Most Internet-tomography problems such as shared congestion detection depend on network measurements. Usually, such measurements are carried out in multiple locations inside the network and relied on local clocks. These clocks usually skewed with time making these measurements unsynchronized and thereby degrading the performance of most techniques. Recently, shared congestion detection has become an important issue in many computer networked applications such as multimedia streaming and
peer-to-peer file sharing. One of the most powerful techniques that employed in literature is based on Discrete Wavelet Transform (DWT) with cross-correlation operation to determine the state of the congestion. Wavelet transform is used as a de-noising tool to reduce the effects of both clock skew and queuing delay fluctuations on the decision of congestion type. Since, classical Discrete Wavelet Transform (DWT) is not shift-invariant transform which is a very useful property particularly in signal de-noising problems. Therefore, another transform called Stationary Wavelet Transform (SWT) that possesses shiftinvariant property is suggested and used instead of DWT. The modified technique exhibits a better performance in terms of the time required to correctly detect the state of congestion especially with the existence of clock skew problem. The suggested technique is tested using simulations under different
environments.
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Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of select
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Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the
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Introduction The abortions reasons in several circumstances yet are mysterious, nevertheless the bacterial toxicities signify a main reason in abortion, where germs seems to be the utmost elaborate pathogens (Khameneh et.al., 2014) and (Oliver and Overton ,2014). Between numerous germs, Humano
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Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E
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The research aims to identify the importance of using analytical procedures in the detection of creative accounting practices. To achieve this goal, (100) questionnaires were prepared and distributed to the auditors in the Federal Financial Supervision Bureau and the authorized auditors' offices and practitioners of the auditing profession in Iraq. For the purpose of testing the research hypothesis and analyzing data, some appropriate statistical methods have been used and the use of the statistical program (SPSS) to analyze the data. The results of the research showed that the analytical procedures and tests applied by the auditor have a role in revealing and limiting creative accounting practices and methods and that auditors u
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Listeria spp. is one of the abortion causative agents in animals, especially in ruminants. This work aimed to detect Listeria spp. in milk and aborted fetus cows in Iraq. A total of 50 organ samples from aborted cow fetuses, including (brain, liver, and spleen), and 50 milk samples from the same aborted cows were collected from Baghdad farms, Iraq from (October 2023- March 2024). The bacteria were identified by conventional culture methods, biochemical tests, and the VITEK2 compact system, followed by molecular confirmation. The antimicrobial resistance pattern assay was performed using the disc diffusion method against eight antibiotic agents, and the L.monocytogenes virulence genes involving prfA,actA, and hylA genes were detected using t
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