Search results for: wealth status prediction
5503 The Best Prediction Data Mining Model for Breast Cancer Probability in Women Residents in Kabul
Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini
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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.Keywords: decision tree, breast cancer, probability, data mining
Procedia PDF Downloads 1385502 The Role of the Rate of Profit Concept in Creating Economic Stability in Islamic Financial Market
Authors: Trisiladi Supriyanto
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This study aims to establish a concept of rate of profit on Islamic banking that can create economic justice and stability in the Islamic Financial Market (Banking and Capital Markets). A rate of profit that creates economic justice and stability can be achieved through its role in maintaining the stability of the financial system in which there is an equitable distribution of income and wealth. To determine the role of the rate of profit as the basis of the profit sharing system implemented in the Islamic financial system, we can see the connection of rate of profit in creating financial stability, especially in the asset-liability management of financial institutions that generate a stable net margin or the rate of profit that is not affected by the ups and downs of the market risk factors, including indirect effect on interest rates. Furthermore, Islamic financial stability can be seen from the role of the rate of profit on the stability of the Islamic financial assets value that are measured from the Islamic financial asset price volatility in the Islamic Bond Market in the Capital Market.Keywords: economic justice, equitable distribution of income, equitable distribution of wealth, rate of profit, stability in the financial system
Procedia PDF Downloads 3145501 Stress Recovery and Durability Prediction of a Vehicular Structure with Random Road Dynamic Simulation
Authors: Jia-Shiun Chen, Quoc-Viet Huynh
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This work develops a flexible-body dynamic model of an all-terrain vehicle (ATV), capable of recovering dynamic stresses while the ATV travels on random bumpy roads. The fatigue life of components is forecasted as well. While considering the interaction between dynamic forces and structure deformation, the proposed model achieves a highly accurate structure stress prediction and fatigue life prediction. During the simulation, stress time history of the ATV structure is retrieved for life prediction. Finally, the hot sports of the ATV frame are located, and the frame life for combined road conditions is forecasted, i.e. 25833.6 hr. If the usage of vehicle is eight hours daily, the total vehicle frame life is 8.847 years. Moreover, the reaction force and deformation due to the dynamic motion can be described more accurately by using flexible body dynamics than by using rigid-body dynamics. Based on recommendations made in the product design stage before mass production, the proposed model can significantly lower development and testing costs.Keywords: flexible-body dynamics, veicle, dynamics, fatigue, durability
Procedia PDF Downloads 3945500 The Viability of Islamic Finance and Its Impact on Global Financial Stability: Evidence from Practical Implications
Authors: Malik Shahzad Shabbir, Muhammad Saarim Ghazi, Amir Khalil ur Rehman
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This study examines the factors which influence and contribute towards the financial viability of Islamic finance and its impact on global financial stability. However, the purpose of this paper is to differentiate the practical implications of both Islamic and conventional finance on global financial stability. The Islamic finance is asset backed financing which creates wealth through trade, commerce and believes in risk and return sharing. Islamic banking is asset driven as against to conventional banking which is liability driven. In order to introduce new financial products for market, financial innovation in Islamic finance must be within the Shari’ah parameters that are tested against the ‘Maqasid al-Shari’ah’. Interest-based system leads to income and wealth inequalities and mis-allocation of resources. Moreover, this system has absence of just and equitable aspect of distribution that may exploit either the debt holder or the financier. Such implications are reached to a tipping point that leaves only one choice: change or face continued decline and misery.Keywords: viability, global financial stability, practical implications, asset driven, tipping point
Procedia PDF Downloads 3035499 Health Promoting Behaviors among Thai Older Adults: Trend and Association with Health Status
Authors: Alongkorn Pekalee, Rossarin Gray
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Various determinants associated with older health include socio-demographic factors and health-promoting behaviors but lack in scholars recommended what factors associated with health status in specific sub-groups of older adults. The current study aims to explore the health-promoting behaviors and to examine and compare the associations of these factors with self-rated health status among three older age cohorts in Thai traditional context. Methods: This study is based on the Survey of Older Persons in Thailand (SOPT), in 2017, conducted by the National Statistical Office (NSO) of Thailand. Participants were classified into three groups by using the Thai contextual recommendation: youngest-old cohort (60-69), old-old cohort (70-79) and oldest old cohort (80 or older). Health promoting behaviors are the behaviors which associated with the health status of older adults include alcohol consumption, smoking, diet, and physical activity. Health status was defined as a subjective measurement by using self-rated health, a simple measure of general health. The socio-demographic factors, health-promoting behaviors, and health status were explained and summarized by descriptive statistics. The binary logistic regression was performed to analyze the data and evaluate the associations between independent and dependent variables. Results: Increase of age contributes to a higher proportion of health-promoting behaviors. All variables were associated with self-reported health status as good health among three older age cohorts statistically significant (p-value = 0.000). However, the influence of income sufficiency on health status is more notable, especially in older adults who aged 60-69 and 70-79. The influence of dietary and physical activity on health status became greater as age increased. Conclusion: the results suggest that income sufficiency should be noted in a plan to promote healthy aging, and co-residence should be more concerned especially in the oldest old cohort. Moreover, the interventions or policies to promote older health behaviors like diet and physical activity should be emphasized in the oldest old cohort more than others.Keywords: health-promoting behaviors, older adults, self- rated health, Thailand
Procedia PDF Downloads 1385498 A Study on Relationship of Lifestyle and Socio-Economic Status with Obesity in Indian Children
Authors: Sushma Ghildyal, Sanjay Kumar Singh
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The present study was undertaken with the purpose to understand the relationship of lifestyle and Socio-Economic status with child obesity among 1000 boys aged from 16 to 18 years of Varanasi District of Uttar Pradesh State in India. The study was conducted in both urban and rural area of the District. Ten schools i.e. five from urban area and five from rural area were selected by using purposive sampling. Healthy boys of class 10th, 11th and 12th were taken as subjects for the study. Prior consent was obtained from school authority. Anthropometric measurements were taken from each subject. Anthropometric measurements were Standing Height, Weight, Biceps skin folds, Triceps skin folds, Sub-scapular skin folds and Supra-iliac skin folds taken by Lange’s skin fold caliper. Lifestyle and Socio-Economic Status were obtained by questionnaires. In order to assess the BMI, Body fat %, Lifestyle and Socio-Economic Status; descriptive analyses were done. To find out the significant association of obesity with lifestyle and Socio-Economic Status Chi-square test was used. To find out significant difference between obesity of Urban and Rural children t-test was applied. Level of significance was set at 0.05 level. The conclusions drawn were: (1) The result showed that in urban area Varanasi District of Uttar Pradesh 0.6% children were in very high level adaptive lifestyle, 6.2% were in high level adaptive lifestyle, 25.4% above average level adaptive lifestyle, 47.8% moderately adaptive lifestyle, 3.6% and 0.4% low and very low level adaptive lifestyle. (2) In rural area Varanasi District of Uttar Pradesh 0.00% children were in very high level adaptive lifestyle, 9.4% were in high level adaptive lifestyle, 24.8% average level adaptive lifestyle, 47.0% moderately adaptive lifestyle, 15.2% below average and 3.0% very low level adaptive lifestyle.(3) In urban area 12.8% were in upper class Socio-Economic Status, 56.6% in upper middle class Socio-Economic Status, 30.2% in middle class Socio-Economic Status and 0.2% in lower middle class Socio-Economic Status. (4) In rural area 1.4% were in upper class Socio-Economic Status, 15.2% in upper middle class Socio-Economic Status, 51.6% in middle class Socio-Economic Status and 0.8% in lower middle class Socio-Economic Status. (5) In urban area 21.2% children of 16-18 years were obese. (6) In rural area 0.2% children of 16-18 years were obese. (7) In overall Varanasi District of Uttar Pradesh 10.7% children of 16-18 years were obese. (8) There was no significant relationship of obesity with Lifestyle of urban area children of 16-18 years. (9) There was significant relationship of obesity with Socio-Economic Status of urban area children of 16-18 years (10) There was no significant relationship of obesity with Lifestyle of rural area children of 16-18 years of Varanasi District Uttar Pradesh. (11) There was significant relationship of obesity with Socio-Economic Status of rural area children of 16-18 years. (12) Results showed significant difference between urban and rural area children of 16-18 years in respect to obesity of Varanasi District of Uttar Pradesh.Keywords: lifestyle, obesity, rural area, socio-economic status, urban area
Procedia PDF Downloads 4805497 The Relationship of Socioeconomic Status and Levels of Delinquency among Senior High School Students with Secured Attachment to Their Mothers
Authors: Aldrin Avergas, Quennie Mariel Peñaranda, Niña Karen San Miguel, Alexis Katrina Agustin, Peralta Xusha Mae, Maria Luisa Sison
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The research is entitled “The Relationship of Socioeconomic Status and Levels of Delinquency among Senior High School Students with Secured Attachment to their Mothers”. The researchers had explored the relationship between socioeconomic status and delinquent tendencies among grade 11 students. The objective of the research is to discover if delinquent behavior will have a relationship with the current socio-economic status of an adolescent student having a warm relationship with their mothers. The researchers utilized three questionnaires that would measure the three variables of the study, namely: (1) 1SEC 2012: The New Philippines Socioeconomic Classification System was used to show the current socioeconomic status of the respondents, (2) Self-Reported Delinquency – Problem Behavior Frequency Scale was utilized to determine the individual's frequency in engaging to delinquent behavior, and (3) Inventory of Parent and Peer Attachment Revised (IPPA-R) was used to determine the attachment style of the respondents. The researchers utilized a quantitative research design, specifically correlation research. The study concluded that there is no significant relationship between socioeconomic status and academic delinquency despite the fact that these participants had secured attachment to their mother hence this research implies that delinquency is not just a problem for students belonging in the lower socio-economic status and that even having a warm and close relationship with their mothers is not sufficient enough for these students to completely be free from engaging in delinquent acts. There must be other factors (such as peer pressure, emotional quotient, self-esteem or etc.) that are might be contributing to delinquent behaviors.Keywords: adolescents, delinquency, high school students, secured attachment style, socioeconomic status
Procedia PDF Downloads 1865496 Free Fatty Acid Assessment of Crude Palm Oil Using a Non-Destructive Approach
Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim, Rashidah Ghazali, Noramli Abdul Razak
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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of prediction models has facilitated the estimation process in recent years. In this study, 110 crude palm oil (CPO) samples were used to build a free fatty acid (FFA) prediction model. 60% of the collected data were used for training purposes and the remaining 40% used for testing. The visible peaks on the NIR spectrum were at 1725 nm and 1760 nm, indicating the existence of the first overtone of C-H bands. Principal component regression (PCR) was applied to the data in order to build this mathematical prediction model. The optimal number of principal components was 10. The results showed R2=0.7147 for the training set and R2=0.6404 for the testing set.Keywords: palm oil, fatty acid, NIRS, regression
Procedia PDF Downloads 5075495 Analyzing Tools and Techniques for Classification In Educational Data Mining: A Survey
Authors: D. I. George Amalarethinam, A. Emima
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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.Keywords: classification technique, data mining, EDM methods, prediction methods
Procedia PDF Downloads 1175494 Reservoir Inflow Prediction for Pump Station Using Upstream Sewer Depth Data
Authors: Osung Im, Neha Yadav, Eui Hoon Lee, Joong Hoon Kim
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Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data.Keywords: artificial neural network, forecasting, reservoir inflow, sewer depth
Procedia PDF Downloads 3175493 Pre-Operative Tool for Facial-Post-Surgical Estimation and Detection
Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary
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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.Keywords: k-nearest neighbor (knn), face detection, vitiligo, bone deformity
Procedia PDF Downloads 1645492 Spatial Variation of WRF Model Rainfall Prediction over Uganda
Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Triphonia Ngailo
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Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region.Keywords: convective parameterization schemes, March-May 2013 rainfall season, spatial variation of parameterization schemes over Uganda, WRF model
Procedia PDF Downloads 3115491 Artificial Neural Networks and Geographic Information Systems for Coastal Erosion Prediction
Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade
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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction
Procedia PDF Downloads 3925490 Remedying the Scourge of Poverty as a Social Problem: The Islamic Perspective
Authors: Maryam Umar Ladan, Arshad Munir
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Poverty has always been a constant feature of society throughout history. It has existed in the lives of people and it is a fact that although the majority of people lives in poverty, the remaining minority lives in luxury. While some countries called the first World countries lives in luxury, the third World countries lives in poverty. It remains an undesirable phenomenon affecting a vast number of people across the globe despite governmental, institutional and private organizations’ interventions with measures aimed at cushioning its adverse effects. Unequal distribution of societal resources, accumulated wealth in the hands of few, lack of access to education and employment, individual responsibility among others, were highlighted as factors associated with poverty. Poverty predisposes the poor individual to malnutrition and starvation, exposure to disease, thereby resulting to violence, crimes, and experiencing lifelong problems. Evidence show that about 50 percent of the world population lives on less than 2.50 dollar a day, 90 percent of whom are from Sub-Saharan Africa and South Asia including countries where Islam is the major if not one adherent religion. As a solution to poverty, Islam prescribes a system of annual Zakat (charity). The Islamic law prescribes that every person who has a saving that reaches a certain limit should give out 2.5 percent of the total annual earning (as in income, money, farm produce) to deserving and prescribed citizens. This is to, among others; reduce the level of inequality through distribution of wealth among the Muslim Ummah (community). Furthermore, Islam encourages the rich in several places in the Qur’an to spend their wealth on poor people other than the compulsory 2.5%. Therefore, it is inarguable that the Islamic system of distribution of resources (as zakat) is the best strategy to poverty eradication. Thus, strongly recommended for desired results in poverty eradication efforts. If every rich person gives Zakat sincerely, poverty will be eradicated in the world, and not a single person will die of want of food or material things.Keywords: Islam, charity, poverty, zakat
Procedia PDF Downloads 2875489 Stock Price Prediction Using Time Series Algorithms
Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava
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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series
Procedia PDF Downloads 1425488 ARIMA-GARCH, A Statistical Modeling for Epileptic Seizure Prediction
Authors: Salman Mohamadi, Seyed Mohammad Ali Tayaranian Hosseini, Hamidreza Amindavar
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In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.Keywords: epileptic seizure prediction , ARIMA, ARCH and GARCH modeling, heteroskedasticity, EEG
Procedia PDF Downloads 4065487 Socio-Economic Inequality in Breastfeeding Patterns in India
Authors: Ankita Shukla
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The promotion and support of breastfeeding is a global priority with benefits for maternal and infant health, especially in low income and middle-income countries where the probability of child survival is still very low. In India too it has been well established that breastfeeding increases the survival of the child. However, the breastfeeding levels are quite low in the country. Examining the socio-economic inequality in breastfeeding pattern can help to the causal pathways responsible for early breastfeeding termination. This paper tries to understand the socio-economic differential in breastfeeding patterns among Indian women. Data is used from nationally representative National Family Health Survey-3. Using Cox regression modelling techniques, the analysis found that the likelihood of having small breastfeeding duration increased with increasing household wealth status similarly education also has negative effect on breastfeeding duration. The considerable gender difference is also visible in India, likelihood of stopping breastfeeding was significantly higher among female children compared with male children. To understand the cultural factors or norms responsible for the early termination of breastfeeding more in depth/qualitative studies are needed.Keywords: breastfeeding, India, socio-economic inequality, women education
Procedia PDF Downloads 2365486 Prediction of Energy Storage Areas for Static Photovoltaic System Using Irradiation and Regression Modelling
Authors: Kisan Sarda, Bhavika Shingote
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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation
Procedia PDF Downloads 3825485 Pres Syndrome in Pregnancy: A Case Series of Five Cases
Authors: Vaibhavi Birle
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Posterior reversible encephalopathy syndrome is a rare clinic-radiological syndrome associated with acute changes in blood pressure during pregnancy. It is characterized symptomatically by headache, seizures, altered mental status, and visual blurring with radiological changes of white matter (vasogenic oedema) affecting the posterior occipital and parietal lobes of the brain. It is being increasingly recognized due to increased institutional deliveries and advances in imaging particularly magnetic resonance imaging (MRI). In spite of the increasing diagnosis the prediction of PRES and patient factors affecting susceptibility is still not clear. Hence, we conducted the retrospective study to analyse the factors associated with PRES at our tertiary centre.Keywords: pres syndrome, eclampsia, maternal outcome, fetal outcome
Procedia PDF Downloads 1515484 A Spatial Information Network Traffic Prediction Method Based on Hybrid Model
Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li
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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model
Procedia PDF Downloads 1475483 Anemia and Nutritional Status as Dominant Factor of the Event Low Birth Weight in Indonesia: A Systematic Review
Authors: Lisnawati Hutagalung
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Background: Low birth weight (LBW) is one cause of newborn death. Babies with low birth weight tend to have slower cognitive development, growth retardation, more at risk of infectious disease event at risk of death. Objective: Identifying risk factors and dominant factors that influence the incidence of LBW in Indonesia. Method: This research used some database of public health such as Google Scholar, UGM journals, UI journals and UNAND journals in 2012-2015. Data were filtered using keywords ‘Risk Factors’ AND ‘Cause LBW’ with amounts 2757 study. The filtrate obtained 5 public health research that meets the criteria. Results: Risk factors associated with LBW, among other environment factors (exposure to cigarette smoke and residence), social demographics (age and socio-economic) and maternal factors (anemia, placental abnormal, nutritional status of mothers, examinations antenatal, preeclampsia, parity, and complications in pregnancy). Anemia and nutritional status become the dominant factor affecting LBW. Conclusions: The risk factors that affect LBW, most commonly found in the maternal factors. The dominant factors are a big effect on LBW is anemia and nutritional status of the mother during pregnancy.Keywords: low birth weight, anemia, nutritional status, the dominant factor
Procedia PDF Downloads 3655482 Legal Judgment Prediction through Indictments via Data Visualization in Chinese
Authors: Kuo-Chun Chien, Chia-Hui Chang, Ren-Der Sun
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Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction.Keywords: legal judgment prediction, deep learning, natural language processing, BERT, data visualization
Procedia PDF Downloads 1215481 Prediction of Marijuana Use among Iranian Early Youth: an Application of Integrative Model of Behavioral Prediction
Authors: Mehdi Mirzaei Alavijeh, Farzad Jalilian
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Background: Marijuana is the most widely used illicit drug worldwide, especially among adolescents and young adults, which can cause numerous complications. The aim of this study was to determine the pattern, motivation use, and factors related to marijuana use among Iranian youths based on the integrative model of behavioral prediction Methods: A cross-sectional study was conducted among 174 youths marijuana user in Kermanshah County and Isfahan County, during summer 2014 which was selected with the convenience sampling for participation in this study. A self-reporting questionnaire was applied for collecting data. Data were analyzed by SPSS version 21 using bivariate correlations and linear regression statistical tests. Results: The mean marijuana use of respondents was 4.60 times at during week [95% CI: 4.06, 5.15]. Linear regression statistical showed, the structures of integrative model of behavioral prediction accounted for 36% of the variation in the outcome measure of the marijuana use at during week (R2 = 36% & P < 0.001); and among them attitude, marijuana refuse, and subjective norms were a stronger predictors. Conclusion: Comprehensive health education and prevention programs need to emphasize on cognitive factors that predict youth’s health-related behaviors. Based on our findings it seems, designing educational and behavioral intervention for reducing positive belief about marijuana, marijuana self-efficacy refuse promotion and reduce subjective norms encourage marijuana use has an effective potential to protect youths marijuana use.Keywords: marijuana, youth, integrative model of behavioral prediction, Iran
Procedia PDF Downloads 5545480 Aggregate Angularity on the Permanent Deformation Zones of Hot Mix Asphalt
Authors: Lee P. Leon, Raymond Charles
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This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.Keywords: aggregate angularity, asphalt concrete, permanent deformation, rutting prediction
Procedia PDF Downloads 4055479 Institutional Preferences of Elites and Society: Paradoxes of Economic Development in Georgia
Authors: Inga Balarjishvili, Ia Natsvlishvili
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Article aims to discuss the controversial character of the institutional preferences of elites and society in modern Georgia. Desktop research method is used to formulate the findings and analyze the outcomes. It is accepted that transformation process in Post-Soviet Georgia went with the prevalence of elites’ institutional preferences over the needs of the society that induced voluntarism in the process of formation of institutions. Hypothesis of 'quasi-inclusion trap' is put forward in the article as an effect of authoritarian modernization that is proved by instable paces of wealth and economic growth in the post-authoritarian period. On the one hand, monopolization of institutional choice by the elites, blocking formation of inclusive political and economic institutions for fear of losing status-quo worsen perspectives for achieving free availability regime. On the other hand, consciousness of the society is dominated by informal institutions, judicial nihilism and orientation on 'self-survival values.' This hinders its consolidation as a 'collective principal' against 'institutional utilitarianism,' result of which is hindered economic development.Keywords: elites, hypothesis of 'quasi-inclusion trap', institutional preferences, post-Soviet Georgia
Procedia PDF Downloads 2555478 Prevalence and Spatial Distribution of Anaemia in Ethiopia using 2011 EDHS
Authors: Bedilu A. Ejigu, Eshetu Wencheko, Kiros Berhane
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Anaemia is a condition in which the haemoglobin concentration falls below an established cut-off value due to a decrease in the number and size of red blood cells. The current study aimed to assess the spatial pattern and identify predictors related to anaemia using the third Ethiopian demographic health survey which was conducted in 2010. To achieve this objective, this study took into account the clustered nature of the data. As a result, multilevel modeling has been used in the statistical analysis. For analysis purpose, only complete cases from 15,909 females, and 13,903 males were considered. Among all subjects who agreed for haemoglobin test, 5.49 %males, and 19.86% females were anaemic. In both binary and ordinal outcome modeling approaches, educational level, age, wealth index, BMI and HIV status were identified to be significant predictors for anaemia prevalence. Furthermore, it was noted that pregnant women were more anaemic than non-pregnant women. As revealed by Moran's I test, significant spatial autocorrelation was noted across clusters. The risk of anaemia was found to vary across different regions, and higher prevalence was observed in Somali and Affar region.Keywords: anaemia, Moran's I test, multilevel models, spatial pattern
Procedia PDF Downloads 4245477 Use of Multistage Transition Regression Models for Credit Card Income Prediction
Authors: Denys Osipenko, Jonathan Crook
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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability
Procedia PDF Downloads 4875476 Mobile Based Long Range Weather Prediction System for the Farmers of Rural Areas of Pakistan
Authors: Zeeshan Muzammal, Usama Latif, Fouzia Younas, Syed Muhammad Hassan, Samia Razaq
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Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain.Keywords: ICTD, farmers, mobile based, Pakistan, rural areas, weather prediction
Procedia PDF Downloads 5725475 Integration of Educational Data Mining Models to a Web-Based Support System for Predicting High School Student Performance
Authors: Sokkhey Phauk, Takeo Okazaki
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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance
Procedia PDF Downloads 1065474 Effect of Group Psychotherapy with Sertraline on Mental Health Status of Adolescents with First-Episode Depression
Authors: Li Yuan
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Objective: The combination of group psychology and Sertraline was used to explore the impact on the mental health status of adolescent patients with first-episode depression. Methods: A total of 118 adolescent depressed patients admitted to Yan'an University Hospital from October 2023 to August 2024 were divided into control group and observation group by random single blind method with 59 patients in each group. The two groups were treated with Sertraline, the control group received usual care, and the observation group used the usual care. The scores of mental health status and sleep quality index were compared between the two groups. Results: In intra-group comparison, the mental health status and sleep quality of the observation and control groups were better than the pre-intervention scores, and the difference was statistically significant (P <0.05). Post-intervention comparison: HAMA and HAMD scores were (12.36 ± 2.13) and (11.78 ± 2.02), significantly lower than (16.52 ± 2.09) and (15.79 ± 2.46), respectively (all P <0.05); PSQI score was (7.66 ± 1.05) and significantly lower (9.88 ± 3.01), with statistically significant difference (P <0.05). Conclusion: Self-regulation can improve their mental health and sleep quality.Keywords: group psychotherapy, Sertraline, adolescent, depression, mental health status
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