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Social Media generates information about news and events in real-time. Given the vast amount of data available and the rate of information propagation, reliably identifying events is a challenge. Most state-of-the-art techniques are post hoc techniques that detect an event after it happened. Our goal is to detect onset of an event as it is happening using the user-generated information from Twitter...
In previous work, several results on rapid real-time decisions from description were simulated using a neural network model including analogs of orbitofrontal cortex, amygdala, anterior cingulate, and striatum [1, 2]. This model marries adaptive resonance theory and fuzzy trace theory to develop categories that selectively weight numerical and qualitative attributes of linguistically presented options...
In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In contrast to traditional binary and multi-class classification, multi-label classification...
HyperLogLog Counting is widely used in cardinality estimation. It is the foundation of many algorithms in data analysis, commodity recommendation and database optimization. Facing the large scale internet business like electronic commerce, internet companies have an urgent requirement of distributed real-time cardinality estimation with high accuracy and low time cost. In this paper, we propose a...
In this paper we deal with one of the most relevant problems in the field of data mining, the real time processing and visualization of data streams. To deal with data streams we propose a novel approach that uses a neighborhood-based clustering. Instead of processing each new element one by one, we propose to process each group of new elements simultaneously. A clustering is applied on each new group...
Spiking neural networks with hardware implementations of Spike Timing Dependent Plasticity (STDP) present a promising solution to energy efficient real-time machine learning. Online real-time learning, however, requires that new training information be considered by an already trained network without reinforcing previous data. Learning new information without severely altering previously learned data...
When a person learns, they observe and interact with their surroundings, and monitor the outcome of these interactions. During this process, the brain only examines single snapshots of information. It does not need to continuously revisit past instances of time to retain learned information. Supervised neural networks, as much as they resemble the human brain, do not learn well incrementally. The...
Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Prior studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. To further test the predictive ability of swarms,...
Due to recent technological advancements in wireless communications and low-power sensor devices, wireless body area networks (WBANs) has become increasingly popular in pervasive healthcare monitoring. However, continuously collecting patients' physiological signs will result in large amounts of monitored data that require a scalable architecture for storage and analysis. This fact motivates the integration...
To elongate the battery life of sensors worn in wireless body area networks, recent studies have advocated compressing the acquired biological signals before transmitting them. The signals are compressed using compressive sensing (CS), by projecting them onto a lower dimension. The original signals are then recovered using CS recovery techniques at the base station, where the computational power is...
In this paper, we propose an approach for automatic detection of bike-riders without helmet using surveillance videos in real time. The proposed approach first detects bike riders from surveillance video using background subtraction and object segmentation. Then it determines whether bike-rider is using a helmet or not using visual features and binary classifier. Also, we present a consolidation approach...
In the last few years, the researchers have spent many efforts in developing advanced systems for activity daily living (ADL) recognition in diverse applicative contexts, as home automation and ambient assisted living. Some of these need to know in real time the actions performed by a user, and this involves a number of additional issues to be taken into account during the recognition. In this paper,...
Social media have become an important source of data and can provide near-instantaneous information which can be analysed to generate predictive models and to support decision making. Much work has been done in short message analysis such as trend analysis, short message classification, etc. However, to generate an accurate and concise conclusion/assertion from all the relevant information remains...
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