scholarly journals An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

2015 ◽  
Vol 8 (3) ◽  
pp. 881-891 ◽  
Author(s):  
M. N. Jebur ◽  
B. Pradhan ◽  
H. Z. M. Shafri ◽  
Z. M. Yusoff ◽  
M. S. Tehrany
Keyword(s):  
Natural Hazard ◽  
Model Performance ◽  
Statistical Modelling ◽  
Belief Function ◽  
Spatial Modelling ◽  
Weight Of Evidence ◽  
Microsoft Excel ◽  
Bivariate Statistical Analysis ◽  
Gis Environment ◽  
User Friendly

Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

scholarly journals An integrated user-friendly ArcMAP tool for bivariate statistical modeling in geoscience applications

2014 ◽  
Vol 7 (5) ◽  
pp. 7239-7265 ◽  
Author(s):  
M. N. Jebur ◽  
B. Pradhan ◽  
H. Z. M. Shafri ◽  
Z. Yusof ◽  
M. S. Tehrany
Keyword(s):  
Spatial Modeling ◽  
Natural Hazard ◽  
Model Performance ◽  
Belief Function ◽  
Microsoft Excel ◽  
Different Types ◽  
Bivariate Statistical Analysis ◽  
Gis Environment ◽  
User Friendly ◽  
Function Models

Abstract. Modeling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modeling. Bivariate statistical analysis (BSA) assists in hazard modeling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, BSM (bivariate statistical modeler), for BSA technique is proposed. Three popular BSA techniques such as frequency ratio, weights-of-evidence, and evidential belief function models are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and is created by a simple graphical user interface, which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

scholarly journals SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

2020 ◽  
Author(s):  
Stevenn Volant ◽  
Pierre Lechat ◽  
Perrine Woringer ◽  
Laurence Motreff ◽  
Christophe Malabat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Web Application ◽  
Graphical Representation ◽  
Statistical Modelling ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Microbiome Research ◽  
Interactive Visualizations ◽  
User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

scholarly journals CNN-based Diagnosis System on Skin Cancer using Ensemble Method Weighted by Cubic Precision

2021 ◽  
Author(s):  
Pengcheng Jiang
Keyword(s):  
User Interface ◽  
Skin Cancer ◽  
Language Processing ◽  
Classification Accuracy ◽  
Model Updating ◽  
Early Stage ◽  
Model Performance ◽  
Diagnosis System ◽  
Cancer Symptoms ◽  
User Friendly

<i>Abstract</i>— One of the most prevalent diseases, skin cancer, has been proven to be treatable at an early stage. Thus, techniques that allow individuals to identify skin cancer symptoms early are in great demand. This paper proposed an interactive skin lesion diagnosis system based on the ensemble of multiple sophisticated CNN models for image classification. The performance of ResNet50, ResNeXt50, ResNeXt101, EfficientNetB4, Mobile-NetV2, MobileNetV3, and MnasNet are investigated separately as ensemble components. Then, using various criteria, we constructed ensembles and compared the accuracy they achieved. Moreover, we designed a method to update the ensemble for new data and examined its performance. In addition, a few natural language processing (NLP) techniques were used to make our system more user-friendly. To integrate all the functionalities, we built a user interface with PyQt5. As a result, MobileNetV3 achieved 91.02% as the best accuracy among all single models; ensemble weighted by cubic precision achieved 92.84% accuracy as the highest one in this study; a notable improvement in accuracy demonstrated the effectiveness of the model updating approach, and a system with all of the desired features was successfully developed. These findings benefit in two aspects. For model performance, applying cubic precisions can increase ensemble learning classification accuracy. For the developed diagnosis system, it can aid in the

scholarly journals Possibilities of including surface runoff barriers in the slope-length factor calculation in the GIS environment and its integration in the user-friendly LS-RUSLE tool

2020 ◽  
Vol 15 (No. 4) ◽  
pp. 246-257
Author(s):  
Jiří Brychta ◽  
Martina Brychtová
Keyword(s):  
Surface Runoff ◽  
Flow Direction ◽  
Slope Angle ◽  
Pixel Resolution ◽  
Slope Length ◽  
Slope Length Factor ◽  
Ephemeral Gullies ◽  
Erosion Modelling ◽  
Gis Environment ◽  
User Friendly

The effect of the morphology is key aspect of erosion modelling. In Universal Soil Loss Equation (USLE) type methods, this effect is expressed by the topographic factor (LS). The LS calculation in GIS is performed by a unit contributing area (UCA) method and can mainly be influenced by the pixel resolution, by the flow direction algorithm and by the inclusion of a hydrologically closed unit (HCU) principle, the cutoff slope angle (CSA) principle and the ephemeral gullies extraction (EG) principle. This research presents a new LS-RUSLE tool created with the inclusion of these principles in the automatic user-friendly GIS tool. The HCU principle using a specific surface runoff interruption algorithm, based on pixels with NoData values at the interruption points (pixels), appears to be key. With this procedure, the occurrence of overestimation results by flow conversion was rapidly reduced. Additionally, the reduction of extreme L and LS values calculated in the GIS environment was reached by the application of the CSA and EG principles. The results of the LS-RUSLE model show the prospective use of this tool in practice.

Soil Organic Carbon Prediction at National Scale (Germany)

2020 ◽  
Author(s):  
Ali Sakhaee ◽  
Anika Gebauer ◽  
Mareike Ließ ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Model Performance ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Agricultural Landscapes ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
National Scale

&lt;p&gt;Soil Organic Carbon (SOC) plays a crucial role in agricultural ecosystems. However, its abundance is spatially variable at different scales. In recent years, machine learning (ML) algorithms have become an important tool in the spatial prediction of SOC at regional to continental scales. Particularly in agricultural landscapes, the prediction of SOC is a challenging task.&lt;/p&gt;&lt;p&gt;In this study, our aim is to evaluate the capability of two ML algorithms (Random Forest and Boosted Regression Trees) for topsoil (0 to 30 cm) SOC prediction in soils under agricultural use at national scale for Germany. In order to build the models, 50 environmental covariates representing topography, climate factors, land use as well as soil properties were selected. The SOC data we used was from the German Agricultural Soil inventory (2947 sampling points). A nested 5-fold cross-validation was used for model tuning and evaluation. Hyperparameter tuning for both ML algorithms was done by differential evolution optimization.&amp;#160;&lt;/p&gt;&lt;p&gt;This approach allows exploring an extensive set of field data in combination with state of the art pedometric tools. With a strict validation scheme, the geospatial-model performance was assessed. Current results indicate that the spatial SOC variation is to a minor extent predictable with the considered covariate data (&lt;30% explained variance). This may partly be explained by a non-steady state of SOC content in agricultural soils with environmental drivers. We discuss the challenges of geo-spatial modelling and the value of ML algorithms in pedometrics.&lt;/p&gt;

scholarly journals GEOINFORMATION APPROACH FOR COMPLEX ANALYSIS OF MULTIPLE NATURAL HAZARD

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 375-381 ◽  
Author(s):  
V. Nikolova ◽  
P. Zlateva
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Complex Analysis ◽  
Natural Hazard ◽  
Public Awareness ◽  
Limiting Factors ◽  
Simultaneous Occurrence ◽  
Suggested Approach ◽  
Natural Components ◽  
Gis Environment

<p><strong>Abstract.</strong> Natural hazards are existence of natural components and processes, which create a situation that could negatively affect people, the economy and the environment. In this concern, they are associated with the probability of negative impacts and they are considered as limiting factors for people's lives and activities. Rising public awareness about natural hazards could improve the quality of life, save financial resources and even save lives. Methodological issues of complex analysis of multiple natural hazards in geographic information system (GIS) environment are presented in the current paper on the example of floods and landslide assessment. The complicated nature of natural hazards and the interrelations between natural components require a complex analysis of natural hazard factors and an integrated assessment taking into account all aspects of different hazards as well as the overall hazard resulting from a probable simultaneous occurrence of several adverse natural phenomena. A special attention is given to the data as one of the most important component of the analysis. Different data formats and particularities of spatial data interpretation in GIS environment are considered. Having regard the nature of the data and the phenomenon being evaluated, different GIS spatial analysis tools (fuzzy overlay, weighted sum, interpolation) are applied together with mathematical analyses. The results of the current research and suggested approach could support decision makers in territorial planning and risk management.</p>

scholarly journals A Method of Teaching the Underlying Theory of XBRL: An Application of Information Modeling and XML Programming with a Microsoft Excel Implementation

2006 ◽  
Vol 1 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Thomas Tribunella ◽  
Heidi Tribunella
Keyword(s):  
System Development ◽  
Financial Statements ◽  
Thinking Skills ◽  
Information Modeling ◽  
Microsoft Excel ◽  
Computer Lab ◽  
Underlying Theory ◽  
Development Life Cycle ◽  
Concept Of Information ◽  
User Friendly

In this paper, we describe a computer lab exercise that will help students understand the underlying theory of XBRL. The lab requires students to build the taxonomy of accounts from a trial balance, create a well structured XML compliant document, load the XML document into Microsoft Excel, and produce a set of financial statements. The exercise follows the system development life cycle, is user friendly, and is very easy to understand. Bloom's taxonomy of thinking skills was used to structure the learning objectives. At two hours in length, the exercise has been used in an AIS class and was received very well by the students. By the end of the lab, students should understand the concept of information modeling, markup technologies, and how they can be applied to accounting.

scholarly journals Penggunaan Microsoft Excel Dalam Penyusunan Laporan Keuangan Untuk Meningkatkan Kualitas Informasi Laporan Keuangan

2020 ◽  
Vol 4 (1) ◽  
pp. 035-047
Author(s):  
Triandi . ◽  
Marina Agustin
Keyword(s):  
Financial Statements ◽  
Daily Basis ◽  
Financial Statement ◽  
Managerial Decision ◽  
Microsoft Excel ◽  
Financial Reports ◽  
Financial Report ◽  
Financial Conditions ◽  
User Friendly ◽  
Qualitative Characteristics

Compiling a financial reportis important because through this statement both external and internal parties may acquire informations about the firm’s financial conditions and its performance. These informations can then be used to make managerial decision for the sake of the firm’s future.   Building an accounting program corresponding to the company’s specific needs can be very expensive and time consuming, not to mention the trials and errors along the way. It takes great efforts to finally come up with ideal accounting program. To solve this issue, Microsoft Excel application provides affordable, simple, fast and accountable accounting program. The aim of this research are to investigate a proper qualitative characteristics of a financial report, to maximize the function of Microsoft Excel application in smaller firms for the purpose of providing a financial statement. The results show that Excel can enchance the informations contained in CV Inovasi Infinita’s financial statements. This application helps with its fast, accurate, complete, user friendly and details. Thus the owner can easily analyse each transaction on the daily basis. The application helps contributing in providing better financial reports.

scholarly journals A User-Friendly Tool to Characterize the Moisture Transfer in Porous Building Materials: FLoW1D

2020 ◽  
Vol 10 (15) ◽  
pp. 5090
Author(s):  
Virginia Cabrera ◽  
Rubén López-Vizcaíno ◽  
Ángel Yustres ◽  
Miguel Ángel Ruiz ◽  
Enrique Torrero ◽  
...  
Keyword(s):  
Building Materials ◽  
Moisture Transfer ◽  
Estimation Of Parameters ◽  
Microsoft Excel ◽  
Porous Building Materials ◽  
Visual Basic For Applications ◽  
Conventional Tests ◽  
User Friendly ◽  
Santa Maria

This paper presents a user-friendly tool—FLoW1D (One-Dimensional Water Flow)—for the estimation of parameters that characterize the unsaturated moisture transfer in porous building materials. FLoW1D has been developed in Visual Basic for Applications and implemented as a function of the well-known Microsoft Excel© spreadsheet application. The aim of our work is to provide a simple and useful tool to improve the analysis and interpretation of conventional tests for the characterization of the hygric behavior of porous building materials. FLoW1D embraces the conceptual model described in EN 15026 for moisture transfer in building elements, and its implementation has been verified and validated correctly. In order to show the scope of the code, an example of an application has been presented. The hygric characterization of the limestone that is mostly employed in the Cathedral of Santa Maria and San Julian in Cuenca (Spain) was conducted based on an analysis of the conventional water absorption by capillarity tests (EN 15801).

Design and Simulation of Computer Business Model for Primary Processing of Hardwood Material

2005 ◽  
Author(s):  
Sudheer K. Padma ◽  
Kenneth M. Ragsdell ◽  
Robert A. Sickler
Keyword(s):  
Decision Making ◽  
Business Model ◽  
Production Cost ◽  
Microsoft Excel ◽  
Flow Paths ◽  
Timber Products ◽  
Revenue Estimates ◽  
User Friendly ◽  
Alternative Activities ◽  
Decision Making Tool

Computer business model of hardwood production gives the user an opportunity to examine different ways and provide the capability of executing scenarios using alternative activities and process flow paths. This paper illustrates the design of business model based on which the simulation will be built. It will be able to simulate the effect of variables in each process and provide the output in terms of cost, quality and quantity of timber products produced at the end of primary hardwood processing. The simulation will act as a powerful decision making tool and be user friendly. It will be built with the combination of Visual Basic.NET and Microsoft Excel. It will provide cost and revenue estimates, identifies process issues and will have the capability to rapidly react to changing markets. The simulation will also be able to study the viability of capturing more product value and reducing production cost.

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