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Recently, the combination of classification systems with semi-supervised learning has attracted researchers in several fields. Usually, for tasks with high complexity such as handwriting based age prediction, individual systems, using one classifier associated with specific data features, cannot provide satisfactory performance. In this paper, we investigate the contribution of the Co-training approach,...
One of the major causes of death in the world is Heart Failure. This disease affects directly the heart's pumping job. Because of this perturbation, nutriments and oxygen are not well circulated and distributed. The New York Heart Association has classified this disease into four different classes based on patient symptoms. In this paper, we are using a data mining technique, more precisely a sequential...
With the development of the aviation industry and the improvement of people's living standard, more and more people choose aircraft as their way of travel, but the airline adjusts the price according to the revenue management in real time. The purpose of this paper is to design different decision-making tools from the customer's perspective, and to provide customers with the relevant information needed...
In the actual production process, the prediction of compressive strength of concrete 28d is of great significance. Prediction of compressive strength of concrete is a typical multi input single output nonlinear systems, which is very close to the BP neural network model. In this paper, the BP neural network is applied to the prediction of the compressive strength of concrete, but the training effect...
Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient...
Most traditional soft sensor modeling requires the labeled training samples that contain both subsidiary and key variables. However, key variables are difficult to be obtained online due to lack of detection information or high measurement cost. In this paper, a novel semi-supervised learning algorithm, called cotraining-style kernel extreme learning machine, is proposed to exploit unlabeled training...
This paper evaluates the performance of four artificial intelligence algorithms for building energy consumption prediction. The backward propagation neural network (BPNN), support vector regression (SVR), adaptive network-based fuzzy inference system (ANFIS) and extreme learning machine (ELM) methods are reviewed and their performances for predicting building energy consumption are compared. A selection...
At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification...
The state monitoring issue of the induced draft fan in a thermal power plant by employing the gravitational searching algorithm optimized BP neural network is investigated in this paper. A new method to estimate the air quantity of the induced draft air of a thermal power plant is proposed based on the historical operation data extracted from the supervisory information system (SIS). In order to predict...
We present an algorithm for test-time combination of a set of reference predictors with unknown parametric forms. Existing multi-task and transfer learning algorithms focus on training-time transfer and combination, where the parametric forms of predictors are known and shared. However, when the parametric form of a predictor is unknown, e.g., for a human predictor or a predictor in a precompiled...
Storage reliability of the ammunition dominates the efforts in achieving the mission reliability goal. Prediction of storage reliability is important in practice to monitor the ammunition quality. In this paper we provided an integrated method where particle swarm optimization (PSO) algorithm is applied to adjust and optimize the BP neural network global parameters (weights and thresholds). The experiment...
One of the most discussed schedule in Brazil is Education, and with it comes a lot of problems to solve. This project use of artificial intelligence, but precisely in the area of Machine Learning, to enable a solution, in relation to student monitoring. The research has done with students of technical high school education at the Federal Institute of Gois — Brazil, to find a method which would be...
Learned boundary maps are known to outperform handcrafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is convolutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method...
The following article is created as a result of the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone. The Challenge goal was to correctly predict which bot would win a bot-vs-bot Hearthstone match based on what was known at the given time. Hearthstone is an online two-players card game with imperfect information (unlike chess and go, and like poker), where the goal of one player is to...
In this paper we apply self-labeling algorithms as Semi-Supervised Classification (SSC) techniques in order to automate the classification of functional and non-functional requirements contained in reviews in the App Store. In this domain, where it is easy collect a large number of reviews but difficult to manually annotate then, we found that SSC techniques can successfully perform this task and...
Future high-performance computing (HPC) systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are among the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs can be harmful and dangerous in that they cannot be detected by hardware....
Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
To overcome the unsatisfying trend prediction results of network public opinion in the present research, this paper put forward a method of Levenberg-Marquardt-based Back-Propagation (LM-BP) neural network algorithm to predict the network public opinion trend. Taking the microblog as the research object, the effectiveness and reliability of the method are proved with some real data in this article...
Authors can be differentiated by their styles of writing. In this paper, we propose features which attempt to classify authors based on their writing styles. The features can be usage of parts of speech, punctuation marks, word lengths, sentence lengths, number of unique words used, etc. This concept is used in many fields like email classification, fraud detection, etc. We propose a module to extract...
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