Affiliation:
1. Vellore Institute of Technology, Chennai, India
Abstract
Social networks play a dominant role in connecting communication devices, which play as essential role in exchanging a large amount of interpersonal data. As a result of exchange of messages, they leave behind some traces, which help in identifying the nature of the network. So, these are helpful in detecting crime users. The algorithms like SVM, Bayesian linear regression will not help in finding out the crime network. It also results in less accuracy for higher amounts of data. So, developing the trace learning system in GAN, which is a higher order of deep learning neural network, larger neural network dataset will be fed into the model, which has digital traces into the neural network done through proposed CrimedetGAN. A trained accuracy system model automatically identifies the digital traces which have been left in the crime natured social network. Experimenting with existing GAN frameworks, namely MaliGAN, seqGAN, LeakGAN, proposed CrimedetGAN came with a test score accuracy of 91.23% on the coherence NLP testing in tracing the relevant data fields for the given input datasets.
Reference35 articles.
1. Adford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
2. Mining criminal networks from unstructured text documents
3. Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study
4. BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis.;A.Brock;International Conference on Learning Representations,2021
5. Internet of things for smart crime detection