A decision support methodology with risk assessment on prediction of terrorism insurgency distribution range radius and elapsing time: An empirical case study in Thailand

Kengpol, Athakorn; Neungrit, Pakorn

doi:10.1016/j.cie.2014.06.003

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…In the experiment, the parameters are set as follows: number of food categories = 10, number of epochs = 20, size of input image = 128 × 128, number of training images = 4,000, and number of testing images = 1,000. The model separates the data with 80% and 20% optimal ratios for training and testing, respectively (Kengpol & Neungrit, 2014). The input shape has the dimensions (k × k × n) 128 × 128 × 3, indicating that the vegetarian food image is 128 × 128 pixels in RGB color, so n is 3 if the image is white and black n is 1.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Knowledge Management of Vegetarian Food for the Elderly Using DCNN

Kengpol

Punyota

2022

International Journal of Knowledge and Systems Science

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According to the literature reviews on knowledge management, no evidence has been found on the knowledge management of vegetarian food among elderly people with chronic diseases. The objective of this research is to apply knowledge management in identifying appropriate vegetarian food for the elderly with chronic disease by using the deep convolutional neural network (DCNN). The contribution of this research is to enable people to use knowledge management and collect knowledge to create a machine learning algorithm system so that the elderly can access knowledge of vegetarian food in relation to chronic disease. The benefits of this research are that the elderly can learn to consume appropriate food based upon their chronic disease, and the food producers can provide food menus accordingly.

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Section: Experiments and Resultsmentioning

confidence: 99%

Knowledge Management of Vegetarian Food for the Elderly Using DCNN

Kengpol

Punyota

2022

International Journal of Knowledge and Systems Science

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“…The ANN involves computations and mathematical models that include diverse processing components comparable with human brain processing, which obtain inputs and transfer output based upon their predetermined activation functions (Kengpol and Neungrit, 2014). The most widely used ANN for a wide range of issues is based upon a supervised procedure that employs multilayer perceptrons (MLPs) with BP learning algorithms.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Then, the activation function is used to compute the optimal solution in the output model through the summation of the input node values multiplied by their assigned weights. The mathematical function of the ANN can be explained as follows (Kengpol and Neungrit, 2014).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Design of hybrid deep learning using TSA with ANN for cost evaluation in the plastic injection industry

Kengpol,

Tabkosai

2024

Front. Mech. Eng.

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In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry.

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“…ANN has been found to be the domain for many successful applications of prediction tasks, in modelling and prediction of energy-engineering systems [22], prediction of the energy consumption of passive solar buildings [23], developing energy system and forecast of energy consumption [24], and analysis of reduction of emissions [25]. There are also some relevant reports of ANN's use based on decision support systems in various subjects such as solving the buffer allocation problem in reliable production [26], developing environmental emergency decision support systems [27], risk assessment on prediction of terrorism insurgency [28] and metamodeling of simulation metamodel [29]. ANN has been used to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings [30].…”

Section: The Use Of Artificial Neural Network and Related Literaturementioning

confidence: 99%

An artificial neural network based decision support system for energy efficient ship operations

Beşikçi

Arslan

Turan

et al. 2016

Computers & Operations Research

217

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Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -'Noon Data' -which provides information on a ship's daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.

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A decision support methodology with risk assessment on prediction of terrorism insurgency distribution range radius and elapsing time: An empirical case study in Thailand

Cited by 13 publications

References 14 publications

Knowledge Management of Vegetarian Food for the Elderly Using DCNN

Knowledge Management of Vegetarian Food for the Elderly Using DCNN

Design of hybrid deep learning using TSA with ANN for cost evaluation in the plastic injection industry

An artificial neural network based decision support system for energy efficient ship operations

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