946 resultados para planning (artificial intelligence)
Resumo:
La scoliose idiopathique de ladolescent (SIA) est une dformation tri-dimensionelle du rachis. Son traitement comprend lobservation, lutilisation de corsets pour limiter sa progression ou la chirurgie pour corriger la dformation squelettique et cesser sa progression. Le traitement chirurgical reste controvers au niveau des indications, mais aussi de la chirurgie entreprendre. Malgr la prsence de classifications pour guider le traitement de la SIA, une variabilit dans la stratgie opratoire intra et inter-observateur a t dcrite dans la littrature. Cette variabilit saccentue dautant plus avec lvolution des techniques chirurgicales et de linstrumentation disponible. Lavancement de la technologie et son intgration dans le milieu mdical a men lutilisation dalgorithmes dintelligence artificielle informatiques pour aider la classification et lvaluation tridimensionnelle de la scoliose. Certains algorithmes ont dmontr tre efficace pour diminuer la variabilit dans la classification de la scoliose et pour guider le traitement. Lobjectif gnral de cette thse est de dvelopper une application utilisant des outils dintelligence artificielle pour intgrer les donnes dun nouveau patient et les vidences disponibles dans la littrature pour guider le traitement chirurgical de la SIA. Pour cela une revue de la littrature sur les applications existantes dans lvaluation de la SIA fut entreprise pour rassembler les lments qui permettraient la mise en place dune application efficace et accepte dans le milieu clinique. Cette revue de la littrature nous a permis de raliser que lexistence de black box dans les applications dveloppes est une limitation pour lintgration clinique ou la justification base sur les vidence est essentielle. Dans une premire tude nous avons dvelopp un arbre dcisionnel de classification de la scoliose idiopathique bas sur la classification de Lenke qui est la plus communment utilise de nos jours mais a t critique pour sa complexit et la variabilit inter et intra-observateur. Cet arbre dcisionnel a dmontr quil permet daugmenter la prcision de classification proportionnellement au temps pass classifier et ce indpendamment du niveau de connaissance sur la SIA. Dans une deuxime tude, un algorithme de stratgies chirurgicales bas sur des rgles extraites de la littrature a t dvelopp pour guider les chirurgiens dans la slection de lapproche et les niveaux de fusion pour la SIA. Lorsque cet algorithme est appliqu une large base de donne de 1556 cas de SIA, il est capable de proposer une stratgie opratoire similaire celle dun chirurgien expert dans prt de 70% des cas. Cette tude a confirm la possibilit dextraire des stratgies opratoires valides laide dun arbre dcisionnel utilisant des rgles extraites de la littrature. Dans une troisime tude, la classification de 1776 patients avec la SIA laide dune carte de Kohonen, un type de rseaux de neurone a permis de dmontrer quil existe des scoliose typiques (scoliose courbes uniques ou double thoracique) pour lesquelles la variabilit dans le traitement chirurgical varie peu des recommandations par la classification de Lenke tandis que les scolioses a courbes multiples ou tangentielles deux groupes de courbes typiques taient celles avec le plus de variation dans la stratgie opratoire. Finalement, une plateforme logicielle a t dveloppe intgrant chacune des tudes ci-dessus. Cette interface logicielle permet lentre de donnes radiologiques pour un patient scoliotique, classifie la SIA laide de larbre dcisionnel de classification et suggre une approche chirurgicale base sur larbre dcisionnel de stratgies opratoires. Une analyse de la correction post-opratoire obtenue dmontre une tendance, bien que non-statistiquement significative, une meilleure balance chez les patients oprs suivant la stratgie recommande par la plateforme logicielle que ceux aillant un traitement diffrent. Les tudes exposes dans cette thse soulignent que lutilisation dalgorithmes dintelligence artificielle dans la classification et llaboration de stratgies opratoires de la SIA peuvent tre intgres dans une plateforme logicielle et pourraient assister les chirurgiens dans leur planification propratoire.
Resumo:
The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.
Resumo:
As a result of the more distributed nature of organisations and the inherently increasing complexity of their business processes, a significant effort is required for the specification and verification of those processes. The composition of the activities into a business process that accomplishes a specific organisational goal has primarily been a manual task. Automated planning is a branch of artificial intelligence (AI) in which activities are selected and organised by anticipating their expected outcomes with the aim of achieving some goal. As such, automated planning would seem to be a natural fit to the BPM domain to automate the specification of control flow. A number of attempts have been made to apply automated planning to the business process and service composition domain in different stages of the BPM lifecycle. However, a unified adoption of these techniques throughout the BPM lifecycle is missing. As such, we propose a new intention-centric BPM paradigm, which aims on minimising the specification effort by exploiting automated planning techniques to achieve a pre-stated goal. This paper provides a vision on the future possibilities of enhancing BPM using automated planning. A research agenda is presented, which provides an overview of the opportunities and challenges for the exploitation of automated planning in BPM.
Resumo:
Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.
Resumo:
The Dissolved Gas Analysis (DGA) a non destructive test procedure, has been in vogue for a long time now, for assessing the status of power and related transformers in service. An early indication of likely internal faults that may exist in Transformers has been seen to be revealed, to a reasonable degree of accuracy by the DGA. The data acquisition and subsequent analysis needs an expert in the concerned area to accurately assess the condition of the equipment. Since the presence of the expert is not always guaranteed, it is incumbent on the part of the power utilities to requisition a well planned and reliable artificial expert system to replace, at least in part, an expert. This paper presents the application of Ordered Ant Mner (OAM) classifier for the prediction of involved fault. Secondly, the paper also attempts to estimate the remaining life of the power transformer as an extension to the elapsed life estimation method suggested in the literature.
Resumo:
The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.
Resumo:
The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.
Resumo:
With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.
