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Titlebook: Victims of Stalking; Case Studies in Invi Jenny Korkodeilou Book 2020 The Editor(s) (if applicable) and The Author(s) 2020 victimisation.in

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21#
發(fā)表于 2025-3-25 03:27:30 | 只看該作者
Doing Qualitative Research on Stalking Victimisation,e need of victims to remain hidden, the lack of a generally accepted definition and persistent socio-cultural misunderstandings about what stalking is and who can perpetrate it. While carrying out my study I often felt walking a tightrope, remaining alert and focused on keeping the balance between m
22#
發(fā)表于 2025-3-25 08:51:19 | 只看該作者
Forget me Not: Stalkers, Modus Operandi and Perceived Motivations,rs seeking to re-establish a relationship or take revenge and control over their partners? This chapter contextualises the experiences of the victims who were interviewed for my study by providing basic demographic information and a discussion of the contextual and motivational characteristics of th
23#
發(fā)表于 2025-3-25 15:24:35 | 只看該作者
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發(fā)表于 2025-3-25 18:04:47 | 只看該作者
25#
發(fā)表于 2025-3-25 20:43:10 | 只看該作者
26#
發(fā)表于 2025-3-26 01:44:47 | 只看該作者
Book 2020alities of victims. Drawing on in-depth interviews with 26 self-defined victims of stalking in England and Wales, it explores the psychological and social effects of this hidden and misunderstood form of interpersonal violence. Korkodeilou‘s work seeks to improve understanding regarding this type of
27#
發(fā)表于 2025-3-26 07:07:02 | 只看該作者
28#
發(fā)表于 2025-3-26 08:31:29 | 只看該作者
and implemen- tion of an essential introductory Bioinformatics course. In addition, we included sections describing specialized noncommercial software, databas978-1-61737-156-1978-1-59259-192-3Series ISSN 1064-3745 Series E-ISSN 1940-6029
29#
發(fā)表于 2025-3-26 16:32:17 | 只看該作者
Jenny Korkodeilountroduced by Cheng and Church in [4]. Since, missing data often occur in the given data matrices for biclustering, usually, missing data are filled by random numbers. However, we show that ignoring the missing data is a better approach and avoids additional noise caused by randomness. Since, an idea
30#
發(fā)表于 2025-3-26 20:26:11 | 只看該作者
Jenny Korkodeilount feature, which is used as the optimal feature subset to train SVM classifier. Finally, the feature subset with the highest SVM classification accuracy was regarded as optimal feature subset. The experimental results on 11 UCI datasets, a real clinical data sets and a gene expression dataset show
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