Search results for: internet data center.

Authors: Kamal Ali Albashiri, Khaled Ahmed Kadouh

Abstract:

A generic and extendible Multi-Agent Data Mining (MADM) framework, MADMF (the Multi-Agent Data Mining Framework) is described. The central feature of the framework is that it avoids the use of agreed meta-language formats by supporting a framework of wrappers. The advantage offered is that the framework is easily extendible, so that further data agents and mining agents can simply be added to the framework. A demonstration MADMF framework is currently available. The paper includes details of the MADMF architecture and the wrapper principle incorporated into it. A full description and evaluation of the framework-s operation is provided by considering two MADM scenarios.

Keywords: Multi-Agent Data Mining (MADM), Frequent Itemsets, Meta ARM, Association Rule Mining, Classifier generator.

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Authors: Mohd Fikri Azli bin Abdullah, Khairul Anwar bin Kamarul Hatta, Esther Jeganathan

Abstract:

WiFi has become an essential technology that is widely used nowadays. It is famous due to its convenience to be used with mobile devices. This is especially true for Internet users worldwide that use WiFi connections. There are many location based services that are available nowadays which uses Wireless Fidelity (WiFi) signal fingerprinting. A common example that is gaining popularity in this era would be Foursquare. In this work, the WiFi signal would be used to estimate the user or client’s location. Similar to GPS, fingerprinting method needs a floor plan to increase the accuracy of location estimation. Still, the factor of inconsistent WiFi signal makes the estimation defer at different time intervals. Given so, an adaptive method is needed to obtain the most accurate signal at all times. WiFi signals are heavily distorted by external factors such as physical objects, radio frequency interference, electrical interference, and environmental factors to name a few. Due to these factors, this work uses a method of reducing the signal noise and estimation using the Nearest Neighbour based on past activities of the signal to increase the signal accuracy up to more than 80%. The repository yet increases the accuracy by using Artificial Neural Network (ANN) pattern matching. The repository acts as the server cum support of the client side application decision. Numerous previous works has adapted the methods of collecting signal strengths in the repository over the years, but mostly were just static. In this work, proposed solutions on how the adaptive method is done to match the signal received to the data in the repository are highlighted. With the said approach, location estimation can be done more accurately. Adaptive update allows the latest location fingerprint to be stored in the repository. Furthermore, any redundant location fingerprints are removed and only the updated version of the fingerprint is stored in the repository. How the location estimation of the user can be predicted would be highlighted more in the proposed solution section. After some studies on previous works, it is found that the Artificial Neural Network is the most feasible method to deploy in updating the repository and making it adaptive. The Artificial Neural Network functions are to do the pattern matching of the WiFi signal to the existing data available in the repository.

Keywords: Adaptive Repository, Artificial Neural Network, Location Estimation, Nearest Neighbour Euclidean Distance, WiFi RSSI Fingerprinting.

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Authors: Nevena Stolba, A Min Tjoa

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Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.

Keywords: data mining, data warehousing, decision-support systems, evidence-based medicine.

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Authors: Denise Levy, Anna Lucia C. H. Villavicencio

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Food irradiation is a processing and preservation technique to eliminate insects and parasites and reduce disease-causing microorganisms. Moreover, the process helps to inhibit sprouting and delay ripening, extending fresh fruits and vegetables shelf-life. Nevertheless, most Brazilian consumers seem to misunderstand the difference between irradiated food and radioactive food and the general public has major concerns about the negative health effects and environmental contamination. Society´s judgment and decision making are directly linked to perceived benefits and risks. The web-based project entitled ‘Scientific information about food irradiation: Internet as a tool to approach science and society’ was created by the Nuclear and Energetic Research Institute (IPEN), in order to offer an interdisciplinary approach to science education, integrating economic, ethical, social and political aspects of food irradiation. This project takes into account that, misinformation and unfounded preconceived ideas impact heavily on the acceptance of irradiated food and purchase intention by the Brazilian consumer. Taking advantage of the potential value of the Internet to enhance communication and education among general public, a research study was carried out regarding the possibilities and trends of Information and Communication Technologies among the Brazilian population. The content includes concepts, definitions and Frequently Asked Questions (FAQ) about processes, safety, advantages, limitations and the possibilities of food irradiation, including health issues, as well as its impacts on the environment. The project counts on eight self-instructional interactive web courses, situating scientific content in relevant social contexts in order to encourage self-learning and further reflections. Communication is a must to improve public understanding of science. The use of information technology for quality scientific divulgation shall contribute greatly to provide information throughout the country, spreading information to as many people as possible, minimizing geographic distances and stimulating communication and development.

Keywords: Food irradiation, multimedia learning tools, nuclear science, society and education.

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Authors: Katsuyuki Umezawa, Takashi Tashiro, Satoru Tezuka

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Recently, various services such as television and the Internet have come to be received through various terminals. However, we could gain greater convenience by receiving these services through cellular phone terminals when we go out and then continuing to receive the same services through a large screen digital television after we have come home. However, it is necessary to go through the same authentication processing again when using TVs after we have come home. In this study, we have developed an authentication method that enables users to switch terminals in environments in which the user receives service from a server through a terminal. Specifically, the method simplifies the authentication of the server side when switching from one terminal to another terminal by using previously authenticated information.

Keywords: Authentication, Cookie, Federation, GBA, IPSec

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Authors: Esti Rahmawati Agustina, Is Esti Firmanesa

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Blind Signature were introduced by Chaum. In this scheme, a signer can “sign” a document without knowing the document contain. This is particularly important in electronic voting. CryptO-0N2 is an electronic voting protocol which is development of CryptO-0N. During its development this protocol has not been furnished with the requirement of blind signature, so the choice of voters can be determined by counting center. In this paper will be presented of implementation of blind signature using RSA algorithm.

Keywords: Blind signature, electronic voting protocol, RSA algorithm.

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Shaun Cox, Michael Oakes, Stefan Wermter, Maurice Hawthorne

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We report on the results of a pilot study in which a data-mining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chi-squared test, and the use of self-organizing maps, an unsupervised neural learning approach.

Keywords: Audiology, data mining, chi-squared, self-organizing maps

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Authors: TamásMatuszka, Attila Kiss

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Due the proliferation of smartphones in everyday use, several different outdoor navigation systems have become available. Since these smartphones are able to connect to the Internet, the users can obtain location-based information during the navigation as well. The users could interactively get to know the specifics of a particular area (for instance, ancient cultural area, Statue Park, cemetery) with the help of thus obtained information. In this paper, we present an Augmented Reality system which uses Semantic Web technologies and is based on the interaction between the user and the smartphone. The system allows navigating through a specific area and provides information and details about the sight an interactive manner.

Keywords: Augmented Reality, Semantic Web, Human Computer Interaction, Mobile Application.

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Authors: Seo Young Kim, Tai Myong Choi

Abstract:

The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others.

Keywords: Clustering, microarray data, Fuzzy-type clustering, Validation

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Authors: Marjan Mansourvar, Norizan Mohd Yasin

Abstract:

The development of Web has affected different aspects of our lives, such as communication, sharing knowledge, searching for jobs, social activities, etc. The web portal as a gateway in the World Wide Web is a starting point for people who are connecting to the Internet. The web portal as the type of knowledge management system provides a rich space to share and search information as well as communication services like free email or content provision for the users. This research aims to discover the university needs to the web portal as a necessary tool for students in the universities to help them in getting the required information. A survey was conducted to gather students' requirements which can be incorporated in to portal to be developed.

Keywords: Knowledge, Knowledge management system, Knowledge sharing, web portal.

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Authors: Mansoor Momeni, Mahmoud Dehghan Nayeri, Ali Faal Ghayoumi, Hoda Ghorbani

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This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.

Keywords: Financial data analysis, Influential data, Outliers, Robust regression.

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Authors: A. R. Ommani, M. Chizari

Abstract:

The purpose of the study reported here was designing Information Dissemination System (IDS) based E-learning in agricultural of Iran. A questionnaire was developed to designing Information Dissemination System. The questionnaire was distributed to 96 extension agents who work for Management of Extension and Farming System of Khuzestan province of Iran. Data collected were analyzed using the Statistical Package for the Social Sciences (SPSS). Appropriate statistical procedures for description (frequencies, percent, means, and standard deviations) were used. In this study there was a significant relationship between the age , IT skill and knowledge, years of extension work, the extend of information seeking motivation, level of job satisfaction and level of education with use of information technology by extension agent. According to extension agents five factors were ranked respectively as five top essential items to designing Information Dissemination System (IDS) based E-learning in agricultural of Iran. These factors include: 1) Establish communication between farmers, coordinators (extension agents), agricultural experts, research centers, and community by information technology. 2) The communication between all should be mutual. 3) The information must be based farmers need. 4) Internet used as a facility to transfer the advanced agricultural information to the farming community. 5) Farmers can be illiterate and speak a local and they are not expected to use the system directly. Knowledge produced by the agricultural scientist must be transformed in to computer understandable presentation. To designing Information Dissemination System, electronic communication, in the agricultural society and rural areas must be developed. This communication must be mutual between all factors.

Keywords: E-learning, information dissemination system, information technology.

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Authors: Rahma Souli-Jbali, Minyar Sassi Hidri, Rahma Ben Ayed

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Grid of computing nodes has emerged as a representative means of connecting distributed computers or resources scattered all over the world for the purpose of computing and distributed storage. Since fault tolerance becomes complex due to the availability of resources in decentralized grid environment, it can be used in connection with replication in data grids. The objective of our work is to present fault tolerance in data grids with data replication-driven model based on clustering. The performance of the protocol is evaluated with Omnet++ simulator. The computational results show the efficiency of our protocol in terms of recovery time and the number of process in rollbacks.

Keywords: Data grids, fault tolerance, chandy-lamport, clustering.

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Authors: Ahmad Habibizad Navin, Mehdi Naghian Fesharaki, Mohamad Teshnelab, Ehsan Shahamatnia

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The anti-lock braking systems installed on vehicles for safe and effective braking, are high-order nonlinear and timevariant. Using fuzzy logic controllers increase efficiency of such systems, but impose a high computational complexity as well. The main concept introduced by this paper is reducing computational complexity of fuzzy controllers by deploying problem-solution data structure. Unlike conventional methods that are based on calculations, this approach is based on data oriented modeling.

Keywords: ABS, Fuzzy controller, PSDS, Time-Memory tradeoff, Data oriented modeling.

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Authors: Erika Martinez Ramirez, Rene V. Mayorga

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Nowadays, more engineering systems are using some kind of Artificial Intelligence (AI) for the development of their processes. Some well-known AI techniques include artificial neural nets, fuzzy inference systems, and neuro-fuzzy inference systems among others. Furthermore, many decision-making applications base their intelligent processes on Fuzzy Logic; due to the Fuzzy Inference Systems (FIS) capability to deal with problems that are based on user knowledge and experience. Also, knowing that users have a wide variety of distinctiveness, and generally, provide uncertain data, this information can be used and properly processed by a FIS. To properly consider uncertainty and inexact system input values, FIS normally use Membership Functions (MF) that represent a degree of user satisfaction on certain conditions and/or constraints. In order to define the parameters of the MFs, the knowledge from experts in the field is very important. This knowledge defines the MF shape to process the user inputs and through fuzzy reasoning and inference mechanisms, the FIS can provide an “appropriate" output. However an important issue immediately arises: How can it be assured that the obtained output is the optimum solution? How can it be guaranteed that each MF has an optimum shape? A viable solution to these questions is through the MFs parameter optimization. In this Paper a novel parameter optimization process is presented. The process for FIS parameter optimization consists of the five simple steps that can be easily realized off-line. Here the proposed process of FIS parameter optimization it is demonstrated by its implementation on an Intelligent Interface section dealing with the on-line customization / personalization of internet portals applied to E-commerce.

Keywords: Artificial Intelligence, Fuzzy Logic, Fuzzy InferenceSystems, Nonlinear Optimization.

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Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

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Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, these projects propose AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project present the best-in-school techniques used to preserve data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptography techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures, and identifies potential correction/mitigation measures.

Keywords: Data privacy, artificial intelligence, healthcare AI, data sharing, healthcare organizations.

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Authors: Quratulain N. Rajput, Sajjad Haider

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This paper applies Bayesian Networks to support information extraction from unstructured, ungrammatical, and incoherent data sources for semantic annotation. A tool has been developed that combines ontologies, machine learning, and information extraction and probabilistic reasoning techniques to support the extraction process. Data acquisition is performed with the aid of knowledge specified in the form of ontology. Due to the variable size of information available on different data sources, it is often the case that the extracted data contains missing values for certain variables of interest. It is desirable in such situations to predict the missing values. The methodology, presented in this paper, first learns a Bayesian network from the training data and then uses it to predict missing data and to resolve conflicts. Experiments have been conducted to analyze the performance of the presented methodology. The results look promising as the methodology achieves high degree of precision and recall for information extraction and reasonably good accuracy for predicting missing values.

Keywords: Information Extraction, Bayesian Network, ontology, Machine Learning

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Authors: Keonwoo Kim, Dowon Hong, Kyoil Chung, Jae-Cheol Ryou

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Cell phone forensics to acquire and analyze data in the cellular phone is nowadays being used in a national investigation organization and a private company. In order to collect cellular phone flash memory data, we have two methods. Firstly, it is a logical method which acquires files and directories from the file system of the cell phone flash memory. Secondly, we can get all data from bit-by-bit copy of entire physical memory using a low level access method. In this paper, we describe a forensic tool to acquire cell phone flash memory data using a logical level approach. By our tool, we can get EFS file system and peek memory data with an arbitrary region from Korea CDMA cell phone.

Keywords: Forensics, logical method, acquisition, cell phone, flash memory.

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Authors: Mohamed Hanine, Abdesadik Bendarag, Omar Boutkhoum

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Currently, the field of data migration is very topical. As the number of applications developed rapidly, the ever-increasing volume of data collected has driven the architectural migration from Relational Database Management System (RDBMS) to NoSQL (Not Only SQL) database. This very recent technology is important enough in the field of database management. The main aim of this paper is to present a methodology for data migration from RDBMS to NoSQL database. To illustrate this methodology, we implement a software prototype using MySQL as a RDBMS and MongoDB as a NoSQL database. Although this is a hard engineering work, our results show that the proposed methodology can successfully accomplish the goal of this study.

Keywords: Data Migration, MySQL, RDBMS, NoSQL, MongoDB.

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Authors: Anurag Sharma, Christian W. Omlin

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Self-organizing map (SOM) is a well known data reduction technique used in data mining. It can reveal structure in data sets through data visualization that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOM, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this paper, we propose the use of an adaptive heuristic particle swarm optimization (PSO) algorithm for finding cluster boundaries directly from the code vectors obtained from SOM. The application of our method to several standard data sets demonstrates its feasibility. PSO algorithm utilizes a so-called U-matrix of SOM to determine cluster boundaries; the results of this novel automatic method compare very favorably to boundary detection through traditional algorithms namely k-means and hierarchical based approach which are normally used to interpret the output of SOM.

Keywords: cluster boundaries, clustering, code vectors, data mining, particle swarm optimization, self-organizing maps, U-matrix.

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Authors: Yung-Gi Wu

Abstract:

With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.

Keywords: Data hiding, vector quantization, watermark.

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Authors: Wen-Chi Hou, Cheng Luo, Zhewei Jiang, Feng Yan

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In this research, we propose to use the discrete cosine transform to approximate the cumulative distributions of data cube cells- values. The cosine transform is known to have a good energy compaction property and thus can approximate data distribution functions easily with small number of coefficients. The derived estimator is accurate and easy to update. We perform experiments to compare its performance with a well-known technique - the (Haar) wavelet. The experimental results show that the cosine transform performs much better than the wavelet in estimation accuracy, speed, space efficiency, and update easiness.

Keywords: DCT, Data Cube

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Authors: Lily Ingsrisawang, Supawadee Ingsriswang, Saisuda Somchit, Prasert Aungsuratana, Warawut Khantiyanan

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This paper presents the methodology from machine learning approaches for short-term rain forecasting system. Decision Tree, Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied to develop classification and prediction models for rainfall forecasts. The goals of this presentation are to demonstrate (1) how feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) what models can be developed and deployed for predicting the accurate rainfall estimates to support the decisions to launch the cloud seeding operations in the northeastern part of Thailand. Datasets collected during 2004-2006 from the Chalermprakiat Royal Rain Making Research Center at Hua Hin, Prachuap Khiri khan, the Chalermprakiat Royal Rain Making Research Center at Pimai, Nakhon Ratchasima and Thai Meteorological Department (TMD). A total of 179 records with 57 features was merged and matched by unique date. There are three main parts in this work. Firstly, a decision tree induction algorithm (C4.5) was used to classify the rain status into either rain or no-rain. The overall accuracy of classification tree achieves 94.41% with the five-fold cross validation. The C4.5 algorithm was also used to classify the rain amount into three classes as no-rain (0-0.1 mm.), few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall accuracy of classification tree achieves 62.57%. Secondly, an ANN was applied to predict the rainfall amount and the root mean square error (RMSE) were used to measure the training and testing errors of the ANN. It is found that the ANN yields a lower RMSE at 0.171 for daily rainfall estimates, when compared to next-day and next-2-day estimation. Thirdly, the ANN and SVM techniques were also used to classify the rain amount into three classes as no-rain, few-rain, and moderate-rain as above. The results achieved in 68.15% and 69.10% of overall accuracy of same-day prediction for the ANN and SVM models, respectively. The obtained results illustrated the comparison of the predictive power of different methods for rainfall estimation.

Keywords: Machine learning, decision tree, artificial neural network, support vector machine, root mean square error.

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Authors: Madhav V. Chitturi, Anshu Manik, Kasthurirangan Gopalakrishnan

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The dynamic or complex modulus test is considered to be a mechanistically based laboratory test to reliably characterize the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes used in the construction of roads. The most common observation is that the data collected from these tests are often noisy and somewhat non-sinusoidal. This hampers accurate analysis of the data to obtain engineering insight. The goal of the work presented in this paper is to develop and compare automated evolutionary computational techniques to filter test noise in the collection of data for the HMA complex modulus test. The results showed that the Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is computationally efficient for filtering data obtained from the HMA complex modulus test.

Keywords: HMA, dynamic modulus, GA, evolutionarycomputation.

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Authors: Sara Sedaghat, Mahmood Barati, Iraj Kazeminezhad

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The ionization energy in semiconductor systems in nano scale was investigated by using effective mass approximation. By introducing the Hamiltonian of the system, the variational technique was employed to calculate the ground state and the ionization energy of a donor at the center and in the case that the impurities are randomly distributed inside a cubic quantum well. The numerical results for GaAs/GaAlAs show that the ionization energy strongly depends on the well width for both cases and it decreases as the well width increases. The ionization energy of a quantum wire was also calculated and compared with the results for the well.

Keywords: quantum well, quantum wire, quantum dot, impuritystate

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Authors: Laila Yasir Al-Harthy, Ali H. Al-Badi

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As an emerging business model, cloud computing has been initiated to satisfy the need of organizations and to push Information Technology as a utility. The shift to the cloud has changed the way Information Technology departments are managed traditionally and has raised many concerns for both, public and private sectors.

The purpose of this study is to investigate the possibility of cloud computing services replacing services provided traditionally by IT departments. Therefore, it aims to 1) explore whether organizations in Oman are ready to move to the cloud; 2) identify the deciding factors leading to the adoption or rejection of cloud computing services in Oman; and 3) provide two case studies, one for a successful Cloud provider and another for a successful adopter.

This paper is based on multiple research methods including conducting a set of interviews with cloud service providers and current cloud users in Oman; and collecting data using questionnaires from experts in the field and potential users of cloud services.

Despite the limitation of bandwidth capacity and Internet coverage offered in Oman that create a challenge in adopting the cloud, it was found that many information technology professionals are encouraged to move to the cloud while few are resistant to change.

The recent launch of a new Omani cloud service provider and the entrance of other international cloud service providers in the Omani market make this research extremely valuable as it aims to provide real-life experience as well as two case studies on the successful provision of cloud services and the successful adoption of these services.

Keywords: Cloud computing, cloud deployment models, cloud service models and deciding factors.

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Authors: Masoud Nasri, Sadat Feiznia, Mohammad Jafari, Hasan Ahmadi

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Today, incorrect use of lands and land use changes, excessive grazing, no suitable using of agricultural farms, plowing on steep slopes, road construct, building construct, mine excavation etc have been caused increasing of soil erosion and sediment yield. For erosion and sediment estimation one can use statistical and empirical methods. This needs to identify land unit map and the map of effective factors. However, these empirical methods are usually time consuming and do not give accurate estimation of erosion. In this study, we applied GIS techniques to estimate erosion and sediment of Menderjan watershed at upstream Zayandehrud river in center of Iran. Erosion faces at each land unit were defined on the basis of land use, geology and land unit map using GIS. The UTM coordinates of each erosion type that showed more erosion amounts such as rills and gullies were inserted in GIS using GPS data. The frequency of erosion indicators at each land unit, land use and their sediment yield of these indices were calculated. Also using tendency analysis of sediment yield changes in watershed outlet (Menderjan hydrometric gauge station), was calculated related parameters and estimation errors. The results of this study according to implemented watershed management projects can be used for more rapid and more accurate estimation of erosion than traditional methods. These results can also be used for regional erosion assessment and can be used for remote sensing image processing.

Keywords: Erosion and sedimentation, Gully, Rill, GIS, GPS, Menderjan Watershed

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Authors: Amar Partap Singh Pharwaha, Shweta Rani

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To develop a reliable and cost effective communication platform for the telemedicine applications, novel antenna design has been presented using bacterial foraging optimization (BFO) technique. The proposed antenna geometry is achieved by etching a modified Koch curve fractal shape at the edges and a square shape slot at the center of the radiating element of a patch antenna. It has been found that the new antenna has achieved 43.79% size reduction and better resonating characteristic than the original patch. Representative results for both simulations and numerical validations are reported in order to assess the effectiveness of the developed methodology.

Keywords: BFO, electrical permittivity, fractals, Koch curve.

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Authors: Ching-I Chen

Abstract:

The continued rise of e-commerce is the main driver of the rapid growth of global online purchase. Consumers can nearly buy everything they want at one occasion through online shopping. The purchase behavior models which focus on single product category are insufficient to describe online shopping behavior. Therefore, analysis of multi-category purchase gets more and more popular. For example, market basket analysis explores customers’ buying tendency of the association between product categories. The information derived from market basket analysis facilitates to make cross-selling strategies and product recommendation system.

To detect the association between different product categories, we use the market basket analysis with the multidimensional scaling technique to build an associated map which describes how likely multiple product categories are bought at the same time. Besides, we also build an inter-purchase time model for associated products to describe how likely a product will be bought after its associated product is bought. We classify inter-purchase time behaviors of multi-category products into nine types, and use a mixture regression model to integrate those behaviors under our assumptions of purchase sequences. Our sample data is from comScore which provides a panelist-label database that captures detailed browsing and buying behavior of internet users across the United States. Finding the inter-purchase time from books to movie is shorter than the inter-purchase time from movies to books. According to the model analysis and empirical results, this research finally proposes the applications and recommendations in the management.

Keywords: Multiple-category purchase behavior, inter-purchase time, market basket analysis.

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