edge. Data sharing and transmission between the various service components may affect performance of the system. The main aim of this research article is to reduce the delay in data transfer between the components. This optimization goal is achieved by new Hybrid Meta-heuristic optimization (HMeO) algorithm. New HMeO algorithm designed for IoT devices to deploy the service components. MHO model is design to optimize the process by selecting the edge computing with minimum latency. Our proposed HMeO algorithm is compared with existing genetic algorithm and ant colony algorithm. The result shows HMeO algorithm provides more performance and efficient in in-depth data analysing and locating the component in big databased cloud environment.

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Section: Literature Surveymentioning

confidence: 99%

Section: Fig 2: Service Dependency Modelmentioning

confidence: 99%

A Survey on Service Level Components in Big-Cloud-IoT Systems with Hybrid Meta-heuristic Techniques

Yin¹,

Chen

2020

International Journal of Advanced Information and Communication Technology

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“…This research has developed prediction techniques to enhance the day-to-day forecast of electricity prices through multi-stage optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA). BFOA has been applied in a number of areas such as face identification [15], [16], biometric verification [17], multimodal functions [18], flexible manufacturing systems (FMS) [19], control and power systems [28], [34]. To date, the authors have found no studies that incorporate LSSVM and BFOA in electricity pricing prediction methods.…”

Section: Introductionmentioning

confidence: 99%

About The Conference

2020

2020 IEEE Student Conference on Research and Development (SCOReD)

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Predicting the price of electricity is an important aspect in the operation and planning of power systems. However, predicting the price of electricity is a relatively challenging task as it faces very uncertain conditions. Hence, this study proposes a hybrid Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) for day-ahead electricity price forecast. The main contribution of this work is the multistage optimization approach of LSSVM-BFOA that can improve the forecasting accuracy and efficiency. This is achieved by optimizing the input features and parameters of LSSVM at the same time. The input features have been reduced by six optimization levels in order to avoid losing any significant input. At the same time, the average MAPE is observed and the second stage of optimization is carried out. These processes are performed until there is no improvement in MAPE is observed. This model is examined in the Ontario power market. The LSSVM-BFOA model developed showed higher prediction accuracy with less complex model structure than most existing models. The day ahead price forecast is beneficial for both power generators and consumers in bidding for electricity prices.

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“…Hence, this study introduces a new technique in electricity price forecast by developing hour-ahead electricity price forecasting model with Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA). BFOA has a fast convergence [12] and has been explored in many fields such as face recognition [13], [14], biometric authentication [15], multimodal function [16], [17], and flexible manufacturing systems (FMS) [18]. Furthermore, researchers in control and power system developed BFOA models for Static Synchronous Series Compensator (SSSC) Damping Controller Design [19], robotic manipulator workspace optimization [20], three phase induction motor and electricity load forecasting [28], [34].…”

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confidence: 99%

An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm

Razak¹,

Abidin²,

Yap³

et al. 2018

IJEECS

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<span lang="EN-US">Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.</span>

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Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Cited by 7 publications

References 19 publications

A Survey on Service Level Components in Big-Cloud-IoT Systems with Hybrid Meta-heuristic Techniques

A Survey on Service Level Components in Big-Cloud-IoT Systems with Hybrid Meta-heuristic Techniques

About The Conference

An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm

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