Breast tumor diagnosis in digital mammograms

Breast cancer has been classified as the most common cancer in most part of the world [1]. Breast cancer is caused by the growth of the abnormal cells in the breast. Breast cancer not only develops in women but also on men. However, the incidents of breast cancer in women are more common than men. Breast cancer is dangerous and may take away one’s life if there is no early detection and treatment are not done to remove the cancer cell present in the breast. Although the prevention methods for breast cancer may be unclear, it is found out that the earlier the detection and treatment conducted to the patients, the higher the survivability of the patients. Digital mammography is a specific type of breast imaging that uses low-dose x-rays to detect cancer early especially before women experience any symptoms [2]. The early signs of breast cancer can be detected in mammograms. Hence, digital mammograms have been classified as one of the best methods to detect breast cancer. In the studies [2] has shown that digital mammograms produce a better result than film mammograms in a group of young women, premenopausal and perimenopausal women, and women with dense breasts. 335 women were found to be infected with breast cancer in the test. However, there is also a limitation present in digital mammograms. High breast density can affect the performance of diagnosis in digital mammography as it increases the difficulty in finding abnormalities in a mammogram. Digital mammograms are only able to yield the best accuracy in the result for the women who are under the age of 50 and absent from menopause or undergoes menopause in a period of less than one year

Forecasting electricity consumption using SARIMA method in IBM SPSS software

Forecasting is a prediction of future values based on historical data. It can be conducted using various methods such as statistical methods or machine learning techniques. Electricity is a necessity of modern life. Hence, accurate forecasting of electricity demand is important. Overestimation will cause a waste of energy but underestimation leads to higher operation costs. Univesity Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian technical university, therefore there is a need to forecast UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. The monthly UTHM electricity consumption data exhibits seasonality-periodic fluctuations. Thus, the seasonal Autoregressive Integrated Moving Average (SARIMA) method was applied in IBM SPSS software to predict UTHM electricity consumption for 2019 via Box-Jenkins method and Expert Modeler. There were a total of 120 observations taken from January year 2009 to December year 2018 to build the models. The best model from both methods is SARIMA(0, 1, 1)(0, 1, 1)12. It was found that the result through the Box-Jenkins method is approximately the same with the result generated through Expert Modeler in SPSS with MAPE of 8.4%

Solitary wave modulation in an artery with stenosis filled with a viscous fluid

In this study, the derivation of mathematical model for the wave modulation through an incompressible viscous fluid contained in a prestressed thin stenosed elastic tube is presented. The artery is assumed to be incompressible, prestressed thin walled elastic tube with a symmetrical stenosis, whereas the blood is considered to be incompressible and Newtonian fluid. By utilizing the nonlinear equations of tube and fluid, the weakly nonlinear wave modulation in such a medium is examined. Employing the reductive perturbation method and considering the long-wave approximation, we showed that the third-order term in the perturbation expansion is governed by the dissipative nonlinear Schrodinger equation with variable coefficient. Our results shown that this type of equation admits a downward bell-shape wave propagates to the right as time increases with decreasing wave amplitude

Energy Poverty Impact on the Economics of Indonesia Using ARDL Approach

Energy poverty is a global threat to human development path. This study is about the cointegration relationship between energy poverty and the economy of Indonesia for the period of 1995 to 2014. Autoregressive Distributed Lag (ARDL) model and vector error correction model (VECM) were used in this study to study the cointegration and causality analysis. Unit root test and stability test were adopted to increase the reliability and accuracy of the model. The analysis shows that parity purchase power (PPP) has a positive relationship with inflation (INF) in both long-run and short-run. Result shows in long-run, the increment of 1% for both energy consumption (EC) and PPP will result -1.12% and 0.032% effect respectively towards inflation in Indonesia. While for 1% increase in energy consumption is expected to give 1.5297% increment on inflation in short-run cases. Granger causality test shows only unidirectional causality between parity purchase power and inflation in both the long-run and short-run. Energy consumption only shows unidirectional causality toward inflation in the long-run. Overall mean increase of PPP or EC has a single direction influence on the inflation rate. The study can aid policy planning in eradication energy poverty

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Study on the gas performance of ceramic membrane from kaolin prepared by phase inversion technique

Membrane for gas application have been widely used. Apart from that, ceramic membrane is gaining much attention towards separation technology due to its characteristics of offering high mechanical strength, chemical resistivity and thermal compatibility. However, production of ceramic membrane for gas separation in term of cost and energy reduce still remains as challenge until now, therefore this work addressed to the development of ceramic membrane from kaolin via simple phase inversion technique. First, ceramic membrane suspension have been prepared by stirring kaolin as raw material, N-methyl-2-pyrollidone (NMP) as solvent and polyethersulfone (PESf) as binder. Phase inversion tchnique conducted by casted the suspension on the glass plate with casting knife. In order to achieve the aims of this study, the development of ceramic membrane from kaolin were conducted into two objectives: (1) effect of particles sizes and, (2) effect of non-solvent coagulant bath. The types of kaolin particle sizes devoted as Type A kaolin (0.4-0.6µm) and Type B kaolin (10-15µm) whereas for different types of non-solvent were distilled water, ethanol and mixture of 70% NMP and 30% distilled water. Overall analysis showed that both effect of particle size and different caogulant generated different structure, properties and characteristic of membrane at two different composition. Polymer phase inversion is dominated at kaolin content of 24 to 34wt.% that caused the formation of finger-like voids of the phase inversed structure with Type A kaolin and strong caogulant of distilled water. An opposed condition was shown at highest kaolin content of 39 wt.% for both parameter that can be correlated to the viscous fingering mechanism in the formation of ceramic membrane structure. A slightly similar results trends and pattern was demostrated with Type B kaolin and weakest coagulant (mixture of 70% NMP and 30% distilled water). Overall performance showed that membrane with Type A kaolin and immersed into ethanol as coagulant at kaolin content of 39 wt.% showed the highest rejection (5.49 and 5.82 for CO2/N2 and O2/N2, respectively)

Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%

Tsunami & Forced Korteweg de Vries Equation

A systematic and comprehensive study of forced solitons in Tsunami waves that can be modelled mathematically by forced Korteweg-de Vries (fKdV). This equation have lost the translation-invariant type of group symmetries due to forcing. The traditional group-theoretical such as inverse scattering method, Backlund transformations and other known approaches can no longer generate analytic solutions of solitons, because there are no infinitely many conservation laws. Numerical simulations and approximate solutions seem the only ways to solve the forced nonlinear evolution equations. Numerical simulations of forced solitons will be implemented by a user-friendly software package FORSO. In this paper we show how approximate scheme can be used to generate forced solitons. Approximate solution also gives various profiles of fKdV such as the depth of depression zone; , amplitude; , speed; s and the period; of generation of forced solitons in Tsunami waves

Nonlinear wave modulation in thin viscoelastic tube filled with inviscid fluid

In the present paper, the modulation of nonlinear wave in a thin viscoelastic tube filled with inviscid fluid is studied. We assumed that the arterial wall material is an incompressible, isotropic and thin viscoelastic tube and considered blood as approximate equations of an incompressible inviscid fluid. Applying the reductive perturbation method, the Nonlinear Schrӧdinger (NLS) type equation as the evolution equation is obtained for the modulation of nonlinear wave in such a medium