ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION

This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively

Predictive Modeling of Cardiac Ischemia

The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware

Priorização de variáveis explicativas na modelagem de acidentes de trânsito utilizando técnicas de aprendizado de máquina

A priorização de variáveis no processo de modelagem de acidentes pode contribuir para otimização de recursos e indicação de quais dados são prioritários para coleta. Assim, este estudo objetivou investigar a influência da priorização de variáveis no ajuste de modelos de previsão de acidentes de resposta multivariada (número de acidentes sem vítimas, número de acidentes com vítimas e número de acidentes com mortes). Duas abordagens foram empregadas: técnicas de agrupamento de árvores de decisão (Random Forest e Boosted Trees) para a priorização inicial e posterior modelagem com uso de redes neurais artificiais (RNA); e, utilização direta de RNA para priorização e modelagem. Os resultados gerais, entretanto, indicaram piora no ajuste dos modelos quando da redução do número de variáveis explicativas. Apesar disso, acredita-se que a evolução de técnicas de aprendizado de máquina de dados que lidem melhor com resposta multivariada, conduzam à identificação adequada das variáveis mais importantes para modelagem.The prioritization of variables in the process of accident modeling can contribute to the optimization of resource and indication of which data are priority to collect. This study aimed to investigate the influence of the prioritization of variables in the adjustment of accident predictive models of multivariate response (number of accidents without victims, number of accidents with victims and number of accidents with deaths). Two approaches were used: decision tree grouping techniques (Random Forest and Boosted Trees) for initial prioritization and later modeling using artificial neural networks (ANN); and, direct use of ANN for both, prioritization and modeling. Overall results, however, indicated a worse fitness of the models when the number of explanatory variables was reduced. Despite this, it is believed that the evolution of machine learning techniques that best deal with multivariate response, lead to the adequate identification of the most important variables for modeling

Priorização de variáveis explicativas na modelagem de acidentes de trânsito utilizando técnicas de aprendizado de máquina

A priorização de variáveis no processo de modelagem de acidentes pode contribuir para otimização de recursos e indicação de quais dados são prioritários para coleta. Assim, este estudo objetivou investigar a influência da priorização de variáveis no ajuste de modelos de previsão de acidentes de resposta multivariada (número de acidentes sem vítimas, número de acidentes com vítimas e número de acidentes com mortes). Duas abordagens foram empregadas: técnicas de agrupamento de árvores de decisão (Random Forest e Boosted Trees) para a priorização inicial e posterior modelagem com uso de redes neurais artificiais (RNA); e, utilização direta de RNA para priorização e modelagem. Os resultados gerais, entretanto, indicaram piora no ajuste dos modelos quando da redução do número de variáveis explicativas. Apesar disso, acredita-se que a evolução de técnicas de aprendizado de máquina de dados que lidem melhor com resposta multivariada, conduzam à identificação adequada das variáveis mais importantes para modelagem.The prioritization of variables in the process of accident modeling can contribute to the optimization of resource and indication of which data are priority to collect. This study aimed to investigate the influence of the prioritization of variables in the adjustment of accident predictive models of multivariate response (number of accidents without victims, number of accidents with victims and number of accidents with deaths). Two approaches were used: decision tree grouping techniques (Random Forest and Boosted Trees) for initial prioritization and later modeling using artificial neural networks (ANN); and, direct use of ANN for both, prioritization and modeling. Overall results, however, indicated a worse fitness of the models when the number of explanatory variables was reduced. Despite this, it is believed that the evolution of machine learning techniques that best deal with multivariate response, lead to the adequate identification of the most important variables for modeling

The Artificial Neural Networks Applied to Servo Control Systems

This chapter utilizes the direct neural control (DNC) based on back propagation neural networks (BPN) with specialized learning architecture applied to the speed control of DC servo motor. The proposed neural controller can be treated as a speed regulator to keep the motor in constant speed, and be applied to DC servo motor speed control. The proposed neural control applied to position control for hydraulic servo system is also studied for some modern robotic applications. A tangent hyperbolic function is used as the activation function, and the back propagation error is approximated by a linear combination of error and error!s differential. The simulation and experiment results reveal that the proposed neural controller is available to DC servo control system and hydraulic servo system with high convergent speed, and enhances the adaptability of the control system

A simplified computer vision system for road surface inspection and maintenance

This paper presents a computer vision system whose aim is to detect and classify cracks on road surfaces. Most of the previous works consisted of complex and expensive acquisition systems, whereas we have developed a simpler one composed by a single camera mounted on a light truck and no additional illumination. The system also includes tracking devices in order to geolocalize the captured images. The computer vision algorithm has three steps: hard shoulder detection, cell candidate proposal, and crack classification. First the region of interest (ROI) is delimited using the Hough transform (HT) to detect the hard shoulders. The cell candidate step is divided into two substeps: Hough transform features (HTF) and local binary pattern (LBP). Both of them split up the image into nonoverlapping small grid cells and also extract edge orientation and texture features, respectively. At the fusion stage, the detection is completed by mixing those techniques and obtaining the crack seeds. Afterward, their shape is improved using a new developed morphology operator. Finally, one classification based on the orientation of the detected lines has been applied following the Chain code. Massive experiments were performed on several stretches on a Spanish road showing very good performance

Predição do tráfego de rede de computadores usando redes neurais tradicionais e de aprendizagem profunda

Neste artigo são comparados quatro abordagens diferentes para a previsão do tráfego de redes de computadores, usando o tráfego de dispositivos de redes de computadores que se conectam a Internet e usando Redes Neurais Artificiais (RNA) para a predição, sendo elas: (1)  Multilayer Perceptron (MLP) com Backpropagation para o treinamento; (2) MLP com Resilient Backpropagation (Rprop); (3) Rede Neural Recorrente (RNN); (4)  Stacked Autoencoder (SAE) com aprendizagem profunda (deep learning). Também é apresentado que um modelo de rede neural mais simples, tais como a RNN e MLP, podem ser mais eficientes do que modelos mais complexos, como o SAE. A predição do tráfego de Internet é uma tarefa importante para muitas aplicações, tais como aplicações adaptativas, controle de congestionamento, controle de admissão, detecção de anomalias e alocação de largura de banda. Além disso, métodos eficientes de gerenciamento de recursos, como a largura de banda, podem ser usados para melhorar o desempenho e reduzir custos, aprimorando a Qualidade de Serviço (QoS). A popularidade das novas redes neurais profundas vêm aumentado em muitas áreas, porém há uma falta de estudos em relação a predição de séries temporais, como o tráfego de Internet

Indoor localization techniques using wireless network and artificial neural network

This research focuses on improving indoor localization using wireless network and artificial neural network (ANN). This involves strategic study on wireless signal behavior and propagation inside buildings, suitable propagation model to simulate indoor propagation and evaluations on different localization methods such as distance based, direction based, time based and signature based. It has been identified that indoor signal propagation impairments are severe, non-linear and custom to a specific indoor location. To accommodate these impairments, an ANN is proposed to provide a viable solution for indoor location prediction as it learns the location specific parameters during training, and then performs positioning based on the trained data, while being robust to severe and non-linear propagation effects. The versatility of ANN allows different setup and optimization possibilities to affect location prediction capabilities. This research identified the best feedforward backpropagation neural network configuration for the generated simulation data and introduced a new optimization method. Indoor-specific received signal strength data were developed with the Lee’s in-building model according to a custom indoor layout. Simulation work was done to test localization performance with different feedforward backpropagation neural network setups with the generated received signal strength data as input. A data preparation method that converts the received signal strength raw data into average, median, min and max values prior to be fed into the neural network process was carried out. The method managed to increase location prediction performance using feedforward neural network with two hidden layers trained with Bayesian Regularization algorithm producing root mean squared error of 0.0821m, which is 50% better in comparison to existing research work. Additional tests conducted with six different relevant scenarios verified the scheme for localization performance robustness. In conclusion, the research has improved the performance of indoor localization using wireless network and ANN

Development of a Neural Network-Based Object Detection for Multirotor Target Tracking

Unmanned aerial vehicles (UAVs) have, for the past few decades, seen an increased popularity in industry and research centres. Despite this intense utilization by both markets there exists an active demand for the development of autonomous guidance, navigation, and control strategies. One need relates to the achievement of a high level of autonomy to identify and track a target object. An elective technique for this set of tasks is neural networks. In the development and study of these networks there is a distinct lack of substantive validation techniques to qualify network performances when implemented in a multirotor UAV. This thesis will first describe the development of a neural network-based object detection subsystem for use in target tracking with an autonomous multirotor UAV. Then, the second part of this thesis will utilize a developed indoor multirotor testbed to externally verify the tracking performance of the multirotor UAV during an object following maneuver

Uso de técnicas de aprendizado de máquina na modelagem da segurança viária: mapeamento sistemático

The road safety modeling is an important alternative in the optimization of resources and efforts to promote safe mobility. This paper presents the mapping study of papers about the development of accident prediction models, especially on highways, using machine learning (ML) techniques. For this purpose, a revision management protocol was applied, using the Portal of Periodicals Capes and Google Scholar as databases. Initially some bibliometric aspects were presented, followed by a qualitative analysis. As a result, the main methodological approaches and their characteristics, model performance and explanatory variables were identified. In this way, the mapping was important to draw the panorama of the area of research, to point out limitations and opportunities of investigation and also, to highlight the potential of the use of ML for analysis of crash accidents.A modelagem da segurança viária se coloca como importante alternativa na otimização de recursos e esforços para promoção de mobilidade segura. Este trabalho apresenta o mapeamento sistemático de artigos que tratam do desenvolvimento de modelos de previsão de acidentes, especialmente em rodovias, com uso de técnicas de aprendizado de máquina (AM). Para tanto, foi aplicado um protocolo de condução da revisão, utilizando como bases de dados o Portal de Periódicos Capes e Google Acadêmico. Inicialmente alguns aspectos bibliométricos foram apresentados, seguido de uma análise qualitativa. Como resultados fez-se a identificação das principais abordagens metodológicas e suas características, desempenho dos modelos e variáveis explicativas. Desta forma, o mapeamento foi importante para traçar o panorama da área de pesquisa, apontar limitações e oportunidades de investigação e ainda, evidenciar o potencial de utilização de AM para análise de acidentes de trânsito