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We present an optimization technique for general object detection and an algorithm for training decision trees. By delaying the calculation of the features as late as possible we drastically reduce the execution time. At detection we alternate between evaluating the necessary features and eliminating candidates. This enables us to have both a rich pool of features and a powerful classifier while keeping...
Graduate employability is an increasingly major concern for academic institutions and assessing student employability provides a way of linking student skills and employer business requirements. Enhancing student assessment methods for employability can improve their understanding about companies in order to get suitable company for them. So, enhanced employability prediction of student can help them...
Cardiac Arrhythmia is a disease dealing with improper beating of heart. The improper condition may be fast beating or slow beating associated with heart. This paper proposes a detection or prediction scheme in the type of cardiac arrhythmia disease. It uses a clustering approach and regression methodology. The clustering approach used is DBSCAN and for regression, multiclass logistic regression is...
This paper focuses on the problem of machine learning classifier choice for network intrusion detection, taking into consideration several ensemble classifiers from the supervised learning category. We have evaluated Bagged trees, AdaBoost, RUSBoost, LogitBoost and GentleBoost algorithms, provided an analysis of the performance of the classifiers and compared their learning capabilities, taking for...
The design of effective financial early warning algorithm is of great significance to the financial management of the company. The weak classification algorithm can be improved to a high classification algorithm with high recognition rate through the ensemble learning. The algorithm can overcome the drawback of low classification accuracy of single classifier. Therefore, this paper combines decision...
This paper presented the research on heat load prediction method of central heating system. The combined simulation data at Xi'an in January was used as the samples for training and predicting. This paper selected the daily average outdoor wind speed, the daily average outdoor temperature, date type, sunshine duration as input variables and the heating load value as output variable. After preprocessing...
For better resolving the safety risk early warning of the apron effectively, the attribute reduction algorithm based on Rough Set is used to simplify the set as the warning index set of the apron is too large. The improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of Support Vector Machine. Combined with the Rough Set and SVM which is optimized by the improved...
Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that...
Genetic mutations are the first warning to the onset of lung cancer. The ability to early predict these mutations could open the door for a targeted treatment options for lung cancer patients. Three top candidate genes previously reported to have the highest frequency of lung cancer mutations. Each gene is encoded as a symbolic sequence of four letters. A novel method for gene representation is introduced...
Individualized blood transfusion management would benefit from the ability to prospectively identify patients at risk of complications of blood transfusion, and target them for closer monitoring or intervention. This study presents a simple and efficient multi-task learning method for predicting multiple surgical outcomes based on the weighted least squares support vector machine. To accelerate the...
Optimal slip rate identifier based on BP neural network has some bugs such that it is easy to fall into local minimum. In order to improve the aircraft's braking efficiency, this paper presents a new method for optimal slip rate identification based on firefly algorithm optimization. The proposed algorithm is used to optimize the weight value of BP neural network and the result shows that the algorithm...
In machine learning applications, there are scenarios of having no labeled training data, due to the data being rare or too expensive to obtain. In these cases, it is desirable to use readily available labeled data, that is similar to, but not the same as, the domain application of interest. Transfer learning algorithms are used to build high-performance classifiers, when the training data has different...
Machine Learning (ML) approaches are widelyused classification/regression methods for data mining applications. However, the time-consuming training process greatly limits the efficiency of ML approaches. We use the example of SVM (traditional ML algorithm) and DNN (state-of-the-art ML algorithm) to illustrate the idea in this paper. For SVM, a major performance bottleneck of current tools is that...
Recommender systems are becoming the crystal ball of the Internet because they can anticipate what the users may want, even before the users know they want it. However, the machine-learning algorithms typically involved in the training of such systems can be computationally expensive, and often may require several days for retraining. Here, we present a distributed approach for load-balancing the...
with the popularity of the Internet and increasingly diversiform commodity, the recommendation system as a common approach came into our daily life, which supports an assistant decision-making when we purchase something on the internet. The traditional recommendation system is based on the user's collaborative filtering algorithm, and Amazon proposed a collaborative filtering algorithm to achieve...
The time-varying Received Signal Strength (RSS) drastically reduces the correlation between signals and location information, which leads to degrade the indoor positioning accuracy in WiFi. And the kernel selection of Support Vector Regression (SVR) is limited by the Mercer theorem, it has a negative influence on the regressive result. In this paper, a new positioning algorithm based on Kernel Direct...
Manifold causes of image blurring make the no-reference evaluation of realistic blurred images very challenging. Previous studies indicate that handcrafted features suffer from poor representation of the intrinsic characteristics of image blurring and thus blind image sharpness assessment (BISA) is unsatisfactory. This paper explores a shallow convolutional neural network (CNN) to address this problem...
In order to avoid the serious loss caused by the fire, to achieve the initial fire alarm, multi-sensor system is widely used in fire prediction. In the processing method, it is essentially different from the traditional classical signal. The multi-sensor information fusion system can be merged at different levels. It can be abstracted distributed into three levels: information fusion layer, feature...
Although there have lots of studies on using static code attributes to identify defective software modules, there still have many challenges. For instance, it is difficult to implement the Apriori-type algorithm to predict defects by learning from an imbalanced dataset. For more accurate and understandable defect prediction, a novel approach based on class-association rules algorithm is proposed....
This work proposes a method that allows the entry of text in smartwatches using gestures based on geometric forms. For this it is proposed the development of a prototype capable of inserting a letter with no more than two user interactions. Gesture recognition is performed using the incremental recognition algorithm. A set of gestures with lines and curves were created to be recognized by the incremental...
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