Opinion Mining on Small and Noisy Samples of Health-Related Texts

The topic of people’s health has always attracted the attention of public and private structures, the patients themselves and, therefore, researchers. Social networks provide an immense amount of data for analysis of health- related issues; however it is not always the case that researchers have enough data to build sophisticated models. In the paper, we artificially create this lim- itation to test performance and stability of different popular algorithms on small samples of texts. There are two specificities in this research apart from the size of a sample: (a) here, instead of usual 5-star classification, we use combined classes reflecting a more practical view on medicines and treatments; (b) we consider both original and noisy data. The experiments were carried out using data extracted from the popular forum AskaPatient. For tuning parameters, GridSearchCV technique was used. The results show that in dealing with small and noisy data samples, GMDH Shell is superior to other methods. The work has a practical orientation

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction

Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs

Model Development of Children Under Mortality Rate with Group Method of Data Handling

In this research we examine aspects of the interdependence between economic development and the use of environmental and natural resources assets from global data published by united nations. for that purpose, we use data mining techniques.data mining techniques applied in this paper were: 1) group method of data handling (gmdh), originally from engineering, introducing principles of evolution - inheritance, mutation and selection - for generating a network structure systematically to develop the automatic model, synthesis, and its validation; 2) step wise regression were also applied for some cases.data sets for this research consist of one sets integration data of air quality data and macroeconomic data of the cross-country data of world development indicator 2003 (wdi 2003). the result shows that the mortality rate of children under five years old is dependent on sanitation and water facilities obtained from gmdh results. however, the results from step-wise regression shows that mortality rate was dependent on annual deforestation, particulate matter, nationally protected area, with the big contribution was from annual deforestation

Model Development of Children Under Mortality Rate With Group Method of Data Handling

in this research we examine aspects of the interdependence between economic development and the use of environmental and natural resources assets from global data published by united nations. for that purpose, we use data mining techniques.data mining techniques applied in this paper were: 1) group method of data handling (gmdh), originally from engineering, introducing principles of evolution - inheritance, mutation and selection - for generating a network structure systematically to develop the automatic model, synthesis, and its validation; 2) step wise regression were also applied for some cases.data sets for this research consist of one sets integration data of air quality data and macroeconomic data of the cross-country data of world development indicator 2003 (wdi 2003). the result shows that the mortality rate of children under five years old is dependent on sanitation and water facilities obtained from gmdh results. however, the results from step-wise regression shows that mortality rate was dependent on annual deforestation, particulate matter, nationally protected area, with the big contribution was from annual deforestation.

Development of sustainable groundwater management methodologies to control saltwater intrusion into coastal aquifers with application to a tropical Pacific island country

Saltwater intrusion due to the over-exploitation of groundwater in coastal aquifers is a critical challenge facing groundwater-dependent coastal communities throughout the world. Sustainable management of coastal aquifers for maintaining abstracted groundwater quality within permissible salinity limits is regarded as an important groundwater management problem necessitating urgent reliable and optimal management methodologies. This study focuses on the development and evaluation of groundwater salinity prediction tools, coastal aquifer multi-objective management strategies, and adaptive management strategies using new prediction models, coupled simulation-optimization (S/O) models, and monitoring network design, respectively. Predicting the extent of saltwater intrusion into coastal aquifers in response to existing and changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMR), an innovative artificial intelligence-based machine learning algorithm, to predict salinity at monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purposes, the prediction results of SVMR are compared with well-established genetic programming (GP) based surrogate models. The prediction capabilities of the two learning machines are evaluated using several measures to ensure their practicality and generalisation ability. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. The performance evaluations suggest that the predictive capability of SVMR is superior to that of GP models. The sensitivity analysis identifies a subset of the most influential pumping rates, which is used to construct new SVMR surrogate models with improved predictive capabilities. The improved predictive capability and generalisation ability of SVMR models, together with the ability to improve the accuracy of prediction by refining the dataset used for training, make the use of SVMR models more attractive. Coupled S/O models are efficient tools that are used for designing multi-objective coastal aquifer management strategies. This study applies a regional-scale coupled S/O methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer in the Pacific Island of Kiribati. A numerical simulation model is developed, calibrated and validated using field data from the Bonriki aquifer. For computational feasibility, SVMR surrogate models are trained and tested utilizing input-output datasets generated using the flow and transport numerical simulation model. The developed surrogate models were externally coupled with a multi-objective genetic algorithm optimization (MOGA) model, as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater. Pumping from barrier wells installed along the coastlines is also considered as a management option to hydraulically control saltwater intrusion. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from barrier wells (which provide a hydraulic barrier) to ensure that the water quality at different monitoring locations remains within pre-specified limits. The executed multi-objective coupled S/O model generated 700 Pareto-optimal solutions. Analysing a large set of Pareto-optimal solution is a challenging task for the decision-makers. Hence, the k-means clustering technique was utilized to reduce the large Pareto-optimal solution set and help solve the large-scale saltwater intrusion problem in the Bonriki aquifer. The S/O-based management models have delivered optimal saltwater intrusion management strategies. However, at times, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management models. The present study explicitly incorporates aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer. To achieve computational efficiency and feasibility of the management model, the calibrated numerical simulation model in the S/O model was is replaced with ensembles of SVMR surrogate models. Each SVMR standalone surrogate model in the ensemble is constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the MOGA model to solve the Bonriki aquifer management problem for ensuring sustainable withdrawal rates that maintain specified salinity limits. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model, established after validation of the optimal solution results, suggests that the implemented constraints of the optimization problem were satisfied; i.e., the salinities at monitoring locations remained within the pre-specified limits. The correct implementation of a prescribed optimal management strategy based on the coupled S/O model is always a concern for decision-makers. The management strategy actually implemented in the field sometimes deviates from the recommended optimal strategy, resulting in field-level deviations. Monitoring such field-level deviations during actual implementation of the recommended optimal management strategy and sequentially updating the strategy using feedback information is an important step towards adaptive management of coastal groundwater resources. In this study, a three-phase adaptive management framework for a coastal aquifer subjected to saltwater intrusion is applied and evaluated for a regional-scale coastal aquifer study area. The methodology adopted includes three sequential components. First, an optimal management strategy (consisting of groundwater extraction from production and barrier wells) is derived and implemented for the optimal management of the aquifer. The implemented management strategy is obtained by solving a homogeneous ensemble-based coupled S/O model. Second, a regional-scale optimal monitoring network is designed for the aquifer system, which considers possible user noncompliance of a recommended management strategy and uncertainty in aquifer parameter estimates. A new monitoring network design is formulated to ensure that candidate monitoring wells are placed at high risk (highly contaminated) locations. In addition, a k-means clustering methodology is utilized to select candidate monitoring wells in areas representative of the entire model domain. Finally, feedback information in the form of salinity measurements at monitoring wells is used to sequentially modify pumping strategies for future time periods in the management horizon. The developed adaptive management framework is evaluated by applying it to the Bonriki aquifer system. Overall, the results of this study suggest that the implemented adaptive management strategy has the potential to address practical implementation issues arising due to user noncompliance, as well as deviations between predicted and actual consequences of implementing a management strategy, and uncertainty in aquifer parameters. The use of ensemble prediction models is known to be more accurate standalone prediction models. The present study develops and utilises homogeneous and heterogeneous ensemble models based on several standalone evolutionary algorithms, including artificial neural networks (ANN), GP, SVMR and Gaussian process regression (GPR). These models are used to predict groundwater salinity in the Bonriki aquifer. Standalone and ensemble prediction models are trained and validated using identical pumping and salinity concentration datasets generated by solving numerical 3D transient density-dependent coastal aquifer flow and transport numerical simulation models. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. The predictive capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation results suggest that the predictive capabilities of the standalone prediction models (ANN, GP, SVMR and GPR) are comparable to those of the groundwater variable-density flow and salt transport numerical simulation model. However, GPR standalone models had better predictive capabilities than the other standalone models. Also, SVMR and GPR standalone models were more efficient (in terms of computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was found to be superior to that of the other homogeneous and heterogeneous ensemble models. Employing data-driven predictive models as replacements for complex groundwater flow and transport models enables the prediction of future scenarios and also helps save computational time, effort and requirements when developing optimal coastal aquifer management strategies based on coupled S/O models. In this study, a new data-driven model, namely Group method for data handling (GMDH) approach is developed and utilized to predict salinity concentration in a coastal aquifer and, simultaneously, determine the most influential input predictor variables (pumping rates) that had the most impact onto the outcomes (salinity at monitoring locations). To confirm the importance of variables, three tests are conducted, in which new GMDH models are constructed using subsets of the original datasets. In TEST 1, new GMDH models are constructed using a set of most influential variables only. In TEST 2, a subset of 20 variables (10 most and 10 least influential variables) are used to develop new GMDH models. In TEST 3, a subset of the least influential variables is used to develop GMDH models. A performance evaluation demonstrates that the GMDH models developed using the entire dataset have reasonable predictive accuracy and efficiency. A comparison of the performance evaluations of the three tests highlights the importance of appropriately selecting input pumping rates when developing predictive models. These results suggest that incorporating the least influential variables decreases model accuracy; thus, only considering the most influential variables in salinity prediction models is beneficial and appropriate. This study also investigated the efficiency and viability of using artificial freshwater recharge (AFR) to increase fresh groundwater pumping rates from production wells. First, the effect of AFR on the inland encroachment of saline water is quantified for existing scenarios. Specifically, groundwater head and salinity differences at monitoring locations before and after artificial recharge are presented. Second, a multi-objective management model incorporating groundwater pumping and AFR is implemented to control groundwater salinization in an illustrative coastal aquifer system. A coupled SVMR-MOGA model is developed for prescribing optimal management strategies that incorporate AFR and groundwater pumping wells. The Pareto-optimal front obtained from the SVMR-MOGA optimization model presents a set of optimal solutions for the sustainable management of the coastal aquifer. The pumping strategies obtained as Pareto-optimal solutions with and without freshwater recharge shows that saltwater intrusion is sensitive to AFR. Also, the hydraulic head lenses created by AFR can be used as one practical option to control saltwater intrusion. The developed 3D saltwater intrusion model, the predictive capabilities of the developed SVMR models, and the feasibility of using the proposed coupled multi-objective SVMR-MOGA optimization model make the proposed methodology potentially suitable for solving large-scale regional saltwater intrusion management problems. Overall, the development and evaluation of various groundwater numerical simulation models, predictive models, multi-objective management strategies and adaptive methodologies will provide decision-makers with tools for the sustainable management of coastal aquifers. It is envisioned that the outcomes of this research will provide useful information to groundwater managers and stakeholders, and offer potential resolutions to policy-makers regarding the sustainable management of groundwater resources. The real-life case study of the Bonriki aquifer presented in this study provides the scientific community with a broader understanding of groundwater resource issues in coastal aquifers and establishes the practical utility of the developed management strategies

Novel Evolutionary-based Methods for the Robust Training of SVR and GMDH Regressors

En los últimos años se han consolidado una serie de diferentes métodos y algoritmos para problemas de aprendizaje máquina y optimización de sistemas, que han dado lugar a toda una corriente de investigación conocida como Soft-Computing. El término de Soft-Computing hace referencia a una colección de técnicas computacionales que intenta estudiar, modelar y analizar fenómenos muy complejos, para los que los métodos convencionales no proporcionan soluciones completas, o no las proporcionan en un tiempo razonable. Dentro de lo que se considera como Soft-Computing existen una gran cantidad de técnicas tales como Redes Neuronales, Máquinas de Vectores Soporte (SVM), Redes Bayesianas, Computación Evolutiva (Algoritmos Genéticos, Algoritmos Evolutivos etc), etc. La investigación de la Tesis está enfocada en dos de estas técnicas, en primer lugar las máquinas de vectores soporte de regresión (SVR) y en segundo lugar a las GMDH (Group Method of Data Handling). Las SVM son una técnica ideada por Vapnik, basada en el principio de minimización del riesgo estructural y la teoría de los métodos kernel, que a partir de un conjunto de datos construye una regla de decisión con la cual intentar predecir nuevos valores para dicho proceso a partir de nuevas entradas. La eficiencia de los sistemas SVM ha hecho que tengan un desarrollo muy significativo en los últimos años y se hayan utilizado en una gran cantidad de aplicaciones tanto para clasificación como para problemas de regresión (SVR). Uno de los principales problemas es la búsqueda de los que se conoce como hiper-parámetros. Estos parámetros no pueden ser calculados de forma exacta, por lo que se hace necesario testear un gran número de combinaciones, para obtener unos parámetros que generen una buena función de estimación. Debido a esto el tiempo de entrenamiento suele ser elevado y no siempre los parámetros encontrados generan una buena solución: ya sea porque el algoritmo de búsqueda tenga un pobre rendimiento o porque el modelo generado está sobre-entrenado. En esta Tesis se ha desarrollado un nuevo algoritmo de tipo evolutivo para el entrenamiento con kernel multi-paramétrico. Este nuevo algoritmo tiene en cuenta un parámetro distinto, para cada una de las dimensiones del espacio de entradas. En este caso, debido al incremento del número de parámetros no puede utilizarse una búsqueda en grid clásica, debido al coste computacional que conllevaría. Por ello, en esta Tesis se propone la utilización de un algoritmo evolutivo para la obtención de los valores óptimos de los parámetros de la SVR y la aplicación de nuevas cotas para los parámetros de este kernel multi-paramétrico. Junto con esto, se han desarrollado nuevos métodos de validación que mejoren el rendimiento de las técnicas de regresión en problemas data-driven. La idea es obtener mejores modelos en la fase de entrenamiento del algoritmo, de tal forma que el desempeño con el conjunto de test mejore, principalmente en lo que a tiempo de entrenamiento se refiere y en el rendimiento general del sistema, con respecto a otros métodos de validación clásicos como son K-Fold cross-validation, etc. El otro foco de investigación de esta Tesis se encuentra en la técnica GMDH, ideada en los años 70 por Ivakhnenko. Es un método particularmente útil para problemas que requieran bajos tiempos de entrenamiento. Es un algoritmo auto-organizado, donde el modelo se genera de forma adaptativa a partir de los datos, creciendo con el tiempo en complejidad y ajustándose al problema en cuestión, hasta que el modelo alcanza un grado de complejidad óptima, es decir, no es demasiado simple ni demasiado complejo. De esta forma el algoritmo construye el modelo en base a los datos de los que dispone y no a una idea preconcebida del investigador, como ocurre en la mayoría de las técnicas de Soft-Computing. Las GMDH también tienen algunos inconvenientes como son los errores debido al sobre-entrenamiento y la multicolinealidad, esto hace que en algunas ocasiones el error sea elevado si lo comparamos con otras técnicas. Esta Tesis propone un nuevo algoritmo de construcción de estas redes basado en un algoritmo de tipo hiper-heurístico. Esta aproximación es un concepto nuevo relacionado con la computación evolutiva, que codifica varios heurísticos que pueden ser utilizados de forma secuencial para resolver un problema de optimización. En nuestro caso particular, varios heurísticos básicos se codifican en un algoritmo evolutivo, para crear una solución hiper-heurística que permita construir redes GMDH robustas en problemas de regresión. Todas las propuestas y métodos desarrollados en esta Tesis han sido evaluados experimentalmente en problemas benchmark, así como en aplicaciones de regresión reales

Social determinants and child survival in Nigeria in the era of Sustainable Development Goals: Progress, challenges, and opportunities

Introduction: Like in many low- and middle-income settings, childhood mortality remains a big challenge in Nigeria—being the second largest contributor to under-five mortality globally, after India. Currently, there is little local evidence to guide policymakers in Nigeria to tailor appropriate social interventions to make the Sustainable Development Goal (SDG) targets of child survival (SDG-3), gender equality (SDG-5), and social inclusiveness (SDG-10) achievable by 2030. In addition, lack of methodological rigor and theoretical foundations of child survival research in Nigeria limit their use for proper planning of child health services. Aims: The basis of this thesis is to understand the complex issues relating to child survival and recommend new approaches to guide policymakers on interventions that will improve child survival in Nigeria. The overarching goal of this thesis is to address the methodological and theoretical shortcomings identified in the previous studies conducted in Nigeria. Using robust interdisciplinary analytic techniques, this thesis assessed the following specific objectives. Objective 1: (a) Compare predictive abilities of the most used conventional statistical time-series methods—ARIMA and Holt-Winters exponential smoothing models, with artificial intelligence technique such as group method of data handling (GMDH)-type artificial neural network (ANN), and (b) estimate the age- and sex-specific mortality trends in child-related SDG indicators (i.e., neonatal and under-five mortality rates) over the 1960s-2017 period, and estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. Objective 2: (a) Identify the social determinants of age-specific childhood (0-59 months) mortalities, which are disaggregated into neonatal mortality (0-27 days), post-neonatal mortality (1-11 months) and child mortality (12-59 months), and (b) estimate the within- and between-community variations of mortality among under-five children in Nigeria. Objective 3: Identify the critical pathways through which social factors (at maternal, household, community levels) determine neonatal, infant, and under-five mortalities in Nigeria. Objective 4: (a) Determine patterns and determinants of geographical clustering of neonatal mortality at the state and regional levels in Nigeria, (b) assess gender inequity for neonatal mortality between urban and rural communities across the regions in Nigeria, and (c) measure gaps in SDG-3 target for neonatal mortality at the state and regional levels in Nigeria. Methods: This thesis is a quantitative study which used two secondary datasets—aggregated historical childhood mortality data from 1960s to 2017 (objective 1), and the latest (2016/2017) Nigeria Multiple Indicator Cluster Survey (MICS) for 36 states and Federal Capital Territory (FCT) in Nigeria (objectives 2-4). To minimize recall bias, analysis was limited to a weighted nationally representative sample of 30,960 live births delivered within five years before the survey. The selection of relevant social determinants of child survival was primarily informed by Mosley-Chen framework. The candidate variables were layered across child, maternal, household, and community-levels. The analytic approaches include artificial intelligence technique (i.e., group method of data handling (GMDH)-type artificial neural network, and multilayer perceptron (MLP) neural network), autoregressive integrated moving average (ARIMA), Holt-Winters exponential smoothing models, spatial cluster analysis, hierarchical path analysis with time-to-event outcome, and multilevel multinomial regression. Results: Progress towards achieving SDG targets – Nigeria is not likely to achieve SDG targets for child survival and, within, gender equity by 2030 at the current annual reduction rates (ARR) under-five mortality rate (U5MR): 1.2%, and neonatal mortality rate (NMR): 2.0%. If the current trend continues, U5MR will begin to increase by 2028. Also, at the end of SDG-era, female deaths will be higher than male deaths (80.9 vs. 62.6 deaths per 1000 live births). To make child-related SDG targets achievable by 2030, Nigeria needs to reduce annual U5MR by 9 times and annual NMR by 4 times the current rate of decrease. Social determinants of childhood mortality – At each stage of early childhood development, there are different factors relating to survival outcomes. Surprisingly, attendance of skilled health providers during delivery was associated with an increased neonatal mortality risk, although its effect disappeared during post-neonatal and toddler/pre-school stages. The observed association requires cautious interpretation because of unavailability of variables on quality of care in MICS dataset to assess how skilled birth delivery impacts child survival in Nigeria. However, there is a possibility of under-reporting under-five mortalities at the community level. Also, it could indicate a functioning referral system that sends the high-risk deliveries to health facilities to a greater extent. There is a large variation (39%) of under-five mortalities across the Nigerian communities, which is accounted for by maternal-level factors (i.e., maternal education, contraceptive use, maternal wealth, parity, death of previous children and quality of perinatal care). Pathways to childhood mortality – Region and area of residence (urban/rural), infrastructural development, maternal education, contraceptive use, marital status, and maternal age at birth were found to operate indirectly on neonatal, infant and under-five survival. Female children, singleton, children whose mothers delivered at least two years apart and aged 20-34 years survived much longer. Specifically, women from Northern areas of Nigeria were less likely to reside in urban cities and towns than those in the Southern areas. This, in turn, limited their access to social infrastructure and acted as a barrier to maternal education. Without adequate education, women were less likely to use contraceptive methods. Women with no history of contraceptive use were more likely to have childbirths closer together (less than two-year gap), which in turn, negatively impacted child survival. Regional inequities in childhood mortality – There was significant state-level clustering of NMR in Nigeria. The states with higher neonatal mortality rates were majorly clustered in the North-West and North-Central regions, and states with lower neonatal mortality rates were clustered in the South-South and South-East regions. Gender inequity was worse in the rural areas of Northern Nigeria, while it was worse in the urban areas of Southern Nigeria. NMR was disproportionately higher among females in urban areas (except North-West and South-West regions). Conversely, male neonates had higher mortality risks in the rural areas for all the regions. Conclusions: This thesis provides more refined age- and sex-specific mortality estimates for Nigeria. At the current rates, Nigeria will not meet SDG targets for child survival. In addition, this thesis identifies the critical intervention pathways to child survival in Nigeria during the SDG-era. The new estimates may be used to improve the design and accelerate the implementation of child health programmes to attain the SDG targets. Also, it is important for stakeholders to implement more impactful policies that promote maternal education and improve living conditions of women (especially in the rural areas). To address gender inequities, gender-sensitive policies, and community mobilization against gender-based discrimination towards girl-child should be implemented. Further research is required to assess the quality of skilled birth attendants in Nigeria