Teaching Neural Networks to Detect the Authors of Texts Using Lexical Descriptors

This paper proposes a means of using an artificial neural network to distinguish the authors of paragraphs. Once the network has been trained, its hidden layer activations are recorded as a representation of the average number of words and average characters of words in a paragraphs of an author. This stored information can then be used to identify the texts written by authors. This computational task is solved by dividing it into a number of computationally simple tasks and then combining the solutions to those tasks. Computational simplicity is achieved by distributing the learning task among a number of experts, which in turn divides the input space into a set of subspaces. The combination of these experts is said to constitute a committee machine. Basically, it fuses knowledge acquired by experts to arrive at an overall decision that is supposedly superior to that attainable by anyone of them acting alone. By this, we succeeded to distinguish the paragraphs authored by Ivo Andrić, from the ones authored by Mehmed Meša Selimović

Principal Component Analysis and Neural Networks for Authorship Attribution

A common problem in statistical pattern recognition is that of feature selection or feature extraction. Feature selection refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. In this paper the data collected by counting selected syntactic characteristics in around a thousand paragraphs of each of the sample books underwent a principal component analysis performed using neural networks. Then, first of the principal components are used to distinguish authors of the texts by the use of multilayer preceptor type artificial neural networks

Detecting the Authors of Texts by Neural Network Committee Machines

This paper proposes a means of using a boosting by filtering algorithm in artificial neural networks to identify the author of a text. This approach involves filtering the training examples by different versions of a weak learning algorithm. It assures the availability of a large source of examples, with the examples being either discarded or kept during training. An advantage of this approach is that it allows for a small memory requirement. Once the network has been trained, its hidden layer activations are recorded as a representation of the selected lexical descriptors of an author. This stored information can then be used to identify the texts written by the same author. Texts studied are literary works of two Bosnian writers, Ivo Andrić  (1892-1975) and M. Meša Selimović (1910-1982). The data collected by counting syntactic characteristics in 1466 paragraphs of "na drini ćupria" by Ivo Andrić, and "derviš i smirt"  by M. Meša Selimović each

Detecting the Authors of Texts by Neural Network Committee Machines

This paper proposes a means of using a boosting by filtering algorithm in artificial neural networks to identify the author of a text. This approach involves filtering the training examples by different versions of a weak learning algorithm. It assures the availability of a large source of examples, with the examples being either discarded or kept during training. An advantage of this approach is that it allows for a small memory requirement. Once the network has been trained, its hidden layer activations are recorded as a representation of the selected lexical descriptors of an author. This stored information can then be used to identify the texts written by the same author. Texts studied are literary works of two Bosnian writers, Ivo Andrić  (1892-1975) and M. Meša Selimović (1910-1982). The data collected by counting syntactic characteristics in 1466 paragraphs of "na drini ćupria" by Ivo Andrić, and "derviš i smirt"  by M. Meša Selimović each

Distinction of The Authors of Texts Using Multilayered Feedforward Neural Networks

This paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a  powerful scheme for learning complex input-output mapping have been used in learning of the average number of words and average characters of words in a paragraphs of an author. The resulting training information we get will be used to identify the texts written by authors. The computational complexity is solved by dividing it into a number of computationally simple tasks where the input space is divided into a set of subspaces and then combining the solutions to those tasks. By this, we have been able to successfully distinguish the books authored by Leo Tolstoy, from the ones authored by George Orwell and Boris Pasternak

Principal Component Analysis for Authorship Attribution

A common problem in statistical pattern recognition is that of feature selection or feature extraction. Feature selection refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. In this paper the data collected by counting words and characters in around a thousand paragraphs of each sample book underwent a principal component analysis performed using neural networks. Then first of the principal components is used to distinguished the books authored by a certain author

Three Variable Cancer Angiogenesis models

In this paper we present several mathematical models of tumor growth and angiogenesis expressed by  systems of ODE’ s encoding the most essential observations and assumptions about the complex hierarchical interactive processes of tumor neo-vascularization (angiogenesis). The simplest modeling option presented merely captures the three independent variables mentioned earlier-tumor size N, total vessel volume V and the amount of protein P. We modify this model assuming that the protein is additionally consumed by growing vessels and obtain a model with protein consumption. Next models with time-delays are introduced. To make our models more realistic, two more compartments representing more complex vascularity and protein effects are introduced

Mathematical Models of Tumor Growth and Angiogenesis

In this paper we present several mathematical models of tumor growth and angiogenesis expressed by  systems of ODE’s encoding the most essential observations and assumptions about the complex hierarchical interactive processes of tumor neo-vascularization (angiogenesis). The simplest modeling option presented merely captures the three independent variables mentioned earlier-tumor size N, total vessel volume V and the amount of protein P. We modify this model assuming that the protein is additionally consumed by growing vessels and obtain a model with protein consumption. Next models with time-delays are introduced. To make our models more realistic, two more compartments representing more complex vascularity and protein effects are introduced. Hence five dimensional models are obtained

Bifurcation Analysis for Metapopulation Models

Two models with four components of chain populations are considered. In the model, a prey population X is predated by individuals of a specialist predator population Y, and another prey population Z is predated by individuals of a generalist predator population U. This model is governed by a system of four nonlinear first order ordinary differential equations. To study the dynamics of the food chain model, the mentioned system of ordinary differential equations solved numerically. One of the biological parameters varied in a sufficiently large range and its effects on the dynamics of the system are observed.  Along the axis of the predating rate of the specialist predator, around four points  we meet chaos. At each time chaos precedes period doublings.

Teaching Neural Networks to Classify the Authors of Texts

A lot of research has been done on author classification using various methodologies. One of them is using artificial neural networks. It is common that the number of descriptors used for author classification exceeds two. In this paper we propose a means of using artificial neural network to classify the authors of texts using only two descriptors: the number of words in a paragraph and a number of characters per word in a paragraph. The approach taken uses committee machines based on ensemble averaging. The basic idea is to solve the complex computational task by dividing it into a number of computationally simple tasks and then combining the solution of these tasks. The high performance achieved is because the committee is much better than the single best constituent in the isolation. Our results show that with the above approach we succeeded to correctly classify the works of Leo Tolstoy and George Orwell