Search results for: corporate credit rating prediction

Authors: Pui Yong Leo, Normy Maziah Mohd Said

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Change of global economy landscape and business environment has led to companies’ decision to go global and enter international markets. As the companies go beyond the comfort zone (i.e. investing in the home country), it is important to ensure a comprehensive risk assessment is carried out. This paper describes a standardized approach for international investment, ensuring identification of risk, risk appetite, risk rating, risk treatment and mitigation plans for respective international investment proposal. The standardized approach is divided into three (3) stages as follows: Stage 1 – Preliminary Risk profiling; with the objective to gauge exposure to countries and high level risk factors as first level assessment. Stage 2 – Risk Parameters; with the objective to define risk appetite for the international investment from the perspective of likelihood and impact. Stage 3 – Detailed Risk Assessments; with the objectives to assess in detail any triggered elements from Stage 1, and project specific risks. The final output will include the mitigation plans for the identified risks for the total investment. Example will be given in this paper to show how comprehensive risk assessment is carried out for an international investment in power energy sector.

Keywords: international investment, mitigation plans, risk appetite, risk assessment

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: Tamar Trop

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Bike sharing systems can be important elements of smart cities as they have the potential for impact on multiple levels. These systems can add a significant alternative to other modes of mass transit in cities that are continuously looking for measures to become more livable and maintain their attractiveness for citizens, businesses and tourism. Bike-sharing began in Europe in 1965, and a viable format emerged in the mid-2000s thanks to the introduction of information technology. The rate of growth in bike-sharing schemes and fleets has been very rapid since 2008 and has probably outstripped growth in every other form of urban transport. Today, public bike-sharing systems are available on five continents, including over 700 cities, operating more than 800,000 bicycles at approximately 40,000 docking stations. Since modern bike sharing systems have become prevalent only in the last decade, the existing literature analyzing these systems and their sustainability is relatively new. The purpose of the presented study is to assess the sustainability of these newly emerging transportation systems, by using the Envision™ rating system as a methodological framework and the Israeli 'Tel -O-Fun' – bike sharing project as a case study. The assessment was conducted by project team members. Envision™ is a new guidance and rating system used to assess and improve the sustainability of all types and sizes of infrastructure projects. This tool provides a holistic framework for evaluating and rating the community, environmental, and economic benefits of infrastructure projects over the course of their life cycle. This evaluation method has 60 sustainability criteria divided into five categories: Quality of life, leadership, resource allocation, natural world, and climate and risk. 'Tel -O-Fun' project was launched in Tel Aviv-Yafo on 2011 and today provides about 1,800 bikes for rent, at 180 rental stations across the city. The system is based on a complex computer terminal that is located in the docking stations. The highest-rated sustainable features that the project scored include: (a) Improving quality of life by: offering a low cost and efficient form of public transit, improving community mobility and access, enabling the flexibility of travel within a multimodal transportation system, saving commuters time and money, enhancing public health and reducing air and noise pollution; (b) improving resource allocation by: offering inexpensive and flexible last-mile connectivity, reducing space, materials and energy consumption, reducing wear and tear on public roads, and maximizing the utility of existing infrastructure, and (c) reducing of greenhouse gas emissions from transportation. Overall, 'Tel -O-Fun' project was highly scored as an environmentally sustainable and socially equitable infrastructure. The use of this practical framework for evaluation also yielded various interesting insights on the shortcoming of the system and the characteristics of good solutions. This can contribute to the improvement of the project and may assist planners and operators of bike sharing systems to develop a sustainable, efficient and reliable transportation infrastructure within smart cities.

Keywords: bike sharing, Envision™, sustainability rating system, sustainable infrastructure

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Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: O. Colot, M. Croquet, L. Cultrera, Y. Fandja Collince

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The objective of our research is to evaluate the impact of the allowance for corporate equity (ACE) on the financial structure of Belgian SME in order to highlight the potential existence of a fiscal leverage. To limit the biases linked to the rationing of the capital further to the financial crisis, we compare first the dynamic evolution of the financial structure of the Belgian firms over the period 2006-2015 by focusing on three sub-periods: 2006-2008, 2009-2012 and 2013-2015. We give then an international size to this comparison by including SMEs from countries adjoining Belgium (France, Germany, Netherlands and the United Kingdom) and within which there is no ACE. This comparison allows better understanding the fiscal advantage linked to the ACE of firms evolving in a relatively unstable economic environment further to the financial crisis of 2008. This research is relevant given the economic and political context in which Belgium operates and the very uncertain future of the Belgian ACE. The originality of this research is twofold: the long study period and the consideration of the effects of the financial and economic crisis on the financing structure of Belgian SMEs. The results of this research, even though they confirm the existence of a positive fiscal leverage for the tax deduction for venture capital on the financing structure of Belgian SMEs, do not allow the extent of this leverage to be clearly quantified. The comparative evolution of financing structures over the period 2006-2015 of Belgian, French, German, Dutch and English SMEs shows a strong similarity in the overall evolution of their financing.

Keywords: allowance for corporate equity, Belgium, financial structure, small and medium sized firms

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Authors: Oladimeji Israel Ajayi

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Agricultural development has long been identified as a cornerstone for wealth creation and economic growth, particularly in emerging economies like Nigeria. This study examines the relationship between agricultural development and wealth creation in Nigeria, emphasizing the sector's potential in reducing poverty, creating employment, and boosting economic stability. Nigeria, endowed with fertile land and a favorable climate, has a significant agricultural base that, if fully leveraged, can transition the economy from oil dependency to a diversified and sustainable growth model. However, challenges such as limited access to credit, poor infrastructure, outdated farming techniques, and climate variability hinder optimal productivity. This research employs a mixed-methods approach, analyzing data from the Nigerian National Bureau of Statistics and the Food and Agriculture Organization to understand how investments in agriculture influence wealth indicators such as GDP growth, employment rates, and rural income levels. The findings reveal a strong positive correlation between agricultural investment and wealth creation, suggesting that strategic policies focusing on mechanization, credit accessibility, and sustainable practices could significantly boost agricultural productivity and contribute to wealth distribution in Nigeria. This study contributes to policy discourse by highlighting agriculture’s role as a transformative tool for economic resilience and sustainable wealth creation in Nigeria.

Keywords: agricultural development, poverty reduction, wealth creation, prosperity

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Ahmad Haidar

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This research paper investigates the concept of modern management, concentrating on the development of managerial practices and the adoption of innovative strategies in response to the fast-changing business landscape caused by Artificial Intelligence (AI). The study begins by examining the historical context of management theories, tracing the progression from classical to contemporary models, and identifying key drivers of change. Through a comprehensive review of existing literature and case studies, this paper provides valuable insights into the principles and practices of modern management, offering a roadmap for organizations aiming to navigate the complexities of the contemporary business world. The paper examines the growing role of digital technology in modern management, focusing on incorporating AI, machine learning, and data analytics to streamline operations and facilitate informed decision-making. Moreover, the research highlights the emergence of new principles, such as adaptability, flexibility, public participation, trust, transparency, and digital mindset, as crucial components of modern management. Also, the role of business leaders is investigated by studying contemporary leadership styles, such as transformational, situational, and servant leadership, emphasizing the significance of emotional intelligence, empathy, and collaboration in fostering a healthy organizational culture. Furthermore, the research delves into the crucial role of environmental sustainability, corporate social responsibility (CSR), and corporate digital responsibility (CDR). Organizations strive to balance economic growth with ethical considerations and long-term viability. The primary research question for this study is: "What are the key principles, practices, and innovations that define modern management, and how can organizations effectively implement these strategies to thrive in the rapidly changing business landscape?." The research contributes to a comprehensive understanding of modern management by examining its historical context, the impact of digital technologies, the importance of contemporary leadership styles, and the role of CSR and CDR in today's business landscape.

Keywords: modern management, digital technology, leadership styles, adaptability, innovation, corporate social responsibility, organizational success, corporate digital responsibility

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Kristine Demilou Santiago

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In a context of a developing nation, how important is the role of Corporate Social Responsibility health programs? Is there a possibility that this will render a large impact in a society where health benefits are insufficient? The Philippine government has been in an unceasing battle to provide its citizens competitive health benefits through launching various health programs. As the efforts are being claimed by the government, the numbers just show that all the health benefits being offered such as PhilHealth health cards, medical missions and other subsidized government health benefits are not effective and sufficient at the minimum level. This is a major characteristic of a developing nation which the Philippine government is focusing on addressing as it becomes a national concern under the effects of poverty. Industrial companies, through Corporate Social Responsibility, are playing an important role in the aspiration to resolve this problem on health programs as supposed to be basic services to citizens of the Philippine government. The rise of commitment by these industrial companies to render health programs to communities as part of their corporate citizenship has covered a large portion of the basic health services that the Filipino citizens are supposed to be receiving. This is the most salient subject that a developing nation should focus on determining the important contribution of industrial companies present in their country as part of the citizens’ access to basic health services. The use of survey forms containing quantitative and qualitative questions which aim to give numerical figures and support answers as to the role of CSR Health programs in helping the communities receive the basic health services they need was the methodological procedure followed in this research. A sample population in a community where the largest industrial company in a province of the Philippines was taken through simple random sampling. The assumption is that this sample population which represents the whole of the community has the highest opportunities to access both the government health services and the CSR health program services of the industrial company located in their community. Results of the research have shown a significant level of participation by industrial companies through their CSR health programs in the attainment of basic health services that should be rendered by the Philippine government to its citizens as part of the state’s health benefits. In a context of a developing nation such as the Philippines, the role of Corporate Social Responsibility is beyond the expectation of initiating to resolve environmental and social issues. It is moving deeper in the concept of the corporate industries being a pillar of the government in catering the support needed by the individuals in the community for its development. As such, the concept of the presence of an industrial company in a community is said to be a parallel progress: by which when an industrial company expands because it is becoming more profitable, so is the community gaining the same step of progress in terms of socioeconomic development.

Keywords: basic health services, CSR health program, health services in a developing nation, Philippines health benefits

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Naod Mekonnen Anega

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The very objective of this paper was to empirically identify technology tailored determinants to the adoption and adoption intensity (extent of use) of agricultural water management technologies in Meda Welabu Woreda, Oromia regional state, Ethiopia. Meda Welabu Woreda which is one of the administrative Woredas of the Oromia regional state was selected purposively as this Woreda is one of the Woredas in the region where small scale irrigation practices and the use of agricultural water management technologies can be found among smallholders. Using the existence water management practices (use of water management technologies) and land use pattern as a criterion Genale Mekchira Kebele is selected to undergo the study. A total of 200 smallholders were selected from the Kebele using the technique developed by Krejeie and Morgan. The study employed the Logit and Tobit models to estimate and identify the economic, social, geographical, household, institutional, psychological, technological factors that determine adoption and adoption intensity of water management technologies. The study revealed that while 55 of the sampled households are adopters of agricultural water management technology the rest 140 were non adopters of the technologies. Among the adopters included in the sample 97% are using river diversion technology (traditional) with traditional canal while the rest 7% percent are using pond with treadle pump technology. The Logit estimation reveled that while adoption of river diversion is positively and significantly affected by membership to local institutions, active labor force, income, access to credit and land ownership, adoption of treadle pump technology is positively and significantly affected by family size, education level, access to credit, extension contact, income, access to market, and slope. The Logit estimation also revealed that whereas, group action requirement, distance to farm, and size of active labor force negative and significantly influenced adoption of river diversion, age and perception has negatively and significantly influenced adoption decision of treadle pump technology. On the other hand, the Tobit estimation reveled that while adoption intensity (extent of use) of agricultural water management is positively and significantly affected by education, credit, and extension contact, access to credit, access to market and income. This study revealed that technology tailored study on adoption of Agricultural water management technologies (AWMTs) should be considered to indentify and scale up best agricultural water management practices. In fact, in countries like Ethiopia, where there is difference in social, economic, cultural, environmental and agro ecological conditions even within the same Kebele technology tailored study that fit the condition of each Kebele would help to identify and scale up best practices in agricultural water management.

Keywords: water management technology, adoption, adoption intensity, smallholders, technology tailored approach

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Authors: Hatthaphone Silimanotham, Michael Henry

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The effective maintenance of road bridge infrastructure is becoming a widely researched topic in the civil engineering field. Deterioration is one of the main issues in bridge performance, and it is necessary to understand how bridges deteriorate to optimally plan budget allocation for bridge maintenance. In Laos, many bridges are in a deteriorated state, which may affect the performance of the bridge. Due to bridge deterioration, the Ministry of Public Works and Transport is interested in the deterioration model to allocate the budget efficiently and support the bridge maintenance planning. A deterioration model can be used to predict the bridge condition in the future based on the observed behavior in the past. This paper analyzes the available inspection data of road bridges on the road classifications network to build deterioration prediction models for the main bridge type found at the provincial level (concrete slab, concrete girder, and steel truss) using probabilistic deterioration modeling by linear regression method. The analysis targets there has three bridge types in the 18 provinces of Laos and estimates the bridge deterioration rating for evaluating the bridge's remaining life. This research thus considers the relationship between the service period and the bridge condition to represent the probability of bridge condition in the future. The results of the study can be used for a variety of bridge management tasks, including maintenance planning, budgeting, and evaluating bridge assets.

Keywords: deterioration model, bridge condition, bridge management, probabilistic modeling

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Authors: Bao Thach Nguyen, Abbas Mohajerani

Abstract:

The California bearing ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments, and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength, and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some fine-grained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, soil physical properties, pavement, soil test

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Authors: Yasaman Ashrafi, Stephen Kajewski, Annastiina Silvennoinen, Madhav Nepal

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Mega projects either in construction, urban development or energy sectors are one of the key drivers that build the foundation of wealth and modern civilizations in regions and nations. Such projects require economic justification and substantial capital investment, often derived from individual and corporate investors as well as governments. Cost overruns and time delays in these mega projects demands a new approach to more accurately predict project costs and establish realistic financial plans. The significance of this paper is that the cost efficiency of megaprojects will improve and decrease cost overruns. This research will assist Project Managers (PMs) to make timely and appropriate decisions about both cost and outcomes of ongoing projects. This research, therefore, examines the oil and gas industry where most mega projects apply the classic methods of Cost Performance Index (CPI) and Schedule Performance Index (SPI) and rely on project data to forecast cost and time. Because these projects are always overrun in cost and time even at the early phase of the project, the probabilistic methods of Monte Carlo Simulation (MCS) and Bayesian Adaptive Forecasting method were used to predict project cost at completion of projects. The current theoretical and mathematical models which forecast the total expected cost and project completion date, during the execution phase of an ongoing project will be evaluated. Earned Value Management (EVM) method is unable to predict cost at completion of a project accurately due to the lack of enough detailed project information especially in the early phase of the project. During the project execution phase, the Bayesian adaptive forecasting method incorporates predictions into the actual performance data from earned value management and revises pre-project cost estimates, making full use of the available information. The outcome of this research is to improve the accuracy of both cost prediction and final duration. This research will provide a warning method to identify when current project performance deviates from planned performance and crates an unacceptable gap between preliminary planning and actual performance. This warning method will support project managers to take corrective actions on time.

Keywords: cost forecasting, earned value management, project control, project management, risk analysis, simulation

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Authors: Petr Gurný

Abstract:

One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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Authors: Jacky Liu

Abstract:

This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

Abstract:

Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Kalbende Krunal Ramesh

Abstract:

The Carbon Capture and Storage Technology (CCS) provides a veritable platform to bridge the gap between the seemingly irreconcilable twin global challenges of ensuring a secure, reliable and diversified energy supply and mitigating climate change by reducing atmospheric emissions of carbon dioxide. Making its proper regulatory policy and making it flexible for the government and private company by law to regulate, also exploring the opportunity in this sector is the main aim of this paper. India's total annual emissions was 1725 Mt CO2 in 2011, which comprises of 6% of total global emission. It is very important to control the greenhouse gas emission for the environment protection. This paper discusses the various regulatory policy and technology adopted by some of the countries for successful using CCS technology. The brief geology of sedimentary basins in India is studied, ranging from the category I to category IV and deep water and potential for mature technology in CCS is reviewed. Areas not suitable for CO2 storage using presently mature technologies were over viewed. CSS and Clean development mechanism was developed for India, considering the various aspects from research and development, project appraisal, approval and validation, implementation, monitoring and verification, carbon credit issued, cap and trade system and its storage potential. The opportunities in oil and gas operations, power sector, transport sector is discussed briefly.

Keywords: carbon credit issued, cap and trade system, carbon capture and storage technology, greenhouse gas

Procedia PDF Downloads 433