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Metabolic Syndrome (MS) is the collection of risk factors for coronary artery disease (CAD). It is responsible for cardiovascular disease (CVD), type 2 diabetes, cancer, renal and mental diseases with the transition from childhood to adulthood. The MS is increasing in recent decades in different societies, especially in Iran. This study is conducted to determine the predictive factors of MS in children and adolescents in Birjand city (Capital of South Khorasan province in Iran) using a data mining approach. The four different analyses for females, males, 6-11 and 12-18 year-old group were carried out using three popular decision tree models. The most important prognostic factors for MS were high level of TG and low level of DBP and HDL in 6-11-year-old group, and high level of waist circumference (WC) and low level of TG for 12-18-year-old group. The most important factors were TG and HDL in females and WC and TG in males. Apprise teens and families to the risk factors and screening for children and teens, Monitor and control the risk factors through life style correction including more physical activity and healthy eating is recommended.
Fatemeh Taheri; Vahide Babaiyan; Mohsen Saffarian; Seyed Mahmood Kazemi; Kokab Namakin; Toba Kazemi; Azra Ramezankhani. Assessment of the Metabolic Syndrome Data in Children and Adolescence in Birjand city of Iran: A data mining based approach. 2021, 1 .
AMA StyleFatemeh Taheri, Vahide Babaiyan, Mohsen Saffarian, Seyed Mahmood Kazemi, Kokab Namakin, Toba Kazemi, Azra Ramezankhani. Assessment of the Metabolic Syndrome Data in Children and Adolescence in Birjand city of Iran: A data mining based approach. . 2021; ():1.
Chicago/Turabian StyleFatemeh Taheri; Vahide Babaiyan; Mohsen Saffarian; Seyed Mahmood Kazemi; Kokab Namakin; Toba Kazemi; Azra Ramezankhani. 2021. "Assessment of the Metabolic Syndrome Data in Children and Adolescence in Birjand city of Iran: A data mining based approach." , no. : 1.
The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.
Moein Hajiabadi; Mahdi Farhadi; Vahide Babaiyan; Abouzar Estebsari. Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities. Smart Cities 2020, 3, 842 -852.
AMA StyleMoein Hajiabadi, Mahdi Farhadi, Vahide Babaiyan, Abouzar Estebsari. Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities. Smart Cities. 2020; 3 (3):842-852.
Chicago/Turabian StyleMoein Hajiabadi; Mahdi Farhadi; Vahide Babaiyan; Abouzar Estebsari. 2020. "Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities." Smart Cities 3, no. 3: 842-852.
Cancer detection can be formulated as a binary classification in a machine learning paradigm. Loss functions are a critical part of almost every machine learning algorithm. While each loss function comes up with its own advantages and disadvantages, in this paper, inspired by ensemble methods, we propose a novel objective function that is a linear combination of single losses. We then integrate the proposed objective function into an Artificial Neural Network (ANN) to diagnose breast cancer. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via backpropagation. As the patients’ data are sometimes very noisy, we evaluate our method by doing comprehensive experiments on Wisconsin Breast Cancer Diagnosis (WBCD) dataset at different noise levels. The experiments show its performance declines very slowly (from 0.97 to 0.96) compared to the peer methods with the increase of noise level.
Hamideh Hajiabadi; Vahide Babaiyan; Davood Zabihzadeh; Moein Hajiabadi. Combination of loss functions for robust breast cancer prediction. Computers & Electrical Engineering 2020, 84, 106624 .
AMA StyleHamideh Hajiabadi, Vahide Babaiyan, Davood Zabihzadeh, Moein Hajiabadi. Combination of loss functions for robust breast cancer prediction. Computers & Electrical Engineering. 2020; 84 ():106624.
Chicago/Turabian StyleHamideh Hajiabadi; Vahide Babaiyan; Davood Zabihzadeh; Moein Hajiabadi. 2020. "Combination of loss functions for robust breast cancer prediction." Computers & Electrical Engineering 84, no. : 106624.