AbstractÐDiscovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRITÐSequential Pattern mIning with Regular expressIon consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like antimonotonicity) are integrated into the mining process. A quantitative exploration of these trade-offs is conducted through an extensive experimental study on synthetic and real-life data sets. The experimental results clearly validate the effectiveness of our approach, showing that speedups of more than an order of magnitude are possible when RE constraints are pushed deep inside the mining process. Our experimentation with real-life data also illustrates the versatility of REs as a user-level tool for focusing on interesting patterns.
In this paper, we present and demonstrate a methodology to improve probabilistic fatigue crack growth (FCG) predictions by using the concept of Bayesian updating using Markov chain Monte Carlo simulations. The methodology is demonstrated on a cracked pipe undergoing fatigue loading. Initial estimates of the FCG rate are made using the Paris law. The prior probability distributions of the Paris law parameters are taken from the tests on specimen made of the same material as that of pipe. Measured data on crack depth over number of loading cycles are used to update the prior distribution using the Markov chain Monte Carlo. The confidence interval on the predicted FCG rate is also estimated. In actual piping placed in a plant, the measured data can be considered equivalent to the data received from in‐service inspection. It is shown that the proposed methodology improves the fatigue life prediction. The number of observations used for updating is found to leave a significant effect on the accuracy of the updated prediction.
Many apps and analyzers based on machine learning have been designed to help and cure the stress issue. The chapter is based on an experimental research work that the authors performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In the research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have lot of stress in their lives, so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In the chapter, they have a website that contains a closed set of questionnaires from SF-36, which have some weight associated with each question.
Earth's atmosphere is mainly made up of two gases, nitrogen and oxygen, which together comprise 99% of gases therein. The other gases include the remaining 1% of the atmosphere. Amongst these are the five major air pollutants (e.g., ground-level ozone, airborne particles or aerosols, carbon monoxide, sulfur dioxide, nitrogen dioxide). Excess of these pollutants in the atmosphere is risky to human health. They are the main ingredients of smog. Air quality is measured with the air quality index. An AQI under 50 is considered as good air quality; however, as the AQI number increases, it becomes a concern for human health. To find a non-conventional solution to air pollution problem, it has been proposed to do Yagya, a fire process with three different samidhas, namely mango wood, bargad wood, and dry cow dung sticks and study their relative emissions and ability to reduce the aerosols PM 2.5 and PM 10. In this paper, the researcher has measured the PM levels (PM 2.5 and PM 10) and carbon dioxide CO2 along with AQI, temperature, and humidity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.