Methodology for the Selection of Intelligence Analysis Tools

2006 ◽  
Author(s):  
Haleh Vafaie ◽  
Nichols Brown ◽  
Lap Truong
Keyword(s):  
Intelligence Analysis ◽  
Analysis Tools ◽  
Selection Of

Finding decision support requirements for effective intelligence analysis tools

2005 ◽  
Author(s):  
William Elm ◽  
Scott Potter ◽  
James Tittle ◽  
David Woods ◽  
Justin Grossman ◽  
...  
Keyword(s):  
Decision Support ◽  
Intelligence Analysis ◽  
Analysis Tools

scholarly journals Open Plot Project: an open-source toolkit for 3-D structural data analysis

Solid Earth ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. Tavani ◽  
P. Arbues ◽  
M. Snidero ◽  
N. Carrera ◽  
J. A. Muñoz
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Structural Data ◽  
Geological Modelling ◽  
Analysis Tools ◽  
Transect Analysis ◽  
Selection Of

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

Overview of Movement Analysis and Gait Features

2011 ◽  
pp. 1-69 ◽  
Author(s):  
Russell Best ◽  
Rezaul Begg
Keyword(s):  
Computational Intelligence ◽  
Movement Analysis ◽  
Research Process ◽  
Machine Learning Techniques ◽  
Intelligence Analysis ◽  
Objective Data ◽  
Analysis Tools ◽  
Learning Techniques ◽  
Gait Features ◽  
Key Features

This chapter provides an overview of the commonly used motion analysis approaches and techniques and the key features that are extracted from movement patterns for characterizing gait. The ultimate goal of gait analysis should be to provide reliable, objective data on which to base clinical decisions (Kaufman, 1998). Thousands of gait features/parameters have been used over the years. Selection of the correct gait features forms an important part of the research process, and often the success of the research outcomes depends heavily on selecting the most appropriate gait features. Analysis tools based on both statistical and machine-learning techniques use various types of gait features, ranging from the basic and directly measurable parameters to parameters that have undergone significant data processing and treatments. In this chapter, we attempt to introduce the commonly used methods to extract these features for use with the various statistical and computational intelligence analysis tools.

Encyclopedia of tools for the analysis of miRNA isoforms

2020 ◽  
Author(s):  
Georges Pierre Schmartz ◽  
Fabian Kern ◽  
Tobias Fehlmann ◽  
Viktoria Wagner ◽  
Bastian Fromm ◽  
...  
Keyword(s):  
Time Range ◽  
Data Sets ◽  
Sequencing Data ◽  
Comprehensive Overview ◽  
Computational Tools ◽  
Web Based ◽  
Analysis Tools ◽  
Research Task ◽  
Publication Time ◽  
Selection Of

Abstract RNA sequencing data sets rapidly increase in quantity. For microRNAs (miRNAs), frequently dozens to hundreds of billion reads are generated per study. The quantification of annotated miRNAs and the prediction of new miRNAs are leading computational tasks. Now, the increased depth of coverage allows to gain deeper insights into the variability of miRNAs. The analysis of isoforms of miRNAs (isomiRs) is a trending topic, and a range of computational tools for the analysis of isomiRs has been developed. We provide an overview on 27 available computational solutions for the analysis of isomiRs. These include both stand-alone programs (17 tools) and web-based solutions (10 tools) and span a publication time range from 2010 to 2020. Seven of the tools were published in 2019 and 2020, confirming the rising importance of the topic. While most of the analyzed tools work for a broad range of organisms or are completely independent of a reference organism, several tools have been tailored for the analysis of human miRNA data or for plants. While 14 of the tools are general analysis tools of miRNAs, and isomiR analysis is one of their features, the remaining 13 tools have specifically been developed for isomiR analysis. A direct comparison on 20 deep sequencing data sets for selected tools provides insights into the heterogeneity of results. With our work, we provide users a comprehensive overview on the landscape of isomiR analysis tools and in that support the selection of the most appropriate tool for their respective research task.

Finding Decision Support Requirements for Effective Intelligence Analysis Tools

2005 ◽  
Vol 49 (3) ◽  
pp. 297-301 ◽  
Author(s):  
William Elm ◽  
Scott Potter ◽  
James Tittle ◽  
David Woods ◽  
Justin Grossman ◽  
...  
Keyword(s):  
Decision Support ◽  
Systems Engineering ◽  
Support Function ◽  
Cognitive Task ◽  
Intelligence Community ◽  
Intelligence Analysis ◽  
Large Set ◽  
Cognitive Systems Engineering ◽  
Support Functions ◽  
Analysis Tools

Within ARDA's GI2Vis program, we developed a unique framework for the definition of decision support requirements for intelligence analysis tools. This framework, based on a first-of-a-kind integration of a model of inferential analysis and principles for designing effective human-computer teams from Cognitive Systems Engineering, has defined the essential support functions to be provided to the intelligence analyst(s). This model has proven to be extremely useful in assessing the support provided by a large set of visualization tools. This assessment has identified clusters of support functions that are addressed by many tools as well as key missing support functions. In this way, the Support Function Model has been used to identify gaps in the support function coverage of existing tools. This can serve as a valuable focusing mechanism for future design and development efforts. In addition, we believe this would be a useful mechanism to enhance cross-discussions among research teams involved in Cognitive Task Analysis efforts within the Intelligence Community. Having others integrate their analytic results with this framework would provide the mechanism for expansion of this model to become a more robust tool and have an even greater impact on the Intelligence Community.

scholarly journals Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

2017 ◽  
Author(s):  
Luke Zappia ◽  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Open Source ◽  
Rapid Development ◽  
Analysis Tool ◽  
Rna Seq ◽  
Link Type ◽  
Analysis Tools ◽  
Rapid Pace ◽  
Cell Data ◽  
Selection Of

AbstractAs single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records of the growth of the field over time.Author summaryIn recent years single-cell RNA-sequeing technologies have emerged that allow scientists to measure the activity of genes in thousands of individual cells simultaneously. This means we can start to look at what each cell in a sample is doing instead of considering an average across all cells in a sample, as was the case with older technologies. However, while access to this kind of data presents a wealth of opportunities it comes with a new set of challenges. Researchers across the world have developed new methods and software tools to make the most of these datasets but the field is moving at such a rapid pace it is difficult to keep up with what is currently available. To make this easier we have developed the scRNA-tools database and website (www.scRNA-tools.org). Our database catalogues analysis tools, recording the tasks they can be used for, where they can be downloaded from and the publications that describe how they work. By looking at this database we can see that developers have focued on methods specific to single-cell data and that they embrace an open-source approach with permissive licensing, sharing of code and preprint publications.

scholarly journals APPLICATION OF INTELLECTUAL ANALYSIS TOOLS FOR SOLVING WAREHOUSE OPERATION OPTIMIZATION PROBLEMS

2019 ◽  
Vol 22 (6) ◽  
pp. 127-135
Author(s):  
R. N. Yakovlev
Keyword(s):  
Optimization Problems ◽  
Working Process ◽  
K Nearest Neighbors ◽  
Operation Optimization ◽  
Training Models ◽  
Analysis Tools ◽  
Optimal Value ◽  
Manual Selection ◽  
Optimal Values ◽  
Selection Of

This work is devoted to the currently relevant issues - optimization of warehousing. Cargo receiving and dispatch processes held in a medium-sized warehouse are being considered in this work in detail. Based on the results of preliminary analysis, several indicators were selected, necessary for the usage of intellectual analysis tools in order to predict the required number of employees at the warehouse entrances. In accordance with the purpose set, models for predicting the required number of employees at the entrances were created to ensure the working process in a way, which corresponds to the optimal value of the indicator “workload of the entrance”. Various methods of machine learning, such as decision tree, k-nearest neighbors regression, random forest, and feedforward neural network are considered in the context of the problem, mentioned above. Each of the models was trained with different values of the model's hyperparameters, which were selected in both manual heuristic-based mode and using specialized software tools for grid search (GridSearchCV) from the scikit-learn library, designed to find the optimal values of the hyperparameters. Using the automated search for hyperparameters when training models yields to a smaller mean-square error in comparison with manual selection of hyperparameters. According to the analysis results of the model prediction quality, it was found that the predicted number of employees closely corresponds to the real situation in comparison with the planned values being used by the company. Based on the obtained results, several recommendations were made to assess the growth of the economic efficiency of the enterprise.

scholarly journals Structured Analytic Techniques: Taxonomy and Technique Selection for Information and Intelligence Analysis Practitioners

2019 ◽  
pp. 115-136
Author(s):  
Paweł Grunt
Keyword(s):  
Learning Process ◽  
Teaching And Learning ◽  
Professional Experience ◽  
Intelligence Analysis ◽  
Analytic Process ◽  
Structured Analysis ◽  
Selection For ◽  
Review Ofthe ◽  
Analytic Problem ◽  
Selection Of

The purpose of this article is to propose a new taxonomy and selection of structured analytictechniques for information and intelligence analysis practitioners. The presented taxonomyand selection of structured analytic techniques are based on the author’s experience in informationand intelligence analysis as well as in training analysts in the use of those techniques.The presented classification and selection of techniques have resulted from the author’sattempts to improve teaching and learning process of the analysts who were novice to structuredanalytic techniques. From the author’s experience, a well-constructed taxonomy aidsnovice analysts in understanding a purpose of used techniques and selecting the best-suitedtechnique for a given analytic problem. In the article the author will first offer definitions ofanalysis, analytic process and analytic spectrum. This will be followed by a review of the fourgeneral categories of analytic methods used in intelligence analysis. The author will furtherexplain the origins, concepts and characteristics of both structured analysis and structuredanalytic techniques. Next, structured analytic techniques will be discussed in the context ofthe Intelligence Cycle. Following this, the author will review several existing taxonomies ofstructured analytic techniques. The article will conclude with a taxonomy and a selection oftechniques for the information and intelligence analysis practitioners, based on a review ofthe literature augmented by the author’s professional experience.

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