Search results for "LABOR"

An Automatic Method for PET Delineation of Cervical Tumors

2015

Aim: PET imaging is increasingly utilized for radiation treatment planning. Nevertheless, accurate segmentation of PET images is a complex and unresolved problem. Aim of this work is the development of an automatic segmentation method of Biological Target Volume (BTV) in patients with cervical cancer. Materials and methods: Random walks (RW) is a graph-based method that represents a DICOM (Digital Imaging and COmmunications in Medicine) image as a graph. The voxels are its nodes and the edges are defined by a cost function which maps a change in image intensity to edge weights. Then, RW partitions the nodes into target and background subsets. To create an automatic method starting from prev…

Are Disembodied Agents Really Autonomous?

2012

Child-display interaction: Exploring avatar-based touchless gestural interfaces

2019

During the last decade, touchless gestural interfaces have been widely studied as one of the most promising interaction paradigms in the context of pervasive displays. In particular, avatars and silhouettes have been proved to be effective in communicating the touchless gestural interactivity supported by displays. In the paper, we take a child-display interaction perspective by exploring avatar-based touchless gestural interfaces. We believe that large displays offer an opportunity to stimulate child experience and engagement, for instance when learning about art, as well as bringing a number of challenges. The purpose of this study is twofold: 1) identifying the relevant aspects of childr…

BAYESIAN APPROACHES TO HUMAN-ROBOT INTERACTION: FROM LANGUAGE GROUNDING TO ACTION LEARNING AND UNDERSTANDING

2012

In human-robot interaction field, the robot is no longer considered as a tool but as a partner, which supports the work of humans. Environments that feature the interaction and collaboration of humans and robots present a number of challenges involving robot learning and interactive capabilities. In order to operate in these environments, the robot must not only be able to do, but also be able to interact and especially to ”understand”. This thesis proposes a unified probabilistic framework that allows a robot to develop basic cognitive skills essential for collaboration. To this aim we embrace the idea of motor simulation - well established in cognitive science and neuroscience - in which …

On a Roadmap to Biologically Inspired Cognitive Agents

2011

A new challenge is proposed for future intelligent artifacts based on biologically inspired cognitive architectures (BICA), called the BICA Challenge. Namely, it is proposed that a BICA agent can only be considered human-level intelligent if it can be accepted and trusted as an equal member (a “person”) by a human community. For example, an agent of this sort would be able to win a political election against human candidates.

Assessing Coastal Sustainability: A Bayesian Approach for Modeling and Estimating a Global Index for Measuring Risk

2013

Integrated Coastal Zone Management is an emerg- ing research area. The aim is to provide a global view of dif- ferent and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate use- ful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second la…

Fast Fingerprints Classification only using the Directional Image

2007

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

An Intelligent Empowering Agent (IEA) to Provide Easily Understood and Trusted Health Information Appropriate to the User Needs

2023

AbstractMost members of the public, including patients, usually obtain health information from Web searches using generic search engines, which is often overwhelming, too generic, and of poor quality. Although patients may be better informed, they are often none the wiser and not empowered to communicate with medical professionals so that their care is compatible with their needs, values, and best interests. Intelligent Empowering Agents (IEA) use AI to filter medical information and assist the user in the understanding of health information about specific complaints or health in general. We have designed and developed a prototype of an IEA that dialogues with the user in simple language, c…

EMBER—Embedding Multiple Molecular Fingerprints for Virtual Screening

2022

In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different “spectra” to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands&…

BioAnalysis: A Framework for Structural and Functional Robustness Analysis of Metabolic Networks

2010

The main objective of this work is to analyze metabolic networks evolution in terms of their robustness and fault tolerance capabilities. In metabolic networks, errors can be seen as random removal of network nodes, while attacks are high-connectivity-degree node deletion aimed at compromising network activity. This paper proposes a software framework, namely BioAnalysis, used to test the robustness and the fault tolerance capabilities of real metabolic networks, when mutations and node deletions affect the network structure. The performed simulations are related to the central metabolic network of the well-known E. coli single-celled bacterium and involve either hub nodes or non-hub nodes,…