A Novel Hybrid SVM-CNN Method for Extracting Characteristics and Classifying Cattle Branding

A tool that can perform the automatic identification of cattle brandings is essential for the government agencies responsible for the record, control and inspection of this activity. This article presents a novel hybrid method that uses Convolutional Neural Networks (CNN) to extract features from images and Support Vector Machines (SVM) to classify the brandings. The experiments were performed using a cattle branding image set provided by the City Hall of Bagé, Brazil. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93.11% in the first experiment with 39 brandings and 1,950 sample images, and 95.34% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 31.661s and 41.749s, respectively

Segmentation and detection of cattle branding images using CNN and SVM classification

This article presents a hybrid method that uses Convolutional Neural Networks (CNN) to segmentation and Support Vector Machines (SVM) to detection the brandings. The experiments were performed using a cattle branding images. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93% in the first experiment with 39 brandings and 1,950 sample images, and 95% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 32s and 42s, respectively

Multi-views Embedding for Cattle Re-identification

People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets. However, little research has been conducted on animal identification and re-identification, even if this knowledge may be useful in a rich variety of different scenarios. Here, we tackle cattle re-identification exploiting deep CNN and show how this task is poorly related to the human one, presenting unique challenges that make it far from being solved. We present various baselines, both based on deep architectures or on standard machine learning algorithms, and compared them with our solution. Finally, a rich ablation study has been conducted to further investigate the unique peculiarities of this task

Visual identification of individual Holstein-Friesian cattle via deep metric learning

Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many cattle unseen during system training are to be identified and re-identified - achieving 98.2% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and underpinning datasets are available publicly.Comment: 37 pages, 14 figures, 2 tables; Submitted to Computers and Electronics in Agriculture; Source code and network weights available at https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml1

Socioeconomic development in the context of Uruguay: a knowledge-based approach

Purpose: The purpose of this exploratory study is to examine the relevance and impact of knowledge in the context of Uruguay’s present and future socioeconomic development through the lens of the knowledge-based theory of the firm (KBTF). Design/methodology/approach: The perspectives of 47 key informants, predominantly representatives of public and private Uruguayan institutions, including chambers of commerce and producer associations, were gathered through unstructured, face-to-face interviews. Findings: Aligned with the KBTF, the significance of tacit knowledge, complemented with explicit knowledge, was revealed, particularly in the more traditional industries. Indeed, industry-based (tacit) knowledge evolving for generations has been strengthened by innovative practices, enhancing the appeal and image of key commodities and the nation’s exports. Additional elements highlighted in the KBTF, such as problem-solving, knowledge integration and application and knowledge specialisation, were identified. Originality/value: Essentially, the study highlights the different associations between the KBTF, the various forms of acquiring knowledge (tacit, explicit), innovation and resulting impacts on food quality and increased product recognition for a developing economy. Moreover, the findings, which illustrate that crucial improvements can be achieved through knowledge-based approaches, could also be considered in the context of other emerging economies that are aiming to attain further socioeconomic development through maximising the benefits of knowledge. In addition, the study addresses a theme that has been sporadically presented in the academic literature, especially when studying developing economies and their industries. © 2017, © Emerald Publishing Limited

Internationalization strategies of companies in the wine industry in Portugal – context, forms of action and performance.

This research project aims to analyze the competitive environment of companies in the wine sector in Portugal and assess the implications in the development of contingent strategic guidelines and different performances.Proposes to apply the methodological framework the IKST – Integrated Key for Strategic Thought for international expansion. The research was carried out at two levels: at a preliminary level – a general characterisation was made of the companies as to their resources, and at a central level – the examination of the strategic aspect of the companies was carried out. The research involved the collection of primary data (survey of 164 companies in the sector) and secondary data (from documentary nature). Explores the strategic aspect, analyzing the sector in terms of global and national context, in order to design a diagnostic context of action, using the models of PEST and 5 Forces. Identifies, based on various statistical techniques, the adopted style of strategic thought and the profile in terms of contextual variables, as well as the underlying economic performance

Shaping Inclusive Markets: How Funders and Intermediaries can Help Markets Move toward Greater Economic Inclusion

Positive progress toward worldwide economic inclusion is not only possible, but can also be made more possible. In Shaping Inclusive Markets, we draw lessons from history on how more inclusive markets have been achieved and highlight ways in which funders and intermediaries can strengthen the conditions for change

Detecting Hidden Patterns in EEG Waveforms of Schizophrenia Patients using Convolutional Neural Network

Schizophrenia is a severe mental disorder that affects 1% of the world’s population and it is characterized by behavioral symptoms such as delusions, hallucinations and disorganized speech. The aim of this research was to develop an artificial intelligence model to detect hidden patterns in electroencephalogram (EEG) waveforms of schizophrenia patients. EEG waveforms of healthy subjects and schizophrenia patients were collected and processed. The data was used to develop a convolutional neural network (CNN) model which can automatically extract features and classify them. CNN does this by comparing the differences between the EEG waveforms of schizophrenia patients and healthy controls. These differences were used to train the classifier to differentiate the schizophrenia patients from the controls. The result of the CNN model showed a test accuracy of 60%, specificity of 55.55% and a precision of 55.55%. This early result shows that the model is promising. The next step will be to improve the accuracy of the model with a larger pool of data and many iterations, which is expected to lead to a better model that can be relied upon for schizophrenia diagnosis. In conclusion, CNN-based models like this one are relatively cheap and will improve the diagnosis of Schizophrenia, especially in low-income economies where the present study has been carried out