Analysis of Oral Diadochokinesis in Progressive Neurological Diseases via Automated Acoustic Analysis

Tato práce se zabývá problematikou automatické klasifikace neurodegenerativních onemocnění pomocí akustické analýzy orální diadochokineze. Dvě varianty algoritmu pro segmentaci řeči, který je nedílnou součástí pro vyhodnocení diadochokineze, jsou navrženy. Jejich přesnosti jsou porovnány a ten lepší je porovnán s předešlými algoritmy. S využitím tohoto algoritmu jsou pak z promluv pacientů extrahovány příznaky, jejichž významnost je vyhodnocena. Posledním krokem je klasifikace onemocnění s pomocí jednoduchého klasifikátoru. Práce je zakončena diskuzí nad výsledky a návrhy k budoucí práci.This thesis deals with an automated assessment of neurodegenerative diseases by acoustic speech analysis using oral diadochokinesis. Two variants of speech segmentation algorithm necessary for the diadochokinetic task are designed. Their performance is evaluated and compared to previously designed algorithms from the literature. Furthermore, the speech segmentation algorithm is used to extract features from the utterances. The features are evaluated in terms of significance, and a simple classifier is built to distinguish between the neurodegenerative diseases. Finally, we discuss the results and make proposals for future work

Lower Bound and Hybrid ILS-VND for a Location Arc Routing Problem

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Recherche Local Itérée pour le TSP avec profits

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A genetic algorithm for the two dimensional bin packing problem with partial conflicts

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Hybrid Metaheuristic for the Team Orienteering Problem with time windows

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An Effective Hybrid Evolutionary Local Search for Orienteering and Team Orienteering Problems with Time Windows

International audienceThe orienteering problem (OP) consists in finding an elementary path over a subset of vertices. Each vertex has associated a profit that is collected on the visitor’s first visit. The objective is to maximize the collected profit with respect to a limit on the path’s length. The team orienteering problem (TOP) is an extension of the OP where a fixed number m of paths must be determined. This paper presents an effective hybrid metaheuristic to solve both the OP and the TOP with time windows. The method combines the greedy randomized adaptive search procedure (GRASP) with the evolutionary local search (ELS). The ELS generates multiple distinct child solutions using a mutation mechanism and a local search. The GRASP provides multiple starting solutions to the ELS. The method is able to improve several best known results on available benchmark instances

An evolutionary algorithm for the bi-objective multiple traveling salesman problem

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A multi-start iterated local search for the traveling salesman with profits.

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An Evolutionary Algorithm with Path Relinking for a Bi-objective Multiple Traveling Salesman Problem with Profits

International audienceThis chapter deals with a bi-objective multiple traveling salesman problem with profits (BOMTSPP), generalizing the classical TSP with profits (TSPP). The TSPP is in fact a generic name for TSP problems taking into account the length of the tour and profits collected at customers. However, all these problems are not really bi-objective: the two criteria are aggregated into a single objective or one of them is replaced by a constraint. Our BOMTSPP aims at building m cycles covering a subset of potential customers so that the total collected profit is maximized and the overall traveling distance is minimized. This new problem generalizes the TSPP in two directions: a true bi-objective treatment and the construction of multiple tours. The proposed solution method is an effective evolutionary algorithm, reinforced by a post-optimization procedure based on path-relinking (PR)

Granular Variable Neiborhood Search for the Team Orienteering Problems with Time Windows

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