Search results for "202"
showing 10 items of 5810 documents
Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine
2020
The rapid deployment in information and communication technologies and internet-based services have made anomaly based network intrusion detection ever so important for safeguarding systems from novel attack vectors. To this date, various machine learning mechanisms have been considered to build intrusion detection systems. However, achieving an acceptable level of classification accuracy while preserving the interpretability of the classification has always been a challenge. In this paper, we propose an efficient anomaly based intrusion detection mechanism based on the Tsetlin Machine (TM). We have evaluated the proposed mechanism over the Knowledge Discovery and Data Mining 1999 (KDD’99) …
The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review
2019
Whenever a disaster occurs, users in social media, sensors, cameras, satellites, and the like generate vast amounts of data. Emergency responders and victims use this data for situational awareness, decision-making, and safe evacuations. However, making sense of the generated information under time-bound situations is a challenging task as the amount of data can be significant, and there is a need for intelligent systems to analyze, process, and visualize it. With recent advancements in Artificial Intelligence (AI), numerous researchers have begun exploring AI, machine learning (ML), and deep learning (DL) techniques for big data analytics in managing disasters efficiently. This paper adopt…
An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders
2020
In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistica…
An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations
2019
A novel measurement approach for power-flow analysis in medium-voltage (MV) networks, based on load power measurements at low-voltage level in each secondary substation (SS) and only one voltage measurement at the MV level at primary substation busbars, was proposed by the authors in previous works. In this paper, the method is improved to cover the case of temporary unavailability of load power measurements in some SSs. In particular, a new load power estimation method based on artificial neural networks (ANNs) is proposed. The method uses historical data to train the ANNs and the real-time available measurements to obtain the load estimations. The load-flow algorithm is applied with the e…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Vector-borne and zoonotic infections and their relationships with regional and socioeconomic statuses: An ID-IRI survey in 24 countries of Europe, Af…
2021
Background: In this cross-sectional, international study, we aimed to analyze vector-borne and zoonotic infections (VBZI), which are significant global threats. Method: VBZIs’ data between May 20–28, 2018 was collected. The 24 Participatingcountries were classified as lower-middle, upper-middle, and high-income. Results: 382 patients were included. 175(45.8%) were hospitalized, most commonly in Croatia, Egypt, and Romania(P = 0.001). There was a significant difference between distributions of VBZIs according to geographical regions(P < 0.001). Amebiasis, Ancylostomiasis, Blastocystosis, Cryptosporidiosis, Giardiasis, Toxoplasmosis were significantly more common in the Middle-East while B…
3D Matrix-Based Visualization System of Association Rules
2017
With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the …
A faint outburst of the accreting millisecond X-ray pulsar SAX J1748.9-2021 in NGC 6440
2018
SAX J1748.9-2021 is an accreting X-ray millisecond pulsar observed in outburst five times since its discovery in 1998. In early October 2017, the source started its sixth outburst, which lasted only ~13 days, significantly shorter than the typical 30 days duration of the previous outbursts. It reached a 0.3-70 keV unabsorbed peak luminosity of $\sim3\times10^{36}$ erg/s. This is the weakest outburst ever reported for this source to date. We analyzed almost simultaneous XMM-Newton, NuSTAR and INTEGRAL observations taken during the decaying phase of its 2017 outburst. We found that the spectral properties of SAX J1748.9-2021 are consistent with an absorbed Comptonization plus a blackbody comp…
Differential Leakage Factor in Electrical Machines Equipped with Asymmetrical Multiphase Windings: a General Investigation
2019
This paper presents an investigation in terms of degree of unbalance and leakage factor of electrical machines equipped with multiphase windings. The analysis has been carried out through 4800 combinations between slots/poles/phases/layers, analyzing the variability of the leakage factor for each condition and determining the optimal region for its minimization. The obtained results demonstrate that the leakage factor could be considerably reduced with the adoption of slightly asymmetrical windings, which represent a favorable option during the early design stage of electrical machines.
Lagrangian Description of Air Masses Associated with Latent Heat Release in Tropical Storm Karl (2016) during Extratropical Transition
2019
Abstract Extratropical transition (ET) of tropical cyclones involves distinct changes of the cyclone’s structure that are not yet well understood. This study presents for the first time a comprehensive Lagrangian description of structure change near the inner core. A large sample of trajectories is computed from a convection-permitting numerical simulation of the ET of Tropical Storm Karl (2016). Three main airstreams are considered: those associated with the inner-core convection, inner-core descent, and the developing warm conveyor belt. Analysis of these airstreams is performed both in thermodynamic and physical space. Prior to ET, Karl is embedded in weak vertical wind shear and its int…