scholarly journals An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs

2020 ◽  
Vol 27 (4) ◽  
pp. 296
Author(s):  
Mirwan Ushada ◽  
Hani Febri Mustika ◽  
Aina Musdholifah ◽  
Tsuyoshi Okayama
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Heart Rate ◽  
Artificial Neural Network ◽  
Optimization Model ◽  
Artificial Neural Network Model ◽  
Fitness Function ◽  
Research Result ◽  
Assessment System ◽  
Optimal Set

Environmental ergonomics in bioproduction of food Small Medium-sized Enterprises (SMEs) become a concern and need to be optimized. An optimization model was developed using a Genetic Algorithm (GA). The weight of an Artificial Neural Network Model was used as a fitness function for GA. The research objectives were: 1) To design an environmental ergonomic assessment system for bioproduction of Food SMEs, 2) To develop an optimization model for environmental ergonomic assessment using a Genetic Algorithm. GA is utilized to search optimal set points of environmental ergonomics based on the predicted fitness values. Each chromosome of GA represents the environmental ergonomics value. The parameters were heart rate, bioproduction temperature, distribution of bioproduction relative humidity and light intensity. The target of the optimization model was the bioproduction temperature set points. The research result indicated the model generated optimum values of environmental ergonomics parameter in bioproduction of food SMEs. The parameters could be used to provide standard workplace environment for the sustainability of food SMEs.

scholarly journals Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study

2013 ◽  
Vol 16 (1) ◽  
pp. 218-230 ◽  
Author(s):  
Gooyong Lee ◽  
Sangeun Lee ◽  
Heekyung Park
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Level ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
River Management ◽  
Water Levels ◽  
Short Term

This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water levels at 15 points of four major rivers in Korea. This case study demonstrates that the approach can be highly compatible with the real river situations, such as hydrological disturbances and water infrastructure under emergencies. Therefore, proper adoption of this approach into a river management system certainly improves the adaptive capacity of the system.

scholarly journals Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3415-3421
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Virtual Machines ◽  
Fitness Function ◽  
Medium Size ◽  
Cloud Environment ◽  
Edos Attacks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Economic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.

Genetic Algorithm–Neural Network Estimation of Cobb Angle from Torso Asymmetry in Scoliosis

2002 ◽  
Vol 124 (5) ◽  
pp. 496-503 ◽  
Author(s):  
Jacob L. Jaremko ◽  
Philippe Poncet ◽  
Janet Ronsky ◽  
James Harder ◽  
Jean Dansereau ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Cobb Angle ◽  
Longitudinal Studies ◽  
X Rays ◽  
Test Set ◽  
Network Estimation ◽  
Optimal Set ◽  
Scoliosis Progression

Scoliosis severity, measured by the Cobb angle, was estimated by artificial neural network from indices of torso surface asymmetry using a genetic algorithm to select the optimal set of input torso indices. Estimates of the Cobb angle were accurate within 5° in two-thirds, and within 10° in six-sevenths, of a test set of 115 scans of 48 scoliosis patients, showing promise for future longitudinal studies to detect scoliosis progression without use of X-rays.

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