ANALYSIS OF TECHNIQUES IN CRIME PREVENTION USING FUZZY

ABSTRAC

Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review

Motivated by the interpretability question in ML models as a crucial element for the successful deployment of AI systems, this paper focuses on rule extraction as a means for neural networks interpretability. Through a systematic literature review, different approaches for extracting rules from feedforward neural networks, an important block in deep learning models, are identified and explored. The findings reveal a range of methods developed for over two decades, mostly suitable for shallow neural networks, with recent developments to meet deep learning models' challenges. Rules offer a transparent and intuitive means of explaining neural networks, making this study a comprehensive introduction for researchers interested in the field. While the study specifically addresses feedforward networks with supervised learning and crisp rules, future work can extend to other network types, machine learning methods, and fuzzy rule extraction

ICE-B 2010:proceedings of the International Conference on e-Business

The International Conference on e-Business, ICE-B 2010, aims at bringing together researchers and practitioners who are interested in e-Business technology and its current applications. The mentioned technology relates not only to more low-level technological issues, such as technology platforms and web services, but also to some higher-level issues, such as context awareness and enterprise models, and also the peculiarities of different possible applications of such technology. These are all areas of theoretical and practical importance within the broad scope of e-Business, whose growing importance can be seen from the increasing interest of the IT research community. The areas of the current conference are: (i) e-Business applications; (ii) Enterprise engineering; (iii) Mobility; (iv) Business collaboration and e-Services; (v) Technology platforms. Contributions vary from research-driven to being more practical oriented, reflecting innovative results in the mentioned areas. ICE-B 2010 received 66 submissions, of which 9% were accepted as full papers. Additionally, 27% were presented as short papers and 17% as posters. All papers presented at the conference venue were included in the SciTePress Digital Library. Revised best papers are published by Springer-Verlag in a CCIS Series book

Discovering Trends in Large Datasets Using Neural Networks

Abstract. A novel knowledge discovery technique using neural networks is presented. A neural network is trained to learn the correlations and relationships that exist in a dataset. The neural network is then pruned and modified to generalize the correlations and relationships. Finally, the neural network is used as a tool to discover all existing hidden trends in four different types of crimes (murder, rape, robbery, and auto theft) in US cities as well as to predict trends based on existing knowledge inherent in the network