Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modelling using regression analysis and artificial neural networks

Today, the landscape aesthetic quality assessment is more technical and quantitative in environmental management. We aimed at developing artificial neural network (ANN) modelling and multiple regression (MLR) analysis approaches to predict the perceptional aesthetic quality of forest landscapes. The methodology, followed in this paper, can be divided into six distinct parts: (i) selection of representative study sites, (ii) mapping of landscape units, (iii) quantification of naturalness indicators, (iv) visibility analysis, (v) assessment of human perceptions, (vi) ANN and MLR modelling and sensitivity analysis. The results of ANN modelling, especially its high accuracy (R<sup>2</sup> = 0.871) in comparison with MLR results (R<sup>2</sup> = 0.782), introduced the forest landscape aesthetic quality model (FLAQM) as a comparative model for an assessment of forest landscape aesthetic quality. According to sensitivity analysis, the values of livestock density, tree harvesting, virgin forest, animal grazing, and tree richness were identified as the most significant variables which influence FLAQM. FLAQM can be used to compare the classes of aesthetic quality of forests.

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Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

Algorithms, Methods, and Applications in Mobile Computing and Communications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5693-0.ch007 ◽

2019 ◽

pp. 158-177

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network

Journal of Healthcare Engineering ◽

10.1155/2016/7035463 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 24

Author(s):

Pei-Fang (Jennifer) Tsai ◽

Po-Chia Chen ◽

Yen-You Chen ◽

Hao-Yuan Song ◽

Hsiu-Mei Lin ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Coronary Atherosclerosis ◽

Inpatient Care ◽

Length Of Hospital Stay ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Christian Hospital

For hospitals’ admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.

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Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

Polymers ◽

10.3390/polym13193219 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3219

Author(s):

Mohammad Saleh Meiabadi ◽

Mahmoud Moradi ◽

Mojtaba Karamimoghadam ◽

Sina Ardabili ◽

Mahdi Bodaghi ◽

...

Keyword(s):

Polylactic Acid ◽

Production Cost ◽

Cost Effective ◽

Fused Filament Fabrication ◽

3D Printers ◽

Artificial Neural ◽

End Use ◽

Artificial Neural Network Ann ◽

Part Thickness

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

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ARTIFICIAL NEURAL NETWORKS FOR PREDICTING THE GENERATION OF ACETALDEHYDE IN PET RESIN IN THE PROCESS OF INJECTION OF PLASTIC PACKAGES

International Journal for Innovation Education and Research ◽

10.31686/ijier.vol9.iss6.3150 ◽

2021 ◽

Vol 9 (6) ◽

pp. 97-119

Author(s):

Mauro Reis Nascimento ◽

David Barbosa de Alencar ◽

Manoel Henrique Reis Nascimento ◽

Carlos Alberto Monteiro

Keyword(s):

Decision Making ◽

Strategic Decision ◽

Drying Temperature ◽

Pet Bottles ◽

Injection Process ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Method Analysis ◽

Plastic Injection

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer's characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin.

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Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

Applied Computer Systems ◽

10.2478/acss-2020-0016 ◽

2020 ◽

Vol 25 (2) ◽

pp. 145-152

Author(s):

Yan Kuchin ◽

Ravil Mukhamediev ◽

Kirill Yakunin ◽

Janis Grundspenkis ◽

Adilkhan Symagulov

Keyword(s):

Neural Network ◽

Automatic Classification ◽

Training Data ◽

Classification Problems ◽

Expert Opinions ◽

Input Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Impact

AbstractMachine learning (ML) methods are nowadays widely used to automate geophysical study. Some of ML algorithms are used to solve lithological classification problems during uranium mining process. One of the key aspects of using classical ML methods is causing data features and estimating their influence on the classification. This paper presents a quantitative assessment of the impact of expert opinions on the classification process. In other words, we have prepared the data, identified the experts and performed a series of experiments with and without taking into account the fact that the expert identifier is supplied to the input of the automatic classifier during training and testing. Feedforward artificial neural network (ANN) has been used as a classifier. The results of the experiments show that the “knowledge” of the ANN of which expert interpreted the data improves the quality of the automatic classification in terms of accuracy (by 5 %) and recall (by 20 %). However, due to the fact that the input parameters of the model may depend on each other, the SHapley Additive exPlanations (SHAP) method has been used to further assess the impact of expert identifier. SHAP has allowed assessing the degree of parameter influence. It has revealed that the expert ID is at least two times more influential than any of the other input parameters of the neural network. This circumstance imposes significant restrictions on the application of ANNs to solve the task of lithological classification at the uranium deposits.

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Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

10.4018/978-1-6684-2408-7.ch022 ◽

2022 ◽

pp. 471-489

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

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Research of Artificial Neural Networks in the Al2O3 Ceramic Laser Milling Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.528.101 ◽

2014 ◽

Vol 528 ◽

pp. 101-106 ◽

Cited By ~ 1

Author(s):

Zhao Mei Xu ◽

Zong Hai Hong ◽

Gang Yang ◽

Qin Gan Wang

Keyword(s):

Laser Power ◽

Scanning Speed ◽

Milling Quality ◽

Laser Process ◽

Laser Milling ◽

The Mean ◽

Ceramic Laser ◽

Artificial Neural ◽

Artificial Neural Network Ann

Based on the artificial neural network (ANN), a model is established to describe the relation of the laser milling quality of the Al2O3ceramics with the ceramics parameters. The milling quality of Al2O3ceramics are predicted with the model in which the input parameters consist of laser power, scanning speed and defocus amount and the output parameters include the milling depth and width. The results show that the mean error is small, and the model has good verifying precision and excellent ability of predicting. The laser process parameters can be chosen easily and accurately to improve the processing quality of laser milling.

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Data Mining using Artificial Neural Network POS_NEG Rules

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v11i6.3046 ◽

2013 ◽

Vol 11 (6) ◽

pp. 2709-2714

Author(s):

Pushkar Shinde ◽

Dr. Varsha Patil

Keyword(s):

Neural Network ◽

Data Mining ◽

Sensitivity Analysis ◽

Artificial Neural Network ◽

Backpropagation Algorithm ◽

The Neural Network ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann ◽

Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

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Reusing Source Task Knowledge via Transfer Approximator in Reinforcement Transfer Learning

Symmetry ◽

10.3390/sym11010025 ◽

2018 ◽

Vol 11 (1) ◽

pp. 25 ◽

Cited By ~ 1

Author(s):

Qiao Cheng ◽

Xiangke Wang ◽

Yifeng Niu ◽

Lincheng Shen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Learning ◽

Mapping Rule ◽

Learning Methods ◽

Target Task ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The One

Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer Learning via Artificial Neural Network Approximator (TL-ANNA). It builds an Artificial Neural Network (ANN) transfer approximator to transfer the related knowledge from the source task into the target task and reuses the transferred knowledge with a Probabilistic Policy Reuse (PPR) scheme. Specifically, the transfer approximator maps the state of the target task symmetrically to states of the source task with a certain mapping rule, and activates the related knowledge (components of the action-value function) of the source task as the input of the ANNs; it then predicts the quality of the actions in the target task with the ANNs. The target learner uses the PPR scheme to bias the RL with the suggested action from the transfer approximator. In this way, the transfer approximator builds a symmetric knowledge path between the target task and the source task. In addition, two mapping rules for the transfer approximator are designed, namely, Full Mapping Rule and Group Mapping Rule. Experiments performed on the RoboCup soccer Keepaway task verified that the proposed transfer learning methods outperform two other transfer learning methods in both jumpstart and time to threshold metrics and are more robust to the quality of source knowledge. In addition, the TL-ANNA with the group mapping rule exhibits slightly worse performance than the one with the full mapping rule, but with less computation and space cost when appropriate grouping method is used.

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