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Suche nach „[Arunraj] [Nari]“ hat 16 Publikationen gefunden
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    DigitalInstitut ProtectITTC Grafenau

    Zeitschriftenartikel

    Karl Leidl, Robert Hable, Michael Fernandes, Nari Arunraj, Michael Heigl

    Comparison of Supervised, Semi-supervised and Unsupervised Learning Methods in Network Intrusion Detection Systems (NIDS) Application

    Anwendungen und Konzepte in der Wirtschaftsinformatik (AKWI), no. 6, pp. 10-19

    2017

    Abstract anzeigen

    With the emergence of the fourth industrial revolution (Industrie 4.0) of cyber physical systems, intrusion detection systems are highly necessary to detect industrial network attacks. Recently, the increase in application of specialized machine learning techniques is gaining critical attention in the intrusion detection community. A wide variety of learning techniques proposed for different network intrusion detection system (NIDS) problems can be roughly classified into three broad categories: supervised, semi-supervised and unsupervised. In this paper, a comparative study of selected learning methods from each of these three kinds is carried out. In order to assess these learning methods, they are subjected to investigate network traffic datasets from an Airplane Cabin Demonstrator. In addition to this, the imbalanced classes (normal and anomaly classes) that are present in the captured network traffic data is one of the most crucial issues to be taken into consideration. From this investigation, it has been identified that supervised learning methods (logistic and lasso logistic regression methods) perform better than other methodswhen historical data on former attacks are available. The results of this study have also showed that the performance of semi-supervised learning method (One class support vector machine) is comparatively better than unsupervised learning method (Isolation Forest) when historical data on former attacks are not available.

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    Beitrag (Sammelband oder Tagungsband)

    Nari Arunraj, Diane Ahrens

    Improving food supply chain using hybrid semiparametric regression model

    Supply Management Research, Wiesbaden

    2017

    DigitalTC Grafenau

    Zeitschriftenartikel

    Michael Fernandes, Nari Arunraj, Diane Ahrens

    Application of SARIMAX model to forecast daily sales in retail industry

    International Journal of Operations Research and Information Systems (IJORIS), vol. 7, no. 2, pp. 1-20

    2016

    DOI: 10.4018/IJORIS.2016040101

    Abstract anzeigen

    Abstract During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Article Preview 1. Introduction Discount retail stores have been a noticeable feature of German retail market since the 1980s. In particular, the growth in number of discount retail stores have significantly increased after reunification of Germany. Recently, there is a growing trend of increasing varieties of fruits and vegetables with year-around availability across all the German discount retail outlets rather than just in their traditional growing season. In order to attract customers and remain competitive in the market, the fruits and vegetables are exported from foreign countries and stocked for longer periods. Particularly, increase in number of retail stores, availability of varieties of fruits and vegetables (in stock) with short shelf-lives, frequent price variations, and different storage conditions increase the complexity and results in huge amount of food waste. In Germany, the retail sector produces the food waste of around 0.5 million tons per year (Kranert et al., 2012). Although the retail sector contributes only 5% of the total food waste in food supply chain, mostly they are avoidable food waste (wasting food which is fit for consumption). The quantity of food waste that occurs in the home (61%) is partially due to the management decisions in the retail sector (e.g. frequent promotions) that stimulate the consumer’s eagerness to purchase, and distract them to equate their demand with the purchase (Arunraj et al., 2014; Gooch et al., 2010). Hence, the proper decision making in the retail sector can help the suppliers and consumers to avoid the food waste. The role of sales forecasting in reducing the food waste in retail stores is a significant topic of discussion in the recent food waste related studies (Mena et al., 2011; Mena et al., 2014). According to Mena et al. (2011) and Stenmarck et al. (2011), the improvement of forecast accuracy is one of the essential remedial measures to reduce the food waste in the retail sector of food supply chain.

    DigitalAngewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Nari Arunraj, Diane Ahrens

    Estimation of Non-Catastrophic Weather Impacts for Retail Industry

    International Journal of Retail & Distribution Management, vol. 44, no. 7, pp. 731-753

    2016

    Abstract anzeigen

    Purpose Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. This study aims at developing a model and to analyse the relationship between weather and retail shopping behavior (i.e., store traffic and sales). Design/methodology/approach. The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represent corresponding weather impacts on the store traffic and the sales. Findings The snowfall has a significant effect on the store traffic and the sales in both food and fashion retail stores. In food retail store, the risk due to snowfall varies depending on the location of stores. There are also significant lagging effects of snowfall in the fashion retail store. However, the rainfall has a significant effect only on the store traffic in the food retail stores. In addition to these effects, the sales in the fashion retail store are highly affected by the temperature deviation. Research limitations/implications Limitations in availability of data for the weather variables and other demand influencing factors (e.g. promotion, tourism, online shopping, demography of customers etc.) may reduce efficiency of the proposed MLR-AR model. In spite of these limitations, this study can be able to quantify the effects of weather variables on the store traffic and the sales Originality/value. This study contributes to the field of retail distribution by providing significant evidence of relationship between weather and retail business. Unlike previous studies, the proposed model tries to consider autocorrelation property, main and interaction effects between weather variables, temperature deviation and lagging effects of snowfall on the store traffic or the sales. The estimated weather impacts from this model can act as a reliable tool for retailers to explain the importance of different non-catastrophic weather events.

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    Zeitschriftenartikel

    Nari Arunraj, Diane Ahrens

    A Hybrid Seasonal Autoregressive Integrated Moving Average and Quantile Regression for Daily Food Sales Forecasting

    International Journal of Production Economics, vol. 170, no. Part A, pp. 321-335

    2015

    DOI: 10.1016/j.ijpe.2015.09.039

    Abstract anzeigen

    In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting of sales which leads to incorrect ordering of products. The time series sales in food retail industry are characterized by high volatility and skewness, which vary by time. So, the interval forecasts are required by the retail companies to set appropriate inventory policy (reorder point or safety stock level). This paper attempts to develop a seasonal autoregressive integrated moving average with external variables (SARIMAX) model to forecast daily sales of a perishable food. The process of fitting a SARIMAX model in this study involves: (i) the development of Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) combining the SARIMA model and the demand influencing factors using linear regression. As the SARIMAX using multiple linear regression (SARIMA-MLR) model produces only mean forecast, the possibility of underestimation and overestimation is very high due to high service level, peak, and sparse sales in food retail industry. Therefore, a hybrid SARIMA and Quantile Regression (SARIMA-QR) is developed to construct high and low quantile predictions. Instead of extrapolating the quantiles from the mean point forecasts of SARIMA-MLR model based on the assumption of normality, the SARIMA-QR model directly forecasts the quantiles. The developed SARIMA-MLR and SARIMA-QR models are applied in modeling and forecasting of sales data, i.e., the daily sales of banana from a discount retail store in Lower Bavaria, Germany. The results show that the SARIMA-MLR and -QR models yield better forecasts at out-sample data when compared to seasonal naïve forecasting, traditional SARIMA, and multi-layered perceptron neural network (MLPNN) models. Unlike the SARIMA-MLR model, the SARIMA-QR model provides better prediction intervals and a deep insight into the effects of demand influencing factors for different quantiles.

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    M. Müller, Michael Fernandes, Nari Arunraj, Diane Ahrens

    Time series sales forecasting to reduce food waste in retail industry

    Proceedings of The 34th International Symposium on Forecasting - Economic Forecasting: Past, Present and Future (June 29th - July 2nd 2014, Rotterdam, The Netherlands)

    2014

    Angewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Nari Arunraj

    Modeling uncertainty in risk assessment: an integrated approach with fuzzy set theory and Monte Carlo simulation

    Accident Analysis and Prevention, vol. 55, no. June, pp. 242-255

    2013

    DOI: 10.1016/j.aap.2013.03.007

    Abstract anzeigen

    Modeling uncertainty during risk assessment is a vital component for effective decision making. Unfortunately, most of the risk assessment studies suffer from uncertainty analysis. The development of tools and techniques for capturing uncertainty in risk assessment is ongoing and there has been a substantial growth in this respect in health risk assessment. In this study, the cross-disciplinary approaches for uncertainty analyses are identified and a modified approach suitable for industrial safety risk assessment is proposed using fuzzy set theory and Monte Carlo simulation. The proposed method is applied to a benzene extraction unit (BEU) of a chemical plant. The case study results show that the proposed method provides better measure of uncertainty than the existing methods as unlike traditional risk analysis method this approach takes into account both variability and uncertainty of information into risk calculation, and instead of a single risk value this approach provides interval value of risk values for a given percentile of risk. The implications of these results in terms of risk control and regulatory compliances are also discussed.

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    Zeitschriftenartikel

    J. Maiti, Nari Arunraj

    Risk-based Maintenance Policy Selection using AHP and Goal Programming

    Safety Science, vol. 48, no. 2, pp. 238-247

    2010

    DOI: 10.1016/j.ssci.2009.09.005

    Abstract anzeigen

    Maintenance policy selection is a multiple criteria decision making. The criteria often considered are cost and reliability of maintenance. There has been a growing interest in using risk of accidents as a criterion for maintenance selection. This paper presents an approach of maintenance selection based on risk of equipment failure and cost of maintenance. Analytic hierarchy process (AHP) and goal programming (GP) are used for maintenance policy selection. A case study in a benzene extraction unit of a chemical plant was done. The AHP results show that considering risk as a criterion, condition based maintenance (CBM) is a preferred policy over time-based maintenance (TBM) as CBM has better risk reduction capability than TBM. Similarly, considering cost as a criterion, corrective maintenance (CM) is preferred. However, considering both risk and cost as multiple criteria, the AHP–GP results show that CBM is a preferred approach for high-risk equipment and CM for low risk equipment.

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    Zeitschriftenartikel

    J. Maiti, Nari Arunraj

    A methodology for overall consequence modeling in chemical industry.

    Journal of Hazardous Materials, vol. 169, no. 1-3, pp. 556-574

    2009

    DOI: 10.1016/j.jhazmat.2009.03.133

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    Zeitschriftenartikel

    J. Maiti, Nari Arunraj

    Environmental risk management and decision making

    International Journal of Environmental Pollution Control and Management, vol. 1, no. 1, pp. 25-40

    2009

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    Zeitschriftenartikel

    J. Maiti, Nari Arunraj

    Development of Environmental Consequence Index (ECI) using fuzzy composite programming

    Journal of Hazardous Materials, vol. 162, no. 1, pp. 29-43

    2009

    DOI: 10.1016/j.jhazmat.2008.05.067

    Abstract anzeigen

    Estimation of environmental consequences of hazardous substances in chemical industries is a very difficult task owing to (i) diversity in the types of hazards and their effects, (ii) location, and (ii) uncertainty in input information. Several indices have been developed over the years to estimate the environmental consequences. In this paper, a critical literature review was done on the existing environmental indices to identify their applications and limitations. The existing indices lack in consideration of all environmental consequence factors such as material hazard factors, dispersion factors, environmental effects, and their uncertainty. A new methodology is proposed for the development of environmental consequence index (ECI), which can overcome the stated limitations. Moreover, the recently developed fuzzy composite programming (FCP) is used to take care of the uncertainty in estimation. ECI is applied to benzene extraction unit (BEU) of a petrochemical industry situated in eastern part of India. The ECI for all the eight sections of BEU are estimated and ranked. The results are compared with well-established indices such as Dow fire and explosion index, safety weight hazard index (SWeHI), and environmental accident index (EAI). The proposed ECI may outperform other indices based on its detailed consideration of the factors and performed equally to Dow F&E index, and EAI in most of the cases for the present application.

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    Zeitschriftenartikel

    J. Maiti, Nari Arunraj

    Risk-based maintenance—Techniques and applications

    Journal of Hazardous Materials, vol. 142, no. 3, pp. 653-661

    2007

    DOI: 10.1016/j.jhazmat.2006.06.069

    Abstract anzeigen

    Plant and equipment, however well designed, will not remain safe or reliable if it is not maintained. The general objective of the maintenance process is to make use of the knowledge of failures and accidents to achieve the possible safety with the lowest possible cost. The concept of risk-based maintenance was developed to inspect the high-risk components usually with greater frequency and thoroughness and to maintain in a greater manner, to achieve tolerable risk criteria. Risk-based maintenance methodology provides a tool for maintenance planning and decision making to reduce the probability of failure of equipment and the consequences of failure. In this paper, the risk analysis and risk-based maintenance methodologies were identified and classified into suitable classes. The factors affecting the quality of risk analysis were identified and analyzed. The applications, input data and output data were studied to understand their functioning and efficiency. The review showed that there is no unique way to perform risk analysis and risk-based maintenance. The use of suitable techniques and methodologies, careful investigation during the risk analysis phase, and its detailed and structured results are necessary to make proper risk-based maintenance decisions.

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    Beitrag (Sammelband oder Tagungsband)

    J. Maiti, Nari Arunraj

    Risk-based maintenance - Techniques and applications

    Proceedings of the Mary Kay O'Connor Process Safety Center 2005 International Symposium 'Beyond Regulatory Compliance, Making Safety Second Nature' (October 25th-26th 2005, Reed Arena, Texas A&M University, College Station, TX, USA)

    2005

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    Beitrag (Sammelband oder Tagungsband)

    Surya Prakasa Rao, K., R. Raja, Nari Arunraj

    Decision support system for multi objective optimization in cotton mixing

    Proceedings of the International Conference on Operational Research and Development (ICORD) [December 27th - 30th 2002, Anna University, Chennai, India]

    2002

    TC Grafenau

    Vortrag

    Michael Fernandes, Nari Arunraj, Martin Müller, Diane Ahrens

    Time series sales forecasting to reduce food waste in retail industry

    34th International Symposium on Forecasting, Rotterdam

    Sonstige

    Vortrag

    Nari Arunraj

    Time Series Sales Forecasting in Food Retail Industry

    1. Bayerisch-Tschechische Wissenschaftskonferenz "Datenanalyse", Jindřichův Hradec, Tschechische Republik