scholarly journals Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification

2021 ◽  
Vol 11 (6) ◽  
pp. 5505
Author(s):  
Noratikah Zawani Mahabob ◽  
Zakiah Mohd Yusoff ◽  
Aqib Fawwaz Mohd Amidon ◽  
Nurlaila Ismail ◽  
Mohd Nasir Taib
Keyword(s):  
Linear Regression ◽  
Multilayer Perceptron ◽  
Mean Squared Error ◽  
Oil Quality ◽  
Study Data ◽  
Research Centre ◽  
Training Algorithms ◽  
Research Areas ◽  
Quality Classification ◽  
Agarwood Oil

<span>Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of on-going research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most <span>accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a</span> benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.</span>

One Versus All Strategies of Multiclass SVM in Modeling Agarwood Oil Quality Classification

2021 ◽  
Vol 69 (6) ◽  
pp. 121-125
Author(s):  
Aqib Fawwaz Mohd Amidon ◽  
Noratikah Zawani Mahabob ◽  
Nurlaila Ismail ◽  
Zakiah Mohd Yusoff ◽  
Mohd Nasir Taib
Keyword(s):  
Oil Quality ◽  
Quality Classification ◽  
Multiclass Svm ◽  
Agarwood Oil

Evaluation of RBF and MLP in SVM kernel tuned parameters for agarwood oil quality classification

2018 ◽  
Author(s):  
Khairul Anis Athirah Kamarulzaini ◽  
Nurlaila Ismail ◽  
Mohd Hezri Fazalul Rahiman ◽  
Mohd Nasir Taib ◽  
Nor Azah Mohd Ali ◽  
...  
Keyword(s):  
Oil Quality ◽  
Quality Classification ◽  
Agarwood Oil

scholarly journals Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator

2021 ◽  
Vol 8 (3) ◽  
pp. 539
Author(s):  
Ayu Ahadi Ningrum ◽  
Iwan Syarif ◽  
Agus Indra Gunawan ◽  
Edi Satriyanto ◽  
Rosmaliati Muchtar
Keyword(s):  
Quality Of Service ◽  
Deep Learning ◽  
Linear Regression ◽  
Multilayer Perceptron ◽  
Mean Squared Error ◽  
Series Data ◽  
Gradient Boosting ◽  
Root Mean Squared Error ◽  
Squared Error

<p>Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode <em>Deep Learning-LSTM. LSTM </em>adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah <em>Root Mean Squared Error</em> (RMSE) dan <em>Squared Correlation (SC</em>). Selain LSTM, penelitian ini juga menerapkan <em>algoritma Multilayer Perceptron, Linear Regression,</em> dan <em>Gradient Boosting Regressor</em> sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode <em>Deep Learning-LSTM</em> mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai <em>Squared Correlation</em>= 0,9690.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em></em><em>The quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690.</em></p>

scholarly journals Intraocular Pressure Reduction after Femtolaser Assisted Cataract Surgery and Its Association with the Use of Ultrasound

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 437
Author(s):  
Hana Abouzeid ◽  
Walter Ferrini ◽  
Murielle Bochud
Keyword(s):  
Intraocular Pressure ◽  
Linear Regression ◽  
Cataract Surgery ◽  
Anterior Chamber ◽  
Anterior Chamber Depth ◽  
Multiple Linear Regression Model ◽  
Study Data ◽  
Department Of Ophthalmology ◽  
Duration Of Surgery ◽  
Ocular Pressure

Background and Objectives: To quantify the change in intraocular pressure (IOP) after phacoemulsification in patients having undergone femtolaser assisted cataract surgery (FLACS), and study the influence of the use of ultrasound on this change. Setting: Jules-Gonin Eye Hospital, University Department of Ophthalmology, Lausanne, Switzerland. Materials and Methods: Interventional study. Methods: All consecutive cases operated with FLACS and with complete data for the studied parameters were selected for inclusion in the study. Data had been prospectively collected and was analysed retrospectively. Linear regression was performed to explore the association of change in IOP with time of measure, ultrasound use, sex, age, and duration of surgery. Results: There was a mean decrease in intraocular pressure of 2.5 mmHg (CI 95% −3.6; −1.4, p < 0.001) postoperatively. No association between the change in intraocular pressure and ultrasound time or effective phaco time was observed when the data were analyzed one at a time or in a multiple linear regression model. There was no association with sex, age, nuclear density, presence of pseudoexfoliation, duration of surgery, and time of ocular pressure measurement. Eyes with preoperative IOP ≥ 21 mmHg had a more significant IOP reduction after surgery (p < 0.0001) as did eyes with an anterior chamber depth <2.5 mm (p = 0.01). Conclusion: There was a decrease in intraocular pressure six months after FLACS in our study similar to that in the published literature for standard phacoemulsification. The use of ultrasound may not influence the size of the decrease, whereas the preoperative IOP and anterior chamber depth do. FLACS may be as valuable as standard phacoemulsification for cases where IOP reduction is needed postoperatively.

Agarwood Oil Quality Grading using OVO Multiclass Support Vector Machine

2021 ◽  
Author(s):  
A. F. M. Amidon ◽  
N. Z. Mahabob ◽  
M. H. Haron ◽  
N. Ismail ◽  
Z. M. Yusoff ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Oil Quality ◽  
Support Vector ◽  
Quality Grading ◽  
Multiclass Support Vector Machine ◽  
Agarwood Oil

scholarly journals Does physiological distribution of blood parameters in children depend on socioeconomic status? Results of a German cross-sectional study

BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e019143 ◽  
Author(s):  
Kristin Rieger ◽  
Mandy Vogel ◽  
Christoph Engel ◽  
Uta Ceglarek ◽  
Kristian Harms ◽  
...  
Keyword(s):  
Physical Activity ◽  
Socioeconomic Status ◽  
Linear Regression ◽  
Family Income ◽  
Blood Parameters ◽  
Research Centre ◽  
Regression Analyses ◽  
Pubertal Stage ◽  
Cross Sectional ◽  
Z Scores

ObjectivesIn the present study, we examined the relation between socioeconomic status (SES) and the physiological distribution of iron-related blood parameters.DesignThis is a cross-sectional analysis of longitudinal population-based cohort study.SettingBased on a sample of healthy participants from a German research centre, various blood parameters and values of clinical examinations and questionnaires were collected.ParticipantsA total of 1206 healthy volunteers aged 2.5 to 19 years, one child per family randomly selected, were included.Primary and secondary outcome measuresAssociations between the SES of children by Winkler-Stolzenberg Index (WSI) and its dimensions (income, education, occupation) and iron-related blood parameters (haemoglobin, ferritin and transferrin) were analysed by linear regression analyses. Gender and pubertal stage were included as covariables. Additionally, associations between SES of children by WSI and physical activity (side-to-side jumps, push-ups) as well as body mass index (BMI) were analysed by linear regression analyses.ResultsChildren with high WSI or family income showed significantly increased z-scores for haemoglobin (P=0.046; P<0.001). Children with increased WSI or family income showed significantly lower z-scores for transferrin (P<0.001). There was a significant correlation between haemoglobin and gender (P<0.001) and between transferrin and pubertal stage (P=0.024). Furthermore, physical activity was positively correlated and BMI was negatively correlated with WSI (P<0.001).DiscussionOur data show an association between SES and the distribution of iron-dependent parameters. Lower SES is correlated with lower values for haemoglobin and higher values for transferrin. Furthermore, we demonstrate that physical activity and BMI are associated with SES. Whereas higher SES is correlated with higher values for physical activity and lower BMI. Our parameters are standardised as z-scores with the advantages that the results are comparable across different age groups and present physiological courses.Trial registration numberNCT02550236; Results.

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