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I-Nicemo Enhanced Algorithm Based On Intersection Angel Geometry, Yifan He, Yulin He, Yongda Cai, Zhexue Huang
I-Nicemo Enhanced Algorithm Based On Intersection Angel Geometry, Yifan He, Yulin He, Yongda Cai, Zhexue Huang
Journal of System Simulation
Abstract: To exactly determine the number of cluster centers and correctly identify the candidate cluster centers, an I-niceMO enhanced(I-niceMOEn) algorithm based on intersection angel geometry is proposed. As many distributions of intersection angles and distances as possible between observation points and data points are utilized to recognize the candidate cluster centers to avoid the neglection of cluster centers. The spectral clustering algorithm is used to automatically merge the candidate cluster centers according to the eigenvalues of Laplacian matrices. The number of final cluster centers is determined by the number of merged candidate cluster centers. The number of clusters can be …
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Applications Of Generative Adversarial Networks In Single Image Datasets, Dylan E. Cramer
Applications Of Generative Adversarial Networks In Single Image Datasets, Dylan E. Cramer
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal
One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.