Download PDF by C. C. Beerepoot, R.N. van Donk, H.E.G.M. Hermans, J.J.M.: Algemene Wet Bijzondere Ziektekosten

By C. C. Beerepoot, R.N. van Donk, H.E.G.M. Hermans, J.J.M. Linders

De invoering van de Zorgverzekeringswet (Zvw) consistent with 1 januari 2006 betekende een drastische wijziging van het zorgstelsel. Na de invoering richtte de politieke aandacht zich vervolgens op de toekomst van de Algemene rainy Bijzondere Ziektekosten (AWBZ). Deze eerste druk van de Algemene rainy Bijzondere Ziektekosten biedt een doorkijkje naar de ontstaansgeschiedenis van de AWBZ, de huidige structuur en juridische inbedding. Ook wordt inzicht gegeven in de politieke ontwikkelingen die leiden (of al hebben geleid) tot een wijziging van de AWBZ en onderliggende regelgeving. De uitvoering van de AWBZ door zorgverzekeraars, de overheveling van herstelgerichte behandeling naar de Zvw, alsmede raakvlakken met andere aanverwante wetgeving worden in dit boek uitgebreid belicht.

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1) [16–18] γ3 = E{[x − E(x)]3 } 3 (E{[x − E(x)]2 }) 2 (1) Malware Detection Using HSA 47 Fig. 2. Architeure of proposed higher order statistical parameters (HSP) E(x) is the mean value of x and E is the expectation operator. For a sample of N values, the sample Skewness is calculated using Eqs. (2) and (3) Skewness = m3 (2) 3 m22 N i=1 N (x − x ˜)3 (x − x ˜)2 and m2 = i=1 (3) N N The Kurtosis is a measure of the heaviness of the tails in the distribution. Distribution has high Kurtosis value with sharp peaks and heavy tail.

1) [16–18] γ3 = E{[x − E(x)]3 } 3 (E{[x − E(x)]2 }) 2 (1) Malware Detection Using HSA 47 Fig. 2. Architeure of proposed higher order statistical parameters (HSP) E(x) is the mean value of x and E is the expectation operator. For a sample of N values, the sample Skewness is calculated using Eqs. (2) and (3) Skewness = m3 (2) 3 m22 N i=1 N (x − x ˜)3 (x − x ˜)2 and m2 = i=1 (3) N N The Kurtosis is a measure of the heaviness of the tails in the distribution. Distribution has high Kurtosis value with sharp peaks and heavy tail.

But it can be effective to make decision by further processing the object and to detect particular families of executable. For example, this approach can be used in a task of automating the identification of obfuscation. A major security threat to the banking industry worldwide is cyber fraud and malware is one of the manifestations of cyber frauds. Malware propagators make use of Application Programming Interface (API) calls to perpetrate these crimes. Malware detection by text and data mining provides a static analysis method to detect Malware based on API call sequences using text and data mining in tandem [8].

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