An evolutionary artificial neural network time series forecasting system

Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may be appealing. However, the main problem with this approach is on the search for the best ANN architecture. Genetic Algorithms (GAs) are suited for problems of combinatorial nature, where other methods seem to fail. Therefore, an integration of ANNs and GAs for TSF, taking the advantages of both methods, may be appealing. ANNs will learn to forecast by back-propagation. Different ANNs architectures will give different forecasts, leading to competition. At the end of the evolutionary process the resulting ANN is expected to return the best possible forecast. It is asserted that the combined strategy exceeded conventional TSF methods on TS of high non-linear degree, particularly for long term forecasts

Revisiting the 1D and 2D laplace transforms

Foundation for Science and Technology of Portugal, under the projects UIDB/00066/2020.The paper reviews the unilateral and bilateral, one- and two-dimensional Laplace transforms. The unilateral and bilateral Laplace transforms are compared in the one-dimensional case, leading to the formulation of the initial-condition theorem. This problem is solved with all generality in the one- and two-dimensional cases with the bilateral Laplace transform. The case of fractional-order systems is also included. General two-dimensional linear systems are introduced and the corresponding transfer function is defined.publishersversionpublishe

A review of sample and hold systems and design of a new fractional algorithm

UIDB/00066/2020Digital systems require sample and hold (S&H) systems to perform the conversion from analog to digital and vice versa. Besides the standard zero and first order holds, we find in the literature other versions, namely the fractional and exponential order holds, involving parameters that can be tuned to produce a superior performance. This paper reviews the fundamental concepts associated with the S&H and proposes a new fractional version. The systems are modeled both in the time and Laplace domains. The new S&H stemming from fractional calculus generalizes these devices. The different S&H systems are compared in the frequency domain and their relationships visualized by means of hierarchical clustering and multidimensional scaling representations. The novel strategy allows a better understanding of the possibilities and limitations of S&H systems.publishersversionpublishe

Anthropopaty and its assessment in virtual entities

The introduction and the natural evaluation of virtual entities presenting human like feelings and behaviours, living in virtual worlds, being based on agents, organizations or other kind of artefacts, has been made, almost exclusively, by an evaluation of such characteristics and assumptions, in terms of a set of quantitative variables. In this paper, it is presented an alternative way to analyse and evaluate an intelligent’s system body of knowledge in terms of its anthropopathic potential, that considers quantitative, qualitative and incomplete information, through and extension to the language of logic programming

Agent based decision support systems in medicine

Embedding Machine Learning technology into Agent Driven Diagnosis Systems adds a new potential to the realm of Medicine, and in particular to the imagiology one. However, despite all the research done in the last years on the development of new methodologies for problem solving, in terms of the design of MultiAgent Systems (MAS) there is none where both the agent and the organizational view can be modelled. Current multi-agent approaches to problem solving either take a centralist, static approach to organizational design or take an emergent view in which agent interactions are not pre-determined, thus making it impossible to make any predictions on the behavior of the whole systems. Most of them also lack a model of the norms in the environment that should rule the behaviour of the agent society as a whole and/or the actions of the individuals. In this paper, it is proposed not only a framework for modelling and run agent organizations, but also to depict the different components of such societies. To illustrate these premises, we will evoke a society with one modality, the Axial Computed Tomography one, where two different but complementary computational paradigms, the Artificial Neural Networks and the Case Based Reasoning are object of attention

Health data management in the medical arena

In this paper it is presented an Agency for Integration, Archive and Diffusion of Medical Information (AIDA), which configures a data warehouse, developed using Multi-Agent technology. AIDA is like a symbiont, with a close association with core applications present at any health care facility, such as the Picture Archive Communication System, the Radiological Information System or the Electronic Medical Record Information System. Multi-Agent Systems also configure a new methodology for problem solving

Data warehousing through multi-agent systems in the medical arena

Comunicação apresentada na International Conference on Knowledge Engineering and Decision Support, 1, Porto, 2004.In this paper it is presented AIDA, an Agency for Integration, Archive and Diffusion of Medical Information. It configures a data warehouse, developed using Multi-Agent technology, that integrates and archives information from heterogeneous sources of a health care unit. AIDA is like a symbiont, with a close association with core applications at any health care facility, namely the Picture Archive Communication System, the Radiological Information System and the Electronic Medical Record Information System, that are built upon pro-active agents and communicate with the AIDA’s ones

Agent driven diagnosis in medicine

Embedding Machine Learning technology into Agent Driven Diagnosis Systems adds a new potential to the realm of Medicine, and in particular to the imagiology one. However, despite all the research done in the last years on the development of methodologies for designing MultiAgent Systems (MAS), there is no methodology suitable for the specification and design of MAS in complex domains where both the agent view and the organizational view can be modelled. Current multi-agent approaches either take a centralist, static approach to organizational design or take an emergent view in which agent interactions are not pre-determined, thus making it impossible to make any predictions on the behavior of the whole systems. Most of them also lack a model of the norms in the environment that should rule the behavior of the agent society as a whole and/or the actions of individuals. In this paper, we propose a framework for modelling agent organizations, and we illustrate the different components of a society with one modality, the Axial Computed Tomography scenario, combining two methodologies for problem solving, the Artificial Neural Networks and the Case Based Reasoning ones

A neural network based time series forecasting system

The Neural Network (NN) arena has suffered in the past years a remarkable development as one of the novel fields for Artificial Intelligence (AI). Time Series Analysis (TSA) based on models of the variability of observations by postulating trends and cyclic effects, with a view to understand the cause of variation and to improve forecasting, suggests the use of NNs that do something like, what is called in statistics, Principal Component Analysis (PCA). The purpose of this work is to present a logical based NN system, along with: (i) Time Series Forecasting (TSF), with its characteristics of strong noise component and non-linearity in data, showing itself as a field in which the use of NN's stuff is particularly advisable; (ii) PCA rules, organized in a default hierarchy as logical theories, competing with one another for the right to represent a particular situation or to predict its successors; i.e., assisting in the process of choosing the best network to forecast each series. Some trials will be conducted, and the basic performance measures used as baselines for comparison with other methods

Deep learning for activity recognition using audio and video

Neural networks have established themselves as powerhouses in what concerns several types of detection, ranging from human activities to their emotions. Several types of analysis exist, and the most popular and successful is video. However, there are other kinds of analysis, which, despite not being used as often, are still promising. In this article, a comparison between audio and video analysis is drawn in an attempt to classify violence detection in real-time streams. This study, which followed the CRISP-DM methodology, made use of several models available through PyTorch in order to test a diverse set of models and achieve robust results. The results obtained proved why video analysis has such prevalence, with the video classification handily outperforming its audio classification counterpart. Whilst the audio models attained on average 76% accuracy, video models secured average scores of 89%, showing a significant difference in performance. This study concluded that the applied methods are quite promising in detecting violence, using both audio and video.This work has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project "Integrated and Innovative Solutions for the well-being of people in complex urban centers" within the Project Scope NORTE-01-0145-FEDER000086. C.N. thank the FCT-Fundacao para a Ciencia e Tecnologia for the grant 2021.06507.BD