Search results for " Machine Learning"
showing 10 items of 300 documents
Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine
2022
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SC…
A Posture Sequence Learning System for an Anthropomorphic Robotic Hand
2003
The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with an human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator.
Online Web Bot Detection Using a Sequential Classification Approach
2019
A significant problem nowadays is detection of Web traffic generated by automatic software agents (Web bots). Some studies have dealt with this task by proposing various approaches to Web traffic classification in order to distinguish the traffic stemming from human users' visits from that generated by bots. Most of previous works addressed the problem of offline bot recognition, based on available information on user sessions completed on a Web server. Very few approaches, however, have been proposed to recognize bots online, before the session completes. This paper proposes a novel approach to binary classification of a multivariate data stream incoming on a Web server, in order to recogn…
Efficient on-the-fly Web bot detection
2021
Abstract A large fraction of traffic on present-day Web servers is generated by bots — intelligent agents able to traverse the Web and execute various advanced tasks. Since bots’ activity may raise concerns about server security and performance, many studies have investigated traffic features discriminating bots from human visitors and developed methods for automated traffic classification. Very few previous works, however, aim at identifying bots on-the-fly, trying to classify active sessions as early as possible. This paper proposes a novel method for binary classification of streams of Web server requests in order to label each active session as “bot” or “human”. A machine learning appro…
CN2-R: Faster CN2 with randomly generated complexes
2011
Among the rule induction algorithms, the classic CN2 is still one of the most popular ones; a great amount of enhancements and improvements to it is to witness this. Despite the growing computing capacities since the algorithm was proposed, one of the main issues is resource demand. The proposed modification, CN2-R, substitutes the star concept of the original algorithm with a technique of randomly generated complexes in order to substantially improve on running times without significant loss in accuracy.
Using self-deferral to achieve fairness between Wi-Fi and NR-U in downlink and uplink scenarios
2022
Wireless networks operating in unlicensed bands generally use one of two channel access paradigms: random access (e.g., Wi-Fi) or scheduled access (e.g., LTE License Assisted Access, LTE LAA and New Radio-Unlicensed, NR-U). The coexistence between these two paradigms is based on listen before talk (LBT), which was, however, designed for random access. Meanwhile, scheduled systems require that their transmissions start at the beginning of a slot boundary. Synchronizing this boundary to the end of LBT usually requires transmitting a reservation signal (RS) to block the channel. Since the RS is a waste of channel resources, we investigate an alternative self-deferral approach (gap-based access…
Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series
2020
L'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fon…
hidden markov random fields and cuckoo search method for medical image segmentation
2020
Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.
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…
Artificial intelligence in the cyber security environment
2019
Artificial Intelligence (AI) is intelligence exhibited by machines. Any system that perceives its environment and takes actions that maximize its chance of success at some goal may be defined as AI. The family of AI research is rich and varied. For example, cognitive computing is a comprehensive set of capabilities based on technologies such as deep learning, machine learning, natural language processing, reasoning and decision technologies, speech and vision technologies, human interface technologies, semantic technology, dialog and narrative generation, among other technologies. Artificial intelligence and robotics have steadily growing roles in our lives and have the potential to transfo…