scholarly journals Non destructive evaluation quality of oil palm fresh fruit bunch (FFB) (Elaeis guineensis Jack) based on optical properties using artificial neural network (ANN)

2021 ◽  
Vol 644 (1) ◽  
pp. 012032
Author(s):  
Melidawati ◽  
D Cherie ◽  
K Fahmy ◽  
M Makky
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Oil Palm ◽  
Elaeis Guineensis ◽  
Fresh Fruit Bunch ◽  
Non Destructive Evaluation ◽  
Artificial Neural Network Ann ◽  
Non Destructive

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

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.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
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.

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

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

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.

Application of Artificial Neural Network Classification to Determine Nutrient Content in Oil Palm Leaves

2018 ◽  
Vol 34 (3) ◽  
pp. 497-504
Author(s):  
Helena Anusia James J. ◽  
Wan Ishak Wan I. ◽  
Nazmi Mat Nawi ◽  
Abdul Rashid M. Shariff ◽  
Saman Abdanan Mehdizadeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Palm ◽  
Spectral Reflectance ◽  
Partial Least Square ◽  
Nutrient Level ◽  
Palm Tree ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract. The classification of oil palm nutrient level based on leaf samples is an important factor to dictate the quality of fresh fruit bunch (FFB). The optimum nutrient level in a palm tree ensures high yield and productivity. This study evaluated a spectroradiometer of spectral bands ranging from 350 to 2500 nm to detect nutrient level in oil palm leaf samples. The features considered were types of nutrient and fronds, explored in spectral reflectance of wavelength for nutrient level determination. Results from statistical analysis using the spectral reflectance of oil palm leaves with partial least square (PLS) models were used for classification of three nutrient levels, comprising of low, optimum, and high amount of fertilization, using the artificial neural network (ANN) to inspect oil palm leaves for contents of nitrogen (N) and potassium (K). From the 90 leaf samples, the ANN models had classification performance of 85.32% accuracy for oil palm nutrient contents determination and 69.42% accuracy for frond identification. Results of this study imply the use of ANN as a prime tool for classification and identification of features in oil palm leaves. Keywords: Artificial neural network (ANN), Oil palm, Nutrients, Spectroradiometer.

scholarly journals Reusing Source Task Knowledge via Transfer Approximator in Reinforcement Transfer Learning

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
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.

Modeling of Moment of Friction under Increasing Load Using Artificial Neural Networks

2008 ◽  
Vol 144 ◽  
pp. 130-135
Author(s):  
Krzysztof Gocman ◽  
Bolesław Giemza ◽  
Tadeusz Kałdoński
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Rotational Speed ◽  
Training Algorithms ◽  
Analysis Of Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Increasing Load

Preliminary results of testing of influence of load and rotational speed on moment of friction are presented in this paper. Tests were carried out under increasing load and within the range of rotational speed of 500 – 1500 rpm. The analysis of results was elaborated and model of moment of friction was developed on the basis of artificial neural network (ANN). Different kind of networks and various training algorithms were applied in order to obtain the best quality of the developed models.

scholarly journals Using an artificial neural network to develop an optimal model of straight punch in boxing and training in punch techniques based on this model and real-time feedback

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259457
Author(s):  
Ilshat Khasanshin ◽  
Aleksey Osipov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Highly Qualified ◽  
Optimal Model ◽  
Data Set ◽  
Punch Force ◽  
Angular Velocities ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The work was aimed to develop an optimal model of a straight punch in boxing based on an artificial neural network (ANN) in the form of a multilayer perceptron, as well as to develop a technique for improving the technique of punches in boxing based on feedback, when each punch delivered by a boxer was compared with the optimal model. The architecture of the neural network optimal punch model included an input layer of 600 nodes—the values of absolute accelerations and angular velocities, four hidden ones, as well as a binary output layer (the best and not the best punch). To measure accelerations and angular velocities, inertial measuring devices were attached to the boxers’ wrists. Highly qualified participated in the data set for the development of the optimal model. The best punches were chosen according to the criteria of strength and speed. The punch force was determined using a boxing pad with the function of measuring the punch force. In order to be able to compare punches, a unified parameter was developed, called the punch quality, which is equal to the product of the effective force and the punch speed. To study the effects of biofeedback, the boxing pads were equipped with five LEDs. The more LEDs were turned on, the more the punch corresponded to the optimal model. As a result of the study, an almost linear relationship was found between the quality of the punch of entry-level boxers and the optimal model. The use of feedback allowed for an increase in the quality of punches from 11 to 25%, which is on average twice as high as in the group where the feedback method was not used. Studies have shown that it is possible to develop an optimal punch model. According to the degree of compliance with this model, you can evaluate and train boxers in the technique.

Artificial Neural Network Modeling for Electrical Discharge Machining Parameters

2014 ◽  
pp. 281-302
Author(s):  
Raja Das ◽  
M. K. Pradhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Electrodes ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The objective of the chapter is to present the application of Artificial Neural Network (ANN) modelling of the Electrical Discharge Machining (EDM) process. It establishes the best ANN model by comparing the prediction from different models under the effect of process parameters. In EDM, the motivation is frequently to get better Material Removal Rate (MRR) with fulfilling better surface quality of machined components. The vital requirements are as small a radial overcut with minimal tool wear rate. The quality of a machined surface is very important to fulfilling the growing demands of higher component performance, durability, and reliability. To improve the reliability of the machine component, it is necessary to have in depth knowledge of the effect of parameters on the aforesaid responses of the components. An extensive chain of experiments has been conducted over a wide range of input parameters, using the full factorial design. More than 150 experiments have been conducted on AISI D2 work piece materials using copper electrodes to get the data for training and testing. The additional experiments were obtained to validate the model predictions. The performance of three neural network models is discussed in the evaluation of the generalization ability of the trained neural network. It was observed that the artificial neural network models could predict the process performance with reasonable accuracy, under varying machining conditions.

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