Internship Effect Prediction for Physical Education Majors Based on Artificial Neural Network

Professional internship offers college students a golden chance to apply their theoretical knowledge to practice. Through internship, physical education (PE) majors can match the professional knowledge and skills learned at school with the competencies required by actual jobs. The relevant studies at home and abroad mainly attempt to improve the internship effect. This paper explores the influence of the diversity of job competencies on the internship effect of PE majors, and establishes a prediction model based on artificial neural network (ANN). Firstly, an evaluation index system (EIS) was constructed for the internship quality of PE majors, and a table was prepared for four types of internship jobs for PE majors, as well as their core competences. Then, the sample data for quality evaluation of PE majors’ internship were preprocessed and subjected to feature extraction, in the light of their sequential property. After that, a prediction model was proposed for the internship quality of PE majors, along with its optimization algorithm. The proposed model was proved effective through experiments.

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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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A Prediction Model of Deflection of Fixing Thin Parts

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.217-219.1526 ◽

2012 ◽

Vol 217-219 ◽

pp. 1526-1529

Author(s):

Yu Mei Liu ◽

Wen Ping Liu ◽

Zhao Liang Jiang ◽

Zhi Li

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Prediction Model ◽

Simulated Data ◽

Artificial Neural ◽

Predicted Values ◽

Artificial Neural Network Ann ◽

Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

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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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Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v16i08.22133 ◽

2021 ◽

Vol 16 (08) ◽

pp. 188

Author(s):

Shu Ji ◽

Jun Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Influencing Factors ◽

Evaluation Model ◽

Intelligent Manufacturing ◽

Evaluation Index System ◽

Vocational Schools ◽

Participation Motivation ◽

Artificial Neural ◽

Artificial Neural Network Ann

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.

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The flood prediction model using Artificial Neural Network (ANN) and weather Application Programming Interface (API) as an alternative effort to flood mitigation in the Jenelata Sub-watershed

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/930/1/012080 ◽

2020 ◽

Vol 930 ◽

pp. 012080

Author(s):

O M Gessang ◽

U Lasminto

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Application Programming Interface ◽

Flood Mitigation ◽

Flood Prediction ◽

Application Programming ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Programming Interface

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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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Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

Jurnal Teknik Industri ◽

10.22219/jtiumm.vol20.no2.152-160 ◽

2019 ◽

Vol 20 (2) ◽

pp. 152

Author(s):

Indra Cahyadi ◽

Heri Awalul Ilhamsah ◽

Ika Deefi Anna

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Speed ◽

Prediction Model ◽

Factorial Design ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Model Performance ◽

Artificial Neural ◽

Artificial Neural Network Ann

In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura. The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.

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An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

Revue d intelligence artificielle ◽

10.18280/ria.340515 ◽

2020 ◽

Vol 34 (5) ◽

pp. 637-644

Author(s):

Jianfeng Cheng

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Principal Component ◽

Principal Evaluation ◽

Evaluation Index System ◽

Benefit Sharing ◽

Optimized Model ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Smart Mobile

With the proliferation of the Internet and smart mobile terminals, great progress has been made in the precision placement and benefit-sharing mechanism of commercial advertisements. Meanwhile, media marketing has become increasingly in-depth and precise. So far, mature theories have been proposed on consumer value and precision marketing. But further research is needed to mine the value from the big data on commercial precision marketing. To improve the accuracy of commercial precision marketing, this paper presents an evaluation index system (EIS) for commercial precision marketing based on improved attention-interest-desire-memory-action (ADIMA) model, and determines the principal evaluation indices through principal component analysis (PCA). Next, an artificial neural network (ANN) was established to evaluate commercial precision marketing, and optimized through k-means clustering (KMC). Finally, the optimized model was realized on MATLAB. The proposed EIS and ANN were proved scientific and effectiveness through simulations. The research results provide a reference for the application of the ANN in other fields of marketing.

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By Using Logarithmic Regression and Artificial Neural Network to Improve Prediction Model of Dead Number Resulted from Road Traffic Accidents in Turkey

Karaelmas Science and Engineering Journal ◽

10.7212/zkufbd.v8i2.1101 ◽

2018 ◽

pp. 446-453

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Traffic Accidents ◽

Road Traffic ◽

Data Sets ◽

Damage Prediction ◽

Independent Variables ◽

Artificial Neural ◽

Artificial Neural Network Ann

Traffic accidents occurred on highway in Turkey cause materially and morally damage. To decrease the damage, prediction model developed. In this study, demographic and traffic data which from 1970 to 2007 are used. These data are consist of dependent and independent variables. Dependent variable is formed Number of Dead (ND). As for independent variables are comprised Population (P), Registered Number of Vehicle (VN), Vehicle-km (VK), Number of Drivers (DN). Models are developed using Artificial Neural Network (ANN) and Logarithmic Regression (LR) enhanced by Smeed. PVNVKDN model developed taking real values logarithm is the best performance of models in LR technique. VKDN created by using historical data sets is the best model in ANN technique. As for models created by randomly selected data, the best model is VKDN. When performances of best models are compared, VKDN is the best model because of lowest error rate.

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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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