Search results for: thick data analytics

Authors: Semih Erdogan

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In this paper, the normal and shear stress distributions in Vlasov beams are determined by two-dimensional triangular finite element formulations. The proposed formulations take into account the warping effects along the beam axis. The shape of the considered beam sections may be arbitrary and varied throughout its length. The stiffness matrices and force vectors are derived for transversal forces, uniform torsion, and nonuniform torsion. The proposed finite element algorithm is validated by comparing the analytical solutions, structural engineering books, and related articles. The numerical examples include beams with different cross-section types such as solid, thick-walled, closed-thin-walled, and open-thin-walled sections. Materials defined in the examples are homogeneous, isotropic, and linearly elastic. Through these examples, the study demonstrates the capability of the proposed method to address a wide range of practical engineering scenarios.

Keywords: Vlasov beams, warping function, nonuniform torsion, finite element method, normal and shear stresses, cross-section properties

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

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Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

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Authors: Jesús A. Pérez, Sam Coppieters, Dimitri Debruyne

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In the past decade, the use of digital image correlation (DIC) techniques has increased significantly in the area of experimental mechanics, especially for materials behavior characterization. This non-contact tool enables full field displacement and strain measurements over a complete region of interest. The DIC algorithm requires a random contrast pattern on the surface of the specimen in order to perform properly. To create this pattern, the specimen is usually first coated using a white matt paint. Next, a black random speckle pattern is applied using any suitable method. If the applied paint coating is too thick, its top surface may not be able to exactly follow the deformation of the specimen, and consequently, the strain measurement might be underestimated. In the present article, a study of the influence of the paint thickness on the strain underestimation is performed for different strain levels. The results are then compared to typical paint coating thicknesses applied by experienced DIC users. A slight strain underestimation was observed for paint coatings thicker than about 30μm. On the other hand, this value was found to be uncommonly high compared to coating thicknesses applied by DIC users.

Keywords: digital image correlation, paint coating thickness, strain

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

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Authors: Milind Chaudhari, Suhail Balasinor

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Due to massive deforestation and an ever-increasing population, the organic content of the soil is depleting at a much faster rate. Due to this, there is a big chance that the entire food production in the world will drop by 40% in the next two decades. Vertical farming can help in aiding food production by leveraging big data and cloud computing to ensure plants are grown naturally by providing the optimum nutrients sunlight by analyzing millions of data points. This paper outlines the most important parameters in vertical farming and how a combination of big data and AI helps in calculating and analyzing these millions of data points. Finally, the paper outlines how different organizations are controlling the indoor environment by leveraging big data in enhancing food quantity and quality.

Keywords: big data, IoT, vertical farming, indoor farming

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Authors: Sawsan M. H. Domi

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Approximately 50% of the energy used in buildings, including houses, provide environmental comfortable levels of thermal living. In Khartoum - the city under study- cooling uses the largest portion of energy and the basic idea of Low energy houses is to minimize energy consumption. Therefore, houses are designed to use natural climate strategies to provide thermal comfort. Strategies such as semi-open spaces, shading devices, small high windows and thick walls. The study aims to review these strategies and then, apply them. It aims to change house microclimate by using vegetation, green areas, and other components. A low energy house is being designed s. It will be the first low energy house in Khartoum designed to create a low-cost energy efficient building without any mechanical systems. Three different types of houses in Khartoum are examined and evaluated according to their energy loads which provides the basis for the designed house. The designed house uses passive design strategies to reduce the need for cooling. These results show that the house reduced energy cooling loads by more than 60% compared to the average of the three given types. The design house is economically viable when taking into consideration the energy prices in Sudan.

Keywords: building envelope, climate, energy loads, ventilation

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Authors: A. Anis, W. Bekheet, A. El Hakim

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Implementing a Road Safety Management System (SMS) in a crowded developing country such as Egypt is a necessity. Beginning a sustainable SMS requires a comprehensive reliable data system for all information pertinent to road crashes. In this paper, a survey for the available data in Egypt and validating it for using in an SMS in Egypt. The research provides some missing data, and refer to the unavailable data in Egypt, looking forward to the contribution of the scientific society, the authorities, and the public in solving the problem of missing or unreliable crash data. The required data for implementing an SMS in Egypt are divided into three categories; the first is available data such as fatality and injury rates and it is proven in this research that it may be inconsistent and unreliable, the second category of data is not available, but it may be estimated, an example of estimating vehicle cost is available in this research, the third is not available and can be measured case by case such as the functional and geometric properties of a facility. Some inquiries are provided in this research for the scientific society, such as how to improve the links among stakeholders of road safety in order to obtain a consistent, non-biased, and reliable data system.

Keywords: road safety management system, road crash, road fatality, road injury

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Authors: Bintao Wu, Xiangfang Xu, Yugang Miao，Duanfeng Han

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Joining of 1 mm thick aluminum 6061 to titanium TC4 was conducted using Bypass-current MIG welding-brazed, and stable welding process and good bead appearance were obtained. The Joint profile and microstructure of Ti/Al joints were observed by optical microscopy and SEM and then the structure of the interfacial reaction layers were analyzed in details. It was found that the intermetallic compound layer at the interfacial top is in the form of columnar crystal, which is in short and dense state. A mount of AlTi were observed at the interfacial layer near the Ti base metal while intermetallic compound like Al3Ti、TiSi3 were formed near the Al base metal, and the Al11Ti5 transition phase was found in the center of the interface layer due to the uneven distribution inside the weld pool during the welding process. Tensile test results show that the average tensile strength of joints is up to 182.6 MPa, which reaches about 97.6% of aluminum base metal. Fracture is prone to occur in the base metal with a certain amount of necking.

Keywords: bypass-current MIG welding-brazed, Al alloy, Ti alloy, joint characteristics, mechanical properties

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Authors: Chejarla Raghunathababu, E. Logashanmugam

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An antenna is an assortment of linked devices that function together to transmit and receive radio waves as a single antenna. Antennas occur in a variety of sizes and forms, but the microstrip patch antenna outperforms other types in terms of effectiveness and prediction. These antennas are easy to generate with discreet benefits. Nevertheless, the antenna's effectiveness will be affected because of the patch's shape above a thick dielectric substrate. As a result, a double-pole rectangular microstrip antenna with nanofillers was suggested in this study. By employing nano-composite substances (Fumed Silica and Aluminum Oxide), which are composites of graphene with nanofillers, the physical characteristics of the microstrip antenna, that is, the elevation of the microstrip antenna substrate and the width of the patch microstrip antenna have been improved in this research. The surface conductivity of graphene may be modified to function at specific frequencies. In order to prepare for future wireless communication technologies, a microstrip patch antenna operating at 93 GHz resonant frequency is constructed and investigated. The goal of this study was to reduce VSWR and increase gain. The simulation yielded results for the gain and VSWR, which were 8.26 dBi and 1.01, respectively.

Keywords: graphene, microstrip patch antenna, substrate material, wireless communication, nanocomposite material

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Authors: Xu Du, Aparna S. Varde

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In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

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Authors: Tabrej Khan, Tamer A. Sebaey, Balbir Singh, M. A. Umarfarooq

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Spread-tow thin-ply-based technology has resulted in the progress of optimized reinforced composite plies with ultra-low thicknesses. There is wide use of composite bolted joints in the aircraft industry for load-bearing structures, and they are regarded as the primary source of stress concentration. The purpose of this study is to look into the bearing strength and structural performance of single shear bolt joint configurations in composite laminates, which are basically a combination of conventional thin-plies and thick-plies in some specific stacking sequence. The placement effect of thin-ply within the configured stack on bearing strength, as well as the potential damages, were investigated. Mechanical tests were used to understand the disfigurement mechanisms of the plies and their reciprocity, as well as to reflect on the single shear bolt joint properties and its load-bearing capacity. The results showed that changing the configuration of laminates by inserting the thin plies inside improved the bearing strength by up to 19%.

Keywords: hybrid composites, delamination, stress concentrations, mechanical testing, single bolt joint, thin-plies

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Authors: Aref Ghafouri, Mohammad javad Mollakazemi, Farhad Asadi

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In this paper, model order reduction method is used for approximation in linear and nonlinearity aspects in some experimental data. This method can be used for obtaining offline reduced model for approximation of experimental data and can produce and follow the data and order of system and also it can match to experimental data in some frequency ratios. In this study, the method is compared in different experimental data and influence of choosing of order of the model reduction for obtaining the best and sufficient matching condition for following the data is investigated in format of imaginary and reality part of the frequency response curve and finally the effect and important parameter of number of order reduction in nonlinear experimental data is explained further.

Keywords: frequency response, order of model reduction, frequency matching condition, nonlinear experimental data

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Authors: Hitesh Tahbildar

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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

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Authors: A. Sharma, S. S. Sandhu, A. Shahi, A. Kumar

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The aim of present investigation was to carry out Finite element modeling of distortion in the case of butt weld. 12mm thick AISI 304L plates were butt welded using three different combinations of groove design namely Double U, Double V and Composite. A full simulation of shielded metal arc welding (SMAW) of nonlinear heat transfer is carried out. Aspects like, temperature-dependent thermal properties of AISI stainless steel above liquid phase, the effect of thermal boundary conditions, were included in the model. Since welding heat dissipation characteristics changed due to variable groove design significant changes in the microhardness tensile strength and impact toughness of the joints were observed. The cumulative distortion was found to be least in double V joint followed by the Composite and Double U-joints. All the joints have joint efficiency more than 100%. CVN value of the Double V-groove weld metal was highest. The experimental results and the FEM results were compared and reveal a very good correlation for distortion and weld groove design for a multipass joint with a standard analogy of 83%.

Keywords: AISI 304 L, Butt joint, distortion, FEM, groove design, SMAW

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Authors: Hatice Kübra Koç

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For academia, collecting reliable and valid data is one of the most significant issues for researchers. However, it is not the same procedure for all different target groups; meanwhile, during data collection from teenagers, young adults, or adults, researchers can use common data collection tools such as questionnaires, interviews, and semi-structured interviews; yet, for young learners and very young ones, these reliable and valid data collection tools cannot be easily designed or applied by the researchers. In this study, firstly, common data collection tools are examined for ‘very young’ and ‘young learners’ participant groups since it is thought that the quality and efficiency of an academic study is mainly based on its valid and correct data collection and data analysis procedure. Secondly, two different data collection instruments for very young and young learners are stated as discussing the efficacy of them. Finally, a suggested data collection tool – a performance-based questionnaire- which is specifically developed for ‘very young’ and ‘young learners’ participant groups in the field of teaching English to young learners as a foreign language is presented in this current study. The designing procedure and suggested items/factors for the suggested data collection tool are accordingly revealed at the end of the study to help researchers have studied with young and very learners.

Keywords: data collection instruments, performance-based questionnaire, young learners, very young learners

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Authors: Fatemeh Mirakhorli

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Porosity is the main issue during welding of aluminum alloys, and surface cleaning has a critical influence to reduce the porosity level by removing the oxidized surface layer before fusion welding. Developing an optimum and economical surface cleaning method has an enormous benefit for aluminum welding industries to reduce costs related to repairing and repeating welds as well as increasing the mechanical properties of the joints. In this study, several mechanical and chemical surface cleaning methods were examined for butt joint welding of 2 mm thick AA6xxx alloys using ER5556 filler metal. The effects of each method on porosity formation and tensile properties are evaluated. It has been found that, compared to the conventional mechanical cleaning method, the use of chemical cleaning leads to an important reduction in porosity level even after a significant delay between cleaning and welding. The effect of the higher porosity level in the fusion zone to reduce the tensile strength of the welds is shown.

Keywords: gas metal arc welding (GMAW), aluminum alloy, surface cleaning, porosity formation, mechanical property

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Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

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Authors: Paulina Solórzano Salgado, Luis Rodrigo Valencia Pérez, Alberto de Jesús Pastrana Palma

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In this research, it is presented a digital maturity framework, which contributes to the development of small and medium-sized enterprises (SMEs) in the commercial sector. This proposal is based on three important concepts: Marketing activities in the enterprise, information and communication technologies ICT, as well as Innovation. Prior to the development of this framework, was formulated a quantitative assessment tool through a literature review, and was validated with a method used by experts, and which determines the relationship of digital marketing and innovation activities in companies. The instrument was applied to 64 Mexican companies from the Made in Mexico database, which allowed both descriptive results and correlation results. These contributed to the development of the methodology, and confirming that the management of digital marketing has a positive relation with innovation activities of companies. Also, that analytics in digital marketing is a source for its development. In this paper, the management stages and activities are presented to be developed by companies in order to generate knowledge, which will allow them to reach its digital maturity.

Keywords: digital marketing, digital maturity, innovation, SMEs

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Authors: John Q. Todd

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Given that modern equipment can provide comprehensive health, status, and error condition data via built-in sensors, maintenance organizations have a new and valuable source of insight to take advantage of. This presentation will expose what these data payloads might look like and how they can be filtered, visualized, calculated into metrics, used for machine learning, and generate alerts for further action.

Keywords: condition based maintenance, equipment data, metrics, alerts

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Authors: Adeolu O. Dairo

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Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making.

Keywords: geospatial, geo-analytics, self-organizing map, customer-centric

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Authors: Dragana Calic

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The openness, availability and the sheer volume of big data have provided, what some regard as, an invaluable and rich dataset. Researchers, businesses, advertising agencies, medical institutions, to name only a few, collect, share, and analyze this data to enable their processes and decision making. However, there are important ethical considerations associated with the use of big data. The rapidly evolving nature of online technologies has overtaken the many legislative, privacy, and ethical frameworks and principles that exist. For example, should we obtain consent to use people’s online data, and under what circumstances can privacy considerations be overridden? Current guidance on how to appropriately and ethically handle big data is inconsistent. Consequently, this paper focuses on two quite distinct but related ethical considerations that are at the core of the use of big data for research purposes. They include empowering the producers of data and empowering researchers who want to study big data. The first consideration focuses on informed consent which is at the core of empowering producers of data. In this paper, we discuss some of the complexities associated with informed consent and consider studies of producers’ perceptions to inform research ethics guidelines and practice. The second consideration focuses on the researcher. Similarly, we explore studies that focus on researchers’ perceptions and experiences.

Keywords: big data, ethics, producers’ perceptions, researchers’ perceptions

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Ankur Mundhra, Shubhadeep Chakraborty, Y. R. Singh, Vishal Das

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Seismic data scaling affects the dynamic range of a data and with present day lower costs of storage and higher reliability of Hard Disk data, scaling is not suggested. However, in dealing with data of different vintages, which perhaps were processed in 16 bits or even 8 bits and are need to be processed with 32 bit available data, scaling is performed. Also, scaling amplifies low amplitude events in deeper region which disappear due to high amplitude shallow events that saturate amplitude scale. We have focused on significance of scaling data to aid interpretation. This study elucidates a proper seismic loading procedure in workstations without using default preset parameters as available in most software suites. Differences and distribution of amplitude values at different depth for seismic data are probed in this exercise. Proper loading parameters are identified and associated steps are explained that needs to be taken care of while loading data. Finally, the exercise interprets the un-certainties which might arise when correlating scaled and unscaled versions of seismic data with synthetics. As, seismic well tie correlates the seismic reflection events with well markers, for our study it is used to identify regions which are enhanced and/or affected by scaling parameter(s).

Keywords: clipping, compression, resolution, seismic scaling

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Authors: Mohannad N. H. Al-Malichi

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Because of its exceptional properties, graphene has become the most promising nanomaterial for the development of a new generation of advanced materials from battery electrodes to structural composites. However, current methods to meet requirements for the mass production of high-quality graphene are limited by harsh oxidation, high temperatures, and tedious processing steps. To extend the scope of the bulk production of graphene, herein, a facile, reproducible and cost-effective approach has been developed. This involved heating a specific mixture of chemical materials at an extremely low temperature (70 C) for a short period (7 minutes) to exfoliate functionalized graphene platelets with high structural integrity. The obtained graphene platelets have an average thickness of 3.86±0.71 nm and a lateral size less than ~2 µm with a low defect intensity ID/IG ~0.06. The thin film (~2 µm thick) exhibited a low surface resistance of ~0.63 Ω/sq⁻¹, confirming its high electrical conductivity. Additionally, these nanoplatelets were decorated with polar functional groups (epoxy and carboxyl groups), thus have the potential to toughen and provide multifunctional polymer nanocomposites. Moreover, such a simple method can be further exploited for the novel exfoliation of other layered two-dimensional materials such as MXenes.

Keywords: functionalized graphene nanoplatelets, high structural integrity graphene, low temperature exfoliation of graphene, functional graphene platelets

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Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data mining

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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