Presenting a New Method of Intrusion Detection in Computer Networks by Ensemble Classification Methods and Feature Selection Methods

Abstract Intrusion detection systems have special importance in computer networks. In present situation, we need high accuracy and relatively good speed systems. We can improve these types of systems by using machine learning algorithm and data mining tools. The purpose of intrusion detection is to recognize unauthorized use, misuse or damage to systems and computer networks by both groups of internal users and foreign attackers. In detecting misuse, recognized intrusion patterns are used for intrusion detection. While in detection methods of unusual behavior, usual behavior of users is considered and as a result each different behavior with it is recognized as attempt for intrusion to system. Decision tree is one of the most famous and the oldest methods of data mining to construct classification model. In classification algorithms based on decision tree, external knowledge is presented as a tree from different states of features. Decision trees are considered because their results are interpretable and they do not need to input parameters. Also processing their structures is relatively fast and flexible. Efficiency of a system severely depends on selection method of features. Since by increasing features number, computing cost of a system is also increased, design and implementation of systems seem necessary with the least number of features

Electric Power Grids Under High-Absenteeism Pandemics: History, Context, Response, and Opportunities.

Widespread outbreaks of infectious disease, i.e., the so-called pandemics that may travel quickly and silently beyond boundaries, can significantly upsurge the morbidity and mortality over large-scale geographical areas. They commonly result in enormous economic losses, political disruptions, social unrest, and quickly evolve to a national security concern. Societies have been shaped by pandemics and outbreaks for as long as we have had societies. While differing in nature and in realizations, they all place the normal life of modern societies on hold. Common interruptions include job loss, infrastructure failure, and political ramifications. The electric power systems, upon which our modern society relies, is driving a myriad of interdependent services, such as water systems, communication networks, transportation systems, health services, etc. With the sudden shifts in electric power generation and demand portfolios and the need to sustain quality electricity supply to end customers (particularly mission-critical services) during pandemics, safeguarding the nation's electric power grid in the face of such rapidly evolving outbreaks is among the top priorities. This paper explores the various mechanisms through which the electric power grids around the globe are influenced by pandemics in general and COVID-19 in particular, shares the lessons learned and best practices taken in different sectors of the electric industry in responding to the dramatic shifts enforced by such threats, and provides visions for a pandemic-resilient electric grid of the future. [Abstract copyright: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Generating GHZ state in 2m-qubit spin network

We consider a pure 2m-qubit initial state to evolve under a particular quantum me- chanical spin Hamiltonian, which can be written in terms of the adjacency matrix of the Johnson network J(2m;m). Then, by using some techniques such as spectral dis- tribution and stratification associated with the graphs, employed in [1, 2], a maximally entangled GHZ state is generated between the antipodes of the network. In fact, an explicit formula is given for the suitable coupling strengths of the hamiltonian, so that a maximally entangled state can be generated between antipodes of the network. By using some known multipartite entanglement measures, the amount of the entanglement of the final evolved state is calculated, and finally two examples of four qubit and six qubit states are considered in details.Comment: 22 page

Neuronal differentiation of rat hair follicle stem cells: The involvement of the neuroprotective factor seladin-1 (DHCR24)

Background: The seladin-1 (selective Alzheimer disease indicator-1), also known as DHCR24, is a gene found to be down-regulated in brain region affected by Alzheimer disease (AD). Whereas, hair follicle stem cells (HFSC), which are affected in with neurogenic potential, it might to hypothesize that this multipotent cell compartment is the predominant source of seladin-1. Our aim was to evaluate seladin-1 gene expression in hair follicle stem cells. Methods: In this study, bulge area of male Wistar rat HFSC were cultured and then characterized with Seladin-1 immunocytochemistry and flow cytometry on days 8 to 14. Next, 9-11-day cells were evaluated for seladin-1 gene expression by real-time PCR. Results: Our results indicated that expression of the seladin-1 gene (DHCR24) on days 9, 10, and 11 may contribute to the development of HFSC. However, the expression of this gene on day 11 was more than day 10 and on 10th day was more than day 9. Also, we assessed HFSC on day 14 and demonstrated these cells were positive for β-III tubulin, and seladin-1 was not expressed in this day. Conclusion: HFSC express seladin-1 and this result demonstrates that these cells might be used to cell therapy for AD in future

Engineering rotating apical-out airway organoid for assessing respiratory cilia motility

Motile cilia project from the airway apical surface and directly interface with inhaled external environment. Owing to cilia\u27s nanoscale dimension and high beating frequency, quantitative assessment of their motility remains a sophisticated task. Here we described a robust approach for reproducible engineering of apical-out airway organoid (AOAO) from a defined number of cells. Propelled by exterior-facing cilia beating, the mature AOAO exhibited stable rotational motion when surrounded by Matrigel. We developed a computational framework leveraging computer vision algorithms to quantify AOAO rotation and correlated it with the direct measurement of cilia motility. We further established the feasibility of using AOAO rotation to recapitulate and measure defective cilia motility caused by chemotherapy-induced toxicity and by CCDC39 mutations in cells from patients with primary ciliary dyskinesia. We expect our rotating AOAO model and the associated computational pipeline to offer a generalizable framework to expedite the modeling of and therapeutic development for genetic and environmental ciliopathies

Electron quantum metamaterials in van der Waals heterostructures

In recent decades, scientists have developed the means to engineer synthetic periodic arrays with feature sizes below the wavelength of light. When such features are appropriately structured, electromagnetic radiation can be manipulated in unusual ways, resulting in optical metamaterials whose function is directly controlled through nanoscale structure. Nature, too, has adopted such techniques -- for example in the unique coloring of butterfly wings -- to manipulate photons as they propagate through nanoscale periodic assemblies. In this Perspective, we highlight the intriguing potential of designer sub-electron wavelength (as well as wavelength-scale) structuring of electronic matter, which affords a new range of synthetic quantum metamaterials with unconventional responses. Driven by experimental developments in stacking atomically layered heterostructures -- e.g., mechanical pick-up/transfer assembly -- atomic scale registrations and structures can be readily tuned over distances smaller than characteristic electronic length-scales (such as electron wavelength, screening length, and electron mean free path). Yet electronic metamaterials promise far richer categories of behavior than those found in conventional optical metamaterial technologies. This is because unlike photons that scarcely interact with each other, electrons in subwavelength structured metamaterials are charged, and strongly interact. As a result, an enormous variety of emergent phenomena can be expected, and radically new classes of interacting quantum metamaterials designed

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery