Results 91 to 100 of about 3,911,620 (341)
Network Flow Classification Methodrule-Based
Journal of Harbin University of Science and Technology, 2017 In this paper，repeated incremental pruning to produce error reduction which is a rule learning ethod is used to solve network traffic classification. The method can be used to dig out the correlations between ttributes and classes，which are utilized to ...
CAO Yan-zhen +3 more
doaj +1 more source
Traffic image sequence classification [PDF]
, 2010 Práce představuje obecný přehled postupů používaných v aplikacích pro monitorování provozu. Jsou zde popsány různé přístupy pro řešení jednotlivých kroků procesu detekce vozidel. Je provedena analýza těchto metod.
Vomela, Miroslav
core
Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams
, 2017 Studying IP traffic is crucial for many applications. We focus here on the detection of (structurally and temporally) dense sequences of interactions, that may indicate botnets or coordinated network scans.
Fournier-S'niehotta, Raphaël +3 more
core +2 more sources
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Advanced Functional Materials, EarlyView.Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Data Augmentation for Traffic Classification
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks.
Chao Wang +4 more
openaire +2 more sources
Classification with NormalBoost: Case Study Traffic Sign Classification
Journal of Intelligent Systems, 2012 NormalBoost is a new boosting algorithm which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity.
Fleyeh Hasan, Davami Erfan
openaire +2 more sources
, 2018 The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities.
Al-Askary, Manar +4 more
core +1 more source
Advanced Functional Materials, EarlyView.Liquid crystalline inverted lipid phases and reverse micelles are self‐assembled lipid nanostructures that enhance the solubility, stability, and delivery of diverse therapeutics. This review integrates their physicochemical principles, formulation strategies, drug loading mechanisms, and biomedical applications, highlighting their growing ...
Numan Eczacioglu +3 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A hydrogel–liquid metal composite peripheral nerve interface (HLB‐PNI) combines electrically durable electrodes and tissue‐adhesive hydrogel for tissue‐adaptive implantation. In nerve‐injured rats, it enables the diagnosis of sensory‐motor connectivity via stimulation and neural signal recording.
Yewon Kim +5 more
wiley +1 more source
Density Classification Quality of the Traffic-majority Rules [PDF]
, 2015 The density classification task is a famous problem in the theory of cellular automata. It is unsolvable for deterministic automata, but recently solutions for stochastic cellular automata have been found.
Redeker, Markus
core