1,853 research outputs found
Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work
Production Optimization Indexed to the Market Demand Through Neural Networks
Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent
production management that facilitates communication between machines, people and
processes and uses technology as the main driver.
Many works in the literature treat maintenance and production management in separate approaches,
but there is a link between these areas, with maintenance and its actions aimed at ensuring the
smooth operation of equipment to avoid unnecessary downtime in production.
With the advent of technology, companies are rushing to solve their problems by resorting to technologies
in order to fit into the most advanced technological concepts, such as industries 4.0 and
5.0, which are based on the principle of process automation. This approach brings together database
technologies, making it possible to monitor the operation of equipment and have the opportunity
to study patterns of data behavior that can alert us to possible failures.
The present thesis intends to forecast the pulp production indexed to the stock market value.The
forecast will be made by means of the pulp production variables of the presses and the stock exchange
variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective
planning. To support the decision of efficient production management, in this thesis algorithms
were developed and validated with from five pulp presses, as well as data from other sources, such
as steel production and stock exchange, which were relevant to validate the robustness of the model.
This thesis demonstrated the importance of data processing methods and that they have great relevance
in the model input since they facilitate the process of training and testing the models. The
chosen technologies demonstrated good efficiency and versatility in performing the prediction of
the values of the variables of the equipment, also demonstrating robustness and optimization in
computational processing. The thesis also presents proposals for future developments, namely
in further exploration of these technologies, so that there are market variables that can calibrate
production through forecasts supported on these same variables.Conectividade, mobilidade e análise de dados em tempo real são pré-requisitos para um novo
modelo de gestão inteligente da produção que facilita a comunicação entre máquinas, pessoas e
processos, e usa a tecnologia como motor principal.
Muitos trabalhos na literatura tratam a manutenção e a gestão da produção em abordagens separadas,
mas existe uma correlação entre estas áreas, sendo que a manutenção e as suas políticas
têm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens
desnecessárias na linha de produção.
Com o advento da tecnologia há uma corrida das empresas para solucionar os seus problemas
recorrendo às tecnologias, visando a sua inserção nos conceitos tecnológicos, mais avançados,
tais como as indústrias 4.0 e 5.0, as quais têm como princípio a automatização dos processos.
Esta abordagem junta as tecnologias de sistema de informação, sendo possível fazer o acompanhamento
do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrões
de comportamento dos dados que nos possam alertar para possíveis falhas.
A presente tese pretende prever a produção da pasta de papel indexada às bolsas de valores. A
previsão será feita por via das variáveis da produção da pasta de papel das prensas e das variáveis
da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo
conseguir um planeamento eficaz. Para suportar a decisão de uma gestão da produção eficiente,
na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de
papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores,
os quais se mostraram relevantes para a validação da robustez dos modelos.
A presente tese demonstrou a importância dos métodos de tratamento de dados e que os mesmos
têm uma grande relevância na entrada do modelo, visto que facilita o processo de treino e testes dos
modelos. As tecnologias escolhidas demonstraram uma boa eficiência e versatilidade na realização
da previsão dos valores das variáveis dos equipamentos, demonstrando ainda robustez e otimização
no processamento computacional.
A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração
mais aprofundada destas tecnologias, de modo a que haja variáveis de mercado que possam calibrar
a produção através de previsões suportadas nestas mesmas variáveis
Data Mining in Smart Grids
Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin
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