Risk Factors of Low Back Pain Using Adaptive Neuro-Fuzzy

Samiei, Sajjad; alefi, mahsa; alaei, zahra; Pourbabaki, Reza

doi:10.18502/aoh.v3i2.672

Cited by 5 publications

(6 citation statements)

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“…3 ), and root means square error (RMSE) (Eq. 4 ) were calculated 31 , 32 : where and are the targets and network outputs, is the mean of target values, and n is the number of samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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show abstract

“…Training data is used to create the optimal model and is evaluated with the validation data set. Finally, the data are used to measure the generalizability and applicability of the model to the new data and determine the actual accuracy of it [ 34 , 45 , 46 ]. To optimize the model accuracy, the number of hidden layers, the number of and activation functions were defined by trials and errors [ 47 – 49 ].…”

Section: Methodsmentioning

confidence: 99%

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach

Saffariha

Jahani²,

Potter

2020

BMC Ecol

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Background Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, −2, −4, −6, −8, −10 and −12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development. Results Comparing to the MLR, the MLP model represents the significant value of R2 in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds. Conclusions Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands.

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“…Dengan kemampuan untuk menggabungkan sistem fuzzy dengan sistem jaringan, ANFIS dapat menghasilkan model numerik sekaligus aliran prediksi dengan kuat (Samiei et al, 2019). Dapat dikatakan bahwa ANFIS memiliki akurasi cukup bagus dalam memprediksi atau mendiagnosis (Dewi & Muslikh, 2013).…”

Section: Pendahuluanunclassified

“…Dapat dikatakan bahwa ANFIS memiliki akurasi cukup bagus dalam memprediksi atau mendiagnosis (Dewi & Muslikh, 2013). Bahwa kemampuan logika fuzzy juga telah digunakan pada banyak penyelesaian masalah kompleks dalam pemodelan, prediksi, dan artificial intelligence (Samiei et al, 2019). Jika dibandingkan dengan metode lain seperti decision tree, memiliki algoritma yang berbeda yaitu dengan memilih satu nilai untuk digunakan sebagai node akar, selanjutnya membuat cabang dari setiap nilai dan membagi kasus dalam cabang tersebut hingga kasus-kasus tersebut terkelompokkan dalam kelas yang sama (Rohmawan, 2018).…”

Section: Pendahuluanunclassified

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS

Khariri

Nurfadila

Novitasari

2022

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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Cedera panggul dan acetabular merupakan cedera yang jarang terjadi, terhitung sekitar 3% hingga 8% dari semua cedera. Meskipun angka kematian cedera panggul hanya terbatas pada 1-2%, apabila disertai dengan perdarahan intra-abdominal atau pada intracranial menimbulkan kematian tertinggi yaitu 50%. Kematian akibat cedera panggul terbilang tinggi ketika penanganan awal dan akurat tidak diperhatikan. Pada penelitian ini dilakukan bertujuan mendeteksi tingkat keparahan penderita cedera panggul menggunakan adaptive neuro-fuzzy inference system (ANFIS). Bertujuan membantu medis dalam memberikan penanganan sesuai dengan tingkat keparahan cedera panggul. Penelitian dengan metode ANFIS untuk mendeteksi keparahan cedera panggul mendapatkan nilai error dengan confusion matrix yang bernilai kecil yaitu 0.3362, sehingga menghasilkan akurasi sebesar 98,1%, presisi sebesar 97.7%, serta nilai dari sensitifitas dan F-skor sebesar 100% dan pada penelitian lain yang menggunakan ANN (Artificial Neural Network) menghasilkan keakuratan deteksi sebesar 87% dan area under the curve (AUC) sebesar 0.94.

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Risk Factors of Low Back Pain Using Adaptive Neuro-Fuzzy

Cited by 5 publications

References 0 publications

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS

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