Fault Detection and Identification in Computer Networks: A soft Computing Approach

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system

A Machine Learning Approach for Big Data in Oil and Gas Pipelines

Abstract-Experienced pipeline operators utilize Magnetic Flux Leakage (MFL) sensors to probe oil and gas pipelines for the purpose of localizing and sizing different defect types. A large number of sensors is usually used to cover the targeted pipelines. The sensors are equally distributed around the circumference of the pipeline; and every three millimeters the sensors measure MFL signals. Thus, the collected raw data is so big that it makes the pipeline probing process difficult, exhausting and error-prone. Machine learning approaches such as neural networks have made it possible to effectively manage the complexity pertaining to big data and learn their intrinsic properties. We concentrate, in this work, on the applicability of artificial neural networks in defect depth estimation and present a detailed study of various network architectures. Discriminant features, which characterize different defect depth patterns, are first obtained from the raw data. Neural networks are then trained using these features. The Levenberg-Marquardt back-propagation learning algorithm is adopted in the training process, during which the weight and bias parameters of the networks are tuned to optimize their performances. Compared with the performance of pipeline inspection techniques reported by service providers such as GE and ROSEN, the results obtained using the method we proposed are promising. For instance, within ±10% error-tolerance range, the proposed approach yields an estimation accuracy at 86%, compared to only 80% reported by GE; and within ±15% error-tolerance range, it yields an estimation accuracy at 89% compared to 80% reported by ROSEN

An Adaptive Neuro-Fuzzy Inference System-Based Approach for Oil and Gas Pipeline Defect Depth Estimation

Abstract-To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the collected MFL data, the defect depth estimation task is cumbersome, timeconsuming, and error-prone. In this paper, we propose an adaptive neuro-fuzzy inference system (ANFIS)-based approach to estimate defect depths from MFL signals. Depth-related features are first extracted from the MFL signals and then are used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. A hybrid learning algorithm that combines least-squares and back propagation gradient descent method is adopted. Moreover, to achieve an optimal performance by the proposed approach, highly-discriminant features are selected from the obtained features by using the weight-based support vector machine (SVM). Experimental work has shown that encouraging results are obtained. Within error-tolerance ranges of ±15%, ±20%, ±25%, and ±30%, the depth estimation accuracies obtained by the proposed technique are 80.39%, 87.75%, 91.18%, and 95.59%, respectively. Moreover, further improvement can be easily achieved by incorporating new and more discriminant features

An Adaptive Thresholding Method for Segmenting Dental X-Ray Images

Thresholding is a way of segmenting an image into foreground and background according to a fixed constant value called a threshold. Image segmentation based on a constant threshold leads to unsatisfactory results with dental X-ray images due to the uneven distribution of pixel intensity. In this paper, an adaptive thresholding method is proposed to attain promising segmentation results for dental X-ray images. The Mean, Median, Midgrey, Niblack, and OTSU thresholding methods are utilized to delineate the acceptable range of threshold values to be applied for segmenting X-ray images. Experimental results showed that the Median method provides consistency in achieving the best range of threshold values which is between 57 and 86 in greyscale

Directed graph-based wireless EEG sensor channel selection approach for cognitive task classification

Wireless electroencephalogram (EEG) sensors have been successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they are low powered devices, and hence an efficient usage of sensor energy resources is critical for any practical application. One way of minimizing energy consumption by the EEG sensors is by reducing the number of EEG channels participating in the classification process. For the purpose of classifying EEG signals, we propose a directed acyclic graph (DAG)-based channel selection algorithm. To achieve this objective, the EEG sensor channels are first realized in a complete undirected graph, where each channel is represented by a node. An edge between any two nodes indicates the collaboration between these nodes in identifying the system state; and the significance of this collaboration is quantified by a weight assigned to the edge. The complete graph is then reduced into a directed acyclic graph that encodes the knowledge of the non-increasing order of the channel ranking for each cognitive task. The channel selection algorithm utilizes this directed graph to find a maximum path such that the total weight of this path satisfies a predefined threshold. It has been demonstrated experimentally that channel utilization has been reduced by 50% in the worst case scenario for a three-state system and an EEG sensor with 14 channels; and the best classification accuracy obtained is 81%. 2016 IEEE.Scopu

Global stabilization of autonomous underactuated underwater vehicles in 3D space

In this paper, global asymptotic stabilization of an autonomous underactuated underwater vehicle (AUUV) is investigated, where the number of actuators of the AUUV is less than the vehicle's degrees of freedom. The model that is considered describes both the kinematics and dynamics of the AUUV with six degrees of freedom and four actuators. To cope with the underactuation characteristics of AUUV a state transformation is proposed to change the model of the vehicle to a cascade nonlinear system. Then a switching control algorithm is proposed where the stability of the whole vehicle is guaranteed based on the stability properties of cascade systems. To illustrate the performance of the proposed approach, simulation results are provided. 2016 IEEE.Scopu

Phytochemical, antioxidant and hepatoprotective effects of different fractions of Moringa oleifera leaves methanol extract against liver injury in animal model

Objective: To evaluate the potential antioxidant and hepatoprotective effects of n-hexane, dichloromethane(DCM), ethyl acetate(EtOAc), n-butanol and aqueous fractions of Moringa oleifera(M. oleifera) leaves methanol extract against carbon tetrachloride(CCl4)-induced liver injury in rats. Methods: These fractions were prepared from the M. oleifera leaves methanol extract by solubilization in water and partitioning in n-hexane, EtOAc, DCM and n-butanol. Their phyto-components were identified by GC-MS analysis. The in vitro antioxidant effect of these fractions was carried out by assessment of 1,1-diphenyl-2-picrylhydrazyl scavenging activity. A total of 40 Sprague Dawley rats were allocated into 8 equal groups: group 1 given olive oil (1 mL/kg b.wt.), group 2 injected with CCl4, group 3 to 7 administered with n-hexane, DCM, EtOAc, n-butanol and aqueous fractions, respectively after CCl4, group 8 administered with silymarin after CCl4. The activities of aspartate aminotransferase, alanine aminotransferase, and the levels of total cholesterol, triglycerides, glucose, total proteins and albumin in serum were determined spectrophotometrically. Glutathione reduced, lipid peroxide by-products levels, glutathione-s-transferase and catalase enzyme activities in the liver homogenate were determined by spectrophotometer. Liver specimens were also examined for histopathological alterations under light microscope. Results: The GC-MS analysis of different fractions of the M. oleifera leaves methanol extract revealed that n-hexane, DCM, EtOAc, n-butanol, and aqueous fractions contained 17, 22, 23, 19 and 32 compounds, respectively. The percent and the molecular structure of each component in each fraction were identified. The n-butanol and EtOAc fractions exhibited the strongest in vitro antioxidant activity against 1,1-diphenyl-2-picrylhydrazyl. CCl4 significantly decreased glutathione reduced and total proteins concentration and glutathione-s-transferase and catalase activities but increased lipid peroxide by-products and total cholesterol levels. The n-hexane followed by aqueous and DCM fractions were the most potent to regulate serum enzyme activities and lipid peroxide by-products levels in the liver homogenate. Conclusions: n-hexane, DCM, and aqueous fractions have the highest effectiveness against CCl4-induced hepatotoxicity. Isolation and purification of the active constituents require further experiments

IN VITRO EVALUATION OF THE ANTIMICROBIAL ACTIVITY OF FIVE HERBAL EXTRACTS AGAINST STREPTOCOCCUS MUTANS

Background: The emergence and extend of antibiotic resistance, together with the development of new strains of illness causes, are of enormous alarm to the global health community. Successful treatment of a illness involves the development of new pharmaceuticals or some promise source of novel drugs. Universally make use of medicinal plants of our community could be an outstanding source of drugs to fight off dental caries. This study is focused on discovering  the antibacterial properties of the plants that are frequently being used as traditional medications. Methods: Five methanol extracts from Salvia officinalis, Commiphora myrrha, Boswellia carteril, Saussurea lappa and Dracaena cinnabari were examined for their antibacterial activities against most common bacterial oral pathogen, Streptococcus mutans. The antibacterial testing was carried out by using the disc diffusion and broth micro-dilution assays. Results: Methanol extracts of the five plants were effective against Streptococcus mutans with diameter  zone of  inhibition ranging from 63.6 to 21.0 mm. The results of the microdilution assay confirmed that the Salvia officinalis, Commiphora myrrha, Saussurea lappa and Dracaena cinnabari were effective against the Streptococcus mutans, exhibiting MIC values, ranging from 0.310 to 0.156 mg/ml. Conclusion: The results of our study indicate that the methanol extracts of plants used in this study have an antibacterial effect even at low concentration against the carcinogenic Streptococcus mutans bacteria, and they may be possible to combat Streptococcus mutans to increase the effectiveness of oral hygiene practices by incorporating the extracts of these plants into anti-caries such as Toothpastes and mouthwash.                        Peer Review History: Received: 18 January 2022; Revised: 13 February; Accepted: 8 March, Available online: 15 March 2022 Academic Editor: Dr. Asia Selman Abdullah, Pharmacy institute, University of Basrah, Iraq, [email protected] UJPR follows the most transparent and toughest ‘Advanced OPEN peer review’ system. The identity of the authors and, reviewers will be known to each other. This transparent process will help to eradicate any possible malicious/purposeful interference by any person (publishing staff, reviewer, editor, author, etc) during peer review. As a result of this unique system, all reviewers will get their due recognition and respect, once their names are published in the papers. We expect that, by publishing peer review reports with published papers, will be helpful to many authors for drafting their article according to the specifications. Auhors will remove any error of their article and they will improve their article(s) according to the previous reports displayed with published article(s). The main purpose of it is ‘to improve the quality of a candidate manuscript’. Our reviewers check the ‘strength and weakness of a manuscript honestly’. There will increase in the perfection, and transparency.  Received file:                Reviewer's Comments: Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.5/10 Reviewers: Dr. Sangeetha Arullappan, Universiti Tunku Abdul Rahman, Malaysia, [email protected] Prof. Dr. Ali Gamal Ahmed Al-kaf, Sana'a university, Yemen, [email protected] Similar Articles: ASSOCIATION BETWEEN THE STREPTOCOCCUS MUTANS BIOFILM FORMATION AND DENTAL CARIES EXPERIENCE AND ANTIBIOTICS RESISTANCE IN ADULT FEMALES ANALYSIS OF BIOFILMS FOR STREPTOCOCCUS MUTANS FROM DENTAL ROOT SURFACES OF ADULT PATIENTS WITH ROOT CARIE