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Real-time crash prediction models are playing a key role in transportation information system. Support vector machine (SVM), a classification learning algorithm, was introduced to evaluate real-time crash risk. The size of traffic dataset is always large with a high accumulating speed. By applying a warm start strategy, an incremental learning algorithm is introduced to update the original model....
An collaborative filtering algorithm based on Denoising Auto-Encoder and item embedding (CDAWE) was proposed to solve the absent analysis of item co-occurrence relation and the cold start of model parameters of the information recommendation algorithm based on Denoising Auto-Encoder. In the proposed information recommendation algorithm, users are viewed as documents and items that users have rated...
In this work, we propose a method collaborating the local similarity and local community paradigm with a tunable parameter to balance the contribution of the energy from these two sources. We show that local similarity e.g., common neighbors and local community paradigm e.g., local community links both play significant roles in network evolution; therefore, one cannot ignore or penalize anyone of...
The key interest of machine learning is conventionally training the machine from data that have underlying distribution such as data should have predetermined distribution. Such a constraint on the problem area leads to the technique for development of learning algorithms with notionally verifiable performance accuracy. However, real-world problems are not able to fit smartly into such restricted...
Random sampling could enhance classification performance by selecting many representative samples to be included in the training dataset. The representative samples usually include the samples located at the border of each class or cluster. In this paper, a new sampling algorithm has been proposed which enforces the training sample to include the border points between classes. Considering a point...
We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured...
In this paper, we consider the problem of event prediction with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex dependencies between the variables combined with asynchronicity and sparsity of the data makes the event prediction problem particularly challenging. Most state-of-art approaches address this either by designing hand-engineered...
Automatic and fast tagging of natural sounds in audio collections is a very challenging task due to wide acoustic variations, the large number of possible tags, the incomplete and ambiguous tags provided by different labellers. To handle these problems, we use a co-regularization approach to learn a pair of classifiers on sound and text. The first classifier maps low-level audio features to a true...
Compared with H.264, High Efficient Video Coding (HEVC) improves the coding efficiency by 50% at the price of significant increase in encoding time, due to Rate Distortion Optimization (RDO) on large variations of block sizes and prediction modes. In this paper, a fast intra coding algorithm is proposed to alleviate the high computational complexity of HEVC intra-frame coding. The proposed algorithm...
The results of counting the size of programs in terms of Lines-of-Code (LOC) depends on the rules used for counting (i.e. definition of which lines should be counted). In the majority of the measurement tools, the rules are statically coded in the tool and the users of the measurement tools do not know which lines were counted and which were not. The goal of our research is to investigate how to use...
In this research, we present a technique of developing university ranking prediction system by analyzing global university performance indicators. Here, we consider standardized dataset of Times higher education world university rankings. Firstly, we perform country wise university ranking data analysis to observe the variation of performance indicators to find out the top influential features. To...
The experimental results show that the classification result with the decision trees algorithm come up over the other classifier. The decision tree algorithm creates a predictive model that predicts the state of the affected tissue by learning simple decision rules inferred while learning.
It is hard to predict student test scores in Mathematics. By being able to predict test scores students that will struggle may be identified. These students could be given more attention. This research uses the K-Nearest Neighbor (KNN) algorithm to predict the categorization of Mathematics test scores. The KNN algorithm initiates with a training data set and a value for parameter K. When evaluating...
The increasing processing power of hearing aids and mobile devices has led to the potential for incorporation of dereverberation algorithms to improve speech quality for the listener. Assessing the effectiveness of deverberation algorithms using subjective listening tests is extremely time consuming and depends on averaging out listener variations over a large number of subjects. Also, most existing...
A RBF-based neural network adaptive particle swarm optimization algorithm is proposed in this paper. In this algorithm, code at particle position adopts quantum bit to realize. The paper adopt particle flight path information to dynamically update the status of quantum bit and introduces quantum non-gate to realize mutation operation so as to avoid local optimization. Then, it is used to train neural...
Aiming at the Smart meters failure prediction problem and based on historical failure data of smart meters in a region of Xinjiang, a smart meter fault identification model is proposed based on C5.0 algorithm: first, after data preprocessing of smart meters history failure database is divided into two parts, training set and testing set; secondly, using C5.0 algorithm for data mining of training set,...
Semi-supervised learning is a key research subject in the field of machine learning. Co-training by Committee is an iterative semi-supervised learning algorithm. During the iteration of this algorithm, the previous committee is used for predicting unlabeled examples. However, the classification accuracy limitation of single committee will bring adverse effect on training of committees. Therefore,...
The prediction of some key parameters in the operational process of vertical roller mill is very important for its safety and reliability. Because the vertical mill works too long in poor conditions, its key operating parameters are nonlinear and time-varying and the traditional prediction methods are difficult to achieve high accuracy. This paper selects the upper shell vibration signal of the vertical...
In order to solve defects of the Slope One algorithm that the effect of recommending is not well because of without considering the time weight, and has the problem of data sparsity and poor real-time performance. A weighted slope one algorithm based on cluster filling and time weight (WSOBCFT) was proposed in this paper. To reduce the time of generating the nearest neighbor, the rating matrix of...
Back-To-Sit and Sit-To-Stand movements are the most important and essential movements for any human being to sustain their life and to accomplish their habitual actions. Not only for human beings, but for robots or assistive devices to accomplish several tasks, these movements are vital. The knee joint has the major functionality to execute such movements and indeed, the torque required at these joints...
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