ANN in Financial Prediction

This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future

ANN in Financial Prediction

This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future

Detecting Selected Network Covert Channels Using Machine Learning

International audienceNetwork covert channels break a computer's security policy to establish a stealthy communication. They are a threat being increasingly used by malicious software. Most previous studies on detecting network covert channels using Machine Learning (ML) were tested with a dataset that was created using one single covert channel tool and also are ineffective at classifying covert channels into patterns. In this paper, selected ML methods are applied to detect popular network covert channels. The capacity of detecting and classifying covert channels with high precision is demonstrated. A dataset was created from nine standard covert channel tools and the covert channels are then accordingly classified into patterns and labelled. Half of the generated dataset is used to train three different ML algorithms. The remaining half is used to verify the algorithms' performance. The tested ML algorithms are Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Deep Neural Networks (DNN). The k-NN model demonstrated the highest precision rate at 98% detection of a given covert channel and with a low false positive rate of 1%

Formulación de un modelo de proceso para ingeniería del conocimiento

Las metodologías de desarrollo de sistemas basados en conocimiento existentes provistas por la Ingeniería de Conocimiento, se centran en el proceso de desarrollo de Bases de Conocimiento. Sin embargo no existe una visión de proceso que identifique fases, tareas, técnicas de representación y procedimientos de ejecución de las tareas; ni que permitan soportar de forma completa e integrada las actividades de administración y desarrollo de Proyectos de Ingeniería de Conocimiento. En este contexto, esta investigación propone cubrir la vacancia desarrollando un Modelo de Proceso para Proyectos de Ingeniería del Conocimiento que integre en fases las actividades y técnicas desarrolladas para la administración y desarrollo de este tipo de proyectos.Eje: Ingeniería de SoftwareRed de Universidades con Carreras en Informática (RedUNCI

Formulación de un modelo de proceso para ingeniería del conocimiento

Las metodologías de desarrollo de sistemas basados en conocimiento existentes provistas por la Ingeniería de Conocimiento, se centran en el proceso de desarrollo de Bases de Conocimiento. Sin embargo no existe una visión de proceso que identifique fases, tareas, técnicas de representación y procedimientos de ejecución de las tareas; ni que permitan soportar de forma completa e integrada las actividades de administración y desarrollo de Proyectos de Ingeniería de Conocimiento. En este contexto, esta investigación propone cubrir la vacancia desarrollando un Modelo de Proceso para Proyectos de Ingeniería del Conocimiento que integre en fases las actividades y técnicas desarrolladas para la administración y desarrollo de este tipo de proyectos.Eje: Ingeniería de SoftwareRed de Universidades con Carreras en Informática (RedUNCI

Formulación de un modelo de proceso para ingeniería del conocimiento

Las metodologías de desarrollo de sistemas basados en conocimiento existentes provistas por la Ingeniería de Conocimiento, se centran en el proceso de desarrollo de Bases de Conocimiento. Sin embargo no existe una visión de proceso que identifique fases, tareas, técnicas de representación y procedimientos de ejecución de las tareas; ni que permitan soportar de forma completa e integrada las actividades de administración y desarrollo de Proyectos de Ingeniería de Conocimiento. En este contexto, esta investigación propone cubrir la vacancia desarrollando un Modelo de Proceso para Proyectos de Ingeniería del Conocimiento que integre en fases las actividades y técnicas desarrolladas para la administración y desarrollo de este tipo de proyectos.Eje: Ingeniería de SoftwareRed de Universidades con Carreras en Informática (RedUNCI

REKAYASA SISTEM KOGNITIF BERBASIS MULTI-AGEN: PENDEKATAN PENALARAN BERBASIS KASUS

Cognitive system modeling first introduced by psychology researchers. Unfortunately, the model has not been sufficient in supporting computer based problem solving. For that reason, artificial intelligence tries to propose a computational model of cognitive system. The main purpose of the computational model is to support human in solving complex problems, especially problems that involve large number of data, uncompleted data, and problem solving that requires systematic approach as human does. This research proposes an engineering of such multiagent based cognitive system, which employs case based reasoning as imitation of human reasoning to maintain the knowledge base

МЕРЕЖЕЦЕНТРИЧНА ВЗАЄМОДІЯ ЕКСПЕРТІВ У ФОРМАТІ НАРАТИВНОГО ДИСКУРСУ

Background. The methodological bases and tools for providing information-analytical activity of experts in the process of using and processing large volumes of information arrays are described. The aim of the work was to determine the methodological principles and tools that ensure the transformation of unstructured documents in the format of interactive knowledge bases. Materials and methods. Results. The basic functional categories of interaction with the distributed information are defined, namely: structuring, classification, criterization, synthesis and estimation. Theoretical bases of transformation of narrative descriptions of documents into a format of interactive systems of knowledge are considered. To do this, a certain set of tools is defined — taxonomy, ontology, indexing, search, intertextual linking, selection, aggregation of cognitive functions, etc. Conclusions. Transdisciplinarity realizes the reflexive and recursive properties of the whole information space. The primary is the formation of taxonomies that reflect the semantic content of each document. The use of these tools creates the conditions for the implementation of information and analytical activities of experts.Описуються методологічні основи та інструменти забезпечення інформаційно-аналітичної діяльності експертів у процесі використання та оброблення великих обсягів інформаційних масивів. Визначаються базові функціональні категорії взаємодії з розподіленою інформацією, а саме: структуризація, класифікація, критеріалізація, синтез і оцінювання. Розглядаються теоретичні засади перетворення наративних описів документів у формат інтерактивних систем знань. Для цього визначається певний набір інструментів — таксономія, онтологія, індексація, пошук, міжконтекстне зв'язування, вибір, агрегація когнітивних функцій тощо. Однак первинним для цих процедур є формування таксономій, що відображають семантичну змістовність кожного документу. Застосування цих інструментів створює умови реалізації інформаційно-аналітичної діяльності експертів

Modelling multicriteria value interactions with Reasoning Maps

Idiographic causal maps are extensively employed in Operational Research to support problem structuring and complex decision making processes. They model means-end or causal discourses as a network of concepts connected by links denoting influence, thus enabling the representation of chains of arguments made by decision-makers. There have been proposals to employ such structures to support the structuring of multicriteria evaluation models, within an additive value measurement framework. However, a drawback of this multi-methodological modelling is the loss of richness of interactions along the means-end chains when evaluating options. This has led to the development of methods that make use of the structure of the map itself to evaluate options, such as the Reasoning Maps method, which employs ordinal scales and ordinal operators for such evaluation. However, despite their potential, Reasoning Maps cannot model explicitly value interactions nor perform a quantitative ranking of options, limiting their applicability and usefulness. In this article we propose extending the Reasoning Maps approach through a multilinear evaluation model structure, built with the MACBETH multicriteria method. The model explicitly captures the value interactions between concepts along the map and employs the MACBETH protocol of questioning to assess the strength of influence for each means-end link. The feasibility of the proposed approach to evaluate options and to deal with multicriteria interactions is tested in a real-world application to support the construction of a population health index

Hemodynamic Analysis for Cognitive Load Assessment and Classification in Motor Learning Tasks Using Type-2 Fuzzy Sets

The paper addresses a novel approach to assess and classify the cognitive load of subjects from their hemodynamic response while engaged in motor learning tasks, such as vehicle-driving. A set of complex motor-activity-learning stimuli for braking, steering-control and acceleration is prepared to experimentally measure and classify the cognitive load of the car-drivers in three distinct classes: High, Medium and Low. New models of General and Interval Type-2 Fuzzy classifiers are proposed to reduce the scope of uncertainty in cognitive load classification due to the fluctuation of the hemodynamic features within and across sessions. The proposed classifiers offer high classification accuracy over 96%, leaving behind the traditional type-1/type-2 fuzzy and other standard classifiers. Experiments undertaken also offer a deep biological insight concerning the shift of brain-activations from the orbito-frontal to the ventro-lateral prefrontal cortex during high-to-low transition in cognitive load. Further, the activation of the dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. The proposed research outcome may directly be utilized to identify driving learners with low cognitive load for difficult motor learning tasks, such as taking a U-turn in a narrow space and motion control on the top of a bridge to avoid possible collision with the car ahead