Search results for: network behavior

Authors: Feng-Chuan Pan, Fang-Yue Liu

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Hospitals in southern Hualien teamed with the Hypertension Joint Care Network. Working with the network, the team provided a special designed health education to the individual who had been identified as a hypertension patient in the outpatient department. Some metabolism improvements achieved. This is a retrospective study by purposively taking 106 patients from a hospital between 2008 and 2010. Records of before and after education intervention of the objects was collected and analyzed to see the how the intervention affected the patients- hypertension control via clinical parameter monitoring. The results showed that the clinical indicators, the LDL-C, the cholesterol and the systolic blood pressure were significantly improved. The study provides evidence for the effectiveness of the network in controlling hypertension.

Keywords: hypertension, joint care management network, cardiovascular diseases, metabolic syndrome.

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Authors: H. Soleimanimehr, M. J. Nategh , S. Amini

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Development of artificial neural network (ANN) for prediction of aluminum workpieces' surface roughness in ultrasonicvibration assisted turning (UAT) has been the subject of the present study. Tool wear as the main cause of surface roughness was also investigated. ANN was trained through experimental data obtained on the basis of full factorial design of experiments. Various influential machining parameters were taken into consideration. It was illustrated that a multilayer perceptron neural network could efficiently model the surface roughness as the response of the network, with an error less than ten percent. The performance of the trained network was verified by further experiments. The results of UAT were compared with the results of conventional turning experiments carried out with similar machining parameters except for the vibration amplitude whence considerable reduction was observed in the built-up edge and the surface roughness.

Keywords: Aluminum, Artificial Neural Network (ANN), BuiltupEdge, Surface Roughness, Tool Wear, Ultrasonic VibrationAssisted Turning (UAT).

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Authors: Satish Kumar Dewangan, Santosh Kumar Senapati

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The present work focuses on the investigation of the effect of non-Newtonian behavior on the oil-water stratified flow in a horizontal channel using ANSYS Fluent. Coupled level set and volume of fluid (CLSVOF) has been used to capture the evolving interface assuming unsteady, coaxial flow with constant fluid properties. The diametric variation of oil volume fraction, mixture velocity, total pressure and pressure gradient has been studied. Non-Newtonian behavior of oil has been represented by the power law model in order to investigate the effect of flow behavior index. Stratified flow pattern tends to assume dispersed flow pattern with the change in the behavior of oil to non-Newtonian. The pressure gradient is found to be very much sensitive to the flow behavior index. The findings could be useful in designing the transportation pipe line in petroleum industries.

Keywords: Oil-water stratified flow, horizontal channel, CLSVOF, non–Newtonian behavior.

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Authors: Péter Restás, Andrea Czibor, Zsolt Péter Szabó

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Purpose: This article aims to rethink the phenomena of employee behavior as a product of a system. Both organizational culture and Complex Adaptive Systems (CAS) theory emphasize that individual behavior depends on the specific system and the unique organizational culture. These two major theories are both represented in the field of organizational studies; however, they are rarely used together for the comprehensive understanding of workplace behavior. Methodology: By reviewing the literature we use key concepts stemming from organizational culture and CAS theory in order to show the similarities between these theories and create an enriched understanding of employee behavior. Findings: a) Workplace behavior is defined here as social cognition issue. b) Organizations are discussed here as complex systems, and cultures which drive and dictate the cognitive processes of agents in the system. c) Culture gives CAS theory a context which lets us see organizations not just as ever-changing and unpredictable, but as such systems that aim to create and maintain stability by recurring behavior. Conclusion: Applying the knowledge from culture and CAS theory sheds light on our present understanding of employee behavior, also emphasizes the importance of novel ways in organizational research and management.

Keywords: Complex adaptive systems theory, employee behavior, organizational culture, stability.

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Authors: Wang Xing-hua, Zhong Shun-an, Zhang Zhuo

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A kind of behavior model for discrete sampling and hold amplifier with charge transmission is analyzed. The transfer function and behavior features are based on the main AC responses of operation amplifier. The result used in pipelined and sigma-delta ADC shows the exact of model of sampling and hold amplifier, and the non-ideal factors are taken into account.

Keywords: SHA, response, behavior, transfer function.

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Authors: Kuen-Long Leu, Yung-Yuan Chen, Chin-Long Wey, Jwu-E Chen, Chung-Hsien Hsu

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The increasing importance of FlexRay systems in automotive domain inspires unceasingly relative researches. One primary issue among researches is to verify the reliability of FlexRay systems either from protocol aspect or from system design aspect. However, research rarely discusses the effect of network topology on the system reliability. In this paper, we will illustrate how to model the reliability of FlexRay systems with various network topologies by a well-known probabilistic reasoning technology, Bayesian Network. In this illustration, we especially investigate the effectiveness of error containment built in star topology and fault-tolerant midpoint synchronization algorithm adopted in FlexRay communication protocol. Through a FlexRay steer-by-wire case study, the influence of different topologies on the failure probability of the FlexRay steerby- wire system is demonstrated. The notable value of this research is to show that the Bayesian Network inference is a powerful and feasible method for the reliability assessment of FlexRay systems.

Keywords: Bayesian Network, FlexRay, fault tolerance, network topology, reliability.

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Authors: Kai Qian, Ming Yang, Minzhe Guo, Prabir Bhattacharya, Lixin Tao

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Computer network courses are essential parts of college computer science curriculum and hands-on networking experience is well recognized as an effective approach to help students understand better about the network concepts, the layered architecture of network protocols, and the dynamics of the networks. However, existing networking labs are usually server-based and relatively cumbersome, which require a certain level of specialty and resource to set up and maintain the lab environment. Many universities/colleges lack the resources and build-ups in this field and have difficulty to provide students with hands-on practice labs. A new affordable and easily-adoptable approach to networking labs is desirable to enhance network teaching and learning. In addition, current network labs are short on providing hands-on practice for modern wireless and mobile network learning. With the prevalence of smart mobile devices, wireless and mobile network are permeating into various aspects of our information society. The emerging and modern mobile technology provides computer science students with more authentic learning experience opportunities especially in network learning. A mobile device based hands-on labware can provide an excellent ‘real world’ authentic learning environment for computer network especially for wireless network study. In this paper, we present our mobile device-based hands-on labware (series of lab module) for computer network learning which is guided by authentic learning principles to immerse students in a real world relevant learning environment. We have been using this labware in teaching computer network, mobile security, and wireless network classes. The student feedback shows that students can learn more when they have hands-on authentic learning experience. 

Keywords: Mobile computing, android, network, labware.

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Authors: Kang Won Lee, Jung Won Lee

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Characterizing the network flow is of fundamental importance to understand the complex dynamics of networks. And passenger flow characteristics of the subway network are very relevant for an effective transportation management in urban cities. In this study, passenger flow of Seoul metropolitan subway network is investigated and characterized through statistical analysis. Traditional betweenness centrality measure considers only topological structure of the network and ignores the transportation factors. This paper proposes a weighted betweenness centrality measure that incorporates monthly passenger flow volume. We apply the proposed measure on the Seoul metropolitan subway network involving 493 stations and 16 lines. Several interesting insights about the network are derived from the new measures. Using Kolmogorov-Smirnov test, we also find out that monthly passenger flow between any two stations follows a power-law distribution and other traffic characteristics such as congestion level and throughflow traffic follow exponential distribution.

Keywords: Betweenness centrality, correlation coefficient, power-law distribution, Korea traffic data base.

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Authors: Saman M. Abdulla, Najla B. Al-Dabagh, Omar Zakaria

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The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.

Keywords: Artificial Neural Network, Attack Features, MisuseIntrusion Detection System, Training Parameters.

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Authors: Xinyan Zhang, Zhipeng Tan, Shan Fan

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The first layer of defense against data loss is the backup data. This paper implements an agent-based network backup system used the backup, server-storage and server-backup agent these tripartite construction, and the snapshot and hierarchical index are used in the NSBS. It realizes the control command and data flow separation, balances the system load, thereby improving efficiency of the system backup and recovery. The test results show the agent-based network backup system can effectively improve the task-based concurrency, reasonably allocate network bandwidth, the system backup performance loss costs smaller and improves data recovery efficiency by 20%.

Keywords: Agent, network backup system, three architecture model, NSBS.

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Authors: S. Swapna Kumar

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A Vehicular Ad-Hoc Network (VANET) is a mobile Ad-Hoc Network that provides connectivity moving device to fixed equipments. Such type of device is equipped with vehicle provides safety for the passengers. In the recent research areas of traffic management there observed the wide scope of design of new methodology of extension of wireless sensor networks and ad-hoc network principal for development of VANET technology. This paper provides the wide research view of the VANET and MANET concept for the researchers to contribute the better optimization technique for the development of effective and fast atomization technique for the large size of data exchange in this complex networks.

Keywords: Ad-Hoc, MANET, Sensors, Security, VANET

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Authors: Khin Thanda Soe

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A model to identify the lifetime of target tracking wireless sensor network is proposed. The model is a static clusterbased architecture and aims to provide two factors. First, it is to increase the lifetime of target tracking wireless sensor network. Secondly, it is to enable good localization result with low energy consumption for each sensor in the network. The model consists of heterogeneous sensors and each sensing member node in a cluster uses two operation modes–active mode and sleep mode. The performance results illustrate that the proposed architecture consumes less energy and increases lifetime than centralized and dynamic clustering architectures, for target tracking sensor network.

Keywords: Network lifetime, Target Localization, TargetTracking, Wireless Sensor Networks.

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Authors: Kunyanuth Kularbphettong, Phanu Waraporn, Cholticha Tongsiri

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This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.

Keywords: Motivation behavior, e-learning, moodle log, association rule mining.

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Authors: Hsiu-Hua Cheng, Chi-Wei Chen

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Lurking behavior is common in information-seeking oriented communities. Transferring users with lurking behavior to be contributors can assist virtual communities to obtain competitive advantages. Based on the ecological cognition framework, this study proposes a model to examine the antecedents of lurking behavior in information-seeking oriented virtual communities. This study argues desire for emotional support, desire for information support, desire for performance-approach, desire for performance -avoidance, desire for mastery-approach, desire for mastery-avoidance, desire for ability trust, desire for benevolence trust, and desire for integrity trust effect on lurking behavior. This study offers an approach to understanding the determinants of lurking behavior in online contexts.

Keywords: Lurking behavior, the ecological cognition framework, Information-seeking oriented virtual communities, Desire.

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Authors: Swapnil Singh, Sanjoy Das

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Mobile Ad-Hoc Network (MANET) is a network without infrastructure dynamically formed by autonomous system of mobile nodes that are connected via wireless links. Mobile nodes communicate with each other on the fly. In this network each node also acts as a router. The battery power and the bandwidth are very scarce resources in this network. The network lifetime and connectivity of nodes depend on battery power. Therefore, energy is a valuable constraint which should be efficiently used. In this paper we survey various energy efficient routing protocols. The energy efficient routing protocols are classified on the basis of approaches they use to minimize the energy consumption. The purpose of this paper is to facilitate the research work and combine the existing solution and to develop a more energy efficient routing mechanism.

Keywords: Delaunay Triangulation, deployment, energy efficiency, MANET.

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Authors: Anita S. Gangal, P. K. Kalra, D. S. Chauhan

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The backpropagation algorithm in general employs quadratic error function. In fact, most of the problems that involve minimization employ the Quadratic error function. With alternative error functions the performance of the optimization scheme can be improved. The new error functions help in suppressing the ill-effects of the outliers and have shown good performance to noise. In this paper we have tried to evaluate and compare the relative performance of complex valued neural network using different error functions. During first simulation for complex XOR gate it is observed that some error functions like Absolute error, Cauchy error function can replace Quadratic error function. In the second simulation it is observed that for some error functions the performance of the complex valued neural network depends on the architecture of the network whereas with few other error functions convergence speed of the network is independent of architecture of the neural network.

Keywords: Complex backpropagation algorithm, complex errorfunctions, complex valued neural network, split activation function.

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Authors: M. Govindarajan, R. M. Chandrasekaran

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.

Keywords: Back propagation, classification accuracy, textmining, time complexity.

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Authors: E. Ahvar, M. Fathy

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Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. We compare the performance of three routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR) , Ad Hoc On-Demand Distance Vector Routing (AODV), location-aided routing(LAR1).The performance differentials are analyzed using varying network load, mobility, and network size. We simulate protocols with GLOMOSIM simulator. Based on the observations, we make recommendations about when the performance of either protocol can be best.

Keywords: Ad hoc Network , Glomosim , routing protocols.

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Authors: I. Oloyede

Abstract:

The study investigates the accuracy and loss of deep learning algorithms with the set of coronavirus (COVID-19) images dataset by comparing Bayesian convolutional neural network and traditional convolutional neural network in low dimensional dataset. 50 sets of X-ray images out of which 25 were COVID-19 and the remaining 20 were normal, twenty images were set as training while five were set as validation that were used to ascertained the accuracy of the model. The study found out that Bayesian convolution neural network outperformed conventional neural network at low dimensional dataset that could have exhibited under fitting. The study therefore recommended Bayesian Convolutional neural network (BCNN) for android apps in computer vision for image detection.

Keywords: BCNN, CNN, Images, COVID-19, Deep Learning.

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Authors: M. R. Mustafa, M. H. Isa, R. B. Rezaur

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The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water resources engineering are being increasing rapidly. Infrastructural applications of ANN in terms of selection of inputs, architecture of networks, training algorithms, and selection of training parameters in different types of neural networks used in water resources engineering have been reported. ANN modeling conducted for water resources engineering variables (river sediment and discharge) published in high impact journals since 2002 to 2011 have been examined and presented in this review. ANN is a vigorous technique to develop immense relationship between the input and output variables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of examples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data before network modeling, which can facilitate to design an efficient network. An appropriate training based ANN model is able to adopt the physical understanding between the variables and may generate more effective results than conventional prediction techniques.

Keywords: ANN, discharge, modeling, prediction, sediment,

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Authors: Lei Guo, Xu Zhang, Weigang Hou

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Since the traditional network is closed and it has no architecture to create applications, it has been unable to evolve with changing demands under the rapid innovation in services. Additionally, due to the lack of the whole network profile, the quality of service cannot be well guaranteed in the traditional network. The Software Defined Network (SDN) utilizes global resources to support on-demand applications/services via open, standardized and programmable interfaces. In this paper, we implement the traffic grooming application under a real SDN environment, and the corresponding analysis is made. In our SDN: 1) we use OpenFlow protocol to control the entire network by using software applications running on the network operating system; 2) several virtual switches are combined into the data forwarding plane through Open vSwitch; 3) An OpenFlow controller, NOX, is involved as a logically centralized control plane that dynamically configures the data forwarding plane; 4) The traffic grooming based on SDN is demonstrated through dynamically modifying the idle time of flow entries. The experimental results demonstrate that the SDN-based traffic grooming effectively reduces the end-to-end delay, and the improvement ratio arrives to 99%.

Keywords: NOX, OpenFlow, software defined network, traffic grooming.

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Authors: Iosif-Viorel Onut, Bin Zhu, Ali A. Ghorbani

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We propose a novel graphical technique (SVision) for intrusion detection, which pictures the network as a community of hosts independently roaming in a 3D space defined by the set of services that they use. The aim of SVision is to graphically cluster the hosts into normal and abnormal ones, highlighting only the ones that are considered as a threat to the network. Our experimental results using DARPA 1999 and 2000 intrusion detection and evaluation datasets show the proposed technique as a good candidate for the detection of various threats of the network such as vertical and horizontal scanning, Denial of Service (DoS), and Distributed DoS (DDoS) attacks.

Keywords: Anomaly Visualization, Network Security, Intrusion Detection.

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Authors: Jong-Bae Lee, Seongsoo Lee

Abstract:

Local interconnect network (LIN) is a communication protocol that combines sensors, actuators, and processors to a functional module in automotive applications. In this paper, a LIN ver. 2.2A controller was designed in Verilog hardware description language (Verilog HDL) and implemented in field-programmable gate array (FPGA). Its operation was verified by making full-scale LIN network with the presented FPGA-implemented LIN controller, commercial LIN transceivers, and commercial processors. When described in Verilog HDL and synthesized in 0.18 μm technology, its gate size was about 2,300 gates.

Keywords: Local interconnect network, controller, transceiver, processor.

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Authors: Abderrahmane Amrouche, Jean Michel Rouvaen

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In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural network (GRNN). The relative performances of the proposed model are compared to the similar recognition systems based on the Multilayer Perceptron (MLP), the Recurrent Neural Network (RNN) and the well known Discrete Hidden Markov Model (HMM-VQ) that we have achieved also. Experimental results obtained with Arabic digits have shown that the use of nonparametric density estimation with an appropriate smoothing factor (spread) improves the generalization power of the neural network. The word error rate (WER) is reduced significantly over the baseline HMM method. GRNN computation is a successful alternative to the other neural network and DHMM.

Keywords: Speech Recognition, General Regression NeuralNetwork, Hidden Markov Model, Recurrent Neural Network, ArabicDigits.

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Authors: D.O. Omondi, M.K. Walingo, G.M. Mbagaya, L.O.A. Othuon

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This study applied the Theory of Planned Behavior model in predicting dietary behavior among Type 2 diabetics in a Kenyan environment. The study was conducted for three months within the diabetic clinic at Kisii Hospital in Nyanza Province in Kenya and adopted sequential mixed methods design combing both qualitative and quantitative phases. Qualitative data was analyzed using grounded theory analysis method. Structural equation modeling using maximum likelihood was used to analyze quantitative data. The results based on the common fit indices revealed that the theory of planned behavior fitted the data acceptably well among the Type 2 diabetes and within dietary behavior {χ2 = 223.3, df = 77, p = .02, χ2/df = 2.9, n=237; TLI = .93; CFI =.91; RMSEA (90CI) = .090(.039, .146)}. This implies that the Theory of Planned Behavior holds and forms a framework for promoting dietary practice among Type 2 diabetics.

Keywords: Dietary practice, Kenya, Theory of PlannedBehavior, Type 2 diabetes, Mixed Methods Design.

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Authors: Himanshu Dehra

Abstract:

This paper presents a paradigm for characterization and checking of human noise behavior. The definitions of ‘Noise’ and ‘Noise Behavior’ are devised. The concept of characterization and examining of Noise Behavior is obtained from the proposed paradigm of Psychoacoustics. The measurement of human noise behavior is discussed through definitions of noise sources and noise measurements. The noise sources, noise measurement equations and noise filters are further illustrated through examples. The theory and significance of solar energy acoustics is presented for life and its activities. Human comfort and health are correlated with human brain through physiological responses and noise protection. Examples of heat stress, intense heat, sweating and evaporation are also enumerated.

Keywords: Human brain, noise behavior, noise characterization, noise filters, physiological responses, psychoacoustics.

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Authors: Bing Yu , Xingshi He

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In this paper, Differential Evolution (DE) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, DE achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.

Keywords: differential evolution, neural network, Rbf function

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Authors: Ji-Hoon Hong, Dong-Hee Kim, Nam-Uk Kim, Tai-Myoung Chung

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Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.

Keywords: Android malware detection, software-defined network.

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Authors: Ali A. Hammadi

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In this work, a practical study on a commissioned Giga Passive Optical Network (GPON) fiber to the home access network in Kuwait is presented. The work covers the framework of the conceptual design of the deployed Passive Optical Networks (PONs), access network, optical fiber cable network distribution, technologies, and standards. The work also describes methodologies applied by system engineers for design of Optical Network Terminals (ONTs) and Optical Line Terminals (OLTs) transceivers with respect to the distance, operating wavelengths, splitting ratios. The results have demonstrated and justified the limitation of transmission distance of a PON link in Fiber to The Premises (FTTP) to not exceed 20 km. Optical Time Domain Reflector (OTDR) test has been carried for this project to confirm compliance with International Telecommunication Union (ITU) specifications regarding the total length of the deployed optical cable, total loss in dB, and loss per km in dB/km with respect to the operating wavelengths. OTDR test results with traces for segments of implemented fiber network will be provided and discussed.

Keywords: Passive optical networks, fiber to the premises, access network, OTDR.

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Authors: T. Hacib, M. R. Mekideche, N. Ferkha

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This paper presents an approach which is based on the use of supervised feed forward neural network, namely multilayer perceptron (MLP) neural network and finite element method (FEM) to solve the inverse problem of parameters identification. The approach is used to identify unknown parameters of ferromagnetic materials. The methodology used in this study consists in the simulation of a large number of parameters in a material under test, using the finite element method (FEM). Both variations in relative magnetic permeability and electrical conductivity of the material under test are considered. Then, the obtained results are used to generate a set of vectors for the training of MLP neural network. Finally, the obtained neural network is used to evaluate a group of new materials, simulated by the FEM, but not belonging to the original dataset. Noisy data, added to the probe measurements is used to enhance the robustness of the method. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Keywords: Inverse problem, MLP neural network, parametersidentification, FEM.

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