The research on the early-warning system model of Operational Risk for commercial banks based on BP Neural Network analysis

2009 ◽  
Author(s):  
Li-Jie Cao ◽  
Li-Jun Liang ◽  
Zhi-Xiang Li
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Early Warning ◽  
Bp Neural Network ◽  
Commercial Banks ◽  
Early Warning System ◽  
Warning System ◽  
Operational Risk ◽  
System Model ◽  
Neural Network Analysis

scholarly journals Artificial Neural Network Models to Build an Early Warning System for Turkish Commercial Banks before and after the 2001 Financial Crisis

2014 ◽  
Author(s):  
Süleyman Bilgin Kılıç ◽  
Kenan Lopcu ◽  
Semin Paksoy
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Commercial Banks ◽  
Early Warning System ◽  
Warning System ◽  
Network Models ◽  
Bank Failures ◽  
Neural Network Models ◽  
Artificial Neural Network Models ◽  
Bank Restructuring

The objective of this paper is to measure the failure risk of Turkish commercial banks. Bank failures bring to bear high costs on economies as well as on governments and eventually on the public and the taxpayers. During the past two decades, many developed and developing economies have experienced large scale bank failures, and estimates for average bank restructuring costs range from 6% to 10% of the Gross Domestic Product. In Turkey the amount of restructuring costs is approximately 30% of the Gross Domestic Product. In this study, we use 29 selected financial ratios of banks across 1996-2012 periods and the Artificial Neural Network Models to build an early warning system. If commercial bank failure were a predictable event, bank restructuring costs could be minimized. Additionally, if early warning systems are used effectively, the regulatory actions necessary to prevent banks from failing could be taken in advance or in the least a more orderly process of bank closures could be administered. The results overall indicate that almost all commercial banks currently operating in the Turkish banking sector are quite sound and far from failure.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

scholarly journals PEMETAAN PERILAKU BERISIKO GAY BERBASIS SNA SEBAGAI EARLY WARNING SYSTEM KASUS HIV/AIDS DI KOTA KENDARI

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Jumartin Gerung
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Betweeness Centrality ◽  
Hiv Aids

AbstrakPada kasus HIV dalam skala nasional, menunjukkan bahwa kelompok heteroseks juga termasuk sebagai kelompokutama yang paling berisiko menderita HIV/AIDS. Peningkatan ini mencolok terijadi sejak 2015 angkanya masih di 4.241 kasus, dan meningkat hingga lebih dari dua kali lipat pada 2016 yang mencapai 13.063 kasus. Data pemetaaninteraksi di sosial media khususnya wilayah Kendari terdapat sekitar 800 akun yang memberi interaksi perihal Gay.Hal ini diindikasikan akan mempengaruhi prevalensi kejadian HIV/AIDS di Kota Kendari. Penelitian ini bertujuan untukmemetakan interaksi perilaku berisiko Gay sebagai early warning system kasus HIV/AIDS. Social Network Analysismerupakan studi yang mempelajari tentang hubungan manusia dengan memanfaatkan teori graf. Penerapan SocialNetworkAnalysis dalam suatu aplikasi mampu menggambarkan relasi atau hubungan antar individu denganmelakukan visualisasi terkait centrality (titik pusat), between centrality (jalur pendek), juga closeness centrality yaknirata-rata jalur terpendek dari interaksi akun di laman FB. Untuk platform Facebook berdasarkan pada hasilpenghitungan diketahui bahwa akun yang berpengaruh terhadap interaksi jejaring sosial adalah akun Gay Kendariyang unggul pada nilai degree centrality,betweeness centrality, dan Closeness centrality. Akun Gay Kendari palingberpengaruh dalam interaksi jaringan sosial Facebook. Melalui social network analysis, penelitian ini memberikangambaran relasi perilaku berisiko LSL/Gay sebagai early warning system kasus HIV/AIDS di kota kendariKata kunci: analisis jaringan sosiai, gay, sistem peringatan dini, HIV/AIDS 

scholarly journals Early warning system model as a resilience of disaster-prone communities

2019 ◽  
Vol 1375 ◽  
pp. 012093
Author(s):  
M Rochim ◽  
A Bajari ◽  
N A Damayanti ◽  
I Bakti
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
System Model
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