Search results for: poverty prediction

Authors: James Ladzekpo

Abstract:

Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

Procedia PDF Downloads 53

Authors: Pallavi Roe

Abstract:

Violent crime is a significant public safety concern in urban areas across the United States, and Washington, DC, is no exception. This research discusses the prevalence and types of crime, particularly violent crime, in Washington, DC, along with the factors contributing to the high rate of violent crime in the city, including poverty, inequality, access to guns, and racial disparities. The organizations working towards ensuring safety in neighborhoods are also listed. The proposal to perform spatial and temporal analysis on violent crime and the use of guns in crime analysis is presented to identify patterns and trends to inform evidence-based interventions to reduce violent crime and improve public safety in Washington, DC. The stakeholders for crime analysis are also discussed, including law enforcement agencies, prosecutors, judges, policymakers, and the public. The anticipated result of the spatial and temporal analysis is to provide stakeholders with valuable information to make informed decisions about preventing and responding to violent crimes.

Keywords: crime analysis, spatial analysis, temporal analysis, violent crime

Procedia PDF Downloads 319

Authors: Hassan Kian

Abstract:

The future of millions of people and consequently, the future of societies and humanity is threatened by a great threat which is called wasted human resources. Perhaps Pasteur, Beethoven and Avicenna, Lavoisier and Einstein and millions of genius individuals and thinkers may have never been discovered and could not found a chance of being known due to various reasons such as poverty or social status, and other problems. So without being able to serve humanity, their talents are fully wasted. While, if a global mechanism exists to discover their talents in different countries and provide to them the right direction, during less than a generation, human society will face to a profound transformation and sustainable social justice will be formed as the basis of sustainable development of human resources. Therefore, the situation of the institution which organizes the affair of discovering and guiding talents was vacant at the level of the international community and its necessity has been felt. So in this plan, the establishment and development of such an organization have been suggested in the international context.

Keywords: talent identification, comparative advantage, sustainable justice, sustainable development

Procedia PDF Downloads 222

Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

Abstract:

Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

Procedia PDF Downloads 520

Authors: Brian Yulianto, Syachrial, Saeful Aziz, Anggita Clara Shinta

Abstract:

Indonesia, with a population of about two hundred and fifty million people in quantity, indicates the outstanding wealth of human resources. Hundreds of millions of the population scattered in various communities in various regions in Indonesia with the different characteristics of economic, social and unique culture. Broadly speaking, the community in Indonesia is divided into two classes, namely urban communities and rural communities. The rural communities characterized by low potential and management of natural and human resources, limited access of development, and lack of social and economic infrastructure, and scattered and isolated population. West Java is one of the provinces with the largest population in Indonesia. Based on data from the Central Bureau of Statistics in 2015 the number of population in West Java reached 46.7096 million souls spread over 18 districts and 9 cities. The big difference in geographical and social conditions of people in West Java from one region to another, especially the south to the north causing the gap is high. It is closely related to the flow of investment to promote the area. Poverty and underdevelopment are the classic problems that occur on a massive scale in the region as the effects of inequity in development. South Cianjur and Tasikmalaya area South became one of the portraits area where the existing potential has not been capable of prospering society. Tri Dharma College not only define the College as a pioneer implementation of education and research to improve the quality of human resources but also demanded to be a pioneer in the development through the concept of public service. Bandung Institute of Technology as one of the institutions of higher education to implement community service system through collaborative community work program "one of the university community" as one approach to developing villages. The program is based Community Service, where students are not only required to be able to take part in community service, but also able to develop a community development strategy that is comprehensive and integrity in cooperation with government agencies and non-government related as a real form of effort alignment potential, position and role from various parties. Areas of western Java in particular have high poverty rates and disparity. On the other hand, there are three fundamental pillars in the development of rural communities, namely economic development, community development, and the integrated infrastructure development. These pillars require the commitment of all components of community, including the students and colleges for upholding success. College’s community program is one of the approaches in the development of rural communities. ITB is committed to implement as one form of student community service as community-college programs that integrate all elements of the community which is called Kuliah Kerja Nyata-Thematic.

Keywords: development in rural area, collaborative, student community service, Kuliah Kerja Nyata-Thematic ITB

Procedia PDF Downloads 221

Authors: Yung-Chung Chuang

Abstract:

The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

Procedia PDF Downloads 140

Authors: Filippo Portera

Abstract:

Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

Procedia PDF Downloads 93

Authors: Yang Liu

Abstract:

The work team has been being adopted by the CCP for special missions in a limited time. Since the 18th National Congress of CCP, the unprecedented practice of the work team has had impacts beyond the original goal of poverty alleviation, their functions in village governance have still not been well studied. As the state agents that come from the outside of the village community, this article argues that the work team is a group that represents the coexistence of political, economic, and cultural capital, which contributes to effectively empower the state, and the village cadres and the peasants. For the state, more accurate bottom-up information could be collected by the work team, and policies could be made scientifically and implemented without distortion. For the village cadres, they can learn leadership skills and share more resources owned or mobilized by the work team. For the peasants, they have more access to participate the public affairs of their village and express their claims. The multiple empowerments have greatly improved the relationship among the state, the peasants, and the village cadres since a series of reforms from 1980s to 2000s that alienated the relationship among them.

Keywords: state, village cadre, empowerment, work team, peasants

Procedia PDF Downloads 113

Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

Procedia PDF Downloads 83

Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

Abstract:

Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

Procedia PDF Downloads 237

Authors: M. Pienaar, J. W. Odendaal, J. C. Smit, J. Joubert

Abstract:

Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

Procedia PDF Downloads 256

Authors: Mohammad Javad Behzadnia, Amir Bahador Boroumand

Abstract:

Introduction: Limited facility situations require allocating the most available resources for most casualties. Accordingly, Traumatic Brain Injury (TBI) is the one that may need to transport the patient as soon as possible. In a mass casualty event, deciding when the facilities are restricted is hard. The Extended Glasgow Outcome Score (GOSE) has been introduced to assess the global outcome after brain injuries. Therefore, we aimed to evaluate the prognostic factors associated with GOSE. Materials and Methods: In a multicenter cross-sectional study conducted on 144 patients with TBI admitted to trauma emergency centers. All the patients with isolated TBI who were mentally and physically healthy before the trauma entered the study. The patient’s information was evaluated, including demographic characteristics, duration of hospital stays, mechanical ventilation on admission laboratory measurements, and on-admission vital signs. We recorded the patients’ TBI-related symptoms and brain computed tomography (CT) scan findings. Results: GOSE assessments showed an increasing trend by the comparison of on-discharge (7.47 ± 1.30), within a month (7.51 ± 1.30), and within three months (7.58 ± 1.21) evaluations (P < 0.001). On discharge, GOSE was positively correlated with Glasgow Coma Scale (GCS) (r = 0.729, P < 0.001) and motor GCS (r = 0.812, P < 0.001), and inversely with age (r = −0.261, P = 0.002), hospitalization period (r = −0.678, P < 0.001), pulse rate (r = −0.256, P = 0.002) and white blood cell (WBC). Among imaging signs and trauma-related symptoms in univariate analysis, intracranial hemorrhage (ICH), interventricular hemorrhage (IVH) (P = 0.006), subarachnoid hemorrhage (SAH) (P = 0.06; marginally at P < 0.1), subdural hemorrhage (SDH) (P = 0.032), and epidural hemorrhage (EDH) (P = 0.037) were significantly associated with GOSE at discharge in multivariable analysis. Conclusion: Our study showed some predictive factors that could help to decide which casualty should transport earlier to a trauma center. According to the current study findings, GCS, pulse rate, WBC, and among imaging signs and trauma-related symptoms, ICH, IVH, SAH, SDH, and EDH are significant independent predictors of GOSE at discharge in TBI patients.

Keywords: field, Glasgow outcome score, prediction, traumatic brain injury.

Procedia PDF Downloads 73

Authors: Arnon Medzini, Lilach Lev Ari

Abstract:

Migration, the driving force behind increased urbanization, has made cities much more diverse places to live in. Nearly one-fifth of all migrants live in the world’s 20 largest cities. In many of these global cities, migrants constitute over a third of the population. Many of contemporary migrants are in fact ‘forced migrants,’ pushed from their countries of origin due to political or ethnic violence and persecution or natural disasters. During the past decade, massive numbers of labor migrants and asylum seekers have migrated from African countries to Israel via Egypt. Their motives for leaving their countries of origin include ongoing and bloody wars in the African continent as well as corruption, severe conditions of poverty and hunger, and economic and political disintegration. Most of the African migrants came to Israel from Eritrea and Sudan as they saw Israel the closest natural geographic asylum to Africa; soon they found their way to the metropolitan Tel-Aviv area. There they concentrated in poor neighborhoods located in the southern part of the city, where they live under conditions of crowding, poverty, and poor sanitation. Today around 45,000 African migrants reside in these neighborhoods, and yet there is no legal option for expelling them due to dangers they might face upon returning to their native lands. Migration of such magnitude to the weakened neighborhoods of south Tel-Aviv can lead to the destruction of physical, social and human infrastructures. The character of the neighborhoods is changing, and the local population is the main victim. These local residents must bear the brunt of the failure of both authorities and the government to handle the illegal inhabitants. The extremely crowded living conditions place a heavy burden on the dilapidated infrastructures in the weakened areas where the refugees live and increase the distress of the veteran residents of the neighborhoods. Some problems are economic and some stem from damage to the services the residents are entitled to, others from a drastic decline in their standard of living. Even the public parks no longer serve the purpose for which they were originally established—the well-being of the public and the neighborhood residents; they have become the main gathering place for the infiltrators and a center of crime and violence. Based on secondary data analysis (for example: The Israel’s Population, Immigration and Border Authority, the hotline for refugees and migrants), the objective of this presentation is to discuss the effects of forced migration to Tel Aviv on the following tensions: between the local population and the immigrants; between the local population and the state authorities, and between human rights groups vis-a-vis nationalist local organizations. We will also describe the changes which have taken place in the urban infrastructure of the city of Tel Aviv, and discuss the efficacy of various Israeli strategic trajectories when handling human problems arising in the marginal urban regions where the forced migrant population is concentrated.

Keywords: African asylum seekers, forced migrants, marginal urban regions, urban infrastructure

Procedia PDF Downloads 251

Authors: Udeh Anastasia Ifeoma

Abstract:

The need to develop the rural areas in developing countries where there have been decades of neglect are on the increase. It is against this background that this paper examined the impact of micro finance contribution to Nigeria’s gross domestic product. Time series data for 12-years period 1999-2010 were collated from Central Bank of Nigeria published annual reports. The least squares (LS) regression was used to analyze the data. The result revealed that microfinance activities have negative and non-significant contribution to gross domestic product in Nigeria. The paper recommends that rural poverty is often a product of poor infrastructural facilities; therefore government should make a conscious effort towards industrializing the rural areas thereby motivating the micro finance institutions to locate their offices and extend credit facilities to rural areas thereby improving rural economic growth.

Keywords: microfinance, rural economic growth, Nigeria, developing countries

Procedia PDF Downloads 449

Authors: Osungwu Emeka

Abstract:

Until recently, Port Harcourt was known as the “Garden City of Nigeria” because of its neatness and the overwhelming presence of vegetation all over the metropolis. But today, the presence of piles of refuse dotting the entire city may have turned Port Harcourt into a “Garbage City”. Indiscriminate dumping of industrial, commercial and household wastes such as food waste, paper, polythene, textiles, scrap metals, glasses, wood, plastic, etc. at street corners and gutters, is still very common. The waste management problem in the state affects the citizens both directly and indirectly. The dumping of waste along the roadside obstructs traffic and, after mixing with rain water may sip underground with the possibility of the leachate contaminating the groundwater. The basic solid waste management processes of collection, transportation, segregation and final disposal appear to be very inefficient. This study was undertaken to assess waste utilization using metal waste scavengers. Highlighting their activities as a part of the informal sector of the solid waste management system with a view to identifying their challenges, prospects and possible contributions to the solid waste management system in the Port Harcourt metropolis. Therefore, the aim was to understand and assess scavenging as a system of solid waste management in Port Harcourt and to identify the main bottlenecks to its efficiency and the way forward. This study targeted people who engage in scavenging metal scraps across 5 major waste dump sites across Port Harcourt. To achieve this, a mixed method study was conducted to provide both experiential evidence on this waste utilization method using a qualitative study and a survey to collect numeric evidence on this subject. The findings from the qualitative string of this study provided insight on scavenging as a waste utilization activity and how their activities can reduce the gross waste generated and collected from the subject areas. It further showed the nature and characteristics of scavengers in the waste recycling system as a means of achieving the millennium development goals towards poverty alleviation, job creation and the development of a sustainable, cleaner environment. The study showed that in Port Harcourt, the waste management practice involves the collection, transportation and disposal of waste by refuse contractors using cart pushers and disposal vehicles at designated dumpsites where the scavengers salvage metal scraps for recycling and reuse. This study further indicates that there is a great demand for metal waste materials/products that are clearly identified as genuinely sustainable, even though they may be perceived as waste. The market for these waste materials shall promote entrepreneurship as a profitable venture for waste recovery and recycling in Port Harcourt. Therefore, the benefit of resource recovery and recycling as a means of the solid waste management system will enhance waste to wealth that will reduce pollution, create job opportunities thereby alleviate poverty.

Keywords: scavengers, metal waste, waste-to-wealth, recycle, Port Harcourt, Nigeria, waste reduction, garden city, waste

Procedia PDF Downloads 97

Authors: Esra Zengin, Sinan Akkar

Abstract:

Reliable and accurate prediction of nonlinear structural response requires specification of appropriate earthquake ground motions to be used in nonlinear time history analysis. The current research has mainly focused on selection and manipulation of real earthquake records that can be seen as the most critical step in the performance based seismic design and assessment of the structures. Utilizing amplitude scaled ground motions that matches with the target spectra is commonly used technique for the estimation of nonlinear structural response. Representative ground motion ensembles are selected to match target spectrum such as scenario-based spectrum derived from ground motion prediction equations, Uniform Hazard Spectrum (UHS), Conditional Mean Spectrum (CMS) or Conditional Spectrum (CS). Different sets of criteria exist among those developed methodologies to select and scale ground motions with the objective of obtaining robust estimation of the structural performance. This study presents ground motion selection and scaling procedure that considers the spectral variability at target demand with the level of ground motion dispersion. The proposed methodology provides a set of ground motions whose response spectra match target median and corresponding variance within a specified period interval. The efficient and simple algorithm is used to assemble the ground motion sets. The scaling stage is based on the minimization of the error between scaled median and the target spectra where the dispersion of the earthquake shaking is preserved along the period interval. The impact of the spectral variability on nonlinear response distribution is investigated at the level of inelastic single degree of freedom systems. In order to see the effect of different selection and scaling methodologies on fragility curve estimations, results are compared with those obtained by CMS-based scaling methodology. The variability in fragility curves due to the consideration of dispersion in ground motion selection process is also examined.

Keywords: ground motion selection, scaling, uncertainty, fragility curve

Procedia PDF Downloads 582

Authors: Vikrant Gupta, Amrit Goswami

Abstract:

The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

Procedia PDF Downloads 136

Authors: Djehich Mohamed Yousri

Abstract:

Nowadays, migration has become a phenomenon that attracts the attention of researchers, countries, agencies, and national and international bodies. Wars and climate change, demographics, poverty, natural disasters, and epidemics are all threats that are contributing daily to forcing more people to migrate. There are those who resort to emigration because of the deteriorating political conditions in their country, others resort to emigration to improve their financial situation, and others emigrate from their country for fear of some penalties and judgments issued against them. In the field of migration, becoming a member of the United Nations as a "relevant organization" gives the United Nations a clear mandate on migration. Its primary goal is to facilitate the management of international migration in an orderly and humane manner. In order to achieve this goal, the organization adopts an international policy to meet the challenges posed in the field of migration. This paper attempts to study the structure of this international organization and its strategy in dealing with the phenomenon of international migration.

Keywords: international organization for migration, immigrants, immigrant rights, resettlement, migration organization strategy

Procedia PDF Downloads 119

Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

Abstract:

Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises

Procedia PDF Downloads 252

Authors: Wang Yichen, Haruka Yamashita

Abstract:

In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

Procedia PDF Downloads 133

Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

Abstract:

Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

Procedia PDF Downloads 429

Authors: Fidelia A. Orji, Julita Vassileva

Abstract:

This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

Procedia PDF Downloads 127

Authors: C. S. Subhashini, H. L. Premaratne

Abstract:

Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

Procedia PDF Downloads 383

Authors: Emir Sunguroglu, Merve Sunguroglu, Yesim Aliefendioglu, Harun Tanrivermis

Abstract:

Basic human needs range from physiological needs such as food, water and shelter to safety needs such as security, protection from natural disasters and even urban terrorism which are extant and not fulfilled even in urban areas where people live civilly in large communities. These basic needs when arose in urban life lead to a different kind of crime set defined as urban crimes. Urban crimes mostly result from differences between socioeconomic conditions in society. Income inequality increases tendency towards urban crimes. Especially in slum areas and suburbs, urban crimes not only threaten public security but they also affect deliverance of public services. It is highlighted that, construction of urban security against problems caused by urban crimes is not only achieved by involvement of urban security in security of the community but also comprises juridical development and staying above a level of legal standards concurrently. The idea of urban transformation emerged as interventions to demolishment and rebuilding of built environment to solve the unhealthy urban environment, inadequate infrastructure and socioeconomic problems came up during the industrialization process. Considering the probability of urbanization process driving citizens to commit crimes, The United Nations Commission on Human Security’s focus on this theme is conferred to be a proper approach. In this study, the analysis and change in security before, through and after urban transformation, which is one of the tools related to urbanization process, is strived to be discussed through the case of Sincan County Saraycik District. The study also aims to suggest improvements to current legislation on public safety, urban resilience, and urban transformation. In spite of Saraycik District residing in a developing County in Ankara, Turkey, from urbanization perspective as well as socioeconomic and demographic indicators the District exhibits a negative view throughout the County and the country. When related to the county, rates of intentional harm reports, burglary reports, the offense of libel and threat reports and narcotic crime reports are higher. The District is defined as ‘crime hotspot’. Interviews with residents of Saraycik claim that the greatest issue of the neighborhood is Public Order and Security (82.44 %). The District becomes prominent with negative aspects, especially with the presence of unlicensed constructions, occurrence of important social issues such as crime and insecurity and complicated lives of inhabitants from poverty and low standard conditions of living. Additionally, the social structure and demographic properties and crime and insecurity of the field have been addressed in this study. Consequently, it is claimed that urban crime rates were related to level of education, employment and household income, poverty trap, physical condition of housing and structuration, accessibility of public services, security, migration, safety in terms of disasters and emphasized that urban transformation is one of the most important tools in order to provide urban security.

Keywords: urban security, urban crimes, urban transformation, Saraycik district

Procedia PDF Downloads 303

Authors: Sabiu Abdullahi Yau

Abstract:

Nigeria is a high fertility country that experiences eye-popping population growth, with no end in sight. However, there is evidence that its large population inhibits government’s efforts in meeting the basic needs of the people. Moreover, past and present governments of Nigeria have been committing huge amount of financial resources to meet the basic infrastructural requirements capable of propelling growth and development. Despite the country’s large population and abundant natural resources, poverty, unemployment, rural-urban migration, deforestation and inadequate infrastructural facilities have been persistently on the increase resulting in consistent failure of government policies to impact positively on the economy. This paper, however, identifies and critically analyses the major development challenges caused by population growth in Nigeria using secondary data. The paper concludes that for the Nigeria’s economy to develop, all the identified challenges posed by rapid population growth must be promptly and squarely addressed.

Keywords: economic development, population, growth, Nigeria

Procedia PDF Downloads 328

Authors: Matar Celine, Gaucel Sebastien, Gontard Nathalie, Guilbert Stephane, Guillard Valerie

Abstract:

To increase the very short shelf life of fresh fruits and vegetable, Modified Atmosphere Packaging (MAP) allows an optimal atmosphere composition to be maintained around the product and thus prevent its decay. This technology relies on the modification of internal packaging atmosphere due to equilibrium between production/consumption of gases by the respiring product and gas permeation through the packaging material. While, to the best of our knowledge, benefit of MAP for fresh fruits and vegetable has been widely demonstrated in the literature, its effect on shelf life increase has never been quantified and formalized in a clear and simple manner leading difficult to anticipate its economic and environmental benefit, notably through the decrease of food losses. Mathematical modelling of mass transfers in the food/packaging system is the basis for a better design and dimensioning of the food packaging system. But up to now, existing models did not permit to estimate food quality nor shelf life gain reached by using MAP. However, shelf life prediction is an indispensable prerequisite for quantifying the effect of MAP on food losses reduction. The objective of this work is to propose an innovative approach to predict shelf life of MAP food product and then to link it to a reduction of food losses and wastes. In this purpose, a ‘Virtual MAP modeling tool’ was developed by coupling a new predictive deterioration model (based on visual surface prediction of deterioration encompassing colour, texture and spoilage development) with models of the literature for respiration and permeation. A major input of this modelling tool is the maximal percentage of deterioration (MAD) which was assessed from dedicated consumers’ studies. Strawberries of the variety Charlotte were selected as the model food for its high perishability, high respiration rate; 50-100 ml CO₂/h/kg produced at 20°C, allowing it to be a good representative of challenging post-harvest storage. A value of 13% was determined as a limit of acceptability for the consumers, permitting to define products’ shelf life. The ‘Virtual MAP modeling tool’ was validated in isothermal conditions (5, 10 and 20°C) and in dynamic temperature conditions mimicking commercial post-harvest storage of strawberries. RMSE values were systematically lower than 3% for respectively, O₂, CO₂ and deterioration profiles as a function of time confirming the goodness of model fitting. For the investigated temperature profile, a shelf life gain of 0.33 days was obtained in MAP compared to the conventional storage situation (no MAP condition). Shelf life gain of more than 1 day could be obtained for optimized post-harvest conditions as numerically investigated. Such shelf life gain permitted to anticipate a significant reduction of food losses at the distribution and consumer steps. This food losses' reduction as a function of shelf life gain has been quantified using a dedicated mathematical equation that has been developed for this purpose.

Keywords: food losses and wastes, modified atmosphere packaging, mathematical modeling, shelf life prediction

Procedia PDF Downloads 181

Authors: Saurabh B. Ganorkar, Atul A. Shirkhedkar

Abstract:

The need for investigations protecting against toxic impurities though seems to be a primary requirement; the impurities which may prove non - toxic can be explored for their therapeutic potential if any to assist advanced drug discovery. The essential role of pharmaceutical analysis can thus be extended effectively to achieve it. The present study successfully achieved these objectives with characterization of major degradation products as impurities for Zileuton which has been used for to treat asthma since years. The forced degradation studies were performed to identify the potential degradation products using Ultra-fine Liquid-chromatography. Liquid-chromatography-Mass spectrometry (Time of Flight) and Proton Nuclear Magnetic Resonance Studies were utilized effectively to characterize the drug along with five major oxidative and hydrolytic degradation products (DP’s). The mass fragments were identified for Zileuton and path for the degradation was investigated. The characterized DP’s were subjected to In-Silico studies as XP Molecular Docking to compare the gain or loss in binding affinity with 5-Lipooxygenase enzyme. One of the impurity of was found to have the binding affinity more than the drug itself indicating for its potential to be more bioactive as better Antiasthmatic. The close structural resemblance has the ability to potentiate or reduce bioactivity and or toxicity. The chances of being active biologically at other sites cannot be denied and the same is achieved to some extent by predictions for probability of being active with Prediction of Activity Spectrum for Substances (PASS) The impurities found to be bio-active as Antineoplastic, Antiallergic, and inhibitors of Complement Factor D. The toxicological abilities as Ames-Mutagenicity, Carcinogenicity, Developmental Toxicity and Skin Irritancy were evaluated using Toxicity Prediction by Komputer Assisted Technology (TOPKAT). Two of the impurities were found to be non-toxic as compared to original drug Zileuton. As the drugs are purposed and repurposed effectively the impurities can also be; as they can have more binding affinity; less toxicity and better ability to be bio-active at other biological targets.

Keywords: UFLC, LC-MS-TOF, NMR, Zileuton, impurities, toxicity, bio-activity

Procedia PDF Downloads 192

Authors: Lejla Mušić

Abstract:

Gender dimension of migration analyzes the intersection in between the world statistics on male and female migrations, around the world, involving the questions of youth migrations. Comparative analyses of world migration statistics as methodology offer the insight into the position of women in labor market around world. There are different forms of youth debris in contemporary world. The main problems are illegal migration, feminization of poverty, kidnapping the girls in Nigeria, femicides in Juarez and Mexico. Illegal migrations involve forced labor, rape and prostitution. Transgender youth share ideas through the online media (anti-bullying videos) and develop their own styles such as anarcho-punk, rave, or rock. Therefore, the stronger gender equality laws and laws for protection of women on work should be enforced.

Keywords: hyperfeminisation, rape, gangs of girls, rent boys masculinities, Varoç in Istanbul, forced labor, rape and prostitution, illegal emigrations

Procedia PDF Downloads 255

Authors: Sunday Olufemi Adesogan

Abstract:

There is a strong link between engineering and development. Engineering as a profession is a call to service by the society. Perhaps next to soldiers, engineers are the most patriotic professionals. However, unlike soldiers, they remain servants of society at all times and in all circumstances. Despite their role to the society, engineering profession seems not to be enjoying the respect due to it probably because of failures associated with some engineering projects. This paper focuses on the need to improve on engineering practices for developments in developing countries using Engineering practice in Nigerian Universities as a tool for argument. Purposeful Survey, interview and focus group discussion were carried out among one hundred and twenty (120) reputable firms in Nigeria. The topic was approached through a few projects that the firms have been involved in from the planning stage, some to completion and beyond into the stage of maintenance and monitoring. It is revealed that some factors which are not determined by the engineers themselves impeded progress and full success of engineering practice in developing countries. The key culprit is corruption whose eradication will put the nation on the solid path of effective engineering development and poverty alleviation.

Keywords: development, engineering, practices, sustainable

Procedia PDF Downloads 331

Authors: Parvin Ghafarian, Mohammadreza Mohammadpur Panchah, Mehri Fallahi

Abstract:

Synoptic patterns from surface up to tropopause are very important for forecasting the weather and atmospheric conditions. There are many tools to prepare and analyze these maps. Reanalysis data and the outputs of numerical weather prediction models, satellite images, meteorological radar, and weather station data are used in world forecasting centers to predict the weather. The forecasting extreme precipitating on the southern coast of the Caspian Sea (CS) is the main issue due to complex topography. Also, there are different types of climate in these areas. In this research, we used two reanalysis data such as ECMWF Reanalysis 5th Generation Description (ERA5) and National Centers for Environmental Prediction /National Center for Atmospheric Research (NCEP/NCAR) for verification of the numerical model. ERA5 is the latest version of ECMWF. The temporal resolution of ERA5 is hourly, and the NCEP/NCAR is every six hours. Some atmospheric parameters such as mean sea level pressure, geopotential height, relative humidity, wind speed and direction, sea surface temperature, etc. were selected and analyzed. Some different type of precipitation (rain and snow) was selected. The results showed that the NCEP/NCAR has more ability to demonstrate the intensity of the atmospheric system. The ERA5 is suitable for extract the value of parameters for specific point. Also, ERA5 is appropriate to analyze the snowfall events over CS (snow cover and snow depth). Sea surface temperature has the main role to generate instability over CS, especially when the cold air pass from the CS. Sea surface temperature of NCEP/NCAR product has low resolution near coast. However, both data were able to detect meteorological synoptic patterns that led to heavy rainfall over CS. However, due to the time lag, they are not suitable for forecast centers. The application of these two data is for research and verification of meteorological models. Finally, ERA5 has a better resolution, respect to NCEP/NCAR reanalysis data, but NCEP/NCAR data is available from 1948 and appropriate for long term research.

Keywords: synoptic patterns, heavy precipitation, reanalysis data, snow

Procedia PDF Downloads 122