Body lengths of harvested fish are key indices for marine resource management. Some fisheries management organisations require fishing vessels to report the lengths of harvested fish. Conventionally, ...body lengths of fish are measured manually using rulers or tape measures. Such methods are, however, time consuming, labour intensive, and subjective. This study proposes an automated method to determine the snout-to-fork length of a fish in complex images. In this approach, images of fish bodies and colour plates with a known dimension were acquired. A convolutional neural network (CNN) classifier was then developed to detect the regions of fish head, tail fork, and colour plate in the images. Snout and fork points of the fish were next determined in the fish head and tail fork regions, respectively, using image processing. Fish body length was subsequently estimated as the distance between the snout and fork points using the pixel-to-distance ratio obtained from the colour plate. The developed CNN classifier reached an accuracy of 98.78% in detecting the regions of fish head, fish fork, and colour plate. The proposed approach reached a mean absolute error and a mean absolute relative error of 5.36 cm and 4.26%, respectively, in estimating the body length of fish.
•An automated method was proposed to measure fish lengths of in complex images.•A CNN classifier was developed to detect fish head and caudal regions in images.•The CNN classifier reached an accuracy of 98.78% in detecting the regions.•Snout and fork points were identified from fish head and caudal regions.•The approach reached an accuracy of 95.74% in estimating fish body length.
The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying ...to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences.
•We combine fragmentation strategies and abstractions to visualize large MDE models.•The approach has been realized in an Eclipse plugin.•We present an evaluation in the embedded systems and reverse ...engineering domains, comparing with tools like Gephi and CDO.•We obtain large gains in terms of time and memory with respect to visualizing monolithic models.
Model-Driven Engineering (MDE) promotes the use of models to conduct all phases of software development in an automated way. However, for complex systems, these models may become large and unwieldy, and hence difficult to process and comprehend.
In order to alleviate this situation, we combine model fragmentation strategies – to split models into more manageable chunks – with model abstraction and visualisation mechanisms, able to provide simpler views of the models. In this paper, we describe the underlying methods and techniques, as well as the supporting tools. The feasibility and benefits of our approach are confirmed based on evaluations in the embedded systems, and the reverse engineering domains, where large benefits in terms of visualisation time (speeds up of up to 55 × ), and reduction in memory consumption (reduction of 97%) are obtained.
This paper introduces a technique to visualise the information content of the kernel matrix and a way to interpret the ingredients of the Support Vector Regression (SVR) model. Recently, the use of ...Support Vector Machines (SVM) for solving classification (SVC) and regression (SVR) problems has increased substantially in the field of chemistry and chemometrics. This is mainly due to its high generalisation performance and its ability to model non-linear relationships in a unique and global manner. Modeling of non-linear relationships will be enabled by applying a kernel function. The kernel function transforms the input data, usually non-linearly related to the associated output property, into a high dimensional feature space where the non-linear relationship can be represented in a linear form. Usually, SVMs are applied as a black box technique. Hence, the model cannot be interpreted like, e.g., Partial Least Squares (PLS). For example, the PLS scores and loadings make it possible to visualise and understand the driving force behind the optimal PLS machinery.
In this study, we have investigated the possibilities to visualise and interpret the SVM model. Here, we exclusively have focused on Support Vector Regression to demonstrate these visualisation and interpretation techniques. Our observations show that we are now able to turn a SVR black box model into a transparent and interpretable regression modeling technique.
OpenSolver is an open source Excel add-in that allows spreadsheet users to solve their LP/IP models using the COIN-OR CBC solver. OpenSolver is largely compatible with the built-in Excel Solver, ...allowing most existing LP and IP models to be solved without change. However, OpenSolver has none of the size limitations found in Solver, and thus can solve larger models. Further, the CBC solver is often faster than the built-in Solver, and OpenSolver provides novel model construction and on-sheet visualisation capabilities. This paper describes Open- Solver’s development and features. OpenSolver can be downloaded free of charge at http://www.opensolver.org.
The present paper highlights business processes as a living system of systems. The main objective of the research is to obtain the visualization of the business model in order to design the system ...architecture tower. The virtual picture is represented as a multi-attribute system of systems description available as a model about reused Artefacts of a classification system. There are two specific artefacts used to model the outbound of a specific system: Influence and External Influence. Therefore, the proposed model is a holistic one, as the business is designed as an open system - the model takes into consideration both the internal processes and the macro-environment.
The increasing popularity of model-based and low-code platforms has raised the need to understand large models - especially in industrial settings. However, current approaches mainly rely on ...graph-based visual metaphors, which do not scale well with large model sizes. To address this issue, we introduce model sensemaking strategies: purposeful model visualisations based on alternative visual metaphors. We define them as reusable patterns that yield tailored visualisations when applied to meta-models. This paper presents a catalogue of domain-specific and domain-agnostic sensemaking strategies, and a recommender that suggests suitable strategies for a given meta-model. To showcase the framework's applicability, we have implemented some of these strategies in Dandelion, an industrial, low-code graphical language workbench for the cloud. Using this platform, we have evaluated the effectiveness of the strategies to visualise large industrial models by the UGROUND company.
Producing graphical views from software and system models is often desirable for communication and comprehension purposes, even when graphical model editing capabilities are not required - because ...the preferred editable concrete syntax of the models is text-based, or for models extracted via reverse engineering. To support such scenarios, we present a novel approach for efficient rule-based generation of transient graphical views from models using lazy model-to-text transformation, and an implementation of the proposed approach in the form of an open-source Eclipse plugin named Picro. PiCTO builds on top of mature visualisation software such as Graphviz and PlantUML and supports, among others, composite views, layers, and multi-model visualisation. We illustrate how Picto can be used to produce various forms of graphical views such as node-edge diagrams, tables and sequence-like diagrams, and we demonstrate the efficiency benefits of lazy view generation approach against batch model-to-text transformation for generating views from large models.
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