Search results for: missing data

Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

Abstract:

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

Procedia PDF Downloads 161

Authors: Paula Simões, Isabel Natário

Abstract:

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

Procedia PDF Downloads 595

Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

Abstract:

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

Procedia PDF Downloads 356

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

Abstract:

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

Procedia PDF Downloads 191

Authors: Husnain Mushtaq, Mary Anjum, Muhammad Aleem

Abstract:

The main objective of this paper is used to develop a high security in Automatic Teller Machine (ATM). In these system bankers will collect the mobile numbers from the customers and then provide a code on their mobile number. In most country existing ATM machine use the magnetic card reader. The customer is identifying by inserting an ATM card with magnetic card that hold unique information such as card number and some security limitations. By entering a personal identification number, first the customer is authenticated then will access bank account in order to make cash withdraw or other services provided by the bank. Cases of card fraud are another problem once the user’s bank card is missing and the password is stolen, or simply steal a customer’s card & PIN the criminal will draw all cash in very short time, which will being great financial losses in customer, this type of fraud has increase worldwide. So to resolve this problem we are going to provide the solution using “Mobile SMS code” and ATM “PIN code” in order to improve the verify the security of customers using ATM system and confidence in the banking area.

Keywords: PIN, inquiry, biometric, magnetic strip, iris recognition, face recognition

Procedia PDF Downloads 366

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

Abstract:

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

Procedia PDF Downloads 248

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

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.

Procedia PDF Downloads 144

Authors: Milind Chaudhari, Suhail Balasinor

Abstract:

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

Procedia PDF Downloads 176

Authors: Radomir Perzina, Jaroslav Ramik

Abstract:

The Analytic Hierarchy Process is frequently used approach for solving decision making problems. There exists wide range of software programs utilizing that approach. Their main disadvantage is that they are relatively expensive and missing intermediate calculations. This work introduces a Microsoft Excel add-in called DAME – Decision Analysis Module for Excel. Comparing to other computer programs DAME is free, can work with scenarios or multiple decision makers and displays intermediate calculations. Users can structure their decision models into three levels – scenarios/users, criteria and variants. Items on all levels can be evaluated either by weights or pair-wise comparisons. There are provided three different methods for the evaluation of the weights of criteria, the variants as well as the scenarios – Saaty’s Method, Geometric Mean Method and Fuller’s Triangle Method. Multiplicative and additive syntheses are supported. The proposed software package is demonstrated on couple of illustrating examples of real life decision problems.

Keywords: analytic hierarchy process, multi-criteria decision making, pair-wise comparisons, Microsoft Excel, scenarios

Procedia PDF Downloads 452

Authors: Oualid Walid Ben Ali

Abstract:

Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

Procedia PDF Downloads 491

Authors: Abdulkabir Abdullahi

Abstract:

The paper is a pre-use evaluation of grammar activities in three primary English course books (two of which are international primary English course books and the other a popular Nigerian primary English course book). The titles are - Cambridge Global English, Collins International Primary English, and Nigeria Primary English – Primary English. Grammar points and grammar activities in the three-course books were identified, grouped, and evaluated. The grammar activity which was most common in the course books, simple past tense, was chosen for evaluation, and the units which present simple past tense activities were selected to evaluate the extent to which the treatment of simple past tense in each of the course books help the young learners of English as a second language in Nigeria, aged 8 – 11, level A1 to A2, who lack the basic grammatical knowledge, to know grammar/communicate effectively. A bespoke checklist was devised, through the modification of existing checklists for the purpose of the evaluation, to evaluate the extent to which the grammar activities promote the communicative effectiveness of Nigerian learners of English as a second language. The results of the evaluation and the analysis of the data reveal that the treatment of grammar, especially the treatment of the simple past tense, is evidently insufficient. While Cambridge Global English’s, and Collins International Primary English’s treatment of grammar, the simple past tense, is underpinned by state-of-the-art theories of learning, language learning theories, second language learning principles, second language curriculum-syllabus design principles, grammar learning and teaching theories, the grammar load is insignificantly low, and the grammar tasks do not promote creative grammar practice sufficiently. Nigeria Primary English – Primary English, on the other hand, treats grammar, the simple past tense, in the old-fashioned direct way. The book does not favour the communicative language teaching approach; no opportunity for learners to notice and discover grammar rules for themselves, and the book lacks the potency to promote creative grammar practice. The research and its findings, therefore, underscore the need to improve grammar contents and increase grammar activity types which engage learners effectively and promote sufficient creative grammar practice in EFL and ESL material design and development.

Keywords: evaluation, activity, second language, activity-types, creative grammar practice

Procedia PDF Downloads 84

Authors: Xu Du, Aparna S. Varde

Abstract:

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

Procedia PDF Downloads 309

Authors: Aref Ghafouri, Mohammad javad Mollakazemi, Farhad Asadi

Abstract:

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

Procedia PDF Downloads 404

Authors: F. O’Dowd, G. Murphy, M. Roche, E. Shudell, F. Keane, M. O’Kane

Abstract:

Intoduction: Apremilast (Otezla®) is an oral phosphodiesterase-4 (PDE4) inhibitor indicated for treatment of adult patients with moderate to severe plaque psoriasis who have contraindications to have failed or intolerant of standard systemic therapy and/or phototherapy; and adult patients with active psoriatic arthritis. Apremilast influences intracellular regulation of inflammatory mediators. Two randomized, placebo-controlled trials evaluating apremilast in 1426 patients with moderate to severe plague psoriasis (ESTEEM 1 and 2) demonstrated that the commonest adverse reactions (AE’s) leading to discontinuation were nausea (1.6%), diarrhoea (1.0%), and headaches (0.8%). The overall proportion of subjects discontinuing due to adverse reactions was 6.1%. At week 16 these trials demonstrated significant more apremilast-treated patients (33.1%) achieved the primary end point PASI-75 than placebo (5.3%). We began prescribing apremilast in July 2015. Aim: To evaluate efficacy and tolerability of apremilast in an Irish teaching hospital psoriasis population. Methods: A proforma documenting clinical evaluation parameters, prior treatment experience and AE’s; was completed prospectively on all patients commenced on apremilast since July 2015 – July 2017. Data was collected at week 0,6,12,24,36 and week 52 with 20/71 patients having passed week 52. Efficacy was assessed using Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI). AE’s documented included GI effects, infections, changes in weight and mood. Retrospective chart review and telephone review was utilised for missing data. Results: A total of 71 adult subjects (38 male, 33 female; age range 23-57), with moderate to severe psoriasis, were evaluated. Prior treatment: 37/71 (52%) were systemic/biologic/phototherapy naïve; 14/71 (20%) has prior phototherapy alone;20/71 (28%) had previous systemic/biologic exposure; 12/71 (17%) had both psoriasis and psoriatic arthritis. PASI responses: mean baseline PASI was 10.1 and DLQI was 15.Week 6: N=71, n=15 (21%) achieved PASI 75. Week 12: N= 48, n=6 (13%) achieved a PASI 100%; n=16 (34.5%) achieved a PASI 75. Week 24: N=40, n=10 (25%) achieved a PASI 100; n=15 (37.5%) achieved a PASI 75. Week 52: N= 20, n=4 (20%) achieved a PASI 100; n= 16 (80%) achieved a PASI 75. (N= number of pts having passed the time point indicated, n= number of pts (out of N) achieving PASI or DLQI responses at that time). DLQI responses: week 24: N= 40, n=30 (75%) achieved a DLQI score of 0; n=5 (12.5%) achieved a DLQI score of 1; n=1 (2.5%) achieved a DLQI score of 10 (due to lack of efficacy). Adverse Events: The proportion of patients that discontinued treatment due to AE’s was n=7 (9.8%). One patient experienced nausea alleviated by dose reduction; another developed significant dysgeusia for certain foods, both continued therapy. Two patients lost 2-3 kg. Conclusion: Initial Irish patient experience of Apremilast appears comparable to that observed in trials with good efficacy and tolerability.

Keywords: Apremilast, introduction, Ireland, clinical audit

Procedia PDF Downloads 149

Authors: Thuan Nguyen

Abstract:

In the present time, data to a data-driven knowledge-based economy is the same as oil to the industrial age hundreds of years ago. Data is everywhere in vast volumes! Big data analytics is expected to help firms not only efficiently improve performance but also completely transform how they should run their business. However, employing the emergent technology successfully is not easy, and assessing the roles of big data in improving firm performance is even much harder. There was a lack of studies that have examined the impacts of big data analytics on organizational performance. This study aimed to fill the gap. The present study suggested using firms’ intellectual capital as a proxy for big data in evaluating its impact on organizational performance. The present study employed the Value Added Intellectual Coefficient method to measure firm intellectual capital, via its three main components: human capital efficiency, structural capital efficiency, and capital employed efficiency, and then used the structural equation modeling technique to model the data and test the models. The financial fundamental and market data of 100 randomly selected publicly listed firms were collected. The results of the tests showed that only human capital efficiency had a significant positive impact on firm profitability, which highlighted the prominent human role in the impact of big data technology.

Keywords: big data, big data analytics, intellectual capital, organizational performance, value added intellectual coefficient

Procedia PDF Downloads 245

Authors: Hitesh Tahbildar

Abstract:

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

Procedia PDF Downloads 407

Authors: Tsafi Timor

Abstract:

The research focuses on the exploration of the unique journey that novice teachers experience in their first year of teaching, among graduates of re-training programs into teaching. The study explores the experiences of success and failure and the factors that underpin positive experiences, as well as the journey (process) of this year with reference to the comparison between novice teachers and new immigrants. The content analysis that was adopted in the study was conducted on texts that were written by the teachers and detailed their first year of teaching. The findings indicate that experiences of success are featured by personal satisfaction, constant need of feedback, high motivation in challenging situations, and emotions. Failure experiences are featured by frustration, helplessness, sense of humiliation, feeling of rejection, and lack of efficacy. Factors that promote and inhibit positive experiences relate to personal, personality, professional and organizational levels. Most teachers reported feeling like new immigrants, and demonstrated different models of the process of the first year of teaching. Further research is recommended on the factors that promote and inhibit positive experiences, and on 'The Missing Link' of the relationship between Teacher Education Programs and the practices in schools.

Keywords: first-year teaching, novice teachers, school practice, teacher education programs

Procedia PDF Downloads 291

Authors: Hatice Kübra Koç

Abstract:

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

Procedia PDF Downloads 94

Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

Procedia PDF Downloads 34

Authors: Pooja Pandey

Abstract:

The role of communication plays an integral part in the management of any disaster, whether natural or human-induced, both require effective and strategic delivery of information. The way any information is conveyed carries the most weight while dealing with the disaster. Hence, integrating communication strategies in disaster risk management (DRM) are extensively acknowledged however, these integration and planning are missing from the practical books. Researchers are continuously exploring integrated DRM and have established substantial vents between research and implementation of the strategies (gaps between science and policy). For this reason, this paper reviews the communication barriers that obstruct effective management of the disaster. Communication between first responders (government agencies, police, medical services) and the public (people directly affected by the disaster) is most critical and lacks proper delivery during a disaster. And these challenges can only be resolved if the foundation of the problem is properly dealt with, which is resolving the issues within the organizations. Through this study, it was found that it is necessary to build the communication gap between the organizations themselves as most of the hindrances occur during the mitigation, preparedness, response and recovery phase of the disaster. The study is concluded with the main aim to review the communication barriers within and at the organizational, technological, and social levels that impact effective DRM. In the end, some suggestions are made to strengthen the knowledge for future improvement in communication between the responders and their organizations.

Keywords: communication, organization, barriers, first responders, disaster risk management

Procedia PDF Downloads 302

Authors: Mobina Bagherianlemraski, Parastoo Namdar

Abstract:

Background: The ongoing coronavirus disease has affected most countries. This virus has high transmission power. Due to the closure of most dental clinics, millions of orthodontic patients missed their appointments during the COVID-19 pandemic. Methods: A questionnaire was developed and sent to patients receiving orthodontic treatment at a public or private clinic. Results: A total of 200 responses were analyzed: These included 153 women (76.5%) and 47 men (23.5%). The mean and standard deviation of their age was 18.92±7.23 years, with an age range of 8 to 40 years. One hundred eighty-nine patients (94.5%) had fixed appliances, and 11 patients (5.5%) had removable appliances. Of all participants, 35% (70) missed their appointments. The highest and lowest reasons for stopping appointments were concerned about the spread of COVID-19 with 28 cases (40%) and the closure of the clinic with 15 cases (21.4%). Of the 53 patients who contacted their orthodontists, 86.8 % (46) communicated via office phone and 5.7% (3) through social media. Conclusion: This study determined that the coronavirus pandemic and quarantine have had an important impact on orthodontic treatments. The greatest concern of orthodontic patients was increasing in treatment duration. Patients who used fixed appliances reported missing dental appointments more than others. Therefore, during COVID 19 Pandemic, orthodontists should prepare patients to solve their problems linked to orthodontic appliances when possible.

Keywords: orthodontic patients, coronavirus pandemic, appointments, COVID-19

Procedia PDF Downloads 139

Authors: Dominique Arrouays, Songchao Chen, Anne C. Richer-De-Forges

Abstract:

Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

Procedia PDF Downloads 184

Authors: Hyun-Woo Cho

Abstract:

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

Procedia PDF Downloads 483

Authors: Hsien-Tsen Wang

Abstract:

Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

Procedia PDF Downloads 97

Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

Abstract:

Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: assembly scheduling, large-scale products, make-to-order, optimization, rescheduling

Procedia PDF Downloads 459

Authors: John Q. Todd

Abstract:

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

Procedia PDF Downloads 189

Authors: Vanessa R. Ralph, Leah J. Scharlott, Megan Y. Deshaye, Ryan L. Stowe

Abstract:

Given the traditions of chemistry teaching, one may not question whether chemical phenomena play a prominent role. Yet, the role of chemical phenomena in an introductory chemistry course may define the extent to which the course is introductory, chemistry, and equitable. Picture, for example, the classic Ideal Gas Law problem. If one envisions a prompt wherein students are tasked with calculating a missing variable, then one envisions a prompt that relies on chemical phenomena as a context rather than as a model to understand the natural world. Consider a prompt wherein students are tasked with applying molecular models of gases to explain why the vapor pressure of a gaseous solution of water differs from that of carbon dioxide. Here, the chemical phenomenon is not only the context but also the subject of the prompt. Deliveries of general and organic chemistry were identified as ranging wildly in the integration of chemical phenomena. The more incorporated the phenomena, the more equitable the assessment task was for students of varying access to pre-college math and science preparation. How chemical phenomena are integrated may very well define whether courses are chemistry, are introductory, and are equitable. Educators of chemistry are invited colleagues to discuss the role of chemical phenomena in their courses and consider the long-lasting impacts of replicating tradition for tradition’s sake.

Keywords: equitable educational practices, chemistry curriculum, content organization, assessment design

Procedia PDF Downloads 197

Authors: Dragana Calic

Abstract:

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

Procedia PDF Downloads 285

Authors: Walid Cherif

Abstract:

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

Procedia PDF Downloads 465

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

Abstract:

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

Procedia PDF Downloads 471