Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

/pdf/fiji-an-open-source-platform-for-biological-image-analysis-1v529iu3cx.pdf

Fiji: an open-source platform for biological-image analysis

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

/pdf/the-weka-data-mining-software-an-update-3zs9onav7o.pdf

The WEKA data mining software: an update

Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8

This article is categorized under: 

Technologies > Classification
Technologies > Machine Learning
Technologies > Prediction
Technologies > Statistical Fundamentals

/pdf/classification-and-regression-trees-1td8f8b4ru.pdf

Classification and regression trees

Anaerobic digestion (AD) is a renewable energy technology that could make a significant contribution to climate change mitigation. Despite early recognition of its potential role within a diverse low-carbon energy mix in the UK, the on-farm AD sector has stubbornly refused to grow beyond the 'early adopter' stage at a time when some other European countries, notably Germany, recorded a huge expansion in uptake. This research investigates the experiences of early adopters in South West England, and examines the factors in making on-farm AD a viable renewable energy enterprise. The findings, drawn from six case studies, indicate that land, labour and capital resource-related requirements, along with significant regulatory and financial barriers, are together a huge hurdle to clear before a would-be on-farm AD plant operator can be confident that a viable business enterprise will result from investment.

Prospects for on-farm anaerobic digestion as a renewable energy technology in the UK: learning from early adopters

Body size is an integral functional trait that underlies pollination-related ecological processes, yet it is often impractical to measure directly. Allometric scaling laws have been used to overcome this problem. However, most existing models rely upon small sample sizes, geographically restricted sampling and have limited applicability for non-bee taxa. Predictive allometric models that consider biogeography, phylogenetic relatedness and intraspecific variation are urgently required to ensure greater accuracy. Here, we measured body size, as dry weight, and intertegular distance (ITD) of 391 bee species (4035 specimens) and 103 hoverfly species (399 specimens) across four biogeographic regions: Australia, Europe, North America and South America. We updated existing models within a Bayesian mixed-model framework to test the power of ITD to predict interspecific variation in pollinator dry weight in interaction with different co-variates: phylogeny or taxonomy, sexual dimorphism and biogeographic region. In addition, we used ordinary least squares (OLS) regression to assess intraspecific dry weight - ITD relationships for 10 bee and five hoverfly species. Including co-variates led to more robust interspecific body size predictions for both bees (Bayesian R2: 0.946; Delta-R2 0.047) and hoverflies (Bayesian R2: 0.821; Delta-R2 0.058) relative to models with ITD alone. In contrast, at the intraspecific level, our results demonstrate that ITD is an inconsistent predictor of body size for bees (R2: 0.02 - 0.66) and hoverflies (R2: -0.11 - 0.44). Therefore, predictive allometry is more suitable for interspecific comparative analyses than assessing intraspecific variation. Collectively, these models form the basis of the dynamic R package, 9pollimetry9, which provides a comprehensive resource for allometric research concerning insect pollinators worldwide.

https://www.biorxiv.org/content/biorxiv/early/2018/08/22/397604.full.pdf

Pollinator size and its consequences: Predictive allometry for pollinating insects

Land Use Intensification: Effects on Agriculture, Biodiversity and Ecological Processes edited by David Lindenmayer, Saul Cunningham and Andrew Young (eds). CSIRO Publishing, Collingwood, 2012. viii + 156 pages. Price A$49.95. ISBN 9780643104075 (paperback).

https://onlinelibrary.wiley.com/doi/pdf/10.1111/aec.13042

Scholarly shortcomings and a lack of evidence beleaguer bee sampling critique: A response to Prendergast and Hogendoorn (2021)

Animal Movement Across Scales. Lars‐Anders Hansson and Susanne Åkesson (eds.). Oxford University Press, Oxford, 2014, xiii + 292 pp. Price AUD $81.95 (paperback). ISBN: 978‐0‐19‐967719‐1

Mark Hall

Papers

Prospects for on-farm anaerobic digestion as a renewable energy technology in the UK: learning from early adopters

Pollinator size and its consequences: Predictive allometry for pollinating insects

Land Use Intensification: Effects on Agriculture, Biodiversity and Ecological Processes edited by David Lindenmayer, Saul Cunningham and Andrew Young (eds). CSIRO Publishing, Collingwood, 2012. viii + 156 pages. Price A$49.95. ISBN 9780643104075 (paperback).

Scholarly shortcomings and a lack of evidence beleaguer bee sampling critique: A response to Prendergast and Hogendoorn (2021)

Animal Movement Across Scales. Lars‐Anders Hansson and Susanne Åkesson (eds.). Oxford University Press, Oxford, 2014, xiii + 292 pp. Price AUD $81.95 (paperback). ISBN: 978‐0‐19‐967719‐1