In recent years, with the growing size and the importance of computer networks, it is very necessary to provide adequate protection for users data from snooping through the use of one of the protection techniques: encryption, firewall and intrusion detection systems etc. Intrusion detection systems is considered one of the most important components in the computer networks that deal with Network security problems. In this research, we suggested the intrusion detection and classification system through merging Fuzzy logic and Artificial Bee Colony Algorithm. Fuzzy logic has been used to build a classifier which has the ability to distinguish between the behavior of the normal user and behavior of the intruder. The artificial bee colony algorithm has been used to build the classifier which was used to classify the intrusion into one of the main types (DoS, R2L , U2R, Prob). The proposed system has the ability to detect and classify intrusion at high speed with a small percentage of false alarms as well as to detect the new attacks. The NSL-KDD dataset used in the training and testing the proposed system.The results of experiments showed that the efficiency of the proposed system performance were (97.59%) for the intrusion detection, and (0.12%) for the false alarms. Also, the Classification rates for classes (DoS, R2L,U2R,Prob) were (97.19, 77.09, 98.43, 93.23) Respectively, which is considered a superior performance comparing with other methods in the literature.
Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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Examination of 241 specimens of two bee-eater species, Merops apiaster and Merops
superciliosus persicus reveal recording of Haemoproteus meropis (Zagar, 1945) emend.
Bennett, 1978 and H. manwelli Bennett, 1978 for the first time in Iraq. A new species
Haemoproteus hudaidensis sp. nov. is described. Microfilariae are also infected the two host
species. The results are discussed with the pertinent literature and the necessary comparision
of morphometric measurements of the recorded parasites with that previously reported is
provided along with a taxonomic key including the newly described haemoproteid.
The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the
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In this paper, we study the effect of group homomorphism on the chain of level subgroups of fuzzy groups. We prove a necessary and sufficient conditions under which the chains of level subgroups of homomorphic images of an a arbitrary fuzzy group can be obtained from that of the fuzzy groups . Also, we find the chains of level subgroups of homomorphic images and pre-images of arbitrary fuzzy groups
Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered
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Ethanolic extracts of Thyme (Thymbra spicata) were tested for their inhibitory action on Paenibacillus larvae the causative agent of American foul brood with different concentration by using disc assay and mixed with culture media . Results showd that 3000mg / disc and 3000 mg / ml was the effective concentroation for the both methods .Thymol was isolated by using TLC technique . The effective concentration of thymol on growth of bacteria was 1000 mg / disc . Thymol and crude extracts of thyme 3000 mg / ml were tested on infected hives by mixed with sugar solution . The symptoms of AFB disease was full disappearance within 21 , 27 days after treatment with thymol and thyme
... Show MoreThe combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.
In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.
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... Show MoreThe first aim in this paper is to introduce the definition of fuzzy absolute value on the vector space of all real numbers then basic properties of this space are investigated. The second aim is to prove some properties that finite dimensional fuzzy normed space have.
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Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative st
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