In recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commercial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested for improving overall computer network security and data privacy. This article gives a thorough overview of recent literature regarding neural networks usage in intrusion detection system area, including surveys and new method proposals. Short tutorial descriptions of neural network architectures, intrusion detection system types and training datasets are also provided.

The aim of this paper is to determine the extent to which infrared (IR) thermal imaging may be used for skin burn depth evaluation. The analysis can be made on the basis of the development of a thermal model of the burned skin. Different methods such as the traditional clinical visual approach and the IR imaging modalities of static IR thermal imaging, active IR thermal imaging and active-dynamic IR thermal imaging (ADT) are analyzed from the point of view of skin burn depth diagnostics. In ADT, a new approach is proposed on the basis of parametric image synthesis. Calculation software is implemented for single-node and distributed systems. The properties of all the methods are verified in experiments using phantoms and subsequently in vivo with animals with a reference histopathological examination. The results indicate that it is possible to distinguish objectively and quantitatively burns which will heal spontaneously within three weeks of infliction and which should be treated conservatively from those which need surgery because they will not heal within this period

This paper is devoted to the problem of class imbalance in machine learning, focusing on the intrusion detection of rare classes in computer networks. The problem of class imbalance occurs when one class heavily outnumbers examples from the other classes. In this paper, we are particularly interested in classifiers, as pattern recognition and anomaly detection could be solved as a classification problem. As still a major part of data network traffic of any organization network is benign, and malignant traffic is rare, researchers therefore have to deal with a class imbalance problem. Substantial research has been undertaken in order to identify these methods or data features that allow to accurately identify these attacks. But the usual tactic to deal with the imbalance class problem is to label all malignant traffic as one class and then solve the binary classification problem. In this paper, however, we choose not to group or to drop rare classes but instead investigate what could be done in order to achieve good multi-class classification efficiency. Rare class records were up-sampled using SMOTE method (Chawla et al., 2002) to a preset ratio targets. Experiments with the 3 network traffic datasets, namely CIC-IDS2017, CSE-CIC-IDS2018 (Sharafaldin et al., 2018) and LITNET-2020 (Damasevicius et al., 2020) were performed aiming to achieve reliable recognition of rare malignant classes available in these datasets. Popular machine learning algorithms were chosen for comparison of their readiness to support rare class detection. Related algorithm hyper parameters were tuned within a wide range of values, different data feature selection methods were used and tests were executed with and without over-sampling to test the multiple class problem classification performance of rare classes. Machine learning algorithms ranking based on Precision, Balanced Accuracy Score, $\bar{G}$, and prediction error Bias and Variance decomposition, show that decision tree ensembles (Adaboost, Random Forest Trees and Gradient Boosting Classifier) performed best on the network intrusion datasets used in this research.

In this paper the optical linear sensor, a representative of low resolution sensors, was investigated in the multi-class recognition of near field hand gestures. The Recurrent Neural Network (RNN) with a GRU (Gated Recurrent Unit) memory cell was utilized as a gestures classifier. A set of 27 gestures was collected from a group of volunteers. The 27 000 sequences obtained were divided into training, validation, and test subsets. The primary research goal was to define the most appropriate model architecture in terms of the accurate recognition of each gesture. An additional aim of the research was to investigate the kind of input data, i.e., raw data, or preprocessed (feature) data, which generally produces better results. Therefore, three datasets were generated: raw data, simple features data, and high level features data (this includes information about hand poses which are already recognized). The random search method was applied to achieve hyperparameter optimization to find the best possible topology for the neural network. The analysis performed shows that selected models were characterized by a test score at a level of 96.89% for the raw data, 95.75% for simple features, and 93.38% for high level features. Results indicate that the direct use of raw data obtained from the optical linear sensor evaluated on the RNN with GRU memory cells allows for the reliable recognition of even complex gestures. Therefore, such solutions may have the potential to serve as a support to, or as an alternative to video based sensors especially for mobile devices.

Recently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring solution for elderly and disabled people. The benefits of the proposed architecture were experimentally verified on the thermal video sequences, acquired in various scenarios to address possible limitations of remote diagnostics: movements of the person performing a diagnose and movements of the examined person. The achieved short processing time (42.05±0.21ms) along with high model accuracy (false positives -0.43%; true positives for the patient focused on a certain task -89.2%) clearly indicates that the current state of the art in the area of image classification and face tracking in thermography was significantly outperformed.