Search results for: type-2 fuzzy relational database.

Authors: M. A. Adeboyejo, O. O. Adeosun

Abstract:

Pioneer networked systems assume that connections are reliable, and a faulty operation will be considered in case of losing a connection. Transient connections are typical of mobile devices. Areas of application of data sharing system such as these, lead to the conclusion that network connections may not always be reliable, and that the conventional approaches can be improved. Nigerian commercial banking industry is a critical system whose operation is increasingly becoming dependent on information technology (IT) driven information system. The proposed solution to this problem makes use of a hierarchically clustered network structure which we selected to reflect (as much as possible) the typical organizational structure of the Nigerian commercial banks. Representative transactions such as data updates and replication of the results of such updates were used to simulate the proposed model to show its applicability.

Keywords: Dependability, reliability, data redundancy.

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Authors: Frederick Wedzerai Nyakudya

Abstract:

In this paper, we examine the relationship between human capital, personal wealth and social capital to explain the differences in start-up rates between female and male entrepreneurs. Since our dependent variable is dichotomous, we examine the determinants of these using a maximum likelihood logit estimator. We used the Global Entrepreneurship Monitor database covering the period 2006 to 2009 with 421 usable cases drawn from the Lower Layer Super Output Areas in East Midlands in the United Kingdom. We found evidence indicating that a female positively moderates the positive relationships between indicators of human capital and personal wealth with start-up activity. The findings have implications for programs, policies, and practices to encourage more females to engage in start-up activity.

Keywords: Entrepreneurship, Global Entrepreneurship Monitor, gender gap, women's entrepreneurship.

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Authors: V. Komarova

Abstract:

The main purpose of this research is the calculation of implicit prices of the environmental level of air quality in the city of Moscow on the basis of housing property prices. The database used contains records of approximately 20 thousand apartments and has been provided by a leading real estate agency operating in Russia. The explanatory variables include physical characteristics of the houses, environmental (industry emissions), neighbourhood sociodemographic and geographic data: GPS coordinates of each house. The hedonic regression results for ecological variables show «negative» prices while increasing the level of air contamination from such substances as carbon monoxide, nitrogen dioxide, sulphur dioxide, and particles (CO, NO2, SO2, TSP). The marginal willingness to pay for higher environmental quality is presented for linear and log-log models.

Keywords: Air pollution, environment, hedonic prices, real estate, willingness to pay.

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Authors: Daniel Siahaan, Aditya Prapanca

Abstract:

The emerging Semantic Web has been attracted many researchers and developers. New applications have been developed on top of Semantic Web and many supporting tools introduced to improve its software development process. Metadata modeling is one of development process where supporting tools exists. The existing tools are lack of readability and easiness for a domain knowledge expert to graphically models a problem in semantic model. In this paper, a metadata modeling tool called RDFGraph is proposed. This tool is meant to solve those problems. RDFGraph is also designed to work with modern database management systems that support RDF and to improve the performance of the query execution process. The testing result shows that the rules used in RDFGraph follows the W3C standard and the graphical model produced in this tool is properly translated and correct.

Keywords: CASE tool, data modeling, semantic web

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Authors: Jung-Ho Moon, Tae Kwon Ha

Abstract:

High temperature deformation behavior of cast Fe-20Cr-5Al alloy has been investigated in this study by performing tensile and compression tests at temperatures from 1100 to 1200oC. Rectangular ingots of which the dimensions were 300×300×100 in millimeter were cast using vacuum induction melting. Phase equilibrium was calculated using the FactSage®, thermodynamic software and database. Tensile strength of cast Fe-20Cr-5Al alloy was 4 MPa at 1200oC. With temperature decreased, tensile strength increased rapidly and reached up to 13 MPa at 1100oC. Elongation also increased from 18 to 80% with temperature decreased from 1200oC to 1100oC. Microstructure observation revealed that M23C6 carbide was precipitated along the grain boundary and within the matrix.

Keywords: Fe-20Cr-5Al alloy, high temperature deformation, aging treatment, microstructure, mechanical properties.

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Authors: C. Ardil

Abstract:

Aircraft have different capabilities and specifications according to the required strategic goals and objectives in operations. With various types on the market with different aircraft characteristics, it becomes difficult to select a suitable aircraft for certain operations and requirements. The entropy weighting method (EWM) is a useful, highly consistent, and reliable method for obtaining the weights of the criteria and is worth integrating with the decision uncertainty distance (DUD) method, which is more applicable and requires less computation than other methods. An illustrative example is presented to demonstrate the validity and usability of the proposed methodology. Comparing the ranking results matches the distance-based approach, which is the technique for order preference by similarity to ideal solution (TOPSIS) method, which shows the robustness of the entropy DUD hybrid method. Validity analysis shows that the proposed hybrid multiple criteria decision-making analysis (MCDMA) methodology is quantitatively stable and reliable.

Keywords: aircraft selection, decision uncertainty distance (DUD), multiple criteria decision making analysis, MCDMA, TOPSIS

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Authors: Mert Bal, Hayri Sever, Oya Kalıpsız

Abstract:

Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the combination of the few mentioned methods are used. In this study, symptom-disease relationships are presented by a framework which is modeled with a formal concept analysis and theory, as diseases, objects and attributes of symptoms. After a concept lattice is formed, Bayes theorem can be used to determine the relationships between attributes and objects. A discernibility relation that forms the base of the rough sets can be applied to attribute data sets in order to reduce attributes and decrease the complexity of computation.

Keywords: Formal Concept Analysis, Rough Set Theory, Granular Computing, Medical Decision Support System.

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Authors: Phruksaphanrat B.

Abstract:

This research proposes a Preemptive Possibilistic Linear Programming (PPLP) approach for solving multiobjective Aggregate Production Planning (APP) problem with interval demand and imprecise unit price and related operating costs. The proposed approach attempts to maximize profit and minimize changes of workforce. It transforms the total profit objective that has imprecise information to three crisp objective functions, which are maximizing the most possible value of profit, minimizing the risk of obtaining the lower profit and maximizing the opportunity of obtaining the higher profit. The change of workforce level objective is also converted. Then, the problem is solved according to objective priorities. It is easier than simultaneously solve the multiobjective problem as performed in existing approach. Possible range of interval demand is also used to increase flexibility of obtaining the better production plan. A practical application of an electronic company is illustrated to show the effectiveness of the proposed model.

Keywords: Aggregate production planning, Fuzzy sets theory, Possibilistic linear programming, Preemptive priority

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Authors: S. Liepina, A. Jemeljanovs

Abstract:

Tasks of the work were study the possible E.coli contamination in red deer meat, identify pathogenic strains from isolated E.coli, determine their incidence in red deer meat and determine the presence of VT1, VT2 and eaeA genes for the pathogenic E.coli. 8 (10%) samples were randomly selected from 80 analysed isolates of E.coli and PCR reaction was performed on them. PCR was done both on initial materials – samples of red deer meat - and for already isolated liqueurs. Two of analysed venison samples contain verotoxin-producing strains of E. coli. It means that this meat is not safe to consumer. It was proven by the sequestration reaction of E. coli and by comparison of the obtained results with the database of microorganism genome available on the internet that the isolated culture corresponds to region 16S rDNS of E. coli thus presenting correctness of the microbiological methods.

Keywords: Deer meat, pathogenic Escherichia coli

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

Abstract:

The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: Pattern recognition, partitional clustering, K-means clustering, Manhattan distance, terrorism data analysis.

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Authors: Dewi Nasien, Siti S. Yuhaniz, Habibollah Haron

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In a handwriting recognition problem, characters can be represented using chain codes. The main problem in representing characters using chain code is optimizing the length of the chain code. This paper proposes to use randomized algorithm to minimize the length of Freeman Chain Codes (FCC) generated from isolated handwritten characters. Feedforward neural network is used in the classification stage to recognize the image characters. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of isolated handwritten when tested on NIST database.

Keywords: Handwriting Recognition, Freeman Chain Code andFeedforward Backpropagation Neural Networks.

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Authors: Khalid Elasnaoui, Brahim Aksasse, Mohammed Ouanan

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In this paper, we are interested in the problem of finding similar images in a large database. For this purpose we propose a new algorithm based on a combination of the 2-D histogram intersection in the HSV space and statistical moments. The proposed histogram is based on a 3x3 window and not only on the intensity of the pixel. This approach overcome the drawback of the conventional 1-D histogram which is ignoring the spatial distribution of pixels in the image, while the statistical moments are used to escape the effects of the discretisation of the color space which is intrinsic to the use of histograms. We compare the performance of our new algorithm to various methods of the state of the art and we show that it has several advantages. It is fast, consumes little memory and requires no learning. To validate our results, we apply this algorithm to search for similar images in different image databases.

Keywords: 2-D histogram, Statistical moments, Indexing, Similarity distance, Histograms intersection.

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Authors: Basheer M. Al-Maqaleh, Kamal K. Bharadwaj

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Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Genetic Programming, Hierarchy, Knowledge Discovery in Database, Subsumption Matrix.

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Authors: Onder Yakut, Serdar Solak, Emine Dogru Bolat

Abstract:

Measuring the Electrocardiogram (ECG) signal is an essential process for the diagnosis of the heart diseases. The ECG signal has the information of the degree of how much the heart performs its functions. In medical diagnosis and treatment systems, Decision Support Systems processing the ECG signal are being developed for the use of clinicians while medical examination. In this study, a modular wireless ECG (WECG) measuring and recording system using a single board computer and e-Health sensor platform is developed. In this designed modular system, after the ECG signal is taken from the body surface by the electrodes first, it is filtered and converted to digital form. Then, it is recorded to the health database using Wi-Fi communication technology. The real time access of the ECG data is provided through the internet utilizing the developed web interface.

Keywords: ECG, e-health sensor shield, raspberry Pi, wifi technology.

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

Abstract:

Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: Arrhythmic beat detection, ECG, HRV, kNN classifier.

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Authors: Rehab H. Alwan, Fadhil J. Kadhim, Ahmad T. Al-Taani

Abstract:

In this study, a novel approach of image embedding is introduced. The proposed method consists of three main steps. First, the edge of the image is detected using Sobel mask filters. Second, the least significant bit LSB of each pixel is used. Finally, a gray level connectivity is applied using a fuzzy approach and the ASCII code is used for information hiding. The prior bit of the LSB represents the edged image after gray level connectivity, and the remaining six bits represent the original image with very little difference in contrast. The proposed method embeds three images in one image and includes, as a special case of data embedding, information hiding, identifying and authenticating text embedded within the digital images. Image embedding method is considered to be one of the good compression methods, in terms of reserving memory space. Moreover, information hiding within digital image can be used for security information transfer. The creation and extraction of three embedded images, and hiding text information is discussed and illustrated, in the following sections.

Keywords: Image embedding, Edge detection, gray level connectivity, information hiding, digital image compression.

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Authors: Gary D. Cantrell, E. Alex Baylot

Abstract:

The radius-of-curvature (ROC) defines the degree of curvature along the centerline of a roadway whereby a travelling vehicle must follow. Roadway designs must encompass ROC in mitigating the cost of earthwork associated with construction while also allowing vehicles to travel at maximum allowable design speeds. Thus, a road will tend to follow natural topography where possible, but curvature must also be optimized to permit fast, but safe vehicle speeds. The more severe the curvature of the road, the slower the permissible vehicle speed. For route planning, whether for urban settings, emergency operations, or even parcel delivery, ROC is a necessary attribute of road arcs for computing travel time. It is extremely rare for a geo-spatial database to contain ROC. This paper will present a procedure and mathematical algorithm to calculate and assign ROC to a segment pair and/or polyline.

Keywords: linear features, radius-of-curvature, roads, routing, traffic, turning

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Authors: Komlan Sedzro

Abstract:

We apply the non-parametric, unconditional, hyperbolic order-α quantile estimator to appraise the relative efficiency of Microfinance Institutions in Africa in terms of outreach. Our purpose is to verify if these institutions, which must constantly try to strike a compromise between their social role and financial sustainability are operationally efficient. Using data on African MFIs extracted from the Microfinance Information eXchange (MIX) database and covering the 2004 to 2006 periods, we find that more efficient MFIs are also the most profitable. This result is in line with the view that social performance is not in contradiction with the pursuit of excellent financial performance. Our results also show that large MFIs in terms of asset and those charging the highest fees are not necessarily the most efficient.

Keywords: Data envelopment analysis, microfinance institutions, quantile estimation of efficiency, social and financial performance.

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Authors: A. Zaher, A. Traore, F. Thiéry, T. Talbert, B. Shaer

Abstract:

In this paper, an intelligent approach is proposed to optimize the orientation of continuous solar tracking systems on cloudy days. Considering the weather case, the direct sunlight is more important than the diffuse radiation in case of clear sky. Thus, the panel is always pointed towards the sun. In case of an overcast sky, the solar beam is close to zero, and the panel is placed horizontally to receive the maximum of diffuse radiation. Under partly covered conditions, the panel must be pointed towards the source that emits the maximum of solar energy and it may be anywhere in the sky dome. Thus, the idea of our approach is to analyze the images, captured by ground-based sky camera system, in order to detect the zone in the sky dome which is considered as the optimal source of energy under cloudy conditions. The proposed approach is implemented using experimental setup developed at PROMES-CNRS laboratory in Perpignan city (France). Under overcast conditions, the results were very satisfactory, and the intelligent approach has provided efficiency gains of up to 9% relative to conventional continuous sun tracking systems.

Keywords: Clouds detection, fuzzy inference systems, images processing, sun trackers.

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Authors: Faisal Aburub, Wael Hadi

Abstract:

Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

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Authors: V. K. Banga

Abstract:

Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: Artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic.

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Authors: Sudhir Jagtap, Kodge B. G., Shinde G. N., Devshette P. M

Abstract:

Numerical analysis naturally finds applications in all fields of engineering and the physical sciences, but in the 21st century, the life sciences and even the arts have adopted elements of scientific computations. The numerical data analysis became key process in research and development of all the fields [6]. In this paper we have made an attempt to analyze the specified numerical patterns with reference to the association rule mining techniques with minimum confidence and minimum support mining criteria. The extracted rules and analyzed results are graphically demonstrated. Association rules are a simple but very useful form of data mining that describe the probabilistic co-occurrence of certain events within a database [7]. They were originally designed to analyze market-basket data, in which the likelihood of items being purchased together within the same transactions are analyzed.

Keywords: Numerical data analysis, Data Mining, Association Rule Mining

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Authors: Jihyun Song, Kyeongjoo Kim, Minsoo Lee

Abstract:

Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.

Keywords: Clustering, data mining, DBSCAN, k-means, k-medoids, sensor data.

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Authors: Radu Dobrescu, Matei Dobrescu, Daniela Hossu

Abstract:

The paper proposes a unified model for multimedia data retrieval which includes data representatives, content representatives, index structure, and search algorithms. The multimedia data are defined as k-dimensional signals indexed in a multidimensional k-tree structure. The benefits of using the k-tree unified model were demonstrated by running the data retrieval application on a six networked nodes test bed cluster. The tests were performed with two retrieval algorithms, one that allows parallel searching using a single feature, the second that performs a weighted cascade search for multiple features querying. The experiments show a significant reduction of retrieval time while maintaining the quality of results.

Keywords: balancing strategies, multimedia databases, parallelprocessing, retrieval algorithms

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Authors: Tae Kyung Kim, Hee Suk Seo

Abstract:

Recent advances in wireless sensor networks have led to many routing methods designed for energy-efficiency in wireless sensor networks. Despite that many routing methods have been proposed in USN, a single routing method cannot be energy-efficient if the environment of the ubiquitous sensor network varies. We present the controlling network access to various hosts and the services they offer, rather than on securing them one by one with a network security model. When ubiquitous sensor networks are deployed in hostile environments, an adversary may compromise some sensor nodes and use them to inject false sensing reports. False reports can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. The interleaved hop-by-hop authentication scheme detects such false reports through interleaved authentication. This paper presents a LMDD (Low energy method for data delivery) algorithm that provides energy-efficiency by dynamically changing protocols installed at the sensor nodes. The algorithm changes protocols based on the output of the fuzzy logic which is the fitness level of the protocols for the environment.

Keywords: Data delivery, routing, simulation.

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Authors: Moustafa A. Bani-Hasan, Yasser M. Kadah, Fatma M. El-Hefnawi

Abstract:

An electrocardiogram (ECG) feature extraction system based on the calculation of the complex resonance frequency employing Prony-s method is developed. Prony-s method is applied on five different classes of ECG signals- arrhythmia as a finite sum of exponentials depending on the signal-s poles and the resonant complex frequencies. Those poles and resonance frequencies of the ECG signals- arrhythmia are evaluated for a large number of each arrhythmia. The ECG signals of lead II (ML II) were taken from MIT-BIH database for five different types. These are the ventricular couplet (VC), ventricular tachycardia (VT), ventricular bigeminy (VB), and ventricular fibrillation (VF) and the normal (NR). This novel method can be extended to any number of arrhythmias. Different classification techniques were tried using neural networks (NN), K nearest neighbor (KNN), linear discriminant analysis (LDA) and multi-class support vector machine (MC-SVM).

Keywords: Arrhythmias analysis, electrocardiogram, featureextraction, statistical classifiers.

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Authors: Hsuan-Chia Yang, Yan-Jhih Haung, Yu-Chuan Li

Abstract:

Researchers of drug-drug interaction alert systems have often suggested that there were high overridden rate for alerts and also too false alerts. However, research about decreasing false alerts is scant. Therefore, the aim of this article attempts to proactive identification of false alert for drug-drug interaction and provide solution to decrease false alerts. This research involved retrospective analysis prescribing database and calculated false alert rate by using MYSQL and JAVA. Results of this study showed 17% of false alerts and the false alert rate in the hospitals (37%) was more than in the clinics. To conclude, this study described the importance that drug-drug interaction alert system should not only detect drug name but also detect frequency or route, as well as in providing solution to decrease false alerts.

Keywords: drug-drug interaction, proactive identification, false alert

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Authors: N. Tuncbag, T. Haliloglu, O. Keskin

Abstract:

Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins.

Keywords: Pair-wise protein interactions, DIP database, functional correlations, biclustering.

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Authors: J. Fernandez, N. Aguilar, R. Fernandez de Canete, J. C. Ramos-Diaz

Abstract:

In this paper, a simulation model of the glucose-insulin system for a patient undergoing diabetes Type 1 is developed by using a causal modeling approach under system dynamics. The OpenModelica simulation environment has been employed to build the so called causal model, while the glucose-insulin model parameters were adjusted to fit recorded mean data of a diabetic patient database. Model results under different conditions of a three-meal glucose and exogenous insulin ingestion patterns have been obtained. This simulation model can be useful to evaluate glucose-insulin performance in several circumstances, including insulin infusion algorithms in open-loop and decision support systems in closed-loop.

Keywords: Causal modeling, diabetes, glucose-insulin system, diabetes, causal modeling, OpenModelica software.

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Authors: N. Benachour, A. Hadjoui, M. Benachour, M. Benguediab

Abstract:

This study reports an empirical investigation of fatigue crack initiation and propagation in 2024 T351 aluminium alloy using constant amplitude loading. In initiation stage, local strain approach at the notch was used and in stable propagation stage NASGRO model was applied. In this investigation, the flat plate of double through crack at hole is used. Based on experimental results (AFGROW Database), effect of stress ratio, R, is highlights on fatigue initiation life (FIL) and fatigue crack growth rate (FCGR). The increasing of dimension of hole characterizing the notch effect decrease the fatigue life.

Keywords: Fatigue crack growth, initiation life, Al-Alloy, stressratio, notch effect

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