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Decision tree model is one of data mining method for builds classification models in the form of a tree structure. These methods are produced various ways of splitting a data set into branch like segments that call nodes. Today, forecasting method is very importance for every side especially agriculture. Because some farmers who want to predict their crops for each semester. This paper describes about...
Noncontact sleep quality monitoring and early diagnosis of sleep disorders has attracted increasing attentions recently due to its advantages over traditional Polysomnography (PSG). In this study, we proposed a novel audio-based method to estimate four sleep stages. The method consists of three steps, breathe/snore detection based on Adaptive Vertical Box (AV-Box), nonlinear feature and sleep pattern...
Fully automated defect detection and classification of automobile components are crucial for solving quality and efficiency problems for automotive manufacturers, due to the rising wage, production costs and warranty claims. However, metrological deviations in form still represent unsolved problems using state-of-the-art techniques, especially for forged or casted components with complex geometry...
In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. This paper conducts an extensive study to infer sensitive personal information from public insensitive attributes in social networks by deploying different machine learning algorithms. The results show that several algorithms can achieve...
Supervision is important in the application of Smart City. Closed-Circuit Television (CCTV) is one of the main tools of Smart City surveillance. Some CCTVs connected with Information and Communication Technology (ICT) form Smart Monitoring. In order to support Smart Mobility, CCTV is installed to monitor road conditions. The problem is the installation of CCTV which is not always appropriate with...
We present our preliminary results on building a framework for analyzing online ratings. There are four major components constructed in this framework including data retrieval, data processing, data analytics, and data visualization. The data retrieval module is responsible for scraping or streaming online rating data. Cleaning, filtering, and parsing unstructured data are done in the data processing...
Typical matching parts of servo valve have high precision requirement and strict fit tolerance, which reduces the matching quality and practical performance. The traditional fit quality control method of the matching parts is based on expert experience and tolerance analysis, which is subjective and indirect. In this paper, a novel method was proposed to determine the matching quality before precision...
Intelligent Tutoring Systems (ITS) are typically designed to offer one-on-one tutoring on a subject to students in an adaptive way so that students can learn the subject at their own pace. The ability to predict student performance enables an ITS to make informed decisions towards meeting the individual needs of students. It is also useful for ITS designers to validate if students are actually able...
Machine learning and text analytics have proven increasingly useful in a number of health-related applications, particularly in the context of analyzing online data for disease epidemics and warning signs of a variety of mental health issues. We follow in this tradition here, but focus our attention on cognitive distortion, a precursor and symptom of disruptive psychological disorders such as anxiety,...
Non-orthogonal multiple access (NOMA) is one of the critical technologies in fifth generation systems. Successive interference cancellation (SIC) is a typical solution of multiple users detection in NOMA systems. However, conventional SIC suffers from error propagation. To alleviate this problem, in this letter, a multiple decision SIC (MD-SIC) strategy is introduced. Instead of choosing one codeword...
In recent years due to increased competition between companies in the services sector, predict churn customer in order to retain customers is so important. The impact of brand loyalty and customer churn in an organization as well as the difficulty of attracting a new customer per lost customer is very painful for organizations. Obtaining a predictive model customer behaviour to plan for and deal with...
Traffic Surveillance System (TSS) plays an important role in extracting necessary information (count, type, speed, etc.). In the area of Traffic Surveillance System (TSS), vehicle detection has emerged as an influential field of study. So far there has been a considerable amount of research to accommodate this subject. However, these studies almost address problems in developed countries where the...
Intrusion Detection Systems (IDSs) are powerful systems which monitor and analyze events in order to detect signs of security problems and take action to stop intrusions. In this paper, the Two Layers Multi-class Detection (TLMD) method used together with the C5.0 method and the Naive Bayes algorithm is proposed for adaptive network intrusion detection, which improves the detection rate as well as...
In this paper, we propose a block-based connected component labeling algorithm, which predicts current block's label by exploiting the information obtained from previous block to reduce memory access. By generating a forest of decision trees according to some of previous block's pixels, which are also needed for current block's label decision, we can reduce trees' depth and number of pixels to check...
In the big data era, machine learning has become an increasingly popular approach for data processing. Data could be in various forms, such as text, images, audios, videos and signals. The essence of machine learning is to learn any patterns from features of data. In the above types of data, the number of features is massively high, which could result in the presence of a large number of irrelevant...
For supporting interpretation, assessment and application of data mining models, explanation-aware methods are crucial. This paper presents an approach for explanation-aware feature selection and assessment using symbolic abstractions of time series. For that, we utilize the symbolic approximate aggregation (SAX) method for data abstraction to be implemented into data mining models. We investigate...
Action recognition in videos is a hot research topic in computer vision because of the popularization of application such as human-machine interaction, intelligent monitoring. Recently, with the aging phenomenon of population becoming more and more serious, the analysis of senior actions is becoming more and more important. Random forest has been wildly used in action recognition because of its efficiency...
Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much...
A human action can be seen as transitions between ones body poses over time, where the transition depicts a temporal relation between two poses. Recognizing actions thus involves learning a classifier sensitive to these pose transitions as well as to static poses. In this paper, we introduce a novel method called transitions forests, an ensemble of decision trees that both learn to discriminate static...
With the rapid development of mobile Internet, android system becomes the absolute overlord of the operating system, which has an unparalleled influence to the smart phone industry, but also closely related with people's lives. However, the feature of open source and lacking of regulatory of Android system led to a lot of security risks. Because of the widespread use of Android Intent Mechanism, the...
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