The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In the field of malware analysis, two basic types, which are static analysis and dynamic analysis, are involved in the process of understanding on how particular malware functions. By using dynamic analysis, malware researchers could collect API call sequences that are very valuable sources of information for identifying malware behavior. The proposed malware classification procedures introduced in...
This paper presents a system for classifying images based on Deep Learning and applied in the recognition of traffic signals aiming to increase road safety increased road safety using autonomous and semi-autonomous intelligent robotic vehicles. This Advanced Driver Assistance System (ADAS) is a system created to automate vehicles, but also to help the human drivers to increase safety and the respect...
Educational data mining has received much attention worldwide due to its significance in the education domain. Among a large number of the educational data mining tasks, early in-trouble student prediction is a popular one. This task focuses on identifying the students who are at risk in their study as soon as possible before the end of the permitted period of study time. For early detection, data...
Classification of Alzheimer 's disease (AD) from normal control (NC) is important for disease abnormality identification and intervention. The current study focused on distinguishing AD from NC based on the multi-feature kernel supervised within- class-similarity discriminative dictionary learning algorithm (MKSCDDL) we introduced previously, which has been derived outperformance in face recognition...
Pedestrian detection for surveillance video, which is the basic of person re-identification, aims to capture the pedestrians in the monitors. However, the existing pedestrian detection algorithms still have two issues: (1) The recall and precision are not applicable for complicated scenes; (2) It is limited for processing the high-resolution video in real-time. Therefore, pedestrian detection algorithm...
Eldercare monitoring using non-wearable sensors is a candidate solution for improving care and reducing costs. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. We propose an unsupervised method for detecting abnormal behavior patterns based on a new context preserving representation of daily activities. A preliminary analysis of the method...
In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
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...
With the emergence of deep-learning algorithms, the accuracy of computer-aided supporting systems advanced., However, their adoption in the field of medicine has been limited, partially due to the challenges of generating reliable and timely results. In this research, we focused on classifying four common cutaneous diseases based on dermoscopic images using deep learning algorithms.
Selecting an efficient classifier for medical data is considered as one of the most important part of today's computer aided diagnosis. The performance of single classifiers such as decision tree classifier can be increased by ensemble method. However, this approach relies on the data quality and missing values. In this paper, we propose a new ensemble classifier to overcome overfitting and biasness...
We propose a new variant of the Correlation-based Feature Selection (CFS) method for coping with longitudinal data – where variables are repeatedly measured across different time points. The proposed CFS variant is evaluated on ten datasets created using data from the English Longitudinal Study of Ageing (ELSA), with different age-related diseases used as the class variables to be predicted. The results...
In given paper offered methods and algorithms of determination of complexity of test questions for formation a database system of the adaptive test control for objective estimation of knowledge of students (pupils) in the course of training learning systems.
Visual object tracking is an important problem in computer vision and has many applications including traffic monitoring, augmented reality and human computer interface. Although it has been investigated in the past decades, designing a robust tracker to cope with different objects under various situations is still a great challenging task. Focusing on the single-target tracking problem, this paper...
The algorithm of extracting pedestrian features based on Local Binary Pattern (LBP) has the problems of being unable to depict the human visual sensitivity. We proposed an Significant Local Binary Pattern (SLBP) which fused the characteristics of human visual pedestrian system. We extracted the significant factor based on Weber's law, and added the significant factor as a weight to the LBP eigenvalue...
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...
Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made...
Open educational resources (OER) are important assets for students or teachers, used to help them search for useful resources. However, it is a challenge to improve the user engagement of OER. In this paper, we propose a system, called resource delivery service system (RDSS), in the Taiwan Open Platform for Educational Resources (TOPER) that actively recommends educational resources to users. RDSS...
People afflicted with Parkinson's Disease (PD) experience impairment of their gait (the way a person walks), which frequently results in falls. In this paper we investigate a machine learning method to assess PD severity using accelerometer data passively crowdsourced from participants' smartphones while they walked. Time and frequency domain features such as entropy rate and peak frequency, and postural...
This work presents a novel algorithm for recognizing activity states which are of interest for assessing the general well-being of cancer, frail and elderly patients. Using the novel idea of two-level classification, misclassification due to unwanted hand motion noise, which is a common source of error in wrist-worn sensing systems, is mitigated. The algorithm is verified using data from 20 subjects...
We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm temporally segments the video sequence into action segments. The optimal temporal segmentation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.