Search results for "artificial intelligence"

Automatic Detection of Infantile Hemangioma using Convolutional Neural Network Approach

2020

Infantile hemangioma is the most common tumor of childhood. This study proposes an automatic detection as a preliminary step for a further accurate monitoring tool to evaluate the clinical status of hemangioma. For the detection of hemangioma pixels, a convolutional neural network (CNN) was trained on patches of two classes (hemangioma and nonhemangioma) from the train dataset, and then it was used to classify all the pixels of the region of interest from the test dataset. In order to evaluate the results of segmentation obtained with CNN, the region of interest of the test dataset was also segmented using two classical methods of segmentation: fuzzy c-means clustering (FCM) and segmentatio…

AI-enabled adaptive learning systems: A systematic mapping of the literature

2021

Abstract Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive le…

Trends in pattern recognition

1993

Aims of this paper are to present a short history of pattern recognition, its current areas of interest and future developments. The term pattern recognition is vague, its related topics including the study of sensorial stimuli, the analysis of physical phenomena and models of reasoning. Here we concentrate our attention on visual patterns and the machines that have been realized in order perform automatic pattern recognition. Some theoretical approaches will be also reviewed.

D Sensor-Based Obstacle Detection Comparing Octrees and Point clouds Using CUDA

2012

This paper presents adaptable methods for achieving fast collision detection using the GPU and Nvidia CUDA together with Octrees. Earlier related work have focused on serial methods, while this paper presents a parallel solution which shows that there is a great increase in time if the number of operations is large. Two dierent models of the environment and the industrial robot are presented, the rst is Octrees at dierent resolutions, the second is a point cloud representation. The relative merits of the two dierent world model representations are shown. In particular, the experimental results show the potential of adapting the resolution of the robot and environment models to the task at h…

Automatic object detection in point clouds based on knowledge guided algorithms

2013

The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strate…

Probabilistic Transition-Based Approach for Detecting Application-Layer DDoS Attacks in Encrypted Software-Defined Networks

2017

With the emergence of cloud computing, many attacks, including Distributed Denial-of-Service (DDoS) attacks, have changed their direction towards cloud environment. In particular, DDoS attacks have changed in scale, methods, and targets and become more complex by using advantages provided by cloud computing. Modern cloud computing environments can benefit from moving towards Software-Defined Networking (SDN) technology, which allows network engineers and administrators to respond quickly to the changing business requirements. In this paper, we propose an approach for detecting application-layer DDoS attacks in cloud environment with SDN. The algorithm is applied to statistics extracted from…

An adaptive probabilistic approach to goal-level imitation learning

2010

Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence…

The Influence of Requirements in Software Model Development in an Industrial Environment

2017

Textual description of requirements is a specification technique that is widely used in industry, where time is key for success. How requirements are specified textually greatly depends on human factors. In order to study how requirements processing is affected by the level of detail in textual descriptions, this paper compares enriched textual requirements specifications with non-enriched ones. To do this, we have conducted an experiment in industry with 19 engineers of CAF (Construcciones y Auxiliares de Ferrocarril), which is a supplier of railway solutions. The experiment is a crossover design that analyzes efficiency, effectiveness, and perceived difficulty starting from a written spec…

An adaptive probabilistic graphical model for representing skills in PbD settings

2010

Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes

2019

BACKGROUND: In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. METHODS: From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings rep…