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Ephraim Nissan
Researcher at University of London
Publications - 172
Citations - 1283
Ephraim Nissan is an academic researcher from University of London. The author has contributed to research in topics: Narrative & Hebrew. The author has an hindex of 17, co-authored 169 publications receiving 1229 citations. Previous affiliations of Ephraim Nissan include University of Manchester & Ben-Gurion University of the Negev.
Papers
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Journal ArticleDOI
Digital technologies and artificial intelligence's present and foreseeable impact on lawyering, judging, policing and law enforcement
TL;DR: This is an overview of the contributions of digital technologies, both artificial intelligence and non-AI smart tools, to both the legal professions and the police.
A Tentative Evaluation of the Spread of Humour Studies Among Journals in Other Domains
TL;DR: In this article, a large sample of the bibliographies from the 1990s of humour studies, in book form or posted online, is used to assess the spread among a vast range of journals from several disciplines.
Book
Computer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation
TL;DR: This book provides an overview of computer techniques and tools especially from artificial intelligence for handling legal evidence, police intelligence, crime analysis or detection, and forensic testing, with a sustained discussion of methods for the modelling of reasoning and forming an opinion about the evidence.
Book
From information to knowledge : conceptual and content analysis by computer
Ephraim Nissan,Klaus M. Schmidt +1 more
TL;DR: The SACAO project - using computation toward textual data analysis in the social sciences computer-assisted text analysis computer tools for cognitive stylistics terrorist rhetoric and knowledge extraction from ethnopoetic texts by multivariate statistical methods.
Journal ArticleDOI
FuelGen: a genetic algorithm-based system for fuel loading pattern design in nuclear power reactors
TL;DR: Tests on well-researched cases have shown that the algorithm is capable of finding a better loading pattern—enabling the reactor to run both longer and more efficiently per cycle—than solutions reported in the domain literature and found by other methods, such as expert systems and simulated annealing.