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Recently, many works have been published for counting people. However, when being applied to real-world train station videos, they have exposed many limitations due to problems such as low resolution, heavy occlusion, various density levels and perspective distortions. In this paper, following the recent trend of regression-based density estimation, we present a linear regression approach based on...
Down syndrome (DS) is a genetic disorder with genome dosage imbalances and micro-duplications of human chromosome 21. It is usually associated with a group of serious diseases, including intellectual disabilities, cardiac diseases, physical abnormalities, and other abnormalities. Currently, since there is no cure for human DS, screening and early detection have become the most efficient way for DS...
To try to decrease the preference of the attribute values for information gain and information gain ratio, in the paper, the authors puts forward a improved algorithm of C4.5 decision tree on the selection classification attribute. The basic thought of the algorithm is as follows: Firstly, computing the information gain of selection classification attribute, and then get an attribute of the information...
Genome-wide association studies (GWAS) of T2D have discovered a number of loci that contribute to susceptibility to the disease. In this paper, we classified and identified the suspected risky Loci of T2D with computational method based on the known T2D GWAS-associated SNPs. The framework includes two parts: we first classified the SNPs based on their features of position and function through a simplified...
Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth...
Traditional vehicle detectors always utilize singletemplate model to represent the vehicle which can not encircle vehicles with different aspect ratios. In this paper, we propose a fast and accurate approach for detecting vehicles which joints classification and aspect ratio regression. The key idea is extending the boosting decision trees method to estimate vehicle's aspect ratio during vehicle detection,...
As a state-of-the-art ensemble method, random forest which exhibits a good ability to predict and generalize on various dataset is often composed of a large number of trees. Redundancy of ensemble and connotative decision rules result in expensive operational costs as well as difficulties in comprehension. In this paper, novel leaf node-level pruning methods for random forest are proposed. Each leaf...
In this empirical study we develop forecasting models for electricity demand using publicly available data and three models based on machine learning algorithms. It compares accuracy of these models using different evaluation metrics. The data consist of several measurements and observations related to the electricity market in Turkey from 2011 to 2016. It is available in different time granularities...
Fraud detection is an enduring topic that pose a threat to banking, insurance, financial sectors and information security systems such as intrusion detection systems (IDS), etc. Data mining and machine learning techniques help to anticipate and quickly detect fraud and take immediate action to minimize costs. This paper starts with the definition of intrusion detection system and its types, focuses...
In data classification mining, the decision tree method is a key algorithm. ID3 (Iterative Dichotomiser 3) algorithm which was presented by Quinlan is a famous decision tree algorithms, but ID3 has some shortcomings such as high complex computation in computing the information entropy expression, multivalue bios problem in the process of selecting an optimal attribute, large scales, etc. In order...
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via...
The paper exposes the behavior of the Decision Trees (DT) algorithms on a big database with many cases and many attributes: Forest Covertype (FC) from UCI Knowledge Discovery in Databases Archive. In classification experiments considered have been taken into account 22 splitting criteria and two pruning methods whose performances were presented in terms of classification error rate on test data, data...
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...
Market research shows that one of the most intolerable issues in the pack of cigarettes is the cigarette missing. This issue makes substantial adverse effects on a company which needs to be avoided completely. Existing research uses a weight detection method to identity packages with issues. However, the accuracy of weight detection methods is low due to instrument error and complex workshop environment...
In this current age, numerous ranges of real word applications with imbalanced dataset is one of the foremost focal point of researcher's inattention. There is the enormous increment of data generation and imbalance within dataset. Processing and knowledge extraction of huge amount of imbalanced data becomes a challenge related with space and time necessities. Generally there is a list of an assortment...
In this paper, we propose an integrated system for scale-variance pedestrian detection. It consists of two cascaded components: a multi-layer detection neural network (MLDNN) for scale-variance pedestrian detection, and a fast decision forest (FDF) for boosting detection performance with only a slight decrease in speed. Experimental results on the Caltech Pedestrian dataset show that our approach...
The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
There has been a great interest in the systems that predict clinical labels from the brain images automatically for the last decade since it is a very important task that helps clinicians for decision making. In this study, clinical labels of the structural brain magnetic resonance (MR) images are predicted automatically using the random forests ensemble method. Morphological measurements like volume...
Usually the static or dynamic characteristics of the flame are extracted for flame detection. But the relationship between the various features of flame could not be distinguished by the human eye. the Gradient Boost Decision Tree (GBDT) is thus proposed to combine and optimize the flame shape and texture features, so as to mine the relationship of flame features. Then the more discriminant new flame...
In this work, we conduct a systematic exploration on the promise and challenges of deep learning for the sparse matrix format selection. We propose a set of novel techniques to solve special challenges to deep learning, including input matrix representations, a late-merging deep neural network structure design, and the use of transfer learning to alleviate cross-architecture portability issues.
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