Search results for: squared prediction risk

Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Azar Baghtaghi

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Financial risk management is critically important as it enables companies to maintain stability and profitability amidst market fluctuations and unexpected events. It involves the precise identification of risks that could impact investments, assets, and potential revenues. By implementing effective risk management strategies, companies can insure themselves against adverse market changes and prevent potential losses. In today's era, where markets are highly complex and influenced by various factors such as macroeconomic policies, exchange rate fluctuations, and natural disasters, the need for meticulous planning to cope with these uncertainties is more pronounced. Ultimately, financial risk management means being prepared for the future and the ability to sustain business in changing environments. A company capable of managing its risks not only achieves sustainable profitability but also gains the confidence of shareholders, investors, and business partners, enhancing its competitive position in the market. In this article, the types of financial risk and risk management strategies for improving financial performance were investigated. By identifying the risks stated in this article and their evaluation techniques, it is possible to improve the organization's financial performance.

Keywords: strategy, risk, risk management, financial performance.

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Sohanur Rahman, Elisabeth Sinnewe, Larelle (Ellie) Chapple, Sarah Osborne

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Greater political attention to global climate change exposes firms to a higher level of political uncertainty, which can lead to adverse capital market consequences. However, a higher level of discourse on environment-specific political risk (EPR) between management and investors can mitigate information asymmetry, followed by less stock price crash risk. This study examines whether EPR discourse in discourse in the earnings conference calls (ECC) reduces firm-level stock price crash risk in the US market. This research also explores if adverse disclosures via media channels further moderates the association between EPR on crash risk. Employing a dataset of 28,933 firm-year observations from 2002 to 2020, the empirical analysis reveals that EPR discourse in ECC reduces future stock price crash risk. However, adverse disclosures via media channels can offset the favourable effect of EPR discourse on crash risk. The results are robust to the potential endogeneity concern in a quasi-natural experiment setting.

Keywords: earnings conference calls, environment, environment-specific political risk discourse, environmental disclosures, information asymmetry, reputation risk, stock price crash risk

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Fereshteh Darash

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Reflective tax management corresponds to the Agency Theory since it determines the motivation of managers for tax management actions and short-term and long-term consequences. Therefore, selection of tax strategy contributes to the tax and financial position of the firm in the future. The aim of the present research is to evaluate the effect of tax management components on risk-taking of firms listed in Tehran stock exchange by using regression analysis method. Results show that tax effective rate, tax risk and tax planning have no significant effect on the firm's future risk. Results suggest that stakeholders assess the effective tax rate and delay in tax payment in line with their benefits. They tend to accept the higher risk cost for reduction of tax payments and benefits of higher liquidity in current period. Hence, effective tax rate and tax risk have no significant effect on future risk of the firm. Moreover, tax planning yields no information regarding the predictability of the future profits and as a result, it has no significant effect on the future risk of the firm since specific goals of financial reporting are in priority for the stakeholders and regardless of the firm’s data analysis, they take investment decisions and they less intend to purchase the stocks in a rational manner.

Keywords: tax management, tax effective rate, tax risk, tax planning, firm risk

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Gregory Michael McMahon

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Risk management for the purposes of minimizing the damages from the operations of dams has met with opposition emerging from organisations and authorities, and their practitioners. It appears that the cause may be a misunderstanding of risk management arising from exchanges that mix deterministic thinking with risk-centric thinking and that do not separate uncertainty from reliability and accuracy from probability. This paper sets out those misunderstandings that arose from dam operations at Wivenhoe in 2011, using a comparison of outcomes that have been based on the methodology and its rules and those that have been operated by applying misunderstandings of the rules. The paper addresses the performance of one risk-centric Flood Manual for Wivenhoe Dam in achieving a risk management outcome. A mixture of engineering, administrative, and legal factors appear to have combined to reduce the outcomes from the risk approach. These are described. The findings are that a risk-centric Manual may need to assist administrations in the conduct of scenario training regimes, in responding to healthy audit reporting, and in the development of decision-support systems. The principal assistance needed from the Manual, however, is to assist engineering and the law to a good understanding of how risks are managed – do not assume that risk management is understood. The wider findings are that the critical profession for decision-making downstream of the meteorologist is not dam engineering or hydrology, or hydraulics; it is risk management. Risk management will provide the minimum flood damage outcome where actual rainfalls match or exceed forecasts of rainfalls, that therefore risk management will provide the best approach for the likely history of flooding in the life of a dam, and provisions made for worst cases may be state of the art in risk management. The principal conclusion is the need for training in both risk management as a discipline and also in the application of risk management rules to particular dam operational scenarios.

Keywords: risk management, flood control, dam operations, deterministic thinking

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Authors: Anders Thorsén, Behrooz Sangchoolie, Peter Folkesson, Ted Strandberg

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As more parts of the power grid become connected to the internet, the risk of cyberattacks increases. To identify the cybersecurity threats and subsequently reduce vulnerabilities, the common practice is to carry out a cybersecurity risk assessment. For safety classified systems and products, there is also a need for safety risk assessments in addition to the cybersecurity risk assessment in order to identify and reduce safety risks. These two risk assessments are usually done separately, but since cybersecurity and functional safety are often related, a more comprehensive method covering both aspects is needed. Some work addressing this has been done for specific domains like the automotive domain, but more general methods suitable for, e.g., intelligent distributed grids, are still missing. One such method from the automotive domain is the Security-Aware Hazard Analysis and Risk Assessment (SAHARA) method that combines safety and cybersecurity risk assessments. This paper presents an approach where the SAHARA method has been modified in order to be more suitable for larger distributed systems. The adapted SAHARA method has a more general risk assessment approach than the original SAHARA. The proposed method has been successfully applied on two use cases of an intelligent distributed grid.

Keywords: intelligent distribution grids, threat analysis, risk assessment, safety, cybersecurity

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Authors: Chih-Hsiang Chang, Fang-Jyun Su

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This paper explores whether stock characteristics influence the herding formation among investors in the US equity market. To extend the research scope of the existing literature, this paper further examines the role that stock risk characteristics play in the US equity market, and the way they influence investors’ decision-making. First, empirical results show that whether general stocks or high-risk stocks, there are no herding behaviors among the investors in the US equity market during the whole research period or during four great events. Moreover, stock characteristics have great influence on investors’ trading decisions. Finally, there is a bidirectional lead-lag relationship of the herding formation between high-risk stocks and low-risk stocks, but the influence of high-risk stocks on the low-risk stocks is stronger than that of low-risk stocks on the high-risk stocks.

Keywords: stock characteristics, herding formation, investment decision, US equity market, lead-lag relationship

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Authors: Edwaldo R. B. Monteiro, Patricia D. M. Plentz, Edson R. De Pieri

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Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e., meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia.

Keywords: deadline missing, historical data, mobile robots, prediction mechanism

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Authors: Chang Su Woo, Hyun Sung Park

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Useful lifetime evaluations of rail-pads were very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of rail pads. In this study, we performed properties and accelerated heat aging tests of rail pads considering degradation factors and all environmental conditions including operation, and then derived a lifetime prediction equation according to changes in hardness, thickness, and static spring constants in the Arrhenius plot to establish how to estimate the aging of rail pads. With the useful lifetime prediction equation, the lifetime of e-clip pads was 2.5 years when the change in hardness was 10% at 25°C; and that of f-clip pads was 1.7 years. When the change in thickness was 10%, the lifetime of e-clip pads and f-clip pads is 2.6 years respectively. The results obtained in this study to estimate the useful lifetime of rail pads for high speed trains can be used for determining the maintenance and replacement schedule for rail pads.

Keywords: rail pads, accelerated test, Arrhenius plot, useful lifetime prediction, mechanical engineering design

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Authors: B. Zerouali, M. Kara, B. Hamaidi, H. Mahdjoub, S. Rouabhia

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In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios.

Keywords: bayesian networks, crude oil tank, fault tree, prediction, safety

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Authors: Nina V. Zaitseva, Pavel Z. Shur, Nina G. Atiskova

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Nowadays harmonization of sanitary and hygienic standards of environmental quality with international standards is crucial part of integration of Russia into the international community. Harmonization of Russian and international ambient air quality standards may be realized by risk-based standards development. In this paper approaches to risk-based standards development and examples of these approaches implementation are presented.

Keywords: harmonization, health risk assessment, evolutionary modelling, benchmark level, nickel, manganese

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Authors: Hyeon-Kyo Lim, Sang-Hun Byun

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Risk assessment is carried out in most industrial plants for accident prevention, but there exists insufficient data for statistical decision making. It is commonly said that risk can be expressed as a product of consequence and likelihood of a corresponding hazard factor. Eventually, therefore, risk assessment involves human decision making which cannot be objective per se. This study was carried out to comprehend perceptive characteristics of human beings in industrial plants. Subjects were shown a set of illustrations describing scenes of industrial plants, and were asked to assess the risk of each scene with not only linguistic variables but also numeric scores in the aspect of consequence and likelihood. After that, their responses were formulated as fuzzy membership functions, and compared with those of university students who had no experience of industrial works. The results showed that risk level of industrial workers were lower than those of any other groups, which implied that the workers might generally have a tendency to neglect more hazard factors in their work fields.

Keywords: fuzzy, hazard, linguistic variable, risk assessment

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Authors: Ibtissam Mharzi Alaoui, Camélia Sehaqui

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The success of a bank depends upon its effective risk management. With the growing complexity and diversity of financial products and services, as well as the accelerating pace of globalization over the past decade, risk management is becoming increasingly difficult. thus, all measurement and monitoring functions must be much more vigorous, relevant and adequate. The Shariah non-compliance risk is specific aspect of Islamic finance which ipso facto, deserves particular attention. It affects the validity of all Islamic financial contracts and it turns out to be likely to result in considerable losses on the overall Islamic financial institutions (IFIs). The purpose of this paper is to review the theoretical literature on Shariah non-compliance risk in order to give a clearer understanding of its sources, causes and consequences. Our intention through this work is to bring added value to the Islamic finance industry all over the world. The findings provide a useful reference work for the Islamic banks in structuring (or restructuring) of their own system of shariah risk management and internal control.

Keywords: Shariah non-compliance, risk management, financial products, Islamic finance.

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Authors: Sirus Sharifi, Arunima Haldar, S. V. D. Nageswara Rao

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The study attempts to analyze the impact of credit risk management practices of Indian scheduled commercial banks on their non-performing assets (NPAs). The data on credit risk practices was collected by administering a questionnaire to risk managers/executives at different banks. The data on NPAs (from 2012 to 2016) is sourced from Prowess, a database compiled by the Centre for Monitoring Indian Economy (CMIE). The model was estimated using cross-sectional regression method. As expected, the findings suggest that there is a negative relationship between credit risk management and NPA growth in Indian banks. The study has implications for Indian banks given the high level of losses, and the implementation of Basel III norms by the central bank, i.e. Reserve Bank of India (RBI). Evidence on credit risk management in Indian banks, and their relationship with non-performing assets held by them.

Keywords: credit risk, identification, Indian Banks, NPAs, ownership

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Authors: Mati Ullah, Shams Ur Rahman

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The purpose of this research study is to investigate the effect of macroeconomic and bank-specific factors on credit risk in Islamic banking in Pakistan. The future of financial institutions largely depends on how well they manage risks. Credit risk is an important type of risk affecting the banking sector. The current study has taken quarterly data for the period of 6 years, from 1st July 2014 to 30 Jun 2020. The data set consisted of secondary data. Data was extracted from the websites of the State Bank and World Bank and from the financial statements of the concerned banks. In this study, the Ordinary least square model was used for the analysis of the data. The results supported the hypothesis that macroeconomic factors and bank-specific factors have a significant effect on credit risk. Macroeconomic variables, Inflation and exchange rates have positive significant effects on credit risk. However, gross domestic product has a negative significant relationship with credit risk. Moreover, the corporate rate has no significant relation with credit risk. Internal variables, size, management efficiency, net profit share income and capital adequacy have been proven to influence positively and significantly the credit risk. However, loan to deposit-has a negative insignificance relationship with credit risk. The contribution of this article is that similar conclusions have been made regarding the influence of banking factors on credit risk.

Keywords: credit risk, Islamic banks, macroeconomic variables, banks specific variable

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Authors: H. A. Mansour

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The objective of this research work is studying the water use prediction, prediction technology for water use by action agencies, and others involved in conservation, planning, and environmental assessment of the Water Erosion Prediction Project (WEPP) simulation model. Models the important physical, processes governing erosion in Egypt (climate, infiltration, runoff, ET, detachment by raindrops, detachment by flowing water, deposition, etc.). Simulation of the non-uniform slope, soils, cropping/management., and Egyptian databases for climate, soils, and crops. The study included important parameters in Egyptian conditions as follows: Water Balance & Percolation, Soil Component (Tillage impacts), Plant Growth & Residue Decomposition, Overland Flow Hydraulics. It could be concluded that we can adapt the WEPP simulation model to determining the previous important parameters under Egyptian conditions.

Keywords: WEPP, adaptation, soil properties, tillage impacts, water balance, soil percolation

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Authors: Mária Hudáková, Mária Lusková

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This report is focused on widening the theoretical knowledge as well as controlling practical application from the risk management point of view, regarding to dynamic business changes that have occurred in Slovakia which recently has been considered to be an environment full of risk and uncertainty. The idea of the report is the proposal of the controlling operation model in the course of risk management process in an enterprise operating in Slovakia, by which the controller is able to identify early risk factors in suggested major areas of the business management upon appropriate business information integration, consecutive control and prognoses and to prepare in time full-value documents in order to suggest measures for reduction thereof. Dealing with risk factors, that can quickly limit the growth potential of the enterprise, is an essential part of managerial activities on each level. This is the reason why mutual unofficial, ergo collegial cooperation of individual departments is necessary for controlling application from the business risk management point of view. An important part of the report is elaborated survey of the most important risk factors existing in major management areas of enterprises operating in Slovakia. The outcome of the performed survey is a catalogue of the most important enterprise risk factors. The catalogue serves for better understanding risk factors affecting the Slovak enterprises, their importance and evaluation.

Keywords: controlling, information, risks, risk factor, crisis

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Authors: Mohammad Ali Hasanzadeh, Thierry Vanelslander, Eddy Van De Voorde

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Nowadays maritime transport is a key element in international trade and global supply chain. Economies of scale in transporting goods are one of the most attractive elements of using ships. Without maritime transport, almost no globalization of economics can be imagined. Within maritime transport, ports are the interface between lands and see. Even though using ships help cargo owners to have a competitive margin but an accident in port during loading or unloading or even moving cargoes within the terminal can diminish such margin. Statistics shows that due to the high-speed notion of activities within ports, collision accidents are the most common type of accidents. To mitigate such accidents, the appropriate risk exposures have to be defined and calculate, later on risk probabilities can be determined for each type of accident, i.e. fatal, severe, moderate and minor ones. Having such risk probabilities help managers to define the effectiveness of each collision risk control option. This research defined travelled distance as main collision risk exposure in container terminals, taking all the related items into consideration, it was calculated for Shahid Rajae container terminals. Following this finding, collision risk probabilities were computed.

Keywords: collision accident, container terminal, maritime transport, risk exposure

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Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

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Authors: Joseph C. Chen, Venkata Mohan Kudapa

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Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

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Authors: Noor A. Hakim

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Several studies in the developed countries investigated the prevalence of diabetes and obesity among individuals from different socioeconomic levels and suggested lower rates among the higher socioeconomic groups. However, studies evaluating diabetes risk and prevalence of obesity among the population of middle- to high-income status in developing countries are limited. The aim of this study is to evaluate the risk of developing type-2 diabetes mellitus (T2DM) and the weight status of female students in private universities in Jeddah City, Saudi Arabia. This is a cross-sectional study of 121 female students aged ≤ 25 years old was conducted; participants were recruited from two private universities. Diabetes risk was evaluated using the Finnish Diabetes Risk Score. Anthropometric measurements were assessed, and body-mass-index (BMI) was calculated. Diabetes risk scores indicated that 35.5% of the female students had a slightly elevated risk, and 10.8% had a moderate to high risk to develop T2DM. One-third of the females (29.7%) were overweight or obese. The majority of the normal weight and underweight groups were classified to have a low risk of diabetes, 22.2% of the overweight participants were classified to have moderate to high risk, and over half of the obese participants (55.5%) were classified to be at the moderate to high-risk category. Conclusions: Given that diabetes risk is alarming among the population in Saudi Arabia, healthcare providers should utilize a simple screening tool to identify high-risk individuals and initiate diabetes preventive strategies to prevent, or delay, the onset of T2DM and improve the quality of life.

Keywords: risk of type 2 diabetes, weight status, college students, socioeconomic status

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Authors: Tio Novita Efriani

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Public sector organizations work in a complex and risky environment. Since the beginning of 2000s, the public sector has paid attention to the need for an effective risk management. The Indonesian public sector has also concerned about this issue and in 2008 it enacted the Government Regulation that gives mandate for the implementation of risk management in government organizations. This paper investigates risk management implementation in the Indonesian public sector organizations and the role of transformational leadership and internal audit activities. Data was collected via survey. A total of 202 effective responses (30% response rate) from employees in 34 government ministries were statistically analyzed by using Partial least square structural equation modelling (PLS-SEM) and the software was SmartPLS 3.0. All the constructs were lower order, except for the risk management implementation construct, which was treated as a second-order construct. A two-stage approach was employed in the analysis of the higher order component. The findings revealed that transformational leadership positively influence risk management implementation. The findings also found that the core and legitimate roles of internal audit in risk management positively affect the implementation of risk management. The final finding showed that internal auditing mediates a relationship between transformational leadership and risk management implementation. These results suggest that the implementation of risk management in the Indonesian public sector was significantly supported by internal auditors and leadership. The findings confirm the importance of transformational leadership and internal audit in the public sector risk management strategies.

Keywords: Indonesian public sector, internal audit, risk management, transformational leadership

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Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

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Authors: Nilamadhab Mohanty

Abstract:

It is essential to use message formats that make communication understandable and correct. It is because; the information format can influence consumer decision on the purchase of a product. This study combines information from qualitative inquiry, media trend analysis, eye tracking experiment, and questionnaire data to examine the impact of specific message format and consumer perceived risk on attention to the information and risk retention. We investigated the influence of message framing (goal framing, attribute framing, and mix framing) on consumer memory, study time, and decisional uncertainty while deciding on the purchase of drugs. Furthermore, we explored the impact of consumer perceived risk (associated with the use of the drug, i.e., RISK-AB and perceived risk associated with the non-use of the drug, i.e., RISK-EB) on message format preference. The study used eye-tracking methods to understand the differences in message processing. Findings of the study suggest that the message format influences information processing, and participants' risk perception impacts message format preference. Eye tracking can be used to understand the format differences and design effective advertisements.

Keywords: message framing, consumer perceived risk, advertising, eye tracking

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Authors: Gunasekaran Raja, Rajakumar Arul, Kottilingam Kottursamy, Ramkumar Jayaraman, Sathya Pavithra, Swaminathan Venkatraman

Abstract:

Wireless Interoperability for Microwave Access (WiMAX) utilizes the IEEE 802.1X mechanism for authentication. However, this mechanism incurs considerable delay during handoffs. This delay during handoffs results in service disruption which becomes a severe bottleneck. To overcome this delay, our article proposes a key caching mechanism based on user path prediction. If the user mobility follows that path, the user bypasses the normal IEEE 802.1X mechanism and establishes the necessary authentication keys directly. Through analytical and simulation modeling, we have proved that our mechanism effectively decreases the handoff delay thereby achieving fast authentication.

Keywords: authentication, authorization, and accounting (AAA), handoff, mobile, user path prediction (UPP) and user pattern

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