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This paper presents an automatic algorithm to recognize the condition of drills on the basis of analysis of the drilling hole images. The algorithm includes the image preprocessing leading to extraction of the diagnostic features, which are used as the input attributes for the classification system. The condition of drill is classified into two groups: the useful one (the sharp enough state) and worn...
Current challenges of clinical breath analysis include large data size and non-clinically relevant variations observed in exhaled breath measurements, which should be urgently addressed with competent scientific data tools. In this study, three different baseline correction methods are evaluated within a previously developed data size reduction strategy for multi capillary column – ion mobility spectrometry...
Due to technical bottlenecks and errors caused by artificial operation, the problem of incomplete data always exists in big data research. Traditional data imputation algorithms incur high complexity and the accuracy cannot reach the desired level. At the same time, analysis and computation involved in mass data makes limitation of traditional algorithms and computing platform more noticeable. In...
Massive Open Online Course(MOOC) is undergoing explosive growth recently, both the number of MOOC platforms and courses are increasing dramatically during these years. One of the major concerns in MOOC is high dropout rate, we study dropout prediction in MOOCs, using student's learning activities data in a period of time to measure how likely students would drop out in next couple of days. We collect...
In order to devise bus lines and make daily scheduling more precisely, this paper uses Back-Propagation(BP) neural network to predict bus traffic. As the time factor and meteorological factor are the two important factors which affect traffic, this paper does the data preprocessing for bus data in Guangzhou from August to December in 2014. BP neural network's information processing capacity is determined...
Nowadays, more and more online examinations are holden in the distributed examination centers, which calls for the deployment of examination surveillance systems. Various related techniques have been proposed for this purpose, for example, video surveillance, face recognition based identification, and etc. However, there is still a lack of flexible and scalable solution to detect the misbehavior of...
Building Automation Systems (BASs) have been developed to provide a safe, comfortable, and energy-efficient indoor environment for households. A tremendous volume and enormous variety of building data are collected and stored in BASs. However, these data could not be completely utilized by traditional data analytics due to the huge volume and heterogeneous nature. In this paper, case studies are carried...
With the development of VLBI technology, the correlator system of observation station is constantly upgrading, so as to adapt to new data format and higher processing speed. Now the conversion of data format from Mark 5B to VDIF has been built by the researchers around the world. Once completed, it will be conducive to VLBI international collaboration. Hence it is very meaningful to study the VDIF...
Extensive clinical trials are required before a drug is placed on the market; yet it is difficult to discover all the side effects for any approved drugs. The United States Food and Drug Administration actively monitors approved medications to identify adverse events. The FDA Adverse Event Reporting System contains a database of adverse drug events (ADE) reported by the healthcare providers and consumers...
The ideological and political education of college students is facing enormous challenge under new situation. Thus, it is necessary to continuously adjust the way of ideological work and strengthen the exchanging of advanced experience, to improve the ideological and political education effects for colleges. Based on the clustering method of data mining, this paper studies its application on ideological...
This paper proposes a recognition approach for Semaphore flag signaling (SFS). We use the improved convolutional neural network (CNN) to classify the SFS. In the experiment we made Semaphore flag signaling system (SFSS), which based on CNN. The image can be directly input into the SFSS. Each alphabetic character or control signal is indicated by a particular flag pattern. We shoot the SFS videos by...
Data preprocessing is applied in many of the clustering or classification problem to produce output with less processing time and less number of features. Fuzzy Cognitive Maps is a relatively new domain in the field of artificial Intelligence. For some problem like the Obesity the number of inputs can vary enormously. Hence the Dimensionality Reduction technique is applied and the simulations are...
To reduce the effect of the unreliability of UDP communication on the test of the network servo system, this study aims to propose an improved method by adding data preprocessing layer between the application layer and the transport layer on the basis of the UDP communication. According to the different functions of the sender and the recipient in the communication, different improvements are used...
The popularity of Bus Rapid Transit (BRT) makes Trans Jogja an alternative of a mass public transportation system for urban mobility. However, without supervision on temporal patterns of passenger's behavior in Trans Jogja on supply and demand, it will result in the decreases of the number of BRT users and the increasing number of private vehicle users, so that traffic jams remain difficult to avoid...
Automatically generating the related work section for a writing paper is useful for researchers as it can save a lot of time and avoid missing related works. The related work section of scientific paper usually introduces other researchers' work published before and makes comparisons with the current author's work. This paper proposes an approach to automatically generating related work by comparing...
In this paper, we propose a novel architecture of polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and space search optimization, which adopts accelerated convergence mechanism instead of purely random search. Two type of polynomials are adopted for constructing discriminate functions in the PNNC to alleviate the limitation of relatively simple geometry using...
Parsing turns unstructured data into structured data suitable for knowledge discovery and querying. The complexity of grammar notations and the difficulty of grammar debugging limit the availability of data parsers. Tier grammars are defined by simply dividing terminals into predefined classes and then splitting elements of some classes into multiple layered sub-groups. The set of predefined terminal...
The capability of artificial Neural Networks to forecast time series with trends has been a topic of dispute. While selected research following Zhang and Qi has indicated that prior removal of trends is required for a Multilayer Perceptron (MLP), others provide evidence that Neural Networks are capable of forecasting trends without data preprocessing, either by choosing input-nodes employing an adequate...
Due to the change in life style and diet, modern people suffer from obesity, diabetes, and other types of diseases. Regular practice of exercise can alleviate the negative effects from the diseases and even cure the diseases in certain cases. In addition, regular practice of exercise improves the quality of life. These facts have drawn much attention and people nowadays recognize the importance of...
In this paper, we propose to deal with the problems of logistic regression with outliers and class imbalance, which are common in a wide range of practical applications. The robust bounded logistic regression with different error costs is developed to reduce the combined influence of outliers and class imbalance. First, inspired by the Correntropy induced loss function, we develop the bounded logistic...
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