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

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

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.

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 ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

10.12783/asc36/35819 ◽

2021 ◽

Author(s):

DEVIN NIELSEN ◽

TYLER LOTT ◽

SOM DUTTA ◽

JUHYEONG LEE

Keyword(s):

Neural Network ◽

Network Architecture ◽

Back Propagation ◽

Training Dataset ◽

Predictive Tool ◽

Ann Models ◽

Artificial Neural ◽

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2021070103 ◽

2021 ◽

Vol 12 (3) ◽

pp. 35-43

Author(s):

Pratibha Verma ◽

Vineet Kumar Awasthi ◽

Sanat Kumar Sahu

Keyword(s):

Neural Network ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Multilayer Perceptron ◽

Artificial Neural ◽

Artery Disease ◽

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Application of artificial neural network for modeling of phenol mineralization by photo-Fenton process using a multi-lamp reactor

Water Science & Technology ◽

10.2166/wst.2013.731 ◽

2013 ◽

Vol 69 (4) ◽

pp. 768-774 ◽

Cited By ~ 6

Author(s):

André L. N. Mota ◽

Osvaldo Chiavone-Filho ◽

Syllos S. da Silva ◽

Edson L. Foletto ◽

José E. F. Moraes ◽

...

Keyword(s):

Neural Network ◽

Uv Light ◽

Fenton Process ◽

Ann Model ◽

Ferrous Ions ◽

Phenol Mineralization ◽

Artificial Neural ◽

An artificial neural network (ANN) was implemented for modeling phenol mineralization in aqueous solution using the photo-Fenton process. The experiments were conducted in a photochemical multi-lamp reactor equipped with twelve fluorescent black light lamps (40 W each) irradiating UV light. A three-layer neural network was optimized in order to model the behavior of the process. The concentrations of ferrous ions and hydrogen peroxide, and the reaction time were introduced as inputs of the network and the efficiency of phenol mineralization was expressed in terms of dissolved organic carbon (DOC) as an output. Both concentrations of Fe2+ and H2O2 were shown to be significant parameters on the phenol mineralization process. The ANN model provided the best result through the application of six neurons in the hidden layer, resulting in a high determination coefficient. The ANN model was shown to be efficient in the simulation of phenol mineralization through the photo-Fenton process using a multi-lamp reactor.

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 ◽

Coronary Atherosclerosis ◽

Inpatient Care ◽

Length Of Hospital Stay ◽

Ann Models ◽

Artificial Neural ◽

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.

Predicting the Required Volume Need for Architectural Work Using Artificial Neural Networks in Hospital Buildings

Journal of the Civil Engineering Forum ◽

10.22146/jcef.39772 ◽

2019 ◽

Vol 5 (1) ◽

pp. 83

Author(s):

Aulia Yudha Prathama

Keyword(s):

Neural Network ◽

Estimation Accuracy ◽

Construction Design ◽

Work Volume ◽

Building Area ◽

Artificial Neural ◽

Input Variables ◽

Decision-making in construction design has an important role. The need for estimation tools of planning and project management aspects needs to develop. This paper discussed the benefits of artificial neural network methodology to overcome the problem of estimated the needs of the volume of wall paired, ceiling worked pairing, and ceramic floor pairing for architectural work at the designed stage of the building. The average architecture cost of state building is 29%-51% of total construction value. Data from 15 projects was used for being trained and tested by Artificial Neural Network (ANN) methods with 5 design input variables. The ANN helped to estimate the value of volume requirement on the architectural working of Pratama Hospital building project in remote areas of Indonesia. Those input variables include building area, average column span distance, the height of the building, the shape of the building, and a number of inpatient rooms. From ANN simulation, the best empirical equation of P2V5 modeling was used to predict the need of hospital architecture work volume at conceptual stage with best ANN structure 5-9-3 (5 input variables, 1 hidden layer with 9 neurons and 3 output) with result of estimation accuracy a maximum of 96.40% was reached.

A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid

Scientific Reports ◽

10.1038/s41598-021-96808-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mohammad Hemmat Esfe ◽

S. Ali Eftekhari ◽

Maboud Hekmatifar ◽

Davood Toghraie

Keyword(s):

Neural Network ◽

Experimental Tests ◽

Hybrid Nanofluid ◽

Trial And Error ◽

Shear Rates ◽

Artificial Neural ◽

Hidden Layer ◽

Increasing Temperature

AbstractIn this study, the influence of different volume fractions ($$\phi$$ ϕ ) of nanoparticles and temperatures on the dynamic viscosity ($$\mu_{nf}$$ μ nf ) of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the $$\mu_{nf}$$ μ nf was derived for 203 various experiments through a series of experimental tests, including a combination of 7 different $$\phi$$ ϕ , 6 various temperatures, and 5 shear rates. These data were then used to train an artificial neural network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward perceptron ANN with two inputs (T and $$\phi$$ ϕ ) and one output ($$\mu_{nf}$$ μ nf ) was used. The best topology of the ANN was determined by trial and error, and a two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. A well-trained ANN is created using the trainbr algorithm and showed an MSE value of 4.3e−3 along 0.999 as a correlation coefficient for predicting $$\mu_{nf}$$ μ nf . The results show that an increase $$\phi$$ ϕ has a significant effect on $$\mu_{nf}$$ μ nf value. As $$\phi$$ ϕ increases, the viscosity of this nanofluid increases at all temperatures. On the other hand, with increasing temperature, the viscosity of this nanofluid decreases. Based on all of the diagrams presented for the trained ANNs, we can conclude that a well-trained ANN can be used as an approximating function for predicting the $$\mu_{nf}$$ μ nf .

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

Revista de Chimie ◽

10.37358/rc.08.10.1988 ◽

2008 ◽

Vol 59 (10) ◽

Author(s):

Gozde Pektas ◽

Erdal Dinc ◽

Dumitru Baleanu

Keyword(s):

Neural Network ◽

Transfer Function ◽

Back Propagation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Artificial Neural ◽

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

Prediction of Drape Coefficient by Artificial Neural Network

Autex Research Journal ◽

10.1515/aut-2015-0045 ◽

2015 ◽

Vol 15 (4) ◽

pp. 266-274 ◽

Cited By ~ 5

Author(s):

Adel Ghith ◽

Thouraya Hamdi ◽

Faten Fayala

Keyword(s):

Neural Network ◽

Back Propagation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Propagation Algorithm ◽

Artificial Neural ◽

Hidden Layer ◽

Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

ANALISIS KECEPATAN KERJA SACO SAAT MANUVER BEBAN DI BANDARA NGURAH RAI MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

Jurnal SPEKTRUM ◽

10.24843/spektrum.2017.v04.i01.p02 ◽

2017 ◽

Vol 4 (1) ◽

pp. 8

Author(s):

Dewa Ngakan Made Barel ◽

I Gede Dyana Arjana ◽

Widyadi Setiawan

Keyword(s):

Neural Network ◽

Artificial Neural ◽

Bandara Ngurah Rai memiliki beban daya sebesar 10.380 kVA, disuplai oleh duapenyulang yakni penyulang Gayatri dan peyulang Bandara. Penyulang Gayatri sebagaipenyulang utama dan penyulang Bandara sebagai penyulang cadangan. Penggunaan duapenyulang ini bertujuan untuk mengatasi gangguan pada jaringan, dikarenakan sensitifitasperalatan listrik di bandara yang tinggi. Namun apabila terjadi gangguan pada penyulangGayatri maka suplai beban secara otomatis akan dipindahkan ke penyulang Bandaramenggunakan sakelar otomatis bernama Switching Automatic Change Over (SACO). Tingkatsensitifitas peralatan listrik Bandara Ngurah Rai yang tinggi mengijinkan batas jatuh teganganmaksimum adalah 0,5 kV dari nilai tegangan nominal penyulang, dari permasalahan tersebutmaka dilakukan analisis perhitungan jatuh tegangan dan hubung singkat pada penyulangGayatri. Analisis ini menggunakan program Artificial Neural Network (ANN). Parameter yangdigunakan antara lain jumlah iterasi yaitu 60000 epoch, kecepatan pembelajaran 0,3 danhidden layer sebanyak 40 hidden layer, dimana nilai target pengujiannya adalah 0,00001.Proses pelatihan dan pengujian yang telah dilakukan menghasilkan nilai jatuh tegangan padatitik 100% (ujung penyulang) adalah 274,7 volt dengan nilai Mean Squarred Error (MSE) yaitu45,5. Nilai waktu tunda rele tercepat yaitu 0,3 detik pada titik gangguan 5%, dan nilai MSEdengan hasil terbaik adalah 0,00032 untuk gangguan 1 fasa ke tanah.