Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

This paper presents the results of the analysis of the network intrusion detection systems using data mining techniques and anomaly detection. Anomaly detection technique is present for a while in the area of data mining. Previous papers that implement data mining techniques to detect anomaly attacks actually use well-known techniques such as classification or clustering. Anomaly detection technique combines all these techniques. They are also facing problem on the fact that many of the attacks do not have some kind of signature on network and transport layer, so it is not easy to train models for these type of attacks. Network dataset that was used in this paper is DARPA 1998 dataset created in MIT Lincoln Laboratory and is used worldwide for the network testing purposes.