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From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.
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Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.
Assuntos
Basidiomycota , Rhodotorula , Basidiomycota/genética , Rhodotorula/genética , Biocombustíveis , LipídeosRESUMO
Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.
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In this work, two new self-tuning collaborative-based mechanisms for jamming detection are proposed. These techniques are named (i) Connected Mechanism and (ii) Extended Mechanism. The first one detects jamming by comparing the performance parameters with respect to directly connected neighbors by interchanging packets with performance metric information, whereas the latter, jamming detection relays comparing defined zones of nodes related with a collector node, and using information of this collector detects a possible affected zone. The effectiveness of these techniques were tested in simulated environment of a quadrangular grid of 7 × 7, each node delivering 10 packets/sec, and defining as collector node, the one in the lower left corner of the grid. The jammer node is sending packets under reactive jamming. The mechanism was implemented and tested in AODV (Ad hoc On Demand Distance Vector), DSR (Dynamic Source Routing), and MPH (Multi-Parent Hierarchical), named AODV-M, DSR-M and MPH-M, respectively. Results reveal that the proposed techniques increase the accurate of the detected zone, reducing the detection of the affected zone up to 15% for AODV-M and DSR-M and up to 4% using the MPH-M protocol.
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Este trabalho busca, com base em pesquisa bibliográfica que relaciona aprendizagem organizacional (AO) e desenvolvimento organizacional (DO), respostas a três questões de pesquisa: Como têm sido definidos e operacionalizados os conceitos de AO? Como têm sido definidos e operacionalizados os conceitos de DO? As definições de DO, confrontadas com as de AO, sugerem algum padrão de relação? Para viabilizar tais respostas, foram selecionados e analisados 13 estudos que empregam método quantitativo multivariado para caracterizar as relações entre AO e DO. Sobre AO, verificou-se a utilização de múltiplas definições: orientação para aprendizagem, cultura, estruturas de aprendizagem e processos. O desempenho é financeiro e não financeiro, relacionando-se ao presente ou ao passado, é estático ou é dinâmico, mede-se em relação a concorrentes, clientes e empregados, focando áreas funcionais ou a totalidade da organização. Há duas formas de relação entre AO e DO: a direta, na qual indicadores de AO se relacionam diretamente com métricas de DO e a indireta, na qual a AO se mostra associada a práticas e comportamentos condutores de maior performance organizacional...
This study aims to answer two research questions: How have the concepts of OL and OP been defined in the literature that relates them? And, do the definitions of OP compared to those of OL suggest any pattern of relationship? To enable an answer to these questions, 13 studies that used multivariate data analysis to define the relationships between OL and OP were selected and analyzed. Regarding OL, it was noticed that multiple definitions were used: target for learning, culture, learning structures and processes. OP can be financial or non-financial, relates to the present or the past, is static or dynamic, is measured in relation to competitors, customers, and employees, focuses on operational processes or overall organizational performance. The relationship between OL and OP takes place in two forms: the first is direct, in which OL indicators have a direct relationship with OP metrics. In the second, OL is associated with practices and behaviors that lead to better OP...
Assuntos
Humanos , Masculino , Feminino , Adulto , Tutoria , Avaliação de Desempenho Profissional , BibliometriaRESUMO
Este trabalho busca, com base em pesquisa bibliográfica que relaciona aprendizagem organizacional (AO) e desenvolvimento organizacional (DO), respostas a três questões de pesquisa: Como têm sido definidos e operacionalizados os conceitos de AO? Como têm sido definidos e operacionalizados os conceitos de DO? As definições de DO, confrontadas com as de AO, sugerem algum padrão de relação? Para viabilizar tais respostas, foram selecionados e analisados 13 estudos que empregam método quantitativo multivariado para caracterizar as relações entre AO e DO. Sobre AO, verificou-se a utilização de múltiplas definições: orientação para aprendizagem, cultura, estruturas de aprendizagem e processos. O desempenho é financeiro e não financeiro, relacionando-se ao presente ou ao passado, é estático ou é dinâmico, mede-se em relação a concorrentes, clientes e empregados, focando áreas funcionais ou a totalidade da organização. Há duas formas de relação entre AO e DO: a direta, na qual indicadores de AO se relacionam diretamente com métricas de DO e a indireta, na qual a AO se mostra associada a práticas e comportamentos condutores de maior performance organizacional.(AU)
This study aims to answer two research questions: How have the concepts of OL and OP been defined in the literature that relates them? And, do the definitions of OP compared to those of OL suggest any pattern of relationship? To enable an answer to these questions, 13 studies that used multivariate data analysis to define the relationships between OL and OP were selected and analyzed. Regarding OL, it was noticed that multiple definitions were used: target for learning, culture, learning structures and processes. OP can be financial or non-financial, relates to the present or the past, is static or dynamic, is measured in relation to competitors, customers, and employees, focuses on operational processes or overall organizational performance. The relationship between OL and OP takes place in two forms: the first is direct, in which OL indicators have a direct relationship with OP metrics. In the second, OL is associated with practices and behaviors that lead to better OP.(AU)