Fuzzy Norm-Explicit Product Quantization for Recommender Systems

As the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques in terms of producing relevant recommendations at a low complexity is Product Quantization (PQ). PQ approaches have continued developing in recent years. This system’s crucial challenge is improving product quantization performance in terms of recall measures without compromising its complexity. This makes the algorithm suitable for problems that require a greater number of potentially relevant items without disregarding others, at high-speed and low-cost to keep up with traffic. This is the case of online shops where the recommendations for the purpose are important, although customers can be susceptible to scoping other products. A recent approach has been exploiting the notion of norm sub-vectors encoded in product quantizers. This research proposes a fuzzy approach to perform norm-based product quantization. Type-2 Fuzzy sets (T2FSs) define the codebook allowing sub-vectors (T2FSs) to be associated with more than one element of the codebook, and next, its norm calculus is resolved by means of integration. Our method finesses the recall measure up, making the algorithm suitable for problems that require querying at most possible potential relevant items without disregarding others. The proposed approach is tested with three public recommender benchmark datasets and compared against seven PQ approaches for Maximum Inner-Product Search (MIPS). The proposed method outperforms all PQ approaches such as NEQ, PQ, and RQ up to +6%, +5%, and +8% by achieving a recall of 94%, 69%, 59% in Netflix, Audio, Cifar60k datasets, respectively. More and over, computing time and complexity nearly equals the most computationally efficient existing PQ method in the state-of-the-art

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory

Otimização da produção de energia elétrica em usinas hidrelétricas

Given the current scenario of greater concern regarding sustainable development, renewable energy sources have become increasingly important. Within this context, in addition to contributing to the greatest share of Brazilian renewable generation, hydroelectric power plants are responsible for most of the energy matrix in general. Since different operation strategies can be applied to a power plant’s set of turbines to supply the same power demand, it is presented in this work an investigation of computational intelligence techniques applied to the optimization of the operation of hydroelectric power plants. Such a study is motivated by the potential to save hydro and financial resources, hence contributing to a better operation planning of the electric system, and also by the simplicity in implementation, and effectiveness that intelligent techniques have presented over the years. The following contributions are proposed: (i) an application not yet explored in the literature involving binary computational intelligence techniques, which, basically, benefits from binary algorithms consolidated in the literature to solve problems whose decision variables are integer; (ii) an adaptation of a well known hydroelectric power plants model which allows the maximum saving of hydro resources through a simple modification in the methodology; and (iii) an innovative approach to the fitting of hydraulic turbines efficiency curves, which aims for a more accurate modeling of this component by applying techniques based on the machine learning concept. Simulations were performed with data of a power plant belonging to the EDP group. Adaptations of the Grey Wolf Optimizer and Sine Cosine Algorithm, and the Ant Colony Optimization algorithm have shown to be highly propitious in solving the given problem when duly configured, given the fact that these were able to reliably providing operation schedules that correspond to global optimum.Diante do cenário atual de maior preocupação com o desenvolvimento sustentável, fontes renováveis de energia têm se tornado cada vez mais importantes. Neste contexto, além de contribuírem com a maior parcela da geração renovável brasileira, usinas hidrelétricas são responsáveis pela maior parte da matriz energética em geral. Como diferentes estratégias de operação podem ser aplicadas a um conjunto de turbinas de uma usina para atender a mesma demanda de potência, este trabalho apresenta uma investigação de técnicas de inteligência computacional aplicadas à otimização da operação diária de usinas hidrelétricas. Tal estudo é motivado pelo potencial em se poupar recursos hídricos e financeiros, contribuindo assim para um melhor planejamento da operação do sistema elétrico, e também pela simplicidade de implementação e eficácia que técnicas de inteligência computacional têm apresentado ao longo dos anos. As seguintes contribuições são propostas: (i) uma aplicação até então não explorada na literatura envolvendo técnicas de inteligência computacional binárias, a qual, basicamente, se beneficia de algoritmos binários consolidados na literatura para solucionar problemas cujas variáveis de decisão são inteiras; (ii) uma adaptação de um modelo renomado de usinas hidrelétricas que viabiliza a economia máxima de recursos hídricos através de uma simples alteração na metodologia; e (iii) uma abordagem inovadora do ajuste de curvas de eficiência de turbinas hidráulicas, a qual almeja uma modelagem mais precisa de tal componente ao se aplicar técnicas baseadas no conceito de aprendizagem de máquinas. Simulações foram realizadas com dados de uma usina hidrelétrica pertencente ao grupo EDP. Adaptações dos algoritmos Grey Wolf Optimizer e Sine Cosine Algorithm e o algoritmo Ant Colony Optimization se mostraram altamente propícios a solucionar o problema em questão quando adequadamente configurados, visto que foram capazes de confiavelmente fornecer cronogramas de operação que correspondem a ótimos globais