Search results for: ANP (analytic network process)

Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision making, management and planning of healthcare and related activities. However, user resistances, unique position of medical data content and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. Success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose a HA process model with features from rational unified process (RUP) model and agile methodology.

Keywords: Agile methodology, health analytics, unified process model, UML.

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: Social networks, community detection, modularity optimization, geographically dispersed communities.

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Authors: Frank Emmert-Streib, Matthias Dehmer

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Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.

Keywords: Dynamic Bayesian networks, microarray data, structure learning, Markov chain Monte Carlo.

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Authors: N. S. Usha, E. A. Mary Anita

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Recent innovations in the field of technology led to the use of   wireless sensor networks in various applications, which consists of a number of small, very tiny, low-cost, non-tamper proof and resource constrained sensor nodes. These nodes are often distributed and deployed in an unattended environment, so as to collaborate with each other to share data or information. Amidst various applications, wireless sensor network finds a major role in monitoring battle field in military applications. As these non-tamperproof nodes are deployed in an unattended location, they are vulnerable to many security attacks. Amongst many security attacks, the node replication attack seems to be more threatening to the network users. Node Replication attack is caused by an attacker, who catches one true node, duplicates the first certification and cryptographic materials, makes at least one or more copies of the caught node and spots them at certain key positions in the system to screen or disturb the network operations. Preventing the occurrence of such node replication attacks in network is a challenging task. In this survey article, we provide the classification of detection schemes and also explore the various schemes proposed in each category. Also, we compare the various detection schemes against certain evaluation parameters and also its limitations. Finally, we provide some suggestions for carrying out future research work against such attacks.

Keywords: Clone node, data security, detection schemes, node replication attack, wireless sensor networks.

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Authors: Hoai-Vu Nguyen, Yongsun Choi

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Distributed denial-of-service (DDoS) attacks pose a serious threat to network security. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve (1) efficient detection with a small number of false alarms and (2) real-time transfer of packets. Here, we introduce a method for proactive detection of DDoS attacks, by classifying the network status, to be utilized in the detection stage of the proposed anti-DDoS framework. Initially, we analyse the DDoS architecture and obtain details of its phases. Then, we investigate the procedures of DDoS attacks and select variables based on these features. Finally, we apply the k-nearest neighbour (k-NN) method to classify the network status into each phase of DDoS attack. The simulation result showed that each phase of the attack scenario is classified well and we could detect DDoS attack in the early stage.

Keywords: distributed denial-of-service (DDoS), k-nearestneighbor classifier (k-NN), anti-DDoS framework, DDoS detection.

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Authors: Marina Gorunescu, Florin Gorunescu, Kenneth Revett

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Hepatocellular carcinoma, also called hepatoma, most commonly appears in a patient with chronic viral hepatitis. In patients with a higher suspicion of HCC, such as small or subtle rising of serum enzymes levels, the best method of diagnosis involves a CT scan of the abdomen, but only at high cost. The aim of this study was to increase the ability of the physician to early detect HCC, using a probabilistic neural network-based approach, in order to save time and hospital resources.

Keywords: Early HCC diagnosis, probabilistic neural network.

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Authors: Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama

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Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance.

Keywords: Ti-5Al-2.5Sn, material removal rate, copper tungsten, positive polarity, artificial neural network, multi-layer perceptron.

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Authors: Babak Saboori, Behzad Saboori, Johan S. Carlson, Rikard Söderberg

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As product life cycle becomes less and less every day, having flexible manufacturing processes for any companies seems more demanding. In the assembling of closures, i.e. opening parts in car body, hemming process is the one which needs more attention. This paper focused on the robot roller hemming process and how to reduce its cycle time by introducing a fast roller hemming process. A robot roller hemming process of a tailgate of Saab 93 SportCombi model is investigated as a case study in this paper. By applying task separation, robot coordination, and robot cell configuration principles in the roller hemming process, three alternatives are proposed, developed, and remarkable reduction in cycle times achieved [1].

Keywords: Cell configuration, cycle time, robot coordination, roller hemming.

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Authors: Hassan Berbia, Faissal Elbouanani, Rahal Romadi, Mostafa Belkasmi

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This paper introduces two decoders for binary linear codes based on Metaheuristics. The first one uses a genetic algorithm and the second is based on a combination genetic algorithm with a feed forward neural network. The decoder based on the genetic algorithms (DAG) applied to BCH and convolutional codes give good performances compared to Chase-2 and Viterbi algorithm respectively and reach the performances of the OSD-3 for some Residue Quadratic (RQ) codes. This algorithm is less complex for linear block codes of large block length; furthermore their performances can be improved by tuning the decoder-s parameters, in particular the number of individuals by population and the number of generations. In the second algorithm, the search space, in contrast to DAG which was limited to the code word space, now covers the whole binary vector space. It tries to elude a great number of coding operations by using a neural network. This reduces greatly the complexity of the decoder while maintaining comparable performances.

Keywords: Block code, decoding, methaheuristic, genetic algorithm, neural network

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Authors: Bhim Kumar Dahal

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Transportation network development in the developing country is in rapid pace. The majority of the network belongs to railway and expressway which passes through diverse topography, landform and geological conditions despite the avoidance principle during route selection. Construction of such networks demand many low to high embankment which required improvement in the foundation soil. This paper is mainly focused on the various advanced ground improvement techniques used to improve the soft soil, modelling approach and its predictability for embankments construction. The ground improvement techniques can be broadly classified in to three groups i.e. densification group, drainage and consolidation group and reinforcement group which are discussed with some case studies.  Various methods were used in modelling of the embankments from simple 1-dimensional to complex 3-dimensional model using variety of constitutive models. However, the reliability of the predictions is not found systematically improved with the level of sophistication.  And sometimes the predictions are deviated more than 60% to the monitored value besides using same level of erudition. This deviation is found mainly due to the selection of constitutive model, assumptions made during different stages, deviation in the selection of model parameters and simplification during physical modelling of the ground condition. This deviation can be reduced by using optimization process, optimization tools and sensitivity analysis of the model parameters which will guide to select the appropriate model parameters.

Keywords: Embankment, ground improvement, modelling, model prediction.

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Authors: Abiodun M. Aibinu, Momoh J. E. Salami, Amir A. Shafie, Athaur Rahman Najeeb

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In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination.

Keywords: Adaptive Learning rate, Adaptive momentum, Autoregressive, Modeling, Neural Network.

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Authors: P. Painmanakul, T. Chintateerachai, S. Lertlapwasin, N. Rojvilavan, T. Chalermsinsuwan, N. Chawaloesphonsiya, O. Larpparisudthi

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Conventional coagulation, advance oxidation process (AOPs), and the combined process were evaluated and compared for its suitability to treat the stabilized cutting-oil wastewater. The 90% efficiency was obtained from the coagulation at Al₂(SO₄)₃ dosage of 150 mg/L and pH 7. On the other hands, efficiencies of AOPs for 30 minutes oxidation time were 10% for acoustic oxidation, 12% for acoustic oxidation with hydrogen peroxide, 76% for Fenton, and 92% sono-Fenton processes. The highest efficiency for effective oil removal of AOPs required large amount of chemical. Therefore, AOPs were studied as a post-treatment after conventional separation process. The efficiency was considerable as the effluent COD can pass the standard required for industrial wastewater discharge with less chemical and energy consumption.

 

Keywords: Cutting oily-wastewater, Advance oxidation process, Sono-Fenton, Combined process.

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Authors: Yael Dubinsky, Orit Hazzan

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There is a constant need for hi-tech innovation to be decentralized to peripheral regions. This work describes how we applied Design Science Research (DSR) principles to define what we refer to as the Sea of Galilee (SoG) method. The goal of the SoG method is to harness existing and new technological initiatives in peripheral regions to create a socio-technological network that can initiate and maintain hi-tech activities. The SoG method consists of a set of principles, a stakeholder network, and actual hi-tech business initiatives, including their infrastructure and practices. The three cycles of DSR, the Relevance, Design, and Rigor cycles, lay out a research framework to sharpen the requirements, collect data from case studies, and iteratively refine the SoG method based on the existing knowledge base. We propose that the SoG method can be deployed by regional authorities that wish to be considered as smart regions (an extension of the notion of smart cities).

Keywords: Design Science Research, socio-technological initiatives, Sea of Galilee method, periphery stakeholder network, hi-tech initiatives.

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Authors: Samina Elyas Mubeen, R. K. Nema, Gayatri Agnihotri

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In this paper the performance of unified power flow controller is investigated in controlling the flow of po wer over the transmission line. Voltage sources model is utilized to study the behaviour of the UPFC in regulating the active, reactive power and voltage profile. This model is incorporated in Newton Raphson algorithm for load flow studies. Simultaneous method is employed in which equations of UPFC and the power balance equations of network are combined in to one set of non-linear algebraic equations. It is solved according to the Newton raphson algorithm. Case studies are carried on standard 5 bus network. Simulation is done in Matlab. The result of network with and without using UPFC are compared in terms of active and reactive power flows in the line and active and reactive power flows at the bus to analyze the performance of UPFC.

Keywords: Newton-Raphson algorithm, Load flow, Unified power flow controller, Voltage source model.

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Authors: Justinian Anatory, N. Theethayi, R. Thottappillil, C. Mwase, N.H. Mvungi

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Power-line networks are widely used today for broadband data transmission. However, due to multipaths within the broadband power line communication (BPLC) systems owing to stochastic changes in the network load impedances, branches, etc., network or channel capacity performances are affected. This paper attempts to investigate the performance of typical medium voltage channels that uses Orthogonal Frequency Division Multiplexing (OFDM) techniques with Quadrature Amplitude Modulation (QAM) sub carriers. It has been observed that when the load impedances are different from line characteristic impedance channel performance decreases. Also as the number of branches in the link between the transmitter and receiver increases a loss of 4dB/branch is found in the signal to noise ratio (SNR). The information presented in the paper could be useful for an appropriate design of the BPLC systems.

Keywords: Communication channel model, Power-line communication, Transfer function, Multipath, Branched network, OFDM, QAM, performance evaluation

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Authors: Asar Khan, Peter D. Widdop, Andrew J. Day, Aliaster S. Wood, Steve, R. Mounce, John Machell

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This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by sensors to construct an empirical model for time series prediction and classification of events. These two components have been installed, tested and verified in an experimental site in a UK water distribution system. Verification of the system has been achieved from a series of simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network management.

Keywords: Detection, leakage, neural networks, sensors, water distribution networks

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Authors: K. Rajasekaran, Kannan Balasubramanian

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A sensory network consists of multiple detection locations called sensor nodes, each of which is tiny, featherweight and portable. A single path routing protocols in wireless sensor network can lead to holes in the network, since only the nodes present in the single path is used for the data transmission. Apart from the advantages like reduced computation, complexity and resource utilization, there are some drawbacks like throughput, increased traffic load and delay in data delivery. Therefore, multipath routing protocols are preferred for WSN. Distributing the traffic among multiple paths increases the network lifetime. We propose a scheme, for the data to be transmitted through a dominant path to save energy. In order to obtain a high delivery ratio, a basic route reconstruction protocol is utilized to reconstruct the path whenever a failure is detected. A basic reconstruction routing (BRR) algorithm is proposed, in which a node can leap over path failure by using the already existing routing information from its neighbourhood while the composed data is transmitted from the source to the sink. In order to save the energy and attain high data delivery ratio, data is transmitted along a multiple path, which is achieved by BRR algorithm whenever a failure is detected. Further, the analysis of how the proposed protocol overcomes the drawback of the existing protocols is presented. The performance of our protocol is compared to AOMDV and energy efficient node-disjoint multipath routing protocol (EENDMRP). The system is implemented using NS-2.34. The simulation results show that the proposed protocol has high delivery ratio with low energy consumption.

Keywords: Multipath routing, WSN, energy efficient routing, alternate route, assured data delivery.

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Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari

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This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].

Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.

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Authors: B. Golchin, N. Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Zukisa Nante, Wang Zenghui

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Face recognition is the problem of identifying or recognizing individuals in an image. This paper investigates a possible method to bring a solution to this problem. The method proposes an amalgamation of Principal Component Analysis (PCA), K-Means clustering, and Convolutional Neural Network (CNN) for a face recognition system. It is trained and evaluated using the ORL dataset. This dataset consists of 400 different faces with 40 classes of 10 face images per class. Firstly, PCA enabled the usage of a smaller network. This reduces the training time of the CNN. Thus, we get rid of the redundancy and preserve the variance with a smaller number of coefficients. Secondly, the K-Means clustering model is trained using the compressed PCA obtained data which select the K-Means clustering centers with better characteristics. Lastly, the K-Means characteristics or features are an initial value of the CNN and act as input data. The accuracy and the performance of the proposed method were tested in comparison to other Face Recognition (FR) techniques namely PCA, Support Vector Machine (SVM), as well as K-Nearest Neighbour (kNN). During experimentation, the accuracy and the performance of our suggested method after 90 epochs achieved the highest performance: 99% accuracy F1-Score, 99% precision, and 99% recall in 463.934 seconds. It outperformed the PCA that obtained 97% and KNN with 84% during the conducted experiments. Therefore, this method proved to be efficient in identifying faces in the images.

Keywords: Face recognition, Principal Component Analysis, PCA, Convolutional Neural Network, CNN, Rectified Linear Unit, ReLU, feature extraction.

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Authors: Wafa' S.Al-Sharafat, Reyadh Naoum

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Society has grown to rely on Internet services, and the number of Internet users increases every day. As more and more users become connected to the network, the window of opportunity for malicious users to do their damage becomes very great and lucrative. The objective of this paper is to incorporate different techniques into classier system to detect and classify intrusion from normal network packet. Among several techniques, Steady State Genetic-based Machine Leaning Algorithm (SSGBML) will be used to detect intrusions. Where Steady State Genetic Algorithm (SSGA), Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and Zeroth Level Classifier system are investigated in this research. SSGA is used as a discovery mechanism instead of SGA. SGA replaces all old rules with new produced rule preventing old good rules from participating in the next rule generation. Zeroth Level Classifier System is used to play the role of detector by matching incoming environment message with classifiers to determine whether the current message is normal or intrusion and receiving feedback from environment. Finally, in order to attain the best results, Modified SSGA will enhance our discovery engine by using Fuzzy Logic to optimize crossover and mutation probability. The experiments and evaluations of the proposed method were performed with the KDD 99 intrusion detection dataset.

Keywords: MSSGBML, Network Intrusion Detection, SGA, SSGA.

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Authors: Jamal Fathi Abu Hasna

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There are two common methodologies to verify signatures: the functional approach and the parametric approach. This paper presents a new approach for dynamic handwritten signature verification (HSV) using the Neural Network with verification by the Conjugate Gradient Neural Network (NN). It is yet another avenue in the approach to HSV that is found to produce excellent results when compared with other methods of dynamic. Experimental results show the system is insensitive to the order of base-classifiers and gets a high verification ratio.

Keywords: Signature Verification, MATLAB Software, Conjugate Gradient, Segmentation, Skilled Forgery, and Genuine.

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: Artificial Neural Network, Decision Support System, drug abuse, drug addiction, Multilayer Perceptron.

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Authors: J. Moshtagh, L. Eslami

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Various studies have showed that about 90% of single line to ground faults occurred on High voltage transmission lines have transient nature. This type of faults is cleared by temporary outage (by the single phase auto-reclosure). The interval between opening and reclosing of the faulted phase circuit breakers is named “Dead Time” that is varying about several hundred milliseconds. For adjustment of traditional single phase auto-reclosures that usually are not intelligent, it is necessary to calculate the dead time in the off-line condition precisely. If the dead time used in adjustment of single phase auto-reclosure is less than the real dead time, the reclosing of circuit breakers threats the power systems seriously. So in this paper a novel approach for precise calculation of dead time in power transmission lines based on the network equivalencing in time domain is presented. This approach has extremely higher precision in comparison with the traditional method based on Thevenin equivalent circuit. For comparison between the proposed approach in this paper and the traditional method, a comprehensive simulation by EMTP-ATP is performed on an extensive power network.

Keywords: Dead Time, Network Equivalencing, High Voltage Transmission Lines, Single Phase Auto-Reclosure.

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Authors: Pakorn Traiprasertpong, Apichit Svang-Ariyaskul

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The production of ethyl tert-butyl ether (ETBE) was simulated through Aspen Plus. The objective of this work was to use the simulation results to be an alternative platform for ETBE production from naphtha cracking wastes for the industry to develop. ETBE is produced from isobutylene which is one of the wastes in naphtha cracking process. The content of isobutylene in the waste is less than 30% weight. The main part of this work was to propose a process to save the environment and to increase the product value by converting a great majority of the wastes into ETBE. Various processes were considered to determine the optimal production of ETBE. The proposed process increased ETBE production yield by 100% from conventional process with the purity of 96% weight. The results showed a great promise for developing this proposed process in an industrial scale.

Keywords: ETBE, process simulation, naphtha cracking, Aspen Plus

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Authors: Ali Payan

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The aim of this paper is to evaluate the performance of the faculties of Islamic Azad University of Zahedan Branch based on two-component (teaching and research) decision making units (DMUs) in data envelopment analysis (DEA). Nowadays it is obvious that most of the systems as DMUs do not act as a simple inputoutput structure. Instead, if they have been studied more delicately, they include network structure. University is such a network in which different sections i.e. teaching, research, students and office work as a parallel structure. They consume some inputs of university commonly and some others individually. Then, they produce both dependent and independent outputs. These DMUs are called two-component DMUs with network structure. In this paper, performance of the faculties of Zahedan branch is calculated by using relative efficiency model and also, a formula to compute relative efficiencies teaching and research components based on DEA are offered.

Keywords: Data envelopment analysis, faculties of Islamic Azad University of Zahedan branch, two-component DMUs.

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Authors: Kyoung-Mi Lee, Youn-Mi Lee

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This paper proposes a way to track persons by making use of multiple non-overlapping cameras. Tracking persons on multiple non-overlapping cameras enables data communication among cameras through the network connection between a camera and a computer, while at the same time transferring human feature data captured by a camera to another camera that is connected via the network. To track persons with a camera and send the tracking data to another camera, the proposed system uses a hierarchical human model that comprises a head, a torso, and legs. The feature data of the person being modeled are transferred to the server, after which the server sends the feature data of the human model to the cameras connected over the network. This enables a camera that captures a person's movement entering its vision to keep tracking the recognized person with the use of the feature data transferred from the server.

Keywords: Person tracking, human model, networked cameras, vision-based surveillance.

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Authors: A. Statkus, S. Paulikas

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With increasing number of wireless devices like laptops, Wi-Fi Web Cams, network extenders, etc., a new kind of problems appeared, mostly related to poor Wi-Fi throughput or communication problems. In this paper an investigation on wireless networks and it-s saturation in Vilnius City and its surrounding is presented, covering the main problems of wireless saturation and network load during day. Also an investigation on wireless channel selection and noise levels were made, showing the impact of neighbor AP to signal and noise levels and how it changes during the day.

Keywords: IEEE 802.11b/g/n, wireless saturation, client activity, channel selection.

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Authors: Е. V. Kutsina, S. A. Tarasova

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In this article we present a methodology which enables preschool and primary school unlanguaged children to remember words, phrases and texts with the help of graphic signs - letters, syllables and words. Reading for a child becomes a support for speech development. Teaching is based on the principle "from simple to complex", "a letter - a syllable - a word - a proposal - a text." Availability of multi-level texts allows using this methodology for working with children who have different levels of speech development.

Keywords: Alalia, analytic-synthetic method, development of coherent speech, formation of vocabulary, learning to read, , sentence formation, three-level stories, unlanguaged children.

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Authors: Alexandre Boum, Salomon Madinatou

Abstract:

This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360x360) serving as an input data sample for the classifiers based on neural networks and a matrix (1x6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.

Keywords: Adaptive neuro-fuzzy, artificial neural network, cardiac arrythmias, fuzzy inference systems.

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