Search results for: interval regression

Authors: Sumit Kumar Das, Laishram Ladusingh

Abstract:

Aim: The aim of the study is to examine the differential of epidemiological transition across fifteen agro-climatic zones of India and its effect on health-seeking behavior. Data and Methods: Unit level data on consumption expenditure on health of India from three decadal rounds conducted by National Sample Survey Organization are used for the analysis. These three rounds are 52nd (1995-96), 60th (2004-05) and 71st (2014-15). The age-adjusted prevalence rate for communicable diseases and non-communicable diseases are estimated for fifteen agro-climatic zones of India for three time periods. Bivariate analysis is used to find out determinants of health-seeking behavior. Multilevel logistic regression is used to examine factors effecting on household health-seeking behavior. Result: The prevalence of communicable diseases is increasing in most of the zones of India. Every South Indian zones, Gujarat plains, and lower Gangetic plain are facing the severe attack of dual burden of diseases. Demand for medical advice has increased in southern zones, and east zones, reliance on private healthcare facilities are increasing in most of the zone. Demographic characteristics of the household head have a significant impact on health-seeking behavior. Conclusion: Proper program implementation is required considering the disease prevalence and differential in the pattern of health seeking behavior. Along with initiation and strengthening of programs for non-communicable, existing programs for communicable diseases need to monitor and supervised strictly.

Keywords: agro-climatic zone, epidemiological transition, health-seeking behavior, multilevel regression

Procedia PDF Downloads 183

Authors: Natasa Todorovic, Jovana Nikolov

Abstract:

Guard detector efficiency, cosmic background, and its variation were determinate using ultra low-level liquid scintillation spectrometer Quantulus 1220, equipped with an anti-Compton guard detector, in the surface laboratory at the University of Novi Sad, Serbia, Atmospheric pressure variation has an observable effect on the anti-Compton guard detector count rate. and the cosmic muon flux is lower during a high-pressure period. Also, the guard detector Compton continuum provides a good view of the level of gamma radiation in the laboratory environment. The efficiency of the guard detector in the channel interval from 750 to 1024 was assessed to 93.45%; efficiency in the entire window (channels 1 to 1024) was 75.23%, which is in good agreement with literature data.

Keywords: cosmic radiation, background reduction, liquid scintillation counting, guard detector efficiency

Procedia PDF Downloads 158

Authors: Ahmad Jawad Fardin

Abstract:

Over 60% of patients with breast cancer in Afghanistan present late with advanced stage III and IV, a major cause for the poor survival rate. The objectives of this study were to identify the contributing factors for the diagnosis and treatment delay and its outcome. This cross-sectional study was conducted on 318 patients with histologically confirmed breast cancer in the oncology department of Jamhoriat hospital, which is the first and only national cancer center in Afghanistan; data were collected from medical records and interviews conducted with women diagnosed with breast cancer, linear regression and logistic regression were used for analysis. Patient delay was defined as the time from first recognition of symptoms until first medical consultation and doctor form first consultation with a health care provider until histological confirmation of breast cancer. The mean age of patients was 49.2+_ 11.5years. The average time for the final diagnosis of breast cancer was 8.5 months; most patients had ductal carcinoma 260.7 (82%). Factors associated with delay were low education level 76% poor socioeconomic and cultural conditions 81% lack of cancer center 73% lack of screening 19%. The stage distribution was as follows stage IV 4 22% stage III 44.4% stage II 29.3% stage I 4.3%. Complex associated factors were identified to delayed the diagnosis of breast cancer and increased adverse outcomes consequently. Raising awareness and education in women, the establishment of cancer centers and providing accessible diagnosis service and screening, training of general practitioners; required to promote early detection, diagnosis and treatment.

Keywords: delayed diagnosis and poor outcome, breast cancer in Afghanistan, poor outcome of delayed breast cancer treatment, breast cancer delayed diagnosis and treatment in Afghanistan

Procedia PDF Downloads 182

Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

Abstract:

The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

Procedia PDF Downloads 91

Authors: Fahad Abbasi

Abstract:

Flow of electrically conducting nanofluids is of pivotal importance in countless industrial and medical appliances. Fluctuations in thermophysical properties of such fluids due to variations in temperature have not received due attention in the available literature. Present investigation aims to fill this void by analyzing the flow of copper-water nanofluid with temperature dependent viscosity past a Riga plate. Strong wall suction and viscous dissipation have also been taken into account. Numerical solutions for the resulting nonlinear system have been obtained. Results are presented in the graphical and tabular format in order to facilitate the physical analysis. An estimated expression for skin friction coefficient and Nusselt number are obtained by performing linear regression on numerical data for embedded parameters. Results indicate that the temperature dependent viscosity alters the velocity, as well as the temperature of the nanofluid and, is of considerable importance in the processes where high accuracy is desired. Addition of copper nanoparticles makes the momentum boundary layer thinner whereas viscosity parameter does not affect the boundary layer thickness. Moreover, the regression expressions indicate that magnitude of rate of change in effective skin friction coefficient and Nusselt number with respect to nanoparticles volume fraction is prominent when compared with the rate of change with variable viscosity parameter and modified Hartmann number.

Keywords: heat transfer, peristaltic flows, radially varying magnetic field, curved channel

Procedia PDF Downloads 166

Authors: Yasuyo Makido, Vivek Shandas, David J. Sailor, M. Salim Ferwati

Abstract:

Mitigating extreme urban heat is challenging in a desert climate such as Doha, Qatar, since outdoor daytime temperature area often too high for the human body to tolerate. Recent studies demonstrate that cities in arid and semiarid areas can exhibit ‘urban cool islands’ - urban areas that are cooler than the surrounding desert. However, the variation of temperatures as a result of the time of day and factors leading to temperature change remain at the question. To address these questions, we examined the spatial and temporal variation of air temperature in Doha, Qatar by conducting multiple vehicle-base local temperature observations. We also employed three statistical approaches to model surface temperatures using relevant predictors: (1) Ordinary Least Squares, (2) Regression Tree Analysis and (3) Random Forest for three time periods. Although the most important determinant factors varied by day and time, distance to the coast was the significant determinant at midday. A 70%/30% holdout method was used to create a testing dataset to validate the results through Pearson’s correlation coefficient. The Pearson’s analysis suggests that the Random Forest model more accurately predicts the surface temperatures than the other methods. We conclude with recommendations about the types of development patterns that show the greatest potential for reducing extreme heat in air climates.

Keywords: desert cities, tree-structure regression model, urban cool Island, vehicle temperature traverse

Procedia PDF Downloads 392

Authors: Trevor C. Brown, David J. Miron

Abstract:

Gas-solid physical adsorption methods are central to the characterization and optimization of the effective surface area, pore size and porosity for applications such as heterogeneous catalysis, and gas separation and storage. Properties such as adsorption uptake, capacity, equilibrium constants and Gibbs free energy are dependent on the composition and structure of both the gas and the adsorbent. However, challenges remain, in accurately calculating these properties from experimental data. Gas adsorption experiments involve measuring the amounts of gas adsorbed over a range of pressures under isothermal conditions. Various constant-parameter models, such as Langmuir and Brunauer-Emmett-Teller (BET) theories are used to provide information on adsorbate and adsorbent properties from the isotherm data. These models typically do not provide accurate interpretations across the full range of pressures and temperatures. The Langmuir adsorption isotherm is a simple approximation for modelling equilibrium adsorption data and has been effective in estimating surface areas and catalytic rate laws, particularly for high surface area solids. The Langmuir isotherm assumes the systematic filling of identical adsorption sites to a monolayer coverage. The BET model is based on the Langmuir isotherm and allows for the formation of multiple layers. These additional layers do not interact with the first layer and the energetics are equal to the adsorbate as a bulk liquid. This BET method is widely used to measure the specific surface area of materials. Both Langmuir and BET models assume that the affinity of the gas for all adsorption sites are identical and so the calculated adsorbent uptake at the monolayer and equilibrium constant are independent of coverage and pressure. Accurate representations of adsorption data have been achieved by extending the Langmuir and BET models to include pressure-varying uptake capacities and equilibrium constants. These parameters are determined using a novel regression technique called flexible least squares for time-varying linear regression. For isothermal adsorption the adsorption parameters are assumed to vary slowly and smoothly with increasing pressure. The flexible least squares for pressure-varying linear regression (FLS-PVLR) approach assumes two distinct types of discrepancy terms, dynamic and measurement for all parameters in the linear equation used to simulate the data. Dynamic terms account for pressure variation in successive parameter vectors, and measurement terms account for differences between observed and theoretically predicted outcomes via linear regression. The resultant pressure-varying parameters are optimized by minimizing both dynamic and measurement residual squared errors. Validation of this methodology has been achieved by simulating adsorption data for n-butane and isobutane on activated carbon at 298 K, 323 K and 348 K and for nitrogen on mesoporous alumina at 77 K with pressure-varying Langmuir and BET adsorption parameters (equilibrium constants and uptake capacities). This modeling provides information on the adsorbent (accessible surface area and micropore volume), adsorbate (molecular areas and volumes) and thermodynamic (Gibbs free energies) variations of the adsorption sites.

Keywords: Langmuir adsorption isotherm, BET adsorption isotherm, pressure-varying adsorption parameters, adsorbate and adsorbent properties and energetics

Procedia PDF Downloads 234

Authors: Kazeem Adesina Dauda, Waheed Babatunde Yahya

Abstract:

To design a tree that maximizes within-node homogeneity, there is a need for a homogeneity measure that is appropriate for event history data with multiple risks. We consider the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in Classification Regression Tree (CART) and compare our results with the results of Fiona (2008) in which homogeneity measures were based on Martingale Residual. Data structure approach was used to validate the performance of our proposed techniques via simulation and real life data. The results of univariate competing risk revealed that: using Deviance and Cox-Snell residuals as a response in within node homogeneity classification tree perform better than using other residuals irrespective of performance techniques. Bone marrow transplant data and double-blinded randomized clinical trial, conducted in other to compare two treatments for patients with prostate cancer were used to demonstrate the efficiency of our proposed method vis-à-vis the existing ones. Results from empirical studies of the bone marrow transplant data showed that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822) in both event of interest and competing risks. Additionally, results from prostate cancer also reveal the performance of proposed model over the existing one in both causes, interestingly, Cox-Snell residual (MSE=0.01783563) outfit both the Martingale residual (MSE=0.1853148) and Deviance residual (MSE=0.8043366). Moreover, these results validate those obtained from the Monte-Carlo studies.

Keywords: within-node homogeneity, Martingale residual, modified Cox-Snell residual, classification and regression tree

Procedia PDF Downloads 272

Authors: Ferydon Salehifar, Hassan Safarikia, Hossein Boromandfar

Abstract:

Karkheh plant in Iran's Khuzestan province and is located in the city Andimeshk. The plant has a production capacity of 400 MW units with water and three hours. One of the important parameters of each country's power grid stability is the stability of the power grid is affected by the voltage and frequency In plants, the amount of active power frequency control is done so that when the unit is placed in the frequency control their productivity is a function of frequency and output power varies with frequency. Produced by hydroelectric power plants with the water level behind the dam has a direct relationship And to decrease and increase the water level behind the dam in order to reduce the power output increases But these changes have a different interval is due to some mechanical problems such as turbine cavitation and vibration are limited. In this study, the range of the frequency control can be Karkheh manufacturing plants have been identified and their effectiveness has been determined.

Keywords: Karkheh power, frequency control system, active power, efficiency

Procedia PDF Downloads 620

Authors: I. Hua Chu, Hsiang-Chi Yu, Hsuan Su

Abstract:

Purpose: To examine the associations between self-determined motivation and physical activity in patients with coronary heart disease (CHD) in a longitudinal study. Methods: Patients with CHD were recruited for this study. Their motivations for exercise were measured by the Behavioral Regulation in Exercise Questionnaire-2 (BREQ-2). Physical activity was assessed using the 7-day physical activity recall questionnaire. Duration and energy expenditure of moderate to vigorous physical activity (MVPA) were used in data analysis. All outcome measures were assessed at baseline and 12 months follow up. Data were analyzed using Pearson correlation analysis and regression analysis. Results: The results of the 45 participants (mean age 60.24 yr; 90.2% male) revealed that there were significant negative correlations between amotivation at baseline and duration (r=-.295, p=.049) and energy expenditure (r=-.300, p=.045) of MVPA at 12 months. In contrast, there were significant positive correlations between calculated relative autonomy index (RAI) at baseline and duration (r=.377, p=.011) and energy expenditure (r=.382, p=.010) of MVPA at 12 months. There was no significant correlation between other subscales of the BREQ-2 and duration or energy expenditure of MVPA. Regression analyses revealed that RAI was a significant predictor of duration (p=.011) and energy expenditure (p=.010) of MVPA at 12 months follow-up. Conclusions: These results suggest that the relative degree of self-determined motivation could predict long-term MVPA behaviors in CHD patients. Physical activity interventions are recommended to target enhancing one’s identified and intrinsic motivation to increase the likelihood of physical activity participation in this population.

Keywords: self-determined motivation, physical activity, coronary heart disease, relative autonomy index (RAI)

Procedia PDF Downloads 428

Authors: Hamid Merzouk, Dajhida Talantikite, Amel Tounsi

Abstract:

Thin films of ZnS have been deposited by chemical route into acidic medium. The deposition time fixed at 5 hours, and the bath temperature varied from 80° C to 95°C with an interval of 5°C. The X-ray diffraction (XRD), UV/ visible spectrophotometry, Fourier Transform Infrared spectroscopy (FTIR) have been used to study the effect of temperature on the structural and optical properties of ZnS thin films. The XRD spectrum of the ZnS layer obtained shows an increase of peaks intensity of ZnS with increasing bath temperature. The study of optical properties exhibit good transmittance (60–80% in the visible region), and the band gap energy of the ZnS thin film decrease from 3.71 eV to 3.64 eV while the refractive index (n) increase with increasing temperature bath. The FTIR analyze confirm our studies and show characteristics bands of vibration of Zn-S.

Keywords: ZnS thin films, XRD spectra, optical gap, XRD

Procedia PDF Downloads 155

Authors: Roselyn Chipojola, Shu-yu Kuo

Abstract:

Background: Breastfeeding self-efficacy in both mothers and fathers play a crucial role in improving exclusive breastfeeding rates. However, less is known on the relationship and predictors of paternal and maternal breastfeeding self-efficacy. This study aimed to examine the relationship and associated factors of breastfeeding self-efficacy (BSE) among mothers and fathers in Malawi. Methods: A cross-sectional study was conducted on 180 pairs of postpartum mothers and fathers at a tertiary maternity facility in central Malawi. BSE was measured using the Breastfeeding Self-Efficacy Scale Short-Form. Depressive symptoms were assessed by the Edinburgh Postnatal Depression Scale. A structured questionnaire was used to collect demographic and health variables. Data were analyzed using multivariable logistic regression and multinomial logistic regression. Results: A higher score of self-efficacy was found in mothers (mean=55.7, Standard Deviation (SD) =6.5) compared to fathers (mean=50.2, SD=11.9). A significant association between paternal and maternal breastfeeding self-efficacy was found (r= 0. 32). Age, employment status, mode of birth was significantly related to maternal and paternal BSE, respectively. Older age and caesarean section delivery were significant factors of combined BSE scores in couples. A higher BSE score in either the mother or her partner predicted higher exclusive breastfeeding rates. BSE scores were lower when couples’ depressive symptoms were high. Conclusion: BSE are highly correlated between Malawian mothers and fathers, with a relatively higher score in maternal BSE. Importantly, a high BSE in couples predicted higher odds of exclusive breastfeeding, which highlights the need to include both mothers and fathers in future breastfeeding promotion strategies.

Keywords: paternal, maternal, exclusive breastfeeding, breastfeeding self‑efficacy, malawi

Procedia PDF Downloads 68

Authors: Keiko Naito, Y. Nakashima, S. Yamauchi, Y. Kunitani, Y. Ishigami, K. Numata, M. Mizobe, Y. Homma, J. Takahashi, T. Inoue, T. Shiga, H. Funakoshi

Abstract:

Background: A presence of severe acidemia, defined as pH < 7.2, is common during endotracheal intubation for critically ill patients in the emergency department (ED). Severe acidemia is widely recognized as a predisposing factor for intubation failure. However, it is unclear that acidemic condition itself actually makes endotracheal intubation more difficult. We aimed to evaluate if a presence of severe acidemia before intubation is associated with the lower first attempt intubation success rate in the ED. Methods: This is a retrospective observational cohort study in the ED of an urban hospital in Japan. The collected data included patient demographics, such as age, sex, and body mass index, presence of one or more factors of modified LEMON criteria for predicting difficult intubation, reasons for intubation, blood gas levels, airway equipment, intubation by emergency physician or not, and the use of the rapid sequence intubation technique. Those with any of the following were excluded from the analysis: (1) no blood gas drawn before intubation, (2) cardiopulmonary arrest, and (3) under 18 years of age. The primary outcome was the first attempt intubation success rates between a severe acidemic patients (SA) group and a non-severe acidemic patients (NA) group. Logistic regression analysis was used to test the first attempt success rates for intubations between those two groups. Results: Over 5 years, a total of 486 intubations were performed; 105 in the SA group and 381 in the NA group. The univariate analysis showed that the first attempt intubation success rate was lower in the SA group than in the NA group (71.4% vs 83.5%, p < 0.01). The multivariate logistic regression analysis identified that severe acidemia was significantly associated with the first attempt intubation failure (OR 1.9, 95% CI 1.03-3.68, p = 0.04). Conclusions: A presence of severe acidemia before endotracheal intubation lowers the first attempt intubation success rate in the ED.

Keywords: acidemia, airway management, endotracheal intubation, first-attempt intubation success rate

Procedia PDF Downloads 248

Authors: Tran Gia Khanh, Dao Phuong Nam, Do Trong Tan, Nguyen Van Huong, Mai Xuan Sinh

Abstract:

This work presents the problem of tube-based robust model predictive controller for a class of continuous-time systems in the presence of input disturbances. The main objective is to point out the state trajectory of closed system being maintained inside a sequence of tubes. An estimation of attraction region of the closed system is pointed out based on input state stability (ISS) theory and linearized model in each time interval. The theoretical analysis and simulation results demonstrate the performance of the proposed algorithm for a wheeled inverted pendulum system.

Keywords: input state stability (ISS), tube-based robust MPC, continuous-time nonlinear systems, wheeled inverted pendulum

Procedia PDF Downloads 220

Authors: Raed Alsufyani, Hissam Tawfik, Victor Chang, Muthu Ramachandran

Abstract:

This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: cloud computing, business intelligence, HOT-fit model, TOE, healthcare and innovation adoption

Procedia PDF Downloads 170

Authors: Mustafa Ümi̇t Ünal, Nafi̇z Çeli̇ktaş, Aysun Şener, Sara Betül Dolgun, Duygu Keser

Abstract:

The aim of this study was to compare acid and base pretreatment of switchgrass for bioethanol production. Switchgrass was pretreated with sulfuric acid and sodium hydroxide at 0.5, 1.0 and 1.5% (v/v) at 120, 140, 180 °C for 10, 60 and 90. Optimization of enzymatic hydrolysis of the pretreated switchgrass samples were carried out using three different enzyme mixtures (22.5 mg cellulase and 75 mg cellobiase /g biomass; 45 mg cellulase and 150 mg cellobiase /g biomass; 90 mg cellulase and 300 mg cellobiase /g biomass). Samples were removed at 24-h interval for fermentable sugar analyses with HPLC. The results showed that use of 90 mg cellulase and 300 mg cellobiase/g biomass resulted in the highest fermentable sugar formation. Furthermore, the highest fermentable sugar yield was obtained by pretreatment at 120 °C for 10 min using 1.0 % sodium hydroxide.

Keywords: switchgrass, acid pretreatment, enzymatic hydrolysis, base pretreatment, ethanol production

Procedia PDF Downloads 530

Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 134

Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

Procedia PDF Downloads 82

Authors: Saheed Olakunle Sanusi, Israel Ajibade Adedeji

Abstract:

The study analysed the effect of microfinance on the profit level of small and medium scale enterprises in Lagos. The data for the study were obtained by simple random sampling, and total of one hundred and fifty (150) small and medium scale enterprises (SMEs) were sampled for the study. Seventy-five (75) each are microfinance users and non-users. Data were analysed using descriptive statistics, logit model, t-test and ordinary least square (OLS) regression. The mean profit of the enterprises using microfinance is ₦16.8m, while for the non-users of microfinance is ₦5.9m. The mean profit of microfinance users is statistically different from the non-users. The result of the logit model specified for the determinant of access to microfinance showed that three of specified variables- educational status of the enterprise head, credit utilisation and volume of business investment are significant at P < 0.01. Enterprises with many years of experience, highly educated enterprise heads and high volume of business investment have more potential access to microfinance. The OLS regression model indicated that three parameters namely number of school years, the volume of business investment and (dummy) participation in microfinance were found to be significant at P < 0.05. These variables are therefore significant determinants of impacts of microfinance on profit level in the study area. The study, therefore, concludes and recommends that to improve the status of small and medium scale enterprises for an increase in profit, the full benefit of access to microfinance can be enhanced through investment in social infrastructure and human capital development. Also, concerted efforts should be made to encouraged non-users of microfinance among SMEs to use it in order to boost their profit.

Keywords: credit utilisation, logit model, microfinance, small and medium enterprises

Procedia PDF Downloads 205

Authors: Maria Anjelica P. Ancheta, Roberto S. Soriano, Erickson L. Llaguno

Abstract:

The main purpose of this study is to examine whether there is a significant relationship between socio-economic class and household water supply demand, through determining or verifying the factors governing water use consumption patterns of households from a sampling from different socio-economic classes in Metro Manila, the national capital region of the Philippines. This study is also an opportunity to augment the lack of local academic literature due to the very few publications on urban household water demand after 1999. In over 600 Metro Manila households, a rapid survey was conducted on their average monthly water consumption and habits on household water usage. The questions in the rapid survey were based on an extensive review of literature on urban household water demand. Sample households were divided into socio-economic classes A-B and C-D. Cluster analysis, dummy coding and outlier tests were done to prepare the data for regression analysis. Subsequently, backward stepwise regression analysis was used in order to determine different statistical models to describe the determinants of water consumption. The key finding of this study is that the socio-economic class of a household in Metro Manila is a significant factor in water consumption. A-B households consume more water in contrast to C-D families based on the mean average water consumption for A-B and C-D households are 36.75 m3 and 18.92 m3, respectively. The most significant proxy factors of socio-economic class that were related to household water consumption were examined in order to suggest improvements in policy formulation and household water demand management.

Keywords: household water uses, socio-economic classes, urban planning, urban water demand management

Procedia PDF Downloads 302

Authors: Jamil Ahmed, Li P. Wong, Yan P. Chua

Abstract:

The United Nation's sustainable development goals include the target to ensure access to water and sanitation for all; however, very few studies have assessed school-based drinking water in Pakistan. The purpose of this study was to characterize water quality in primary schools of Pakistan and to characterize how recent WASH interventions were associated with school water quality. We conducted a representative cross-sectional study of primary schools in the Sindh province of Pakistan. We used structured observations and structured interviews to ascertain the school’s WASH conditions. Our primary exposures of interest were the implementation of previous WASH interventions in the school and the water source type. Outcomes of interest included water quality (measured by various chemical and microbiological indicators) and water availability at the school’s primary drinking water source. We used log-binomial regression to characterize how WASH exposures were associated with water quality outcomes. We collected data from 256 schools. Groundwater was the primary drinking water source at most schools (87%). Water testing showed that 14% of the school’s water had arsenic above the WHO recommendations, and over 50% of the water samples exceeded recommendations for both lead and cadmium. A majority of the water sources (52%) had fecal coliform contamination. None of the schools had nitrate contamination (0%), and few had fluoride contamination (5%). Regression results indicated that having a recent WASH intervention at the school was not associated with either arsenic contamination (prevalence ratio=0.97; 95% CI: 0.46-2.1) or with fecal coliform contamination (PR=0.88; 95% CI: 0.67-1.17). Our assessment unveiled several water quality gaps that exist, including high heavy metal and fecal contamination. Our findings will help various stakeholders to take suitable action to improve water quality in Pakistani schools.

Keywords: WASH interventions, water quality, primary school children, heavy metals

Procedia PDF Downloads 141

Authors: Xu Zhang

Abstract:

This article unveils an indicator of language contact between English and Cantonese in one of the Outer Circle Englishes, Hong Kong (HK) English, through an empirical investigation into 1000 tokens from the Global Web-based English (GloWbE) corpus, employing frequency analysis and logistic regression analysis. It is perceived that Cantonese and general Chinese are contextually marked by an integral underlying thinking pattern. Chinese speakers exhibit a reliance on semantic context over syntactic rules and lexical forms. This linguistic trait carries over to their use of English, affording greater flexibility to formal elements in constructing English sentences. The study focuses on the syntactic positioning of the focusing subjunct ‘also’, a linguistic element used to add new or contrasting prominence to specific sentence constituents. The English language generally allows flexibility in the relative position of 'also’, while there is a preference for close marking relationships. This article shifts attention to Hong Kong, where Cantonese and English converge, and 'also' finds counterparts in Cantonese ‘jaa’ and Mandarin ‘ye’. Employing a corpus-based data-driven method, we investigate the syntactic position of 'also' in both HK and GB English. The study aims to ascertain whether HK English exhibits a greater 'syntactic freedom,' allowing for a more distant marking relationship with 'also' compared to GB English. The analysis involves a random extraction of 500 samples from both HK and GB English from the GloWbE corpus, forming a dataset (N=1000). Exclusions are made for cases where 'also' functions as an additive conjunct or serves as a copulative adverb, as well as sentences lacking sufficient indication that 'also' functions as a focusing particle. The final dataset comprises 820 tokens, with 416 for GB and 404 for HK, annotated according to the focused constituent and the relative position of ‘also’. Frequency analysis reveals significant differences in the relative position of 'also' and marking relationships between HK and GB English. Regression analysis indicates a preference in HK English for a distant marking relationship between 'also' and its focused constituent. Notably, the subject and other constituents emerge as significant predictors of a distant position for 'also.' Together, these findings underscore the nuanced linguistic dynamics in HK English and contribute to our understanding of language contact. It suggests that future pedagogical practice should consider incorporating the syntactic variation within English varieties, facilitating leaners’ effective communication in diverse English-speaking environments and enhancing their intercultural communication competence.

Keywords: also, Cantonese, English, focus marker, frequency analysis, language contact, logistic regression analysis

Procedia PDF Downloads 56

Authors: Shorena Pharjiani

Abstract:

The Present empirical paper investigates the relationship between FDI and economic growth by 10 selected industries in 10 Central and Eastern European countries from the period 1995 to 2012. Different estimation approaches were used to explore the connection between FDI and economic growth, for example OLS, RE, FE with and without time dummies. Obtained empirical results leads to some main consequences: First, the Central and East European countries (CEEC) attracted foreign direct investment, which raised the productivity of industries they entered in. It should be concluded that the linkage between FDI and output growth by industries is positive and significant enough to suggest that foreign firm’s participation enhanced the productivity of the industries they occupied. There had been an endogeneity problem in the regression and fixed effects estimation approach was used which partially corrected the regression analysis in order to make the results less biased. Second, it should be stressed that the results show that time has an important role in making FDI operational for enhancing output growth by industries via total factor productivity. Third, R&D positively affected economic growth and at the same time, it should take some time for research and development to influence economic growth. Fourth, the general trends masked crucial differences at the country level: over the last 20 years, the analysis of the tables and figures at the country level show that the main recipients of FDI of the 11 Central and Eastern European countries were Hungary, Poland and the Czech Republic. The main reason was that these countries had more open door policies for attracting the FDI. Fifth, according to the graphical analysis, while Hungary had the highest FDI inflow in this region, it was not reflected in the GDP growth as much as in other Central and Eastern European countries.

Keywords: central and East European countries (CEEC), economic growth, FDI, panel data

Procedia PDF Downloads 237

Authors: Hafiz Arbab Sakandar, Sabahat Yaqub, Ayesha Sameen, Muhammad Imran, Sarfraz Ahmad

Abstract:

Yoghurt is one of the famous nutritious fermented milk products which have myriad of positive health effects on human beings and curable against different intestinal diseases. This research was conducted to observe effects of different artificial sweeteners on the quality parameters of yoghurt with relation to storage. Some people are allergic to natural sweeteners so artificial sweetener will be helpful for them. Physical-chemical, Microbiology and various sensory evaluation tests were carried out with the interval of 7, 14, 21, and 28 days. It was outcome from this study that addition of artificial sweeteners in yoghurt has shown much harmful effects on the yoghurt microorganisms and other physicochemical parameters from quality point of view. Best results for acceptance were obtained when aspartame was added in yoghurt at level of 0.022 percent. In addition, growth of beneficial microorganisms in yoghurt was also improved as well as other sensory attributes were enhanced by the addition of aspartame.

Keywords: yoghurt, artificial sweetener, storage, quality parameters

Procedia PDF Downloads 476

Authors: Sizar Abed Mohammed, Zahraa Ghazi Sadeeq

Abstract:

The use of the classic method, least squares (OLS) to estimate the linear regression parameters, when they are available assumptions, and capabilities that have good characteristics, such as impartiality, minimum variance, consistency, and so on. The development of alternative statistical techniques to estimate the parameters, when the data are contaminated with outliers. These are powerful methods (or resistance). In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from Duhok company for manufacturing furniture, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). Important conclusions that this study came up with are: a number of typical values detected by using four methods in the furniture line and very close to the data. This refers to the fact that close to the normal distribution of standard errors, but typical values in the doors line data, using OLS less than that detected by the powerful ways. This means that the standard errors of the distribution are far from normal departure. Another important conclusion is that the estimated values of the parameters by using the lifeline is very far from the estimated values using powerful methods for line doors, gave LTS- destined better results using standard MSE, and gave the M- estimator better results using standard MAPE. Moreover, we noticed that using standard MSAE, and MM- estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data.

Keywords: Robest, LTS, M estimate, MSE

Procedia PDF Downloads 232

Authors: O.O. Ehinmowo, A.I. Fatuase, D.F. Oke

Abstract:

Nigeria is endowed with resources that could boost the economy as well as generate income and provide jobs to the teaming populace. One of the keys of attaining this is by making the environment conducive for the entrepreneurs to excel in their respective enterprises so that more income could be accrued to the entrepreneurs. This study therefore examines income inequality among selected entrepreneurs in Ondo State, Nigeria using primary data. A multistage sampling technique was used to select 200 respondents for the study with the aid of structured questionnaire and personal interview. The data collected were subjected to descriptive statistics, Lorenz curve, Gini coefficient and Double - Log regression model. Results revealed that majority of the entrepreneurs (63%) were males and 90% were married with an average age of 44 years. About 40% of the respondents spent at most 12 years in school with 81% of the respondents had 4-6 members per household, while hair dressing (43.5%) and fashion designing (31.5%) were the most common enterprises among the sampled respondents. The findings also showed that majority of the entrepreneurs in hairdressing, fashion designing and laundry service earned below N200,000 per annum while the majority of those in restaurant and food vending earned between N400,000 – N600,000 followed by the entrepreneurs in pure water enterprise where majority earned N800,000 and above per annum. The result of the Gini coefficient (0.58) indicated that there was presence of inequality among the entrepreneurs which was also affirmed by the Lorenz curve. The Regression results showed that gender, household size and number of employees significantly affected the income of the entrepreneurs in the study area. Therefore, more female households should be encouraged into entrepreneurial businesses and government should give incentive cum conductive environment that could bridge the disparity in the income of the entrepreneurs in their various enterprises.

Keywords: entrepreneurs, Gini coefficient, income inequality, Lorenz curve

Procedia PDF Downloads 350

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

Procedia PDF Downloads 40

Authors: Laura E. Hall, Joel Monárrez-Espino, Luz María Tejada Tayabas

Abstract:

College students are at risk of weight gain and poor dietary habits, and health behaviours established during this period have been shown to track into midlife. They may therefore be an important target group for health promotion strategies, yet there is a lack of literature regarding dietary intakes and associated factors in this group, particularly in middle-income countries such as Mexico. The aim of this exploratory research was to describe and compare reported dietary intakes among nursing and nutrition college students at two public universities in Mexico, and to explore the relationship between demographic, behavioural and other health-related factors and the risk of low diet quality. Mexican college students (n=444) majoring in nutrition or nursing at two urban universities completed questionnaires regarding dietary and health-related behaviours and risks. Dietary intake was assessed via 24-hour recall. Weight, height and abdominal circumference were measured. Descriptive statistics were reported and nutrient intakes were compared between colleges and study tracks using Student’s t tests, odds ratios and Pearson chi square tests. Two dietary quality scores were constructed to explore the relationship between demographic, behavioural and other health-related factors and the diet quality scores using binary logistic regression. Analysis was performed using SPSS statistics, with differences considered statistically significant at p<0.05. The response rate to the survey was 91%. When macronutrients were considered as a percentage of total energy, the majority of students had protein intakes within recommended ranges, however one quarter of students had carbohydrate and fat intakes exceeding recommended levels. Three quarters had fibre intakes that were below recommendations. More than half of the students reported intakes of magnesium, zinc, vitamin A, folate and vitamin E that were below estimated average requirements. Students studying nutrition reported macronutrient and micronutrient intakes that were more compliant with recommendations compared to nursing students, and students studying in central-north Mexico were more compliant than those studying in southeast Mexico. Breakfast skipping (Adjusted Odds Ratio (OR) = 5.3; 95% Confidence Interval (CI) = 1.2-22.7), risk of anxiety (OR = 2.3; CI = 1.3-4.4), and university location (OR = 1.6; CI = 1.03-2.6) were associated with a greater risk of having a low macronutrient score. Caloric intakes <1800kcal (OR = 5.8; CI = 3.5-9.7), breakfast skipping (OR = 3.7; CI = 1.4-10.3), vigorous exercise ≤1h/week (OR = 2.6; CI = 1.3-5.2), soda consumption >250mls/day (OR = 2.0; CI = 1.2-3.3), unhealthy diet perception (OR = 1.9; CI = 1.2-3.0), and university location (OR = 1.8; CI = 1.1-2.8) were significantly associated with greater odds of having a low micronutrient score. College students studying nursing and nutrition did not report ideal diets, and these students should not be overlooked in public health interventions. Differences in dietary intakes between universities and study tracks were evident, with more favourable profiles evident in nutrition compared to nursing, and North-central compared to Southeast students. Further, demographic, behavioural and other health-related factors were associated with diet quality scores, warranting further research.

Keywords: college student, diet quality, nutrient intake, young adult

Procedia PDF Downloads 452

Authors: Jae Young Park, Hwansu Jung, Jong Tae Kim

Abstract:

Whether the data has been well parallelized is an important factor in the Solid-State-Drive (SSD) performance. SSD parallelization is affected by allocation scheme and it is directly connected to SSD performance. There are dynamic allocation and static allocation in representative allocation schemes. Dynamic allocation is more adaptive in exploiting write operation parallelism, while static allocation is better in read operation parallelism. Therefore, it is hard to select the appropriate allocation scheme when the workload is mixed read and write operations. We simulated conditions on a few mixed data patterns and analyzed the results to help the right choice for better performance. As the results, if data arrival interval is long enough prior operations to be finished and continuous read intensive data environment static allocation is more suitable. Dynamic allocation performs the best on write performance and random data patterns.

Keywords: dynamic allocation, NAND flash based SSD, SSD parallelism, static allocation

Procedia PDF Downloads 339

Authors: Viliam Makis, Farnoosh Naderkhani, Leila Jafari

Abstract:

In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example.

Keywords: Bayesian control chart, semi-Markov decision process, quality control, partially observable process

Procedia PDF Downloads 319