Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Applied deep learning in intelligent transportation systems and embedding exploration

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization and data retrieval. The first part of this dissertation is about a Spatio-TEmporal Fuzzy neural Network (STEF-Net) which accurately predicts passenger demand by incorporating the complex interaction of all known important factors, such as temporal, spatial and external information. Specifically, a convolutional long short-term memory network is employed to simultaneously capture spatio-temporal feature interaction, and a fuzzy neural network to model external factors. A novel feature fusion method with convolution and an attention layer is proposed to keep the temporal relation and discriminative spatio-temporal feature interaction. Experiments on a large-scale real-world dataset show the proposed model outperforms the state-of-the-art approaches. The second part is a light-weight and energy-efficient system which detects transportation modes using only accelerometer sensors in smartphones. Understanding people’s transportation modes is beneficial to many civilian applications, such as urban transportation planning. The system collects accelerometer data in an efficient way and leverages a convolutional neural network to determine transportation modes. Different architectures and classification methods are tested with the proposed convolutional neural network to optimize the system design. Performance evaluation shows that the proposed approach achieves better accuracy than existing work in detecting people’s transportation modes. The third component of this dissertation is a deep reinforcement learning model, based on Q learning, to control the traffic light. Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. In the proposed model, the complex traffic scenario is quantified as states by collecting data and dividing the whole intersection into grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards, which is further optimized by several components, such as dueling network, target network, double Q-learning network, and prioritized experience replay. The simulation results in Simulation of Urban MObility (SUMO) show the efficiency of the proposed model in controlling traffic lights. The last part of this dissertation studies the hierarchical structure in an embedding system. Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which generates storage-inefficient representation and fails to effectively encode the internal semantic structure of data. A regularized autoencoder framework is proposed to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of data points, aiming at capturing semantic structures of data. Experimental results on synthetic and real-world datasets show that the proposed HKD embedding can effectively reveal the semantic structure of data via visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy

Malware detection based on call graph similarities

S rostoucím množstvím škodlivých souborů se stalo využití strojového učení pro jejich detekci nezbytností. Autoři škodlivých souborů vytváří důmyslnější programy, aby překonali stále se zlepšující antivirovou ochranu. Windows OS zůstává nejčastějším cílem útoků. Viry se často šíří ve formátu Portable Executable (PE). PE soubory mohou být zkoumány pomocí metod statické analýzy, které se hodí pro zpracovávání velkého množství dat. Mnoho antivirových systémů disassembluje soubory a zkoumá jejich kód, který nabízí vhled do funkcionality souboru. Assembly kód je členěn do funkcí. Vztahy mezi funkcemi zachycuje graf volání funkcí (GVF). Tento graf byl zkoumán v literatuře a jeho struktura byla využita k hledání podobností mezi soubory. V poslední době začaly být úspěšně využívány grafové neuronové sítě (GNN) ke zpracování těchto grafů. V naší práci zkoumáme různé druhy a architektury GNN a vzájemně je porovnáváme. Po tom, co vybereme nejlepší GNN model, ho srovnáme s modelem, který nevyužívá grafovou strukturu GVF, abychom zjistili zda tato struktura zlepšuje klasifikační modely. Naši studii provádíme na velkém datasetu o více než 5 milionech PE souborů.Machine learning-powered malware detection systems became a necessity to fight the rising volume of malware. Malware authors create more sophisticated programs to overcome always improving antivirus engines. Windows OS remains the most targeted system, and the malicious payload commonly comes in the Portable executable (PE) file format. PE files can be analyzed with the static analysis methods, which are suitable for processing large amounts of data. Many engines disassemble binaries and study the code, which carries valuable insight into binary behavior. The assembly code is divided into functions that carry the functionality. The relations between functions form a Function Call Graph (FCG). FCG has been studied in the literature, and the graph structure was employed to find similarities between files. Recently, Graph Neural Networks (GNNs) have been adapted to work upon FCGs and are claimed to be performing well. In this work, we study and compare different GNN models and their architectures. After selecting the best GNN model, we compare it with a non-structural model to verify if an FCG structure improves classification models. We perform our empirical study on a large dataset of more than 5 million PE files