Chaos game mechanism is a procedure for creating fractal phenomena from another fractal using a polygon and random walk. Here, the authors propose a novel identification approach and efficient application of Chaos Game theory towards signature identification. In fact, a signature is produced with hand scripting with a special rhythm that belongs to a specific person. Although a signature is a behavioural feature among the human biometric features, this behavioural feature expresses a chaotic property and can be analysed with chaotic systems and fractal theory. With authors' technique, by using a Chaos Game theory, a new fractal is created for each signature instance and, during the creation of the fractal, new features are extracted. These features express the fractal properties of the signature and are unique. In addition, by using fractal theory, this technique benefits from the advantages of fractal phenomena such as stability against rotation, losing some parts of the signature and scale that is desirable for biometrics applications. Authors' approach for offline signature analysis can segregate and identify many signature instances with a desirable time complexity. The authors name the technique that the authors present here the chaos game signature identification (CGSI).
By increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.
In the last decade, identification by biometric features such as iris and fingerprint has been considered very much. Last introduced methods, in fact, could achieve high accuracy, but one of the most common problems in these methods is the lack of scalability. So these methods are suitable for use in small databases of iris. One solution for this problem is using the hierarchy classification. In this paper, fractal dimension of iris and effective range of color in RGB layers are used as first and second layers of classification in iris images respectively in order to increase the performance of different methods in human identification. The result of simulation on Phoenix database's data shows that this method is suitably efficient in the classification step.
Traveler Salesman Problem (TSP) is one the most famous and important problems in the field of operation research and optimization. This problem is a NP-Hard problem and it is aimed to find a minimum Hamiltonian cycle in a connected and weighed graph. In the last decades, many innovative algorithms have been presented to solve this problem but most of them are inappropriate and inefficient and have high complexity. In this paper, we combined Hopfield neural network with genetic algorithm to solve this problem, and showed that the results of the algorithm are more efficient that the other similar algorithms.
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