2,842 research outputs found
Performance and Health Monitoring and Analysis of Hive Scales Portal Web Application
Honey bee health and colony collapse disorder (CCD), a term coined in 2006, have been drawing global attention as bees are dying at a rapid rate. The University of Maryland reported that beekeepers across the US lost more than 40 percent of their honey bee colonies during the year spanning April 2014 to April 2015[1]. With seventy out of the top 100 human food crops, which supply about 90 percent of the world’s nutrition, being pollinated by bees [4], this puts our food supply at serious risk!
Formed from a grant from the US Department of Agriculture, the Bee Informed Partnership (BIP) is a collaborative effort to address the declining honey bee population. A major part of this effort is being led by GVSU through the Hive Scales Portal web application.
This application aggregates and stores data, collected from hive scales around the world, for researchers and scientists with the intent of identifying patterns that lead to best practices for caring for bees.
As the quantity of contributing hives scales increase, the hive scale portal will need to scale proportionally. Knowing when or how much to scale is a guessing game without the proper visibility.
The purpose of this project is to analyze the application and ensure it can scale. The key piece of this analysis is to integrate the necessary tools to ensure site health and performance visibility is available for continued monitoring
Implementation of an Automated Image Processing System for Observing the Activities of Honey Bees
This research designed and implemented an automated system to collect data on honey bees using computer science techniques. This system utilizes image processing techniques to extract data from the videos taken in front or at the top of the hive’s entrance. Several web-based applications are used to obtain temperature and humidity data from National weather Service to supplement the data that are collected at the hive locally. All the weather data and those extracted from the images are stored in a MySQL database for analysis and accessed by an iPhone App that is designed as part of this research
Personalized Web Content with Fuzzy System
In this paper, we will develop a new computational intelligence methodology to automatically analyze and summarize web content during a user surfing sessions. The output of this process is meaningful keywords or phrases which will be used to bring the user other contents such as images that closely relate to the web pages that he or she is currently surfing
eHive: An Artificial Intelligence workflow system for genomic analysis
<p>Abstract</p> <p>Background</p> <p>The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future.</p> <p>Results</p> <p>We present eHive, a new fault tolerant distributed processing system initially designed to support comparative genomic analysis, based on blackboard systems, network distributed autonomous agents, dataflow graphs and block-branch diagrams. In the eHive system a MySQL database serves as the central blackboard and the autonomous agent, a Perl script, queries the system and runs jobs as required. The system allows us to define dataflow and branching rules to suit all our production pipelines. We describe the implementation of three pipelines: (1) pairwise whole genome alignments, (2) multiple whole genome alignments and (3) gene trees with protein homology inference. Finally, we show the efficiency of the system in real case scenarios.</p> <p>Conclusions</p> <p>eHive allows us to produce computationally demanding results in a reliable and efficient way with minimal supervision and high throughput. Further documentation is available at: <url>http://www.ensembl.org/info/docs/eHive/</url>.</p
Metadata enrichment for digital heritage: users as co-creators
This paper espouses the concept of metadata enrichment through an expert and user-focused approach to metadata creation and management. To this end, it is argued the Web 2.0 paradigm enables users to be proactive metadata creators. As Shirky (2008, p.47) argues Web 2.0’s social tools enable “action by loosely structured groups, operating without managerial direction and outside the profit motive”. Lagoze (2010, p. 37) advises, “the participatory nature of Web 2.0 should not be dismissed as just a popular phenomenon [or fad]”. Carletti (2016) proposes a participatory digital cultural heritage approach where Web 2.0 approaches such as crowdsourcing can be sued to enrich digital cultural objects. It is argued that “heritage crowdsourcing, community-centred projects or other forms of public participation”. On the other hand, the new collaborative approaches of Web 2.0 neither negate nor replace contemporary standards-based metadata approaches. Hence, this paper proposes a mixed metadata approach where user created metadata augments expert-created metadata and vice versa. The metadata creation process no longer remains to be the sole prerogative of the metadata expert. The Web 2.0 collaborative environment would now allow users to participate in both adding and re-using metadata. The case of expert-created (standards-based, top-down) and user-generated metadata (socially-constructed, bottom-up) approach to metadata are complementary rather than mutually-exclusive. The two approaches are often mistakenly considered as dichotomies, albeit incorrectly (Gruber, 2007; Wright, 2007) .
This paper espouses the importance of enriching digital information objects with descriptions pertaining the about-ness of information objects. Such richness and diversity of description, it is argued, could chiefly be achieved by involving users in the metadata creation process. This paper presents the importance of the paradigm of metadata enriching and metadata filtering for the cultural heritage domain. Metadata enriching states that a priori metadata that is instantiated and granularly structured by metadata experts is continually enriched through socially-constructed (post-hoc) metadata, whereby users are pro-actively engaged in co-creating metadata. The principle also states that metadata that is enriched is also contextually and semantically linked and openly accessible. In addition, metadata filtering states that metadata resulting from implementing the principle of enriching should be displayed for users in line with their needs and convenience. In both enriching and filtering, users should be considered as prosumers, resulting in what is called collective metadata intelligence
Bee Hive Monitoring System - Solutions for the automated health monitoring
Cerca de um terço da produção global de alimentos depende da polinização das abelhas, tornando-as vitais para a economia mundial. No entanto, existem diversas ameaças à sobrevivência das espécies de abelhas, tais como incêndios florestais, stress humano induzido, subnutrição, poluição, perda de biodiversidade, agricultura intensiva e predadores como as vespas asiáticas. Destes problemas, pode-se observar um aumento da necessidade de soluções automatizadas que possam auxiliar na monitorização remota de colmeias de abelhas. O objetivo desta tese é desenvolver soluções baseadas em Aprendizagem Automática para problemas que podem ser identificados na apicultura, usando técnicas e conceitos de Deep Learning, Visão Computacional e Processamento de Sinal. Este documento descreve o trabalho da tese de mestrado, motivado pelo problema acima exposto, incluindo a revisão de literatura, análise de valor, design, planeamento de testes e validação e o desenvolvimento e estudo computacional das soluções. Concretamente, o trabalho desta tese de mestrado consistiu no desenvolvimento de soluções para três problemas – classificação da saúde de abelhas a partir de imagens e a partir de áudio, e deteção de abelhas e vespas asiáticas. Os resultados obtidos para a classificação da saúde das abelhas a partir de imagens foram significativamente satisfatórios, excedendo os que foram obtidos pela metodologia definida no trabalho base utilizado para a tarefa, que foi encontrado durante a revisão da literatura. No caso da classificação da saúde das abelhas a partir de áudio e da deteção de abelhas e vespas asiáticas, os resultados obtidos foram modestos e demonstram potencial aplicabilidade das respetivas metodologias desenvolvidas nos problemas-alvo. Pretende-se que as partes interessadas desta tese consigam obter informações, metodologias e perceções adequadas sobre o desenvolvimento de soluções de IA que possam ser integradas num sistema de monitorização da saúde de abelhas, incluindo custos e desafios inerentes à implementação das soluções. O trabalho futuro desta dissertação de mestrado consiste em melhorar os resultados dos modelos de classificação da saúde das abelhas a partir de áudio e de deteção de objetos, incluindo a publicação de artigos para obter validação pela comunidade científica.Up to one third of the global food production depends on the pollination of honey bees, making them vital for the world economy. However, between forest fires, human-induced stress, poor nutrition, pollution, biodiversity loss, intensive agriculture, and predators such as Asian Hornets, there are plenty of threats to the honey bee species’ survival. From these problems, a rise of the need for automated solutions that can aid with remote monitoring of bee hives can be observed. The goal of this thesis is to develop Machine Learning based solutions to problems that can be identified in beekeeping and apiculture, using Deep Learning, Computer Vision and Signal Processing techniques and concepts. The current document describes master thesis’ work, motivated from the above problem statement, including the literature review, value analysis, design, testing and validation planning and the development and computational study of the solutions. Specifically, this master thesis’ work consisted in developing solutions to three problems – bee health classification through images and audio, and bee and Asian wasp detection. Results obtained for the bee health classification through images were significantly satisfactory, exceeding those reported by the baseline work found during literature review. On the case of bee health classification through audio and bee and Asian wasp detection, these obtained results were modest and showcase potential applicability of the respective developed methodologies in the target problems. It is expected that stakeholders of this thesis obtain adequate information, methodologies and insights into the development of AI solutions that can be integrated in a bee health monitoring system, including inherent costs and challenges that arise with the implementation of the solutions. Future work of this master thesis consists in improving the results of the bee health classification through audio and the object detection models, including publishing of papers to seek validation by the scientific community
Personalised Learning: Educational, Technological and Standardisation Perspective
The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective
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