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Suche nach „[Fernandes] [Michael]“ hat 7 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.

    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.

    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

    DigitalNachhaltigTC Grafenau

    Vortrag

    Michael Fernandes

    Food-Scanner: Lebensmittelqualität einfach bestimmen

    Posterpräsentation

    5. Tag der Forschung, Deggendorf

    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

    TC Grafenau

    Vortrag

    Michael Fernandes

    Intelligentes Prognose- und Dispositionsverfahren im Lebensmitteleinzelhandel

    EssensWert Fachtagung, München/Fürstenfeldbruck

    TC Grafenau

    Vortrag

    Michael Fernandes

    Das Auge analysiert mit - Datenanalyse und Visualisierung

    3. Tag der Forschung - Themenbereiche Wirtschaft und Gesundheit, Deggendorf