Search results for: data compression

Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from trav-eling vehicles, such as taxis through installed global positioning sys-tem (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: Waqed H. Almohammed, Mohammed Y. Fattah, Sajjad E. Rasheed

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Dynamic traffic loads cause deformation of underground pipes, resulting in vehicle discomfort. This makes it necessary to reinforce the layers of soil above underground pipes. In this study, the subbase layer was reinforced. Finite element software (PLAXIS 3D) was used to in the simulation, which includes geocell reinforcement, vehicle loading, soil layers and Glass Fiber Reinforced Plastic (GRP) pipe. Geocell reinforcement was modeled using a geogrid element, which was defined as a slender structure element that has the ability to withstand axial stresses but not to resist bending. Geogrids cannot withstand compression but they can withstand tensile forces. Comparisons have been made between the numerical models and experimental works, and a good agreement was obtained. Using the mathematical model, the performance of three different pipes of diameter 600 mm, 800 mm, and 1000 mm, and three different vehicular speeds of 20 km/h, 40 km/h, and 60 km/h, was examined to determine their impact on surface settlement and vertical pressure at the pipe crown for two cases: with and without geocell reinforcement. The results showed that, for a pipe diameter of 600 mm under geocell reinforcement, surface settlement decreases by 94 % when the speed of the vehicle is 20 km/h and by 98% when the speed of the vehicle is 60 km/h. Vertical pressure decreases by 81 % when the diameter of the pipe is 600 mm, while the value decreases to 58 % for a pipe with diameter 1000 mm. The results show that geocell reinforcement causes a significant and positive reduction in surface settlement and vertical stress above the pipe crown, leading to an increase in pipe safety.

Keywords: Dynamic loading, geocell reinforcement, GRP pipe, PLAXIS 3D, surface settlement.

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Authors: Harleen Kaur

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Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting patterns according to some measures, from databases. It is of vital importance to define good measures of interestingness that would allow the system to discover only the useful patterns. Measures of interestingness are divided into objective and subjective measures. Objective measures are those that depend only on the structure of a pattern and which can be quantified by using statistical methods. While, subjective measures depend only on the subjectivity and understandability of the user who examine the patterns. These subjective measures are further divided into actionable, unexpected and novel. The key issues that faces data mining community is how to make actions on the basis of discovered knowledge. For a pattern to be actionable, the user subjectivity is captured by providing his/her background knowledge about domain. Here, we consider the actionability of the discovered knowledge as a measure of interestingness and raise important issues which need to be addressed to discover actionable knowledge.

Keywords: Data Mining Community, Knowledge Discovery inDatabases (KDD), Interestingness, Subjective Measures, Actionability.

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Authors: Luminita Dumitriu, Cristina Segal, Marian Craciun, Adina Cocu, Lucian P. Georgescu

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There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.

Keywords: association rules, classification, data mining, Quantitative Structure - Activity Relationship.

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Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

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The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyse huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic wellbeing is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that support the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: COVID-19, big data, data analysis, indexing, NoSQL, sharding, scalability, poverty.

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Authors: Valentin P. Velikov

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Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: Computer science, graphical user interface, user dialog interface, dialog frames, data modeling, subject area modeling.

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Authors: Linnea Stenliden

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The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors within Actor-network theory (ANT). The learning conditions are found to be distinguished by broad complexity, characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: Analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation.

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Authors: Necati Içer

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Crop-hail insurance plays a vital role in managing risks and reducing the financial consequences of hail damage on crop production. Predicting insurance premium rates with short-term data is a major challenge in numerous nations because of the unique characteristics of hailstorms. This study aims to suggest a feasible approach for establishing equitable premium rates in crop-hail insurance for nations with short-term insurance data. The primary goal of the rate-making process is to determine premium rates for high and zero loss costs of villages and enhance their credibility. To do this, a technique was created using the author's practical knowledge of crop-hail insurance. With this approach, the rate-making method was developed using a range of temporal and spatial factor combinations with both hypothetical and real data, including extreme cases. This article aims to show how to incorporate the temporal and spatial elements into determining fair premium rates using short-term insurance data. The article ends with a suggestion on the ultimate premium rates for insurance contracts.

Keywords: Crop-hail insurance, premium rate, short-term insurance data, spatial and temporal parameters.

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Authors: Ayad Salih Sabbar, Amin Chegenizadeh, Hamid Nikraz

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Investigation of liquefaction susceptibility of materials that have been used in embankments, slopes, dams, and foundations is very essential. Many catastrophic geo-hazards such as flow slides, declination of foundations, and damage to earth structure are associated with static liquefaction that may occur during abrupt shearing of these materials. Many artificial backfill materials are mixtures of sand with fines and other composition. In order to provide some clarifications and evaluations on the role of fines in static liquefaction behaviour of sand sandy soils, the effect of fines on the liquefaction susceptibility of sand was experimentally examined in the present work over a range of fines content, relative density, and initial confining pressure. The results of an experimental study on various sand-fines mixtures are presented. Undrained static triaxial compression tests were conducted on saturated Perth sand containing 5% bentonite at three different relative densities (10, 50, and 90%), and saturated Perth sand containing both 5% bentonite and slag (2%, 4%, and 6%) at single relative density 10%. Undrained static triaxial tests were performed at three different initial confining pressures (100, 150, and 200 kPa). The brittleness index was used to quantify the liquefaction potential of sand-bentonite-slag mixtures. The results demonstrated that the liquefaction susceptibility of sand-5% bentonite mixture was more than liquefaction susceptibility of clean sandy soil. However, liquefaction potential decreased when both of two fines (bentonite and slag) were used. Liquefaction susceptibility of all mixtures decreased with increasing relative density and initial confining pressure.  

Keywords: Bentonite, brittleness index, liquefaction, slag.

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Authors: C. Yaiprasert, K. Jaroensutasinee, M. Jaroensutasinee

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To develop a process of extracting pixel values over the using of satellite remote sensing image data in Thailand. It is a very important and effective method of forecasting rainfall. This paper presents an approach for forecasting a possible rainfall area based on pixel values from remote sensing satellite images. First, a method uses an automatic extraction process of the pixel value data from the satellite image sequence. Then, a data process is designed to enable the inference of correlations between pixel value and possible rainfall occurrences. The result, when we have a high averaged pixel value of daily water vapor data, we will also have a high amount of daily rainfall. This suggests that the amount of averaged pixel values can be used as an indicator of raining events. There are some positive associations between pixel values of daily water vapor images and the amount of daily rainfall at each rain-gauge station throughout Thailand. The proposed approach was proven to be a helpful manual for rainfall forecasting from meteorologists by which using automated analyzing and interpreting process of meteorological remote sensing data.

Keywords: Pixel values, satellite image, water vapor, rainfall, image processing.

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Authors: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh

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Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.

Keywords: Malmquist Index, Grey's Theory, Charnes Cooper & Rhodes (CCR) Model, network data envelopment analysis, Iran electricity power chain.

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Authors: Yiannis Smirlis, Dimitris K. Despotis

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This study performs a comparative analysis of the 21 Greek Universities in terms of their public funding, awarded for covering their operating expenditure. First it introduces a DEA/MCDM model that allocates the fund into four expenditure factors in the most favorable way for each university. Then, it presents a common, consensual assessment model to reallocate the amounts, remaining in the same level of total public budget. From the analysis it derives that a number of universities cannot justify the public funding in terms of their size and operational workload. For them, the sufficient reduction of their public funding amount is estimated as a future target. Due to the lack of precise data for a number of expenditure criteria, the analysis is based on a mixed crisp-ordinal data set.

Keywords: Data envelopment analysis, Greek universities, operating expenditures, ordinal data.

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Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: Resistivity, inversion, optimization.

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Authors: Chris Ewe, Naoum Jamous, Holger Schrödl

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Like most entrepreneurs, data center operators pursue goals such as profit-maximization, improvement of the company’s reputation or basically to exist on the market. Part of those aims is to guarantee a given quality of service. Quality characteristics are specified in a contract called the service level agreement. Central part of this agreement is non-functional properties of an IT service. The system availability is one of the most important properties as it will be shown in this paper. To comply with availability requirements, data center operators can use virtualization technologies. A clear model to assess the effect of virtualization functions on the parts of a data center in relation to the system availability is still missing. This paper aims to introduce a basic model that shows these connections, and consider if the identified effects are positive or negative. Thus, this work also points out possible disadvantages of the technology. In consequence, the paper shows opportunities as well as risks of data center virtualization in relation to system availability.

Keywords: Availability, cloud computing IT service, quality of service, service level agreement, virtualization.

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

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In this paper we introduce new data oriented modeling of uniform random variable well-matched with computing systems. Due to this conformity with current computers structure, this modeling will be efficiently used in statistical inference.

Keywords: Uniform random variable, Data oriented modeling, Statistical inference, Prodigraph, Statistically complete tree, Uniformdigital probability digraph, Uniform n-complete probability tree.

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Authors: Inci Develioglu, Hasan Firat Pulat

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Sustainable development is one of the most important topics in today's world, and it is also an important research topic for geoenvironmental engineering. Dredging process is performed to expand the river and port channel, flood control and accessing harbors. Every year large amount of sediment are dredged for these purposes. Dredged marine soils can be reused as filling materials, road and foundation embankments, construction materials and wildlife habitat developments. In this study, geotechnical engineering properties and compressibility behavior of dredged soil obtained from the Izmir Bay were investigated. The samples with four different organic matter contents were obtained and particle size distributions, consistency limits, pH and specific gravity tests were performed. The consolidation tests were conducted to examine organic matter content (OMC) effects on compressibility behavior of dredged soil. This study has shown that the OMC has an important effect on the engineering properties of dredged soils. The liquid and plastic limits increased with increasing OMC. The lowest specific gravity belonged to sample which has the maximum OMC. The specific gravity values ranged between 2.76 and 2.52. The maximum void ratio difference belongs to sample with the highest OMC (De_11% = 0.38)_. As the organic matter content of the samples increases, the change in the void ratio has also increased. The compression index increases with increasing OMC.

Keywords: Compressibility, consolidation, geotechnical properties, organic matter content, organic soils.

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Authors: Phumelele P. Kubheka, Pius A. Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users share their opinions on different subjects. Twitter can be considered a great source for mining text due to the high volumes of data generated through the platform daily. Many industries such as telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model in this experiment. A higher topic coherence score indicates better performance of the model.

Keywords: Big data, latent Dirichlet allocation, latent semantic indexing, Telco, topic modeling, Twitter.

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Authors: Yogesh Aggarwal, Paratibha Aggarwal

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The paper presents a comparative performance of the models developed to predict 28 days compressive strengths using neural network techniques for data taken from literature (ANN-I) and data developed experimentally for SCC containing bottom ash as partial replacement of fine aggregates (ANN-II). The data used in the models are arranged in the format of six and eight input parameters that cover the contents of cement, sand, coarse aggregate, fly ash as partial replacement of cement, bottom ash as partial replacement of sand, water and water/powder ratio, superplasticizer dosage and an output parameter that is 28-days compressive strength and compressive strengths at 7 days, 28 days, 90 days and 365 days, respectively for ANN-I and ANN-II. The importance of different input parameters is also given for predicting the strengths at various ages using neural network. The model developed from literature data could be easily extended to the experimental data, with bottom ash as partial replacement of sand with some modifications.

Keywords: Self compacting concrete, bottom ash, strength, prediction, neural network, importance factor.

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Authors: D. Koren, V. Kilar

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The modern energy-efficient houses are often founded on a thermal insulation (TI) layer placed under the building’s RC foundation slab.The purpose of the paper is to identify the potential problems of the buildings founded on TI layer from the seismic point of view. The two main goals of the study were to assess the seismic behavior of such buildings, and to search for the critical structural parameters affecting the response of the superstructure as well as of the extruded polystyrene (XPS) layer. As a test building a multi-storeyed RC frame structure with and without the XPS layer under the foundation slab has been investigated utilizing nonlinear dynamic (time-history) and static (pushover) analyses. The structural response has been investigated with reference to the following performance parameters: i) Building’s lateral roof displacements, ii) Edge compressive and shear strains of the XPS, iii) Horizontal accelerations of the superstructure, iv) Plastic hinge patterns of the superstructure, v) Part of the foundation in compression, and vi) Deformations of the underlying soil and vertical displacements of the foundation slab (i.e. identifying the potential uplift). The results have shown that in the case of higher and stiff structures lying on firm soil the use of XPS under the foundation slab might induce amplified structural peak responses compared to the building models without XPS under the foundation slab. The analysis has revealed that the superstructure as well as the XPS response is substantially affected by the stiffness of the foundation slab.

Keywords: Extruded polystyrene (XPS), foundation on thermal insulation, energy-efficient buildings, nonlinear seismic analysis, seismic response, soil–structure interaction.

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient, but not the magnitude. A neural network with two hidden layers was then used to learn the coefficient magnitudes, along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: Quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction.

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Authors: Se Kyung Oh, Young Hwan Yoon, Ary Bachtiar Krishna

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Nowadays, a passenger car suspension must has high performance criteria with light weight, low cost, and low energy consumption. Pilot controlled proportional valve is designed and analyzed to get small pressure change rate after blow-off, and to get a fast response of the damper, a reverse damping mechanism is adapted. The reverse continuous variable damper is designed as a HS-SH damper which offers good body control with reduced transferred input force from the tire, compared with any other type of suspension system. The damper structure is designed, so that rebound and compression damping forces can be tuned independently, of which the variable valve is placed externally. The rate of pressure change with respect to the flow rate after blow-off becomes smooth when the fixed orifice size increases, which means that the blow-off slope is controllable using the fixed orifice size. Damping forces are measured with the change of the solenoid current at the different piston velocities to confirm the maximum hysteresis of 20 N, linearity, and variance of damping force. The damping force variance is wide and continuous, and is controlled by the spool opening, of which scheme is usually adapted in proportional valves. The reverse continuous variable damper developed in this study is expected to be utilized in the semi-active suspension systems in passenger cars after its performance and simplicity of the design is confirmed through a real car test.

Keywords: Blow-off, damping force, pilot controlledproportional valve, reverse continuous damper.

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Authors: M. Sadighi, H. Pouriayevali, M. Saadati

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This paper deals with analysis of flexural stiffness, indentation and their energies in three point loading of sandwich beams with composite faces from Eglass/epoxy and cores from Polyurethane or PVC. Energy is consumed in three stages of indentation in laminated beam, indentation of sandwich beam and bending of sandwich beam. Theory of elasticity is chosen to present equations for indentation of laminated beam, then these equations have been corrected to offer better results. An analytical model has been used assuming an elastic-perfectly plastic compressive behavior of the foam core. Classical theory of beam is used to describe three point bending. Finite element (FE) analysis of static indentation sandwich beams is performed using the FE code ABAQUS. The foam core is modeled using the crushable foam material model and response of the foam core is experimentally characterized in uniaxial compression. Three point bending and indentation have been done experimentally in two cases of low velocity and higher velocity (quasi-impact) of loading. Results can describe response of beam in terms of core and faces thicknesses, core material, indentor diameter, energy absorbed, and length of plastic area in the testing. The experimental results are in good agreement with the analytical and FE analyses. These results can be used as an introduction for impact loading and energy absorbing of sandwich structures.

Keywords: Three point Bending, Indentation, Foams, Composite laminated beam, Sandwich beams, Finite element

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Over the past epoch a rampant amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Furthermore several algorithms and methods have been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of new methods in the sphere of data mining and also the incessant interest in inventing new algorithms, makes obligatory to scrutinize a critical analysis of the existing techniques and the future novelty. This paper exposes the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this speculative and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.

Keywords: Clustering, Cluster Ensemble methods, Co-association matrix, Consensus function, Median partition.

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Authors: Chuen Tse Kuah, Kuan Yew Wong, Farzad Behrouzi

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This paper is aimed to give an illustration on the application of Data Envelopment Analysis (DEA) as a tool to assess Quality Management (QM) efficiency. A variant of DEA, slack based measure (SBM) is used for this purpose. From this study, it is found that DEA is suitable to measure QM efficiency and give improvement suggestions to the inefficient QM.

Keywords: Quality Management, Data Envelopment Analysis, Slack Based Measure, Efficiency Measurement.

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Authors: Wang Yang

Abstract:

Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map.

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Authors: Christian Mosch, Reiner Anderl, Antonio Álvaro de Assis Moura, Klaus Schützer

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Due to short product life cycles, increasing variety of products and short cycles of leap innovations manufacturing companies have to increase the flexibility of factory structures. Flexibility of factory structures is based on defined factory planning processes in which product, process and resource data of various partial domains have to be considered. Thus factory planning processes can be characterized as iterative, interdisciplinary and participative processes [1]. To support interdisciplinary and participative character of planning processes, a federative factory data management (FFDM) as a holistic solution will be described. FFDM is already implemented in form of a prototype. The interim results of the development of FFDM will be shown in this paper. The principles are the extracting of product, process and resource data from documents of various partial domains providing as web services on a server. The described data can be requested by the factory planner by using a FFDM-browser.

Keywords: BRAGECRIM, Factory Planning Process, FactoryData Management, Web Services

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Authors: Shaban Pazi, Chris Chatwin, Rupert Young, Philip Birch

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Internet infrastructures in most places of the world have been supported by the advancement of optical fiber technology, most notably wavelength division multiplexing (WDM) system. Optical technology by means of WDM system has revolutionized long distance data transport and has resulted in high data capacity, cost reductions, extremely low bit error rate, and operational simplification of the overall Internet infrastructure. This paper analyses and compares the system impairments, which occur at data transmission rates of 2.5Gb/s and 10 Gb/s per wavelength channel in our proposed optical WDM system for Internet infrastructure in Tanzania. The results show that the data transmission rate of 2.5 Gb/s has minimum system impairments compared with a rate of 10 Gb/s per wavelength channel, and achieves a sufficient system performance to provide a good Internet access service.

Keywords: Internet infrastructure, WDM system, standard single mode fibers, system impairments.

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

Abstract:

Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, unmanned aerial vehicle, UAV, random, Kriging.

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Authors: Usman Qamar, Younus Javed

Abstract:

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and roughsets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: Rough-sets, Classification, Feature Selection, Entropy, Outliers, Frequent itemset mining.

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Authors: Ilma Robo, Saimir Heta, Nedja Hysi, Vera Ostreni

Abstract:

Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values ​​according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.

Keywords: Marginal gingivitis, cross-sectional, retrograde, prevalence.

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