Search results for: secure data aggregation

Authors: Hosuk Shin, Young-Hyun Seo, Eunhak Lee, Seung-Young Kho

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Currently, in Seoul, users have the privilege to avoid riding crowded buses with the installation of Bus Information System (BIS). BIS has three levels of on-board bus ridership level information (spacious, normal, and crowded). However, there are flaws in the system due to it being real time which could provide incomplete information to the user. For example, a bus comes to the station, and on the BIS it shows that the bus is crowded, but on the stop that the user is waiting many people get off, which would mean that this station the information should show as normal or spacious. To fix this problem, this study predicts the bus ridership level using smart card data to provide more accurate information about the passenger ridership level on the bus. An Artificial Neural Network (ANN) is an interconnected group of nodes, that was created based on the human brain. Forecasting has been one of the major applications of ANN due to the data-driven self-adaptive methods of the algorithm itself. According to the results, the ANN algorithm was stable and robust with somewhat small error ratio, so the results were rational and reasonable.

Keywords: smartcard data, ANN, bus, ridership

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Ping Bo, Meng Yunshan

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Satellite-derived sea surface temperature (SST) is a key parameter for many operational and scientific applications. However, the disadvantage of SST data is a high percentage of missing data which is mainly caused by cloud coverage. Data Interpolating Empirical Orthogonal Function (DINEOF) algorithm is an EOF-based technique for reconstructing the missing data and has been widely used in oceanographic field. The reconstruction of SST images within a long time series using DINEOF can cause large discontinuities and one solution for this problem is to filter the temporal covariance matrix to reduce the spurious variability. Based on the previous researches, an algorithm is presented in this paper to improve the temporal correlations in EOF expansion. Similar with the previous researches, a filter, such as Laplacian filter, is implemented on the temporal covariance matrix, but the temporal relationship between two consecutive images which is used in the filter is considered in the presented algorithm, for example, two images in the same season are more likely correlated than those in the different seasons, hence the latter one is less weighted in the filter. The presented approach is tested for the monthly nighttime 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST for the long-term period spanning from 1989 to 2006. The results obtained from the presented algorithm are compared to those from the original DINEOF algorithm without filtering and from the DINEOF algorithm with filtering but without taking temporal relationship into account.

Keywords: data interpolating empirical orthogonal function, image reconstruction, sea surface temperature, temporal filter

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Authors: Fanqiang Kong, Chending Bian

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In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined l_2,p-norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy.

Keywords: hyperspectral unmixing, joint-sparse, low-rank representation, abundance estimation

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Authors: Rishi Kanth Saripalle

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Medical experts highly recommend to include physical activity in everyone’s daily routine irrespective of gender or age as it helps to improve various medical issues or curb potential issues. Simultaneously, experts are also diligently trying to provide various healthcare services (interventions, plans, exercise routines, etc.) for promoting healthy living and increasing physical activity in one’s ever increasing hectic schedules. With the introduction of wearables, individuals are able to keep track, analyze, and visualize their daily physical activities. However, there seems to be no common agreed standard for representing, gathering, aggregating and analyzing an individual’s physical activity data from disparate multiple sources (exercise pans, multiple wearables, etc.). This issue makes it highly impractical to develop any data-driven physical activity applications and healthcare programs. Further, the inability to integrate the physical activity data into an individual’s Electronic Health Record to provide a wholistic image of that individual’s health is still eluding the experts. This article has identified three primary reasons for this potential issue. First, there is no agreed standard, both structure and semantic, for representing and sharing physical activity data across disparate systems. Second, various organizations (e.g., LA fitness, Gold’s Gym, etc.) and research backed interventions and programs still primarily rely on paper or unstructured format (such as text or notes) to keep track of the data generated from physical activities. Finally, most of the wearable devices operate in silos. This article identifies the underlying problem, explores the idea of reusing existing standards, and identifies the essential modules required to move forward.

Keywords: electronic physical activity record, physical activity in EHR EIM, tracking physical activity data, physical activity data standards

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Authors: Gregory Kelly, Gary Chai, Sittampalam Manoharan, Deborah Delaney

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A Structural Number (SN) can be calculated for a road pavement from the properties and thicknesses of the surface, base course, sub-base, and subgrade. Historically, the cost of collecting structural data has been very high. Data were initially collected using Benkelman Beams and now by Falling Weight Deflectometer (FWD). The structural strength of pavements weakens over time due to environmental and traffic loading factors, but due to a lack of data, no structural deterioration curve for pavements has been implemented in a Pavement Management System (PMS). International Roughness Index (IRI) is a measure of the road longitudinal profile and has been used as a proxy for a pavement’s structural integrity. This paper offers two conceptual methods to develop Pavement Structural Deterioration Curves (PSDC). Firstly, structural data are grouped in sets by design Equivalent Standard Axles (ESA). An ‘Initial’ SN (ISN), Intermediate SN’s (SNI) and a Terminal SN (TSN), are used to develop the curves. Using FWD data, the ISN is the SN after the pavement is rehabilitated (Financial Accounting ‘Modern Equivalent’). Intermediate SNIs, are SNs other than the ISN and TSN. The TSN was defined as the SN of the pavement when it was approved for pavement rehabilitation. The second method is to use Traffic Speed Deflectometer data (TSD). The road network already divided into road blocks, is grouped by traffic loading. For each traffic loading group, road blocks that have had a recent pavement rehabilitation, are used to calculate the ISN and those planned for pavement rehabilitation to calculate the TSN. The remaining SNs are used to complete the age-based or if available, historical traffic loading-based SNI’s.

Keywords: conceptual, pavement structural number, pavement structural deterioration curve, pavement management system

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Authors: Yakup Ulusu, Numan Eczacıoğlu, İsa Gökçe, Helen Waller, Jeremy H. Lakey

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Besides having the appropriate amino acid sequence to perform the function of proteins, it is important to have correct conformation after this sequence to process. To consist of this conformation depends on the amino acid sequence at the primary structure, hydrophobic interaction, chaperones and enzymes in charge of folding etc. Misfolded proteins are not functional and tend to be aggregated. Cysteine originating disulfide cross-links make stable this conformation of functional proteins. When two of the cysteine amino acids come side by side, disulfide bond is established that forms a cystine bridge. Due to this feature cysteine plays an important role on the formation of three-dimensional structure of many proteins. There are two cysteine amino acids (C44, C69) in the Tol-A-III protein. Unlike protein disulfide bonds from within his own, any non-specific cystine bridge causes a change in the three dimensional structure of the protein. Proteins can be expressed in various host cells as directly or fusion (chimeric). As a result of overproduction of the recombinant proteins, aggregation of insoluble proteins in the host cell can occur by forming a crystal structure called inclusion body. In general fusion proteins are produced for provide affinity tags to make proteins more soluble and production of some toxic proteins via fusion protein expression system like pTolT. Proteins can be modified by using a site-directed mutagenesis. By this way, creation of non-specific disulfide crosslinks can be prevented at fusion protein expression system via the present cysteine replaced by another amino acid such as serine, glycine or etc. To do this, we need; a DNA molecule that contains the gene that encodes for the target protein, required primers for mutation to be designed according to site directed mutagenesis reaction. This study was aimed to be replaced cysteine encoding codon TGT with serine encoding codon AGT. For this sense and reverse primers designed (given below) and used site-directed mutagenesis reaction. Several new copy of the template plasmid DNA has been formed with above mentioned mutagenic primers via polymerase chain reaction (PCR). PCR product consists of both the master template DNA (wild type) and the new DNA sequences containing mutations. Dpn-l endonuclease restriction enzyme which is specific for methylated DNA and cuts them to the elimination of the master template DNA. E. coli cells obtained after transformation were incubated LB medium with antibiotic. After purification of plasmid DNA from E. coli, the presence of the mutation was determined by DNA sequence analysis. Developed this new plasmid is called PtolT-δ.

Keywords: site directed mutagenesis, Escherichia coli, pTolT, protein expression

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Authors: Nader Parsazadeh

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The variety of bed sediment load relationships, insufficient information and data, and the influence of river conditions make the selection of an optimum relationship for a given river extremely difficult. Hence, in order to select the best formulae, the bed load equations should be evaluated. The affecting factors need to be scrutinized, and equations should be verified. Also, re-evaluation may be needed. In this research, sediment bed load of Dez Dam at Tal-e Zang Station has been studied. After reviewing the available references, the most common formulae were selected that included Meir-Peter and Muller, using MS Excel to compute and evaluate data. Then, 52 series of already measured data at the station were re-measured, and the sediment bed load was determined. 1. The calculated bed load obtained by different equations showed a great difference with that of measured data. 2. r difference ratio from 0.5 to 2.00 was 0% for all equations except for Nilsson and Shields equations while it was 61.5 and 59.6% for Nilsson and Shields equations, respectively. 3. By reviewing results and discarding probably erroneous measured data measurements (by human or machine), one may use Nilsson Equation due to its r value higher than 1 as an effective equation for estimating bed load at Tal-e Zang Station in order to predict activities that depend upon bed sediment load estimate to be determined. Also, since only few studies have been conducted so far, these results may be of assistance to the operators and consulting companies.

Keywords: bed load, empirical relation ship, sediment, Tale Zang Station

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Authors: C. J. Tien, C. S. Chen, S. F. Huang, Z. X. Wang

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Algae in soil ecosystems can produce organic matters and oxygen by photosynthesis. Heterocyst-forming cyanobacteria can fix nitrogen to increase soil nitrogen contents. Secretion of mucilage by some algae increases the soil water content and soil aggregation. These actions will improve soil quality and fertility, and further increase abundance and diversity of soil microorganisms. In addition, some mixotrophic and heterotrophic algae are able to degrade petroleum hydrocarbons. Therefore, the objectives of this study were to analyze the effects of algal addition on the degradation of total petroleum hydrocarbons (TPH), diversity and activity of bacteria and algae in the diesel-contaminated soil under different nutrient contents and frequency of plowing and irrigation in order to assess the potential bioremediation technique using edaphic algae. The known amount of diesel was added into the farmland soil. This diesel-contaminated soil was subject to five settings, experiment-1 with algal addition by plowing and irrigation every two weeks, experiment-2 with algal addition by plowing and irrigation every four weeks, experiment-3 with algal and nutrient addition by plowing and irrigation every two weeks, experiment-4 with algal and nutrient addition by plowing and irrigation every four weeks, and the control without algal addition. Soil samples were taken every two weeks to analyze TPH concentrations, diversity of bacteria and algae, and catabolic genes encoding functional degrading enzymes. The results show that the TPH removal rates of five settings after the two-month experimental period were in the order: experiment-2 > expermient-4 > experiment-3 > experiment-1 > control. It indicated that algal addition enhanced the degradation of TPH in the diesel-contaminated soil, but not for nutrient addition. Plowing and irrigation every four weeks resulted in more TPH removal than that every two weeks. The banding patterns of denaturing gradient gel electrophoresis (DGGE) revealed an increase in diversity of bacteria and algae after algal addition. Three petroleum hydrocarbon-degrading algae (Anabaena sp., Oscillatoria sp. and Nostoc sp.) and two added algal strains (Leptolyngbya sp. and Synechococcus sp.) were sequenced from DGGE prominent bands. The four hydrocarbon-degrading bacteria Gordonia sp., Mycobacterium sp., Rodococcus sp. and Alcanivorax sp. were abundant in the treated soils. These results suggested that growth of indigenous bacteria and algae were improved after adding edaphic algae. Real-time polymerase chain reaction results showed that relative amounts of four catabolic genes encoding catechol 2, 3-dioxygenase, toluene monooxygenase, xylene monooxygenase and phenol monooxygenase were appeared and expressed in the treated soil. The addition of algae increased the expression of these genes at the end of experiments to biodegrade petroleum hydrocarbons. This study demonstrated that edaphic algae were suitable biomaterials for bioremediating diesel-contaminated soils with plowing and irrigation every four weeks.

Keywords: catabolic gene, diesel, diversity, edaphic algae

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Authors: Xudong He, Jian Wang, Jiqiang Liu, Lei Han, Yang Yu, Shaohua Lv

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Nowadays, the threats of the internet are enormous and increasing; however, the classification of huge alert messages generated in this environment is relatively monotonous. It affects the accuracy of the network situation assessment, and also brings inconvenience to the security managers to deal with the emergency. In order to deal with potential network threats effectively and provide more effective data to improve the network situation awareness. It is essential to build a hierarchical filtering method to prevent the threats. In this paper, it establishes a model for data monitoring, which can filter systematically from the original data to get the grade of threats and be stored for using again. Firstly, it filters the vulnerable resources, open ports of host devices and services. Then use the entropy theory to calculate the performance changes of the host devices at the time of the threat occurring and filter again. At last, sort the changes of the performance value at the time of threat occurring. Use the alerts and performance data collected in the real network environment to evaluate and analyze. The comparative experimental analysis shows that the threat filtering method can effectively filter the threat alerts effectively.

Keywords: correlation analysis, hierarchical filtering, multisource data, network security

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Authors: A. Satty, H. Mwambi

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Much clinical trials data-based research are characterized by the unavoidable problem of dropout as a result of missing or erroneous values. This paper aims to review some of the various techniques to address the dropout problems in longitudinal clinical trials. The fundamental concepts of the patterns and mechanisms of dropout are discussed. This study presents five general techniques for handling dropout: (1) Deletion methods; (2) Imputation-based methods; (3) Data augmentation methods; (4) Likelihood-based methods; and (5) MNAR-based methods. Under each technique, several methods that are commonly used to deal with dropout are presented, including a review of the existing literature in which we examine the effectiveness of these methods in the analysis of incomplete data. Two application examples are presented to study the potential strengths or weaknesses of some of the methods under certain dropout mechanisms as well as to assess the sensitivity of the modelling assumptions.

Keywords: incomplete longitudinal clinical trials, missing at random (MAR), imputation, weighting methods, sensitivity analysis

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Authors: Claerchille Jhulia Robin

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This paper discusses the perspective of ESL learners towards pronunciation instruction. It sought to determine how these learners view the type of feedback their speech teacher gives and its impact on their own classroom practice of providing feedback. This study utilized a quantitative-qualitative approach to the problem. The respondents were Education students majoring in English. A survey questionnaire and interview guide were used for data gathering. The data from the survey was tabulated using frequency count and the data from the interview were then transcribed and analyzed. Results showed that ESL learners favor immediate corrective feedback and they do not find any issue in being corrected in front of their peers. They also practice the same corrective technique in their own classroom.

Keywords: ESL, feedback, learner perspective, pronunciation instruction

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Authors: Sadao Araki, Daisuke Gondo, Satoshi Imasaka, Hideki Yamamoto

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The separation of organic compounds from aqueous solutions is a key technology for recycling valuable organic compounds and for the treatment of wastewater. The wastewater from chemical plants often contains organic compounds such as ethyl acetate (EA), methylethyl ketone (MEK) and isopropyl alcohol (IPA). In this study, we prepared hydrophobic silica membranes by a sol-gel method. We used phenyltrimethoxysilane (PhTMS), ethyltrimethoxysilan (ETMS), Propyltrimethoxysilane (PrTMS), N-butyltrimethoxysilane (BTMS), N-Hexyltrimethoxysilane (HTMS) as silica sources to introduce each functional groups on the membrane surface. Cetyltrimethyl ammonium bromide (CTAB) was used as a molecular template to create suitable pore that enable the permeation of organic compounds. These membranes with five different functional groups were characterized by SEM, FT-IR, and permporometry. Thicknesses and pore diameters of silica layer for all membrane were about 1.0 μm and about 1 nm, respectively. In other words, functional groups had an insignificant effect on the membrane thicknesses and the formation of the pore by CTAB. We confirmed the effect of functional groups on the flux and separation factor for ethyl acetate (EA), methyl ethyl ketone, acetone and 1-butanol (1-BtOH) /water mixtures. All membranes showed a high flux for ethyl acetate compared with other compounds. In particular, the hydrophobic silica membrane prepared by using BTMS showed 0.75 kg m-2 h-1 of flux for EA. For all membranes, the fluxes of organic compounds showed the large values in the order corresponding to EA > MEK > acetone > 1-BtOH. On the other hand, carbon chain length of functional groups among ETMS, PrTMS, BTMS, PrTMS and HTMS did not have a major effect on the organic flux. Although we confirmed the relationship between organic fluxes and organic molecular diameters or fugacity of organic compounds, these factors had a low correlation with organic fluxes. It is considered that these factors affect the diffusivity. Generally, permeation through membranes is based on the diffusivity and solubility. Therefore, it is deemed that organic fluxes through these hydrophobic membranes are strongly influenced by solubility. We tried to estimate the organic fluxes by Hansen solubility parameter (HSP). HSP, which is based on the cohesion energy per molar volume and is composed of dispersion forces (δd), intermolecular dipole interactions (δp), and hydrogen-bonding interactions (δh), has recently attracted attention as a means for evaluating the resolution and aggregation behavior. Evaluation of solubility for two substances can be represented by using the Ra [(MPa)1/2] value, meaning the distance of HSPs for both of substances. A smaller Ra value means a higher solubility for each substance. On the other hand, it can be estimated that the substances with large Ra value show low solubility. We established the correlation equation, which was based on Ra, of organic flux at low concentrations of organic compounds and at 295-325 K.

Keywords: hydrophobic, membrane, Hansen solubility parameter, functional group

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Authors: Chayanika Hazarika Bordoloi

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This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

Procedia PDF Downloads 197

Authors: M. Abdullah, Al-Wadud

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Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian, based classification, activity recognition, sensor data, object-usage model

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Authors: Nastaran Moosavi, Mohammad Mokhtari

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Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.

Keywords: density, p-impedance, s-impedance, post-stack seismic inversion, pre-stack seismic inversion

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Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

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Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

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Authors: Ahmed M. Jamel

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Electronic government initiatives and public participation to them are among the indicators of today's development criteria of the countries. After consequent two wars, Iraq's current position in, for example, UN's e-government ranking is quite concerning and did not improve in recent years, either. In the preparation of this work, we are motivated with the fact that handling geographic data of the public facilities and resources are needed in most of the e-government projects. Geographical information systems (GIS) provide most common tools not only to manage spatial data but also to integrate such type of data with nonspatial attributes of the features. With this background, this paper proposes that establishing a working GIS in the health sector of Iraq would improve e-government applications. As the case study, investigating hospital locations in Erbil is chosen.

Keywords: e-government, GIS, Iraq, Erbil

Procedia PDF Downloads 392

Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: V. Ayishwarya Bharathi, S. M. Hasker, J. Indhu, M. Mohamed Azarudeen, G. Gowthami, R. Vinoth Rajan, N. Vijayarangan

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Identification of water quality conditions in a river system based on limited observations is an essential task for meeting the goals of environmental management. The traditional method of water quality testing is to collect samples manually and then send to laboratory for analysis. However, it has been unable to meet the demands of water quality monitoring today. So a set of automatic measurement and reporting system of water quality has been developed. In this project specifies Water quality parameters collected by multi-parameter water quality probe are transmitted to data processing and monitoring center through GPRS wireless communication network of mobile. The multi parameter sensor is directly placed above the water level. The monitoring center consists of GPRS and micro-controller which monitor the data. The collected data can be monitor at any instant of time. In the pollution control board they will monitor the water quality sensor data in computer using Visual Basic Software. The system collects, transmits and processes water quality parameters automatically, so production efficiency and economy benefit are improved greatly. GPRS technology can achieve well within the complex environment of poor water quality non-monitored, and more specifically applicable to the collection point, data transmission automatically generate the field of water analysis equipment data transmission and monitoring.

Keywords: multiparameter sensor, GPRS, visual basic software, RS232

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Authors: E. Fathallahi Aghdam, V. Hosseini

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Environmental pollution is not limited to a specific region or country; that is why sustainable development, as a necessary process for improvement, pays attention to issues such as destruction of natural resources, degradation of biological system, global pollution, and climate change in the world, especially in the developing countries. According to the World Health Organization, as a developing city, Tehran (capital of Iran) is one of the most polluted cities in the world in terms of air pollution. In this study, three pollutants including particulate matter less than 10 microns, nitrogen oxides, and sulfur dioxide were evaluated in Tehran using data mining techniques and through Crisp approach. The data from 21 air pollution measuring stations in different areas of Tehran were collected from 1999 to 2013. Commercial softwares Clementine was selected for this study. Tehran was divided into distinct clusters in terms of the mentioned pollutants using the software. As a data mining technique, clustering is usually used as a prologue for other analyses, therefore, the similarity of clusters was evaluated in this study through analyzing local conditions, traffic behavior, and industrial activities. In fact, the results of this research can support decision-making system, help managers improve the performance and decision making, and assist in urban studies.

Keywords: data mining, clustering, air pollution, crisp approach

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Authors: Anuradha Chug, Sunali Gandhi

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Regression Testing is a very expensive and time-consuming process carried out to ensure the validity of modified software. Due to the availability of insufficient resources to re-execute all the test cases in time constrained environment, efforts are going on to generate test data automatically without human efforts. Many search based techniques have been proposed to generate efficient, effective as well as optimized test data, so that the overall cost of the software testing can be minimized. The generated test data should be able to uncover all potential lapses that exist in the software or product. Inspired from the natural behavior of bat for searching her food sources, current study employed a meta-heuristic, search-based bat algorithm for optimizing the test data on the basis certain parameters without compromising their effectiveness. Mathematical functions are also applied that can effectively filter out the redundant test data. As many as 50 Java programs are used to check the effectiveness of proposed test data generation and it has been found that 86% saving in testing efforts can be achieved using bat algorithm while covering 100% of the software code for testing. Bat algorithm was found to be more efficient in terms of simplicity and flexibility when the results were compared with another nature inspired algorithms such as Firefly Algorithm (FA), Hill Climbing Algorithm (HC) and Ant Colony Optimization (ACO). The output of this study would be useful to testers as they can achieve 100% path coverage for testing with minimum number of test cases.

Keywords: regression testing, test case selection, test case prioritization, genetic algorithm, bat algorithm

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Authors: Agria Rhamdhan

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WhatsApp as the most popular mobile messaging app has been used as evidence in many criminal cases. As the use of mobile messages generates large amounts of data, forensic investigation faces the challenge of large data problems. The hardest part of finding this important evidence is because current practice utilizes tools and technique that require manual analysis to check all messages. That way, analyze large sets of mobile messaging data will take a lot of time and effort. Our work offers methodologies based on forensic triage to reduce large data to manageable sets resulting easier to do detailed reviews, then show the results through interactive visualization to show important term, entities and relationship through intelligent ranking using Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) Model. By implementing this methodology, investigators can improve investigation processing time and result's accuracy.

Keywords: forensics, triage, visualization, WhatsApp

Procedia PDF Downloads 173

Authors: Mohkammad Nur Cahyadi, Hepi Hapsari Handayani, Agus Budi Raharjo, Ronny Mardianto, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

Abstract:

PDAM (local water company) determines customer charges by considering the customer's building or house. Charges determination significantly affects PDAM income and customer costs because the PDAM applies a subsidy policy for customers classified as small households. Periodic updates are needed so that pricing is in line with the target. A thorough customer survey in Surabaya is needed to update customer building data. However, the survey that has been carried out so far has been by deploying officers to conduct one-by-one surveys for each PDAM customer. Surveys with this method require a lot of effort and cost. For this reason, this research offers a technology called moblie mapping, a mapping method that is more efficient in terms of time and cost. The use of this tool is also quite simple, where the device will be installed in the car so that it can record the surrounding buildings while the car is running. Mobile mapping technology generally uses lidar sensors equipped with GNSS, but this technology requires high costs. In overcoming this problem, this research develops low-cost mobile mapping technology using a webcam camera sensor added to the GNSS and IMU sensors. The camera used has specifications of 3MP with a resolution of 720 and a diagonal field of view of 78⁰. The principle of this invention is to integrate four camera sensors, a GNSS webcam, and GPS to acquire photo data, which is equipped with location data (latitude, longitude) and IMU (roll, pitch, yaw). This device is also equipped with a tripod and a vacuum cleaner to attach to the car's roof so it doesn't fall off while running. The output data from this technology will be analyzed with artificial intelligence to reduce similar data (Cosine Similarity) and then classify building types. Data reduction is used to eliminate similar data and maintain the image that displays the complete house so that it can be processed for later classification of buildings. The AI method used is transfer learning by utilizing a trained model named VGG-16. From the analysis of similarity data, it was found that the data reduction reached 50%. Then georeferencing is done using the Google Maps API to get address information according to the coordinates in the data. After that, geographic join is done to link survey data with customer data already owned by PDAM Surya Sembada Surabaya.

Keywords: mobile mapping, GNSS, IMU, similarity, classification

Procedia PDF Downloads 84

Authors: Abraham Motlhabane

Abstract:

This research analysed the written views of science education students regarding the teaching of laboratory work using the online mode. The research adopted the qualitative methodology. The qualitative research was aimed at investigating small and distinct groups normally regarded as a single-site study. Qualitative research was used to describe and analyze the phenomena from the student’s perspective. This means the research began with assumptions of the world view that use theoretical lenses of research problems inquiring into the meaning of individual students. The research was conducted with three groups of students studying for Postgraduate Certificate in Education, Bachelor of Education and honors Bachelor of Education respectively. In each of the study programmes, the science education module is compulsory. Five science education students from each study programme were purposively selected to participate in this research. Therefore, 15 students participated in the research. In order to analysis the data, the data were first printed and hard copies were used in the analysis. The data was read several times and key concepts and ideas were highlighted. Themes and patterns were identified to describe the data. Coding as a process of organising and sorting data was used. The findings of the study are very diverse; some students are in favour of online laboratory whereas other students argue that science can only be learnt through hands-on experimentation.

Keywords: online learning, laboratory work, views, perceptions

Procedia PDF Downloads 149

Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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Modern experiments in high energy physics impose great demands on the reliability, the efficiency, and the data rate of Data Acquisition Systems (DAQ). This contribution focuses on the development and deployment of the new communication library DIALOG for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. The iFDAQ utilizing a hardware event builder is designed to be able to readout data at the maximum rate of the experiment. The DIALOG library is a communication system both for distributed and mixed environments, it provides a network transparent inter-process communication layer. Using the high-performance and modern C++ framework Qt and its Qt Network API, the DIALOG library presents an alternative to the previously used DIM library. The DIALOG library was fully incorporated to all processes in the iFDAQ during the run 2016. From the software point of view, it might be considered as a significant improvement of iFDAQ in comparison with the previous run. To extend the possibilities of debugging, the online monitoring of communication among processes via DIALOG GUI is a desirable feature. In the paper, we present the DIALOG library from several insights and discuss it in a detailed way. Moreover, the efficiency measurement and comparison with the DIM library with respect to the iFDAQ requirements is provided.

Keywords: data acquisition system, DIALOG library, DIM library, FPGA, Qt framework, TCP/IP

Procedia PDF Downloads 320

Authors: A. Anastasiou, K. S. Tingay

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Background: Discovering suitable datasets is an important part of health research, particularly for projects working with clinical data from patients organized in cohorts (cohort data), but with the proliferation of so many national and international initiatives, it is becoming increasingly difficult for research teams to locate real world datasets that are most relevant to their project objectives. We present a method for identifying healthcare institutes in the European Union (EU) which may hold haemato-oncology (HO) data. A key enabler of this research was the bibInsight platform, a scientometric data management and analysis system developed by the authors at Swansea University. Method: A PubMed search was conducted using HO clinical terms taken from previous work. The resulting XML file was processed using the bibInsight platform, linking affiliations to the Global Research Identifier Database (GRID). GRID is an international, standardized list of institutions, including the city and country in which the institution exists, as well as a category of the main business type, e.g., Academic, Healthcare, Government, Company. Countries were limited to the 28 current EU members, and institute type to 'Healthcare'. An article was considered valid if at least one author was affiliated with an EU-based healthcare institute. Results: The PubMed search produced 21,310 articles, consisting of 9,885 distinct affiliations with correspondence in GRID. Of these articles, 760 were from EU countries, and 390 of these were healthcare institutes. One affiliation was excluded as being a veterinary hospital. Two EU countries did not have any publications in our analysis dataset. The results were analysed by country and by individual healthcare institute. Networks both within the EU and internationally show institutional collaborations, which may suggest a willingness to share data for research purposes. Geographical mapping can ensure that data has broad population coverage. Collaborations with industry or government may exclude healthcare institutes that may have embargos or additional costs associated with data access. Conclusions: Data reuse is becoming increasingly important both for ensuring the validity of results, and economy of available resources. The ability to identify potential, specific data sources from over twenty thousand articles in less than an hour could assist in improving knowledge of, and access to, data sources. As our method has not yet specified if these healthcare institutes are holding data, or merely publishing on that topic, future work will involve text mining of data-specific concordant terms to identify numbers of participants, demographics, study methodologies, and sub-topics of interest.

Keywords: data reuse, data discovery, data linkage, journal articles, text mining

Procedia PDF Downloads 117

Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

Procedia PDF Downloads 380

Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

Procedia PDF Downloads 326