A neural network approach to audio-assisted movie dialogue detection

A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of indicator functions and the magnitude of the corresponding cross-power spectral density are fed as input to neural networks for dialogue detection. Several types of artificial neural networks, including multilayer perceptrons, voted perceptrons, radial basis function networks, support vector machines, and particle swarm optimization-based multilayer perceptrons are tested. Experiments are carried out to validate the feasibility of the aforementioned approach by using ground-truth indicator functions determined by human observers on 6 different movies. A total of 41 dialogue instances and another 20 non-dialogue instances is employed. The average detection accuracy achieved is high, ranging between 84.78%±5.499% and 91.43%±4.239%

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Neural networks in control engineering

The purpose of this thesis is to investigate the viability of integrating neural networks into control structures. These networks are an attempt to create artificial intelligent systems with the ability to learn and remember. They mathematically model the biological structure of the brain and consist of a large number of simple interconnected processing units emulating brain cells. Due to the highly parallel and consequently computationally expensive nature of these networks, intensive research in this field has only become feasible due to the availability of powerful personal computers in recent years. Consequently, attempts at exploiting the attractive learning and nonlinear optimization characteristics of neural networks have been made in most fields of science and engineering, including process control. The control structures suggested in the literature for the inclusion of neural networks in control applications can be divided into four major classes. The first class includes approaches in which the network forms part of an adaptive mechanism which modulates the structure or parameters of the controller. In the second class the network forms part of the control loop and replaces the conventional control block, thus leading to a pure neural network control law. The third class consists of topologies in which neural networks are used to produce models of the system which are then utilized in the control structure, whilst the fourth category includes suggestions which are specific to the problem or system structure and not suitable for a generic neural network-based-approach to control problems. Although several of these approaches show promising results, only model based structures are evaluated in this thesis. This is due to the fact that many of the topologies in other classes require system estimation to produce the desired network output during training, whereas the training data for network models is obtained directly by sampling the system input(s) and output(s). Furthermore, many suggested structures lack the mathematical motivation to consider them for a general structure, whilst the neural network model topologies form natural extensions of their linear model based origins. Since it is impractical and often impossible to collect sufficient training data prior to implementing the neural network based control structure, the network models have to be suited to on-line training during operation. This limits the choice of network topologies for models to those that can be trained on a sample by sample basis (pattern learning) and furthermore are capable of learning even when the variation in training data is relatively slow as is the case for most controlled dynamic systems. A study of feedforward topologies (one of the main classes of networks) shows that the multilayer perceptron network with its backpropagation training is well suited to model nonlinear mappings but fails to learn and generalize when subjected to slow varying training data. This is due to the global input interpretation of this structure, in which any input affects all hidden nodes such that no effective partitioning of the input space can be achieved. This problem is overcome in a less flexible feedforward structure, known as regular Gaussian network. In this network, the response of each hidden node is limited to a -sphere around its center and these centers are fixed in a uniform distribution over the entire input space. Each input to such a network is therefore interpreted locally and only effects nodes with their centers in close proximity. A deficiency common to all feedforward networks, when considered as models for dynamic systems, is their inability to conserve previous outputs and states for future predictions. Since this absence of dynamic capability requires the user to identify the order of the system prior to training and is therefore not entirely self-learning, more advanced network topologies are investigated. The most versatile of these structures, known as a fully recurrent network, re-uses the previous state of each of its nodes for subsequent outputs. However, despite its superior modelling capability, the tests performed using the Williams and Zipser training algorithm show that such structures often fail to converge and require excessive computing power and time, when increased in size. Despite its rigid structure and lack of dynamic capability, the regular Gaussian network produces the most reliable and robust models and was therefore selected for the evaluations in this study. To overcome the network initialization problem, found when using a pure neural network model, a combination structure· _in which the network operates in parallel with a mathematical model is suggested. This approach allows the controller to be implemented without any prior network training and initially relies purely on the mathematical model, much like conventional approaches. The network portion is then trained during on-line operation in order to improve the model. Once trained, the enhanced model can be used to improve the system response, since model exactness plays an important role in the control action achievable with model based structures. The applicability of control structures based on neural network models is evaluated by comparing the performance of two network approaches to that of a linear structure, using a simulation of a nonlinear tank system. The first network controller is developed from the internal model control (IMC) structure, which includes a forward and inverse model of the system to be controlled. Both models can be replaced by a combination of mathematical and neural topologies, the network portion of which is trained on-line to compensate for the discrepancies between the linear model _ and nonlinear system. Since the network has no dynamic ·capacity, .former system outputs are used as inputs to the forward and inverse model. Due to this direct feedback, the trained structure can be tuned to perform within limits not achievable using a conventional linear system. As mentioned previously the IMC structure uses both forward and inverse models. Since the control law requires that these models are exact inverses, an iterative inversion algorithm has to be used to improve the values produced by the inverse combination model. Due to deadtimes and right-half-plane zeroes, many systems are furthermore not directly invertible. Whilst such unstable elements can be removed from mathematical models, the inverse network is trained directly from the forward model and can not be compensated. These problems could be overcome by a control structure for which only a forward model is required. The neural predictive controller (NPC) presents such a topology. Based on the optimal control philosophy, this structure uses a model to predict several future outputs. The errors between these and the desired output are then collected to form the cost function, which may also include other factors such as the magnitude of the change in input. The input value that optimally fulfils all the objectives used to formulate the cost function, can then be found by locating its minimum. Since the model in this structure includes a neural network, the optimization can not be formulated in a closed mathematical form and has to be performed using a numerical method. For the NPC topology, as for the neural network IMC structure, former system outputs are fed back to the model and again the trained network approach produces results not achievable with a linear model. Due to the single network approach, the NPC topology furthermore overcomes the limitations described for the neural network IMC structure and can be extended to include multivariable systems. This study shows that the nonlinear modelling capability of neural networks can be exploited to produce learning control structures with improved responses for nonlinear systems. Many of the difficulties described are due to the computational burden of these networks and associated algorithms. These are likely to become less significant due to the rapid development in computer technology and advances in neural network hardware. Although neural network based control structures are unlikely to replace the well understood linear topologies, which are adequate for the majority of applications, they might present a practical alternative where (due to nonlinearity or modelling errors) the conventional controller can not achieve the required control action

Application of neural networks and sensitivity analysis to improved prediction of trauma survival

Application of neural networks and sensitivity analysis to improved prediction of trauma surviva

Algumas aplicações da Inteligência Artificial em Biotecnologia

The present work is a revision about neural networks. Initially presents a little introduction to neural networks, fuzzy logic, a brief history, and the applications of Neural Networks on Biotechnology. The chosen sub-areas of the applications of Neural Networks on Biotechnology are, Solid-State Fermentation Optimization, DNA Sequencing, Molecular Sequencing Analysis, Quantitative Structure-Activity Relationship, Soft Sensing, Spectra Interpretation, Data Mining, each one use a special kind of neural network like feedforward, recurrent, siamese, art, among others. Applications of the Neural-Networks in spectra interpretation and Quantitative Structure-activity relationships, is a direct application to Chemistry and consequently also to Biochemistry and Biotechnology. Soft Sensing is a special example for applications on Biotechnology. It is a method to measure variables that normally can’t be directly measure. Solid state fermentation was optimized and presenting, as result, a strong increasing of production efficiency.O presente trabalho é uma revisão sobre redes neurais. Inicialmente apresenta uma breve introdução a redes neurais, lógica difusa, um breve histórico, e aplicações de Redes Neurais em Biotecnologia. As subáreas escolhidas para aplicação das redes neurais são, Otimização da Fermentação no Estado-Sólido, Sequenciamento de DNA, Análise Molecular Sequencial, Relação Quantitativa Strutura-Atividade, Sensores inteligentes, Interpretação de espectros, Mineração de Dados, sendo que cada um usa um tipo especial de rede neural, tais como feed forward, recorrente, siamesa, art, entre outros. Aplicações de Redes Neurais em interpretação de espectros e Relação Quantitativa Estrutura-Atividade, como uma aplicação direta à química e consequentemente também para a Bioquímica e Biotecnologia. Os sensores Inteligentes são um exemplo especial de aplicação em Biotecnologia. É um método de medir variáveis que normalmente não podem ser medidas de forma direta. Fermentações no Estado-sólido foram otimizadas e, apresentaram como resultado um forte aumento do rendimento na produção final