Search results for: Survey data visualization.

Authors: Roza Dastres, Mohsen Soori

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In order to electronically exchange information between network users in the web of data, different software such as outlook is presented. So, the traffic of users on a site or even the floors of a building can be decreased as a result of applying a secure and reliable data sharing software. It is essential to provide a fast, secure and reliable network system in the data sharing webs to create an advanced communication systems in the users of network. In the present research work, different encoding methods and algorithms in data sharing systems is studied in order to increase security of data sharing systems by preventing the access of hackers to the transferred data. To increase security in the networks, the possibility of textual conversation between customers of a local network is studied. Application of the encryption and decryption algorithms is studied in order to increase security in networks by preventing hackers from infiltrating. As a result, a reliable and secure communication system between members of a network can be provided by preventing additional traffic in the website environment in order to increase speed, accuracy and security in the network and web systems of data sharing.

Keywords: Secure Socket Layer, Security of networks.

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Authors: Justina I. R. Udotong, Ime R. Udotong, Offiong U. Eka

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The concentrations of heavy metals in sediments of Qua Iboe River Estuary (QIRE) were monitored at four different sampling locations in wet and dry seasons. A preliminary survey to determine the four sampling stations along the river continuum showed that the area spanned between <0.1‰ salinity at the control station and 21.5‰ at the fourth station along the river continuum. A preliminary survey to determine the four sampling locations along the river estuary showed variations in salinity and other physicochemical parameters. The estuary was found to be polluted with heavy metals from point and nonpoint sources at varying degrees. Mean values of 7.80 mg/kg, 4.97 mg/kg and 2.80 mg/kg of nickel were obtained for sediment samples from Douglas creek, Qua Iboe and Atlantic sampling locations, respectively in the dry season. The wet season nickel concentrations were however lower. The entire study area was grossly contaminated by iron. At Douglas creek, the concentration of iron in sediment was 9274 ± 9.54mg/kg while copper, nickel, lead and vanadium were <0.5mg/kg each as compared to iron. Bioaccumulation was therefore suspected within the study area as values of 31.00 ± 0.79, 36.00 ± 0.10 and 55.00 ± 0.05 mg/kg of zinc were recorded in sediment at Douglas creek, Atlantic and the control sampling locations. The results from this study showed that the source of these heavy metals were from point sources like the corrosion of metal steel pipes from old bridges as well as oily sludge wastes from the Qua Iboe Terminal / tank farm located within the vicinity of the study area.

Keywords: Heavy metal, Qua Iboe River Estuary, seasonal variations, sediment.

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Authors: Abeer K. Jameel, Harry Evdorides

Abstract:

Iraq is a middle income country where the road safety issue is considered one of the leading causes of deaths. To control the road risk issue, the Iraqi Ministry of Planning, General Statistical Organization started to organise a collection system of traffic accidents data with details related to their causes and severity. These data are published as an annual report. In this paper, a review of the available crash data in Iraq will be presented. The available data represent the rate of accidents in aggregated level and classified according to their types, road users’ details, and crash severity, type of vehicles, causes and number of causalities. The review is according to the types of models used in road safety studies and research, and according to the required road safety data in the road constructions tasks. The available data are also compared with the road safety dataset published in the United Kingdom as an example of developed country. It is concluded that the data in Iraq are suitable for descriptive and exploratory models, aggregated level comparison analysis, and evaluation and monitoring the progress of the overall traffic safety performance. However, important traffic safety studies require disaggregated level of data and details related to the factors of the likelihood of traffic crashes. Some studies require spatial geographic details such as the location of the accidents which is essential in ranking the roads according to their level of safety, and name the most dangerous roads in Iraq which requires tactic plan to control this issue. Global Road safety agencies interested in solve this problem in low and middle-income countries have designed road safety assessment methodologies which are basing on the road attributes data only. Therefore, in this research it is recommended to use one of these methodologies.

Keywords: Data availability, Iraq, road safety.

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Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

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Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: Human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing.

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Authors: Renáta Iváncsy, Sándor Juhász

Abstract:

Web usage mining has become a popular research area, as a huge amount of data is available online. These data can be used for several purposes, such as web personalization, web structure enhancement, web navigation prediction etc. However, the raw log files are not directly usable; they have to be preprocessed in order to transform them into a suitable format for different data mining tasks. One of the key issues in the preprocessing phase is to identify web users. Identifying users based on web log files is not a straightforward problem, thus various methods have been developed. There are several difficulties that have to be overcome, such as client side caching, changing and shared IP addresses and so on. This paper presents three different methods for identifying web users. Two of them are the most commonly used methods in web log mining systems, whereas the third on is our novel approach that uses a complex cookie-based method to identify web users. Furthermore we also take steps towards identifying the individuals behind the impersonal web users. To demonstrate the efficiency of the new method we developed an implementation called Web Activity Tracking (WAT) system that aims at a more precise distinction of web users based on log data. We present some statistical analysis created by the WAT on real data about the behavior of the Hungarian web users and a comprehensive analysis and comparison of the three methods

Keywords: Data preparation, Tracking individuals, Web useridentification, Web usage mining

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Authors: Wei Zhang

Abstract:

With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: Deep learning, field programmable gate array, FPGA, hardware acceleration, convolutional neural networks, CNN.

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Authors: Mohammadreza Amini, Omid Moradtalab, Ebadollah Zohrevandi

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In real-time networks a large number of application programs are relying on video data and heterogeneous data transmission techniques. The aim of this research is presenting a method for end-to-end vouch quality service in surface applicationlayer for sending video data in comparison form in wireless heterogeneous networks. This method tries to improve the video sending over the wireless heterogeneous networks with used techniques in surface layer, link and application. The offered method is showing a considerable improvement in quality observing by user. In addition to this, other specifications such as shortage of data load that had require to resending and limited the relation period length to require time for second data sending, help to be used the offered method in the wireless devices that have a limited energy. The presented method and the achieved improvement is simulated and presented in the NS-2 software.

Keywords: Heterogeneous wireless networks, adaptation mechanism, multi-level, Handoff, stop mechanism, graceful degrades, application layer.

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Authors: Jinsuk Yang, Choongjae Joo, Kyoungsu Oh

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Existing methods in which the animation data of all frames are stored and reproduced as with vertex animation cannot be used in mobile device environments because these methods use large amounts of the memory. So 3D animation data reduction methods aimed at solving this problem have been extensively studied thus far and we propose a new method as follows. First, we find and remove frames in which motion changes are small out of all animation frames and store only the animation data of remaining frames (involving large motion changes). When playing the animation, the removed frame areas are reconstructed using the interpolation of the remaining frames. Our key contribution is to calculate the accelerations of the joints of individual frames and the standard deviations of the accelerations using the information of joint locations in the relevant 3D model in order to find and delete frames in which motion changes are small. Our methods can reduce data sizes by approximately 50% or more while providing quality which is not much lower compared to original animations. Therefore, our method is expected to be usefully used in mobile device environments or other environments in which memory sizes are limited.

Keywords: Data Reduction, Interpolation, Vertex Animation, 3D Animation.

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

Abstract:

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.

As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO.

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Authors: Fan Ye

Abstract:

Adverse weather conditions, particularly those with low visibility, are critical to the driving tasks. However, the direct relationship between visibility distances and traffic flow/roadway safety is uncertain due to the limitation of visibility data availability. The recent growth of deployment of Road Weather Information Systems (RWIS) makes segment-specific visibility information available which can be integrated with other Intelligent Transportation System, such as automated warning system and variable speed limit, to improve mobility and safety. Before applying the RWIS visibility measurements in traffic study and operations, it is critical to validate the data. Therefore, an attempt was made in the paper to examine the validity and viability of RWIS visibility data by comparing visibility measurements among RWIS, airport weather stations, and weather information recorded by police in crash reports, based on Ohio data. The results indicated that RWIS visibility measurements were significantly different from airport visibility data in Ohio, but no conclusion regarding the reliability of RWIS visibility could be drawn in the consideration of no verified ground truth in the comparisons. It was suggested that more objective methods are needed to validate the RWIS visibility measurements, such as continuous in-field measurements associated with various weather events using calibrated visibility sensors.

Keywords: Low visibility, RWIS, traffic safety, visibility.

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Authors: Vichada Chokesikarin

Abstract:

The study aims to determine which factors account for customer satisfaction and to investigate the relationship between entrepreneurial orientation and business success, in particular, context of the information understanding of hostel business in Pranakorn district, Bangkok and the significant element of entrepreneurship in tourism industry. This study covers 352 hostels customers and 61 hostel owners/managers nearby Khao San road. Data collection methods were used by survey questionnaire and a series of hypotheses were developed from services marketing literature. The findings suggest the customer satisfaction most influenced by image, service quality, room quality and price accordingly. Furthermore the findings revealed that significant relationships exist between entrepreneurial orientation and business success; while competitive aggressiveness was found unrelated. The ECSI model’s generic measuring customer satisfaction was found partially mediate the business success. A reconsideration of other variables applicable should be supported with the model of hostel business. The study provides context and overall view of hostel business while discussing from the entrepreneurial orientation to customer satisfaction, thereby reducing decision risk on hostel investment.

Keywords: Customer satisfaction, ECSI Model, entrepreneurial orientation, small hotels, hostel, business performance.

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Authors: T. Suresh Kumar, M. Arthanari, N. Shanthi

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Nowadays, the Web has become one of the most pervasive platforms for information change and retrieval. It collects the suitable and perfectly fitting information from websites that one requires. Data mining is the form of extracting data’s available in the internet. Web mining is one of the elements of data mining Technique, which relates to various research communities such as information recovery, folder managing system and simulated intellects. In this Paper we have discussed the concepts of Web mining. We contain generally focused on one of the categories of Web mining, specifically the Web Content Mining and its various farm duties. The mining tools are imperative to scanning the many images, text, and HTML documents and then, the result is used by the various search engines. We conclude by presenting a comparative table of these tools based on some pertinent criteria.

Keywords: Data Mining, Web Mining, Web Content Mining, Mining Tools, Information retrieval.

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Authors: Myint Myint Yi, Khin Sandar Linn, Marlar Kyaw

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This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF.

Keywords: Neural network, Load forecast, Time series, wavelettransform.

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

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Authors: Sirbu R., Negreanu-Pirjol T., Leca M., Bechir A., Maris M., Maris D.

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In the paper we presented the possibility of application collagen gels with active principle-s from marine algae extract and chlorhexidine salt in dental medicine. The hydro-alcoholic extracts from marine algae have been used as they have been obtained. The extracts from marine algae and chlorhexidine salt (digluconate) are incorporated in type I non-denatured fibrillar collagen matrixes. In order to obtain therapeutic effects at nanostructure level, it is important to know the rheological characteristics of the relevant mixtures of collagen gels and extracts from marine algae selected for use. In this survey we have studied mixtures made of non-denatured fibrillar collagen hydro-gels where different concentrations of marine algae have been incorporated. Based on the data obtained for the shearing tensions, we have traced the rheograms – the diagrams for shearing tensions depending on the shearing speed values – from which we have calculated the apparent viscosities as ratios between shearing tension and speed values, which have been figured in relation to the shearing speed values, with a view to levelling dependency.

Keywords: rheological properties, fibrillar collagen hydro-gel, marine algae, chlorhexidine salt, dental medicine

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: Concrete bridges, deterioration, Markov chains, probability matrix.

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Authors: Resmi Nair, Campbell Wilson, Bala Srinivasan

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Data warehouse is a dedicated database used for querying and reporting. Queries in this environment show special characteristics such as multidimensionality and aggregation. Exploiting the nature of queries, in this paper we propose a query driven design framework. The proposed framework is general and allows a designer to generate a schema based on a set of queries.

Keywords: Conceptual schema, data warehouse, queries, requirements.

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Authors: T. Qasim, M. Obeidat, S. Al-Sharairi

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The objective of this study is to investigate the effect of the excessive use of smartphones. Smartphones have enormous effects on the human body in that some musculoskeletal disorders (MSDs) and health problems might evolve. These days, there is a wide use of the smartphones among all age groups of society, thus, the focus on smartphone effects on human behavior and health, especially on the young and elderly people, becomes a crucial issue. This study was conducted in Jordan on smartphone users for different genders and ages, by conducting a survey to collect data related to the symptoms and MSDs that are resulted from the excessive use of smartphones. A total of 357 responses were used in the analysis. The main related symptoms were numbness, fingers pain, and pain in arm, all linked to age and gender for comparative reasons. A statistical analysis was performed to find the effects of extensive usage of a smartphone for long periods of time on the human body. Results show that the significant variables were the vision problems and the time spent when using the smartphone that cause vision problems. Other variables including age of user and ear problems due to the use of the headsets were found to be a border line significant.

Keywords: Smartphone, age group, musculoskeletal disorders (MSDs), health problems.

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Authors: Kerem Ok, Serafettin Senturk, Serdar Aktas, Cem Cevikbas

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Mobile technology is very popular nowadays and it provides a digital world where users can experience many value-added services. Service Providers are also eager to offer diverse value-added services to users such as digital identity, mobile financial services and so on. In this context, the security of data storage in smartphones and the security of communication between the smartphone and service provider are critical for the success of these services. In order to provide the required security functions, the SIM card is one acceptable alternative. Since SIM cards include a Secure Element, they are able to store sensitive data, create cryptographically secure keys, encrypt and decrypt data. In this paper, we design and implement a SIM and a smartphone framework that uses a SIM card for secure key generation, key storage, data encryption, data decryption and digital signing for mobile financial services. Our frameworks show that the SIM card can be used as a controlled Secure Element to provide required security functions for popular e-services such as mobile financial services.

Keywords: SIM Card, mobile financial services, cryptography, secure data storage.

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Authors: R. Goede, E. Taylor

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This paper demonstrates how the soft systems methodology can be used to improve the delivery of a module in data warehousing for fourth year information technology students. Graduates in information technology needs to have academic skills but also needs to have good practical skills to meet the skills requirements of the information technology industry. In developing and improving current data warehousing education modules one has to find a balance in meeting the expectations of various role players such as the students themselves, industry and academia. The soft systems methodology, developed by Peter Checkland, provides a methodology for facilitating problem understanding from different world views. In this paper it is demonstrated how the soft systems methodology can be used to plan the improvement of data warehousing education for fourth year information technology students.

Keywords: Data warehousing, education, soft systems methodology, stakeholders, systems thinking.

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Authors: S.S.Mohanavalli, Sheila Anand

Abstract:

This paper proposes a novel architecture for At- Home medical care which enables senior citizens, patients with chronic ailments and patients requiring post- operative care to be remotely monitored in the comfort of their homes. This architecture is implemented using sensors and wireless networking for transmitting patient data to the hospitals, health- care centers for monitoring by medical professionals. Patients are equipped with sensors to measure their physiological parameters, like blood pressure, pulse rate etc. and a Wearable Data Acquisition Unit is used to transmit the patient sensor data. Medical professionals can be alerted to any abnormal variations in these values for diagnosis and suitable treatment. Security threats and challenges inherent to wireless communication and sensor network have been discussed and a security mechanism to ensure data confidentiality and source authentication has been proposed. Symmetric key algorithm AES has been used for encrypting the data and a patent-free, two-pass block cipher mode CCFB has been used for implementing semantic security.

Keywords: data confidentiality, integrity, remotemonitoring, source authentication

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Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

Abstract:

With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: Machine learning, Imbalanced data, Data mining, Big data.

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Authors: Mansour Tarafdar, Mohammad Saniee Abade

Abstract:

This paper investigates the problem of sampling from transactional data streams. We introduce CFISDS as a content based sampling algorithm that works on a landmark window model of data streams and preserve more informed sample in sample space. This algorithm that work based on closed frequent itemset mining tasks, first initiate a concept lattice using initial data, then update lattice structure using an incremental mechanism.Incremental mechanism insert, update and delete nodes in/from concept lattice in batch manner. Presented algorithm extracts the final samples on demand of user. Experimental results show the accuracy of CFISDS on synthetic and real datasets, despite on CFISDS algorithm is not faster than exist sampling algorithms such as Z and DSS.

Keywords: Sampling, data streams, closed frequent item set mining.

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Authors: Chansheng He, Carlo DeMarchi

Abstract:

A physically based, spatially-distributed water quality model is being developed to simulate spatial and temporal distributions of material transport in the Great Lakes Watersheds of the U.S. Multiple databases of meteorology, land use, topography, hydrography, soils, agricultural statistics, and water quality were used to estimate nonpoint source loading potential in the study watersheds. Animal manure production was computed from tabulations of animals by zip code area for the census years of 1987, 1992, 1997, and 2002. Relative chemical loadings for agricultural land use were calculated from fertilizer and pesticide estimates by crop for the same periods. Comparison of these estimates to the monitored total phosphorous load indicates that both point and nonpoint sources are major contributors to the total nutrient loads in the study watersheds, with nonpoint sources being the largest contributor, particularly in the rural watersheds. These estimates are used as the input to the distributed water quality model for simulating pollutant transport through surface and subsurface processes to Great Lakes waters. Visualization and GIS interfaces are developed to visualize the spatial and temporal distribution of the pollutant transport in support of water management programs.

Keywords: Distributed Large Basin Runoff Model, Great LakesWatersheds, nonpoint source pollution, and point sources.

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Authors: Anis S. Shuib, Peter R. Hoskins, William J. Easson

Abstract:

Cardiovascular diseases, principally atherosclerosis, are responsible for 30% of world deaths. Atherosclerosis is due to the formation of plaque. The fatty plaque may be at risk of rupture, leading typically to stroke and heart attack. The plaque is usually associated with a high degree of lumen reduction, called a stenosis.It is increasingly recognized that the initiation and progression of disease and the occurrence of clinical events is a complex interplay between the local biomechanical environment and the local vascular biology. The aim of this study is to investigate the flow behavior through a stenosed artery. A physical experiment was performed using an artery model and blood analogue fluid. An axisymmetric model constructed consists of contraction and expansion region that follow a mathematical form of cosine function. A 30% diameter reduction was used in this study. The flow field was measured using particle image velocimetry (PIV). Spherical particles with 20μm diameter were seeded in a water-glycerol-NaCl mixture. Steady flow Reynolds numbers are 250. The area of interest is the region after the stenosis where the flow separation occurs. The velocity field was measured and the velocity gradient was investigated. There was high particle concentration in the recirculation zone. High velocity gradient formed immediately after the stenosis throat created a lift force that enhanced particle migration to the flow separation area.

Keywords: Stenosis artery, Biofluid mechanics, PIV

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Authors: Rosziati Ibrahim, Siow Yen Yen

Abstract:

System development life cycle (SDLC) is a process uses during the development of any system. SDLC consists of four main phases: analysis, design, implement and testing. During analysis phase, context diagram and data flow diagrams are used to produce the process model of a system. A consistency of the context diagram to lower-level data flow diagrams is very important in smoothing up developing process of a system. However, manual consistency check from context diagram to lower-level data flow diagrams by using a checklist is time-consuming process. At the same time, the limitation of human ability to validate the errors is one of the factors that influence the correctness and balancing of the diagrams. This paper presents a tool that automates the consistency check between Data Flow Diagrams (DFDs) based on the rules of DFDs. The tool serves two purposes: as an editor to draw the diagrams and as a checker to check the correctness of the diagrams drawn. The consistency check from context diagram to lower-level data flow diagrams is embedded inside the tool to overcome the manual checking problem.

Keywords: Data Flow Diagram, Context Diagram, ConsistencyCheck, Syntax and Semantic Rules

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Authors: S. Saraç, F. Kara, C.Vural

Abstract:

High-frequency (HF) communications have been used by military organizations for more than 90 years. The opportunity of very long range communications without the need for advanced equipment makes HF a convenient and inexpensive alternative of satellite communications. Besides the advantages, voice and data transmission over HF is a challenging task, because the HF channel generally suffers from Doppler shift and spread, multi-path, cochannel interference, and many other sources of noise. In constructing an HF data modem, all these effects must be taken into account. STANAG 4539 is a NATO standard for high-speed data transmission over HF. It allows data rates up to 12800 bps over an HF channel of 3 kHz. In this work, an efficient implementation of STANAG 4539 on a single Texas Instruments- TMS320C6747 DSP chip is described. The state-of-the-art algorithms used in the receiver and the efficiency of the implementation enables real-time high-speed data / digitized voice transmission over poor HF channels.

Keywords: High frequency, modem, STANAG 4539.

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Authors: S. Turkan

Abstract:

In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: Data envelopment analysis, super efficiency, financial ratios, BCC model.

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Authors: Mahesh Pal

Abstract:

This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one 'internal texture' and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combination of multispectral, panchromatic band and its texture were used and results were compared with those obtained by using multispectral data alone. A decision tree classifier with and without boosting were used to classify different datasets. Results from this study suggest that the dataset consisting of panchromatic band, four of its texture features and multispectral data was able to increase the classification accuracy by about 2%. In comparison, a boosted decision tree was able to increase the classification accuracy by about 3% with the same dataset.

Keywords: Internal texture; GLCM; decision tree; boosting; classification accuracy.

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Authors: Vesna Bosilj Vukšić, Darija Ivandić Vidović, Ljubica Milanović Glavan

Abstract:

Information technology plays an irreplaceable role in introducing and improving business process orientation in a company. It enables implementation of the theoretical concept, measurement of results achieved and undertaking corrective measures aimed at improvements. Information technology is a key concept in the development and implementation of the business process management systems as it establishes a connection to business operations. Both in the literature and practice, insurance companies are often seen as highly process oriented due to the nature of their business and focus on customers. They are also considered leaders in using information technology for business process management. The research conducted aimed to investigate whether the perceived leadership status of insurance companies is well deserved, i.e. to establish the level of process orientation and explore the practice of information technology use in insurance companies in the region. The main instrument for primary data collection within this research was an electronic survey questionnaire sent to the management of insurance companies in the Republic of Croatia, Bosnia and Herzegovina, Slovenia, Serbia and Macedonia. The conducted research has shown that insurance companies have a satisfactory level of process orientation, but that there is also a huge potential for improvement, especially in the segment of information technology and its connection to business processes.

Keywords: Business processes management, process orientation, information technology, insurance companies.

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