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Activity recognition with triaxial accelerometer embedded in mobile phone is an important research topic in pervasive computing field. The research results can be widely used in many healthcare or data mining applications. Numerous classification algorithms have been applied into the activity recognition tasks. Among these algorithms, ELM (Extreme Learning Machine) shows its advantages in generalization...
With the availability of traffic sensors data, various techniques have been proposed to make congestion prediction by utilizing those datasets. One key challenge in predicting traffic congestion is how much to rely on the historical data v.s. The real-time data. To better utilize both the historical and real-time data, in this paper we propose a novel online framework that could learn the current...
The accuracy of recommendation trends to be worse towards the long tail of the popularity distribution of items, but items in the long tail are generally considered to be valuable as they occupy a majority part of entire data. In this paper, we develop an instance-level cost-sensitive Factorization Machine (FM) to tackle the problem. The new algorithm allows the FM model to automatically leverage...
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able...
Since link prediction helps improve our understandings about the structure, functions, and evolution of networks, it has drawn much attention from both computer science and physical communities. Among many mainstream proposed algorithms, the common-neighbor based ones show prominent efficiency but neglect the influence of community structure. Based on the assumption that in the same communities common...
Recommender systems are the software or technical tools that help user to find out items/things according to his/her preferences from a wide range of items/things. For example, selecting a movie from a large database of movies from on-line or selecting a song of his/her own kind from a large number of songs available in the internet and much more. In order to generate recommendations for the users...
Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental...
In the most industrial processes, weighing machines are designed to automatically fill one or more types of the containers with a predetermined weight of particulates raw material using a controller. The performance of the machine is closely related to the accuracy of measurement and speed of dynamic response, which are contradictory. For a gravimetric filling machine, a novel adaptive dynamic state...
Particle swarm optimisation (PSO) algorithms have been successfully used to solve many complex real-world optimisation problems. Since their introduction in 1995, the focus of research in PSOs has largely been on the algorithmic side with many new variations proposed on the original PSO algorithm. Relatively little attention has been paid to the study of problems with respect to PSO performance. The...
This study investigates the evaluation of machine learning models based on multiple criteria. The criteria included are: predictive model accuracy, model complexity, and algorithmic complexity (related to the learning/adaptation algorithm and prediction delivery) captured by monitoring the execution time. Furthermore, it compares the models generated from optimising the criteria using two approaches...
Many indoor positioning algorithms have been proposed in the last decade, most of which are based on WiFi RSS fingerprints. However, the environment has changed dramatically since the original algorithms using only a few Access Points (APs). A typical building with densely deployed APs might contain hundreds of APs. The explosive growth of the number of APs introduces new challenges to these WiFi-based...
Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15–20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and...
Hierarchical Multi-label Classification (HMC) is a challenging real-world problem that naturally emerges in several areas. This work proposes two new algorithms using a Probabilistic Graphical Model based on Dependency Networks (DN) to solve the HMC problem of classifying gene functions into pre-established class hierarchies. DNs are especially attractive for their capability of using traditional,...
Speech recognition systems are either based on parametric approach or non-parametric approach. Parametric based systems such as HMMs have been the dominant technology for speech recognition in the past decade. Despite a lot of advancements and enhancements in the design of these systems: key problems such as long term temporal dependence, etc. Has not yet been solved. Recently due to availability...
In this paper, we study on the popularity prediction of online user-generated contents, where high quality predictions give us much more flexibility and preparing time in deploying limited resources (such as advertising budget, monitoring capacity) into more popular contents. However the high retrieval cost of data used in prediction is a big challenge due to the large amount of users and contents...
Ensuring high reliability of large-scale clusters is becoming more critical as the size of these machines continues to grow, since this increases the complexity and amount of interactions between different nodes and thus results in a high failure frequency. For this reason, predicting node failures in order to prevent errors from happening in the first place has become extremely valuable. A common...
Adverse drug events (ADEs) are grossly under-reported in electronic health records (EHRs). This could be mitigated by methods that are able to detect ADEs in EHRs, thereby allowing for missing ADE-specific diagnosis codes to be identified and added. A crucial aspect of constructing such systems is to find proper representations of the data in order to allow the predictive modeling to be as accurate...
Chloroplasts are organelles in most green plant and some algal cells. Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations...
We propose a robust diabetes prediction model by examining how predictions from several learning algorithms, performing the same task, can be exploited to yield a higher performance than the best individual learning algorithm. The task was to forecast the onset of non-insulin dependent diabetes within a five year period using previous vital sign examination information. Experimental data is a 768...
There is an important relationship between the stability of protein complex and hot region. Research has shown that in protein-protein interaction (PPI), residues are denser around the hot region. Therefore, this paper proposed an algorithm based on Gi statistics, regional division rule and regional amplification principle to form residue dense region (RDR); Then, according to the results of cascade...
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