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Security of communication network is essential for the smooth functioning of smart grid. In this paper, an intrusion detection system is proposed for early detection of threats in advanced metering infrastructure of smart grid. The proposed intrusion detection system has a multi-support vector machine classifier with mutual information based feature selection technique to detect attacks in Neighborhood...
The availability of Electronic Health Records (EHR) in health care settings provides terrific opportunities for early detection of patients' potential diseases. While many data mining tools have been adopted for EHR-based disease early detection, Linear Discriminant Analysis (LDA) is one of the most widely-used statistical prediction methods. To improve the performance of LDA for early detection of...
This paper studied automatic identification of malaria infected cells using deep learning methods. We used whole slide images of thin blood stains to compile an dataset of malaria-infected red blood cells and non-infected cells, as labeled by a group of four pathologists. We evaluated three types of well-known convolutional neural networks, including the LeNet, AlexNet and GoogLeNet. Simulation results...
Energy saving in buildings has attracted many researchers attention. One of the research topics is occupancy detection for each room to automatically control airconditioner, light, heating, etc. by monitoring the temperature, humidity, light, and co2 using the corresponding sensors. For existing data analysis, traditional regression methods such as CART, RF, LDA are often used to predict the occupancy...
Objective: The goal of this project was development of a software tool to detect documentation of Pediatric Appendicitis Score (PAS) within electronic emergency department (ED) notes. The overarching purpose was assessment of diagnostic imaging practices when PAS falls outside of a certain range, since minimizing patients' radiation exposure is desired. Methods: 15074 ED notes were collected from...
In operating conditions, real time non-destructive testing (NDT) is needed for the identification of tensile damage process of high-speed train gearbox shell. This paper focuses on the application of an acoustic emission (AE) method to study tensile damage. First, tensile tests with AE monitoring were employed to collect AE signals and tensile damage data. Second, feature extraction was performed...
Object detection is a challenging task in the field of pattern recognition. The objective of object detection is to locate the target objects in the testing images. In this paper, we use SVM trained active basis model as a sparse coding model for representing objects. The sparse coding model represents each image as the linear superposition of a small number of Gabor wavelets selected from an over-complete...
This work presents results of effectiveness analyses of using deep neural networks models for syntactic parsing of SynTagRus dataset. A set of modular neural network topologies based on composition of Stack long short-term memory layers, multilayer perceptron has been compared with widely used algorithms of classification on base of Gradient boosting trees and Support vector machine. Results allow...
The use of different evaluation measures for classification tasks have gained a significant amount of attention in the past decade, specially for those problems with multiple and imbalanced classes [1], [2]. However, the optimization of classifiers with respect to these measures is still heuristic, using ad-hoc rules with classical accuracy-optimized classifiers. We propose a classifier designed specifically...
This paper presents a user authentication system based on mouse movement data. An available logging tool named Recording User Input (RUI) is used to collect three types of mouse actions — Mouse Move, Point-and-Click on Left or Right mouse button and Drag-and-Drop. Collected data are divided into N-number of blocks consisting of specific number of actions. From each block seventy four features are...
The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous response while consuming the video can be leveraged to improve video emotion tagging, but this capability has not been thoroughly exploited yet. In this paper, we propose an implicit hybrid video emotion tagging approach by integrating video content and users' multiple physiological responses, which are only...
In this paper, we focus on the problem of group detection in crowd, which is a task of partitioning a set of pedestrians in a scene into small subsets called groups based on their trajectories. Most of previous methods use only a single model for representing a relationship between trajectories of pedestrians who belong to the same group. However, such relationship would vary depending on the activity...
Cloud computing is gaining significant attention, however, security is the biggest hurdle in its wide acceptance. Users of cloud services are under constant fear of data loss, security threats and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning techniques. However, the major challenge in these...
A Regionlet model explored here provides a new object representation strategy for generic object detection, which integrates local deformation handling into object classifier learning and feature extraction. Generic object detection deals with different degrees of variations in discrete object classes with tractable computations and hence faces problems. This generates a need for representational...
Multi-label classification (MLC), allowing instances to have multiple labels, has been received a surge of interests in recent years due to its wide range of applications such as image annotation and document tagging. One of simplest ways to solve MLC problems is label-power set method (LP) that regards all possible label subsets as classes. LP validates traditional multi-classification classifiers...
Today the Remote health monitoring play significant role for the patients who suffer from high risks and chronic disorders. The patient data and verification are significant factors in transmission of patient data remotely for suggestion and take decision of further treatment. Generally it can be used for human identification through remotely monitoring a patient's through Electrocardiogram (ECG)...
Regression testing is the common task of retesting software that has been changed or extended (e.g., by new features) during software evolution. As retesting the whole program is not feasible with reasonable time and cost, usually only a subset of all test cases is executed for regression testing, e.g., by executing test cases according to test case prioritization. Although a vast amount of methods...
It's difficult to review a large number of examination surveillance videos at the same time. To reduce the workload of censors, we propose a method to analyze abnormal behaviors during examinations. This paper mainly includes two parts. Firstly, it employs a two-layer classifier with Histograms of Oriented Gradients feature to detect head-shoulder part of examinees, thus we can report the presence...
It is very difficult to implement an efficient analysis by using the customary techniques currently available; this is due to the fact that the data size has had a huge increase. Many complications were faced because of the numerous characteristics of big data; some of them include complexity, value, variability, variety, velocity, and volume. The objective of this paper is to implement classification...
A method is proposed to distinguish patients with schizophrenia from healthy controls based on data measured by functional near-infrared spectroscopy (fNIRS) during a cognitive task, which combines principal component analysis (PCA) and support vector machine (SVM). Firstly, a data reduction technique is applied prior to PCA, and then PCA is used to extract features on oxygenated hemoglobin (oxy-Hb)...
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