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The Box-Cox (BC) transformation is widely used in data analysis for achieving approximate normality in the trans-formed scale. The transformation is only possible for non-negative data. This positiveness requirement implies a truncation to the distribution on the transformed scale and the distribution in the transformed scale is truncated normal. This fact has consequences for the estimation of the parameters specially if the truncated probability is high. In the seminal paper Box and Cox proposed to estimate parameters using the normal distribution which in practice means to ignore any consequences of the truncation on the estimation process. In this work we present the framework for exact likelihood estimation on the PN distribution to which we call method m1 and how to calculate the parameters estimates using consistent estimators. We also present a pseudo-Likelihood function for the same model not taking into account truncation and allowing to replace parameters μ and σ for their estimates. We call m2 to this estimation method. We conclude that for cases where the truncated probability is low both methods give good estimation results. However for larger values of the truncated probability the m2 method does not present the same efficiency. [ABSTRACT FROM AUTHOR]

The Details of accidents on roadways, in workplaces, or public areas will be addressed in this study paper, it can have severe repercussions. In recent years, there has been an increase in interest in studying mishap data in a bid to comprehend their causes as well as create effective protection strategies. This paper will investigate the different approaches and methods used in the analysis of mishap data and associated effects, emphasizing the advantages and disadvantages of this approach, and giving instances of effective uses in various contexts. Analyzing mishap data and associated factors aids in the identification of patterns and trends, the development of predictive models, and the creation of measures to reduce the risk of accidents as well as improve overall safety. [ABSTRACT FROM AUTHOR]

Climate change is getting more attention in recent years as it is bringing more devastating natural disasters such as heat waves, drought and floods around the world. Human activities such as the burning of fossil fuels, which produce heat-trapping gases, are the main driver of climate change. The enormous amount of greenhouse gases that are released into the atmosphere accelerates the pace of global warming. On the other hand, renewable energy is usually clean and sustainable. Thus, turning down the role of fossil fuel in the energy sector should be a priority and facilitating the use of renewable energy is the best solution to mitigate climate change. To have a clear picture on the current situation, the paper will include a literature review about climate change, greenhouse gases and renewable energy. The methodology, analysis methods and results will be included in the paper. Data collection, pre-processing, data analysis and data visualisation dashboard will be involved during the process. A data analysis dashboard will be created to incorporate the background of the story and the results of the data analysis. The users will be exploring the trends of renewable energy and greenhouse gas emissions with data analysis tools to gain useful insights from the data. [ABSTRACT FROM AUTHOR]

The first, and at the same time one of the most important tasks in building a predictive system is the task of analyzing the original data set. The next task is to process the data according to the purpose and objectives of the predictive system. These two tasks are often not given enough attention and, as a consequence, the developer does not get the desired result as a result of building the system. This paper discusses methods and algorithms for data analysis and processing that can help in building a predictive system. The data set from the machine learning competition "ASHRAE - Great Energy Predictor III" is used as input data. The analysis phase focuses on constructing graphs and drawing conclusions from them. The paper shows how to use graphical information to find anomalies in the data and eliminate them. Examples of graphs that provide information useful for building machine learning models are given. The data processing phase describes the transformations performed on the data. It considers the transformations that lead to positive results as well as those that lead to negative results. [ABSTRACT FROM AUTHOR]

The hybrid education room is an important content in the art course, but the teaching method has the problem of contradictory teaching path and teaching method. At present, artificial intelligence is widely used in the field of education, which also has an impact on blended teaching and solves the problem of incompatibility between methods and paths in blended education. Therefore, this paper applies artificial intelligence to the teaching of art courses and optimizes the teaching methods and teaching paths. First, hybrid teaching methods are used for data analysis to form initial data and incorporate uncertainty. Then, artificial intelligence methods are used to plan the hybrid path, form a teaching path that meets the requirements, and verify the effectiveness of the path. The actual simulation results show that the artificial intelligence method can improve the effectiveness of the hybrid teaching method and optimize the teaching and art teaching methods of art courses. [ABSTRACT FROM AUTHOR]

The advent of extensive data analysis has fundamentally transformed data processing, equipping organizations with the capacity to efficiently manage vast data volumes. It delves into the significance of technological synergies in harnessing the poten-tial of extensive data and its relevance across diverse sectors, including social networks, agriculture, and real estate. Furthermore, the paper scrutinizes the current state of extensive data analysis, appraising its applications and advancements. Additionally, it explores emerging trends in extensive data analysis, such as intelligent farming and electronic commerce, spotlighting the forth-coming potential of this technology. [ABSTRACT FROM AUTHOR]

Crime is considered one of the largest troubles in our society and its prevention could be very crucial undertaking to all of us. Every day there are big numbers of crimes happening in our society. This calls for preserve tracking of all the crimes and maintaining a database which can be used for future reference. The contemporary issues facing are keeping the proper dataset of crime and studying this information to assist in predicting and fixing crimes in destiny. The goal of this paper is to research dataset which may consist of several crimes and predicting the kind of crime which could take place in future depending upon numerous conditions [ABSTRACT FROM AUTHOR]

Power plants become critical facilities that need to run every time without failure. The component in the power plant system has been equipped with sensors. These sensors will monitor the element's condition by periodically sending the related metric data to the control room or storing it for later evaluation. These sensor data can be analyzed for early anomaly detection on power plants. Traditional methods still need humans as experts to detect these anomalies; hence, human error is a factor. Using deep learning, we can take advantage of abundant data to learn the usual pattern and detect anomalies in future data. Many deep learning algorithms have been implemented in this case. However, the selection of these algorithms depends on the data's characteristics. This paper aims to provide a comprehensive, structured analysis of the data features and the algorithm used to detect anomalies in the data. Twenty-two recent power plant anomaly detection studies were compiled as a systematic review. This review will discuss power plant sensor data characteristics that need to be considered, such as raw data format, label usage, data dependence, and detection results. This review also discusses methods used to process unsupervised data, the relation of the multivariate data, and anomaly detection. This review aims to ease the selection of the appropriate anomaly detection algorithm for future research by considering the data characteristics. [ABSTRACT FROM AUTHOR]

In this paper, technical and statistical analysis of Smart Living, one of the most important aspects of the next generation, has been provided with detailed information. Smart living refers to using technology and the internet of things (IoT) to pursue a more efficient, controllable, and productive lifestyle. In this century, with the increase in the global population, smart living is a significantly important criterion to ensure equal opportunities for all. Here a complete overview and case implementation are discussed, followed by an analysis of statistical data about the demand for smart living from a Techno-Socio point of view. Lastly, a few aspects and issues of Smart Living have been discussed, and a proposal is given to improvise it further for future research and possible implementation. [ABSTRACT FROM AUTHOR]

The paper discusses the growth of MOOCs documents Worldwide and in Indonesia. This research method adopts the five-stage method: determine search keyword, initial research, refinement of search results, compile preliminary data statistic and data analysis. The initial search results using the MOOCs keyword getting 5,729 documents from scopus databased spread throughout the world, while the MOOCs keyword is limited in Indonesia only getting 56 documents, the data is specified based on the type of journal article document and obtained 2000 documents spread throughout the world and 27 documents in Indonesia, then journal article data was analyzed to determine the growth of MOOCs research every year, countries with the most MOOCs research, publishers who published the most MOOCs research and articles with the highest citations, then the data was also visualized into VOSViewer to analyze the keywords used, namely the keywords that were most frequently used, newly researched keywords, keyword cluster division and who are the prolific authors who write MOOC topics. From the results of this study it can be concluded that most of the MOOCs research is in the United States, while Indonesia occupies the 24th position out of 104 countries Worldwide, this shows that research on MOOCs in Indonesia is still very small. [ABSTRACT FROM AUTHOR]

The paper solves the problem of generating informative patterns, which is the most important stage of logical data analysis. A greedy heuristic algorithm for pattern search is proposed, with the help of which a chain of patterns of different coverage and homogeneity is generated. Such a chain of patterns is an approximation of the Pareto optimal set of patterns, maximum in coverage of observations of the target class and minimum in coverage of observations of other classes. Computational experiments show that the choice of the pattern variant with the best informativeness (according to the used criterion of informativeness) avoids both the effect of retraining due to excessive selectivity of patterns and insufficient homogeneity of the pattern due to its excessive generalization. [ABSTRACT FROM AUTHOR]

Mental stress is one of the major contributors to a variety of health issues. Various measures and diagnoses have been created by neurologists and psychiatrists to determine the intensity of mental stress in its early phases. In the literature, several neuroimaging devices and methods for assessing mental stress have been presented. The key candidate chosen is the electroencephalogram (EEG) signal which contains valuable information regarding mental states and conditions. This paper presents reviews of current works on EEG signal analysis for assessing mental stress. The reviews emphasize the significant disparities in the research outcomes and claims of different results from various data analysis methodologies. Accordingly, methods of EEG signals analysis will be used to study the effect of various extracted features and classification methods that associate with mental stress. Apart from that, the utilization of Artificial Intelligence (AI) approach is also investigated to study its significance towards stress detection. [ABSTRACT FROM AUTHOR]

Since their emergence, high dimensional data have imposed a big challenge to researchers given their complexities. To deal with this problem, using feature selection is the best solution, it plays a very important role in high dimensional data analysis. This process will first allow us to extract the relevant features, remove redundancy and noise, decrease the computation time and improve the classification performances. In the literature, there are several methods for feature selection, of which three categories can be distinguished. In this paper, we will focus on the filter methods for feature selection, this category estimates the quality of the variables individually independently of any learning algorithm based on statistical measures. Among the most popular filters, there is the Relief method created by Kira and Rendell as an instance-based learning-inspired idea, it gives weight to each feature and selects the highest-scored features. In this study, we propose a filter method for feature selection called ReliefTP. ReliefTP is a combination of the Relief concepts and the Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS). Several experiments were carried out on four biomedical datasets the most famous including a comparison with the original Relief. The classification results achieved by the different classification methods confirm the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an approach how to develop a system dedicated to analyze the problems and challenges in a relatively novel area - intelligent data analysis in a manufacturing organization in terms of operational decision making in order to achieve a production efficiency. The importance of this area for decision making through manufacturing analytics is highlighted with a description of modules for such system. Basic steps in the decision-making process in the production management are outlined and structured as well as the modules which are necessary to be built a business intelligence analysis system are described. [ABSTRACT FROM AUTHOR]

Survival analysis is a collection of statistical approaches for data analysis when the outcome variable of interest is the time until an event occurs. The time to failure of an experimental unit may be censored and this can be right, left, interval, and Partly Interval Censored (PIC). PIC contain the exact observations and interval-censored. In this paper, more than 25% of exact observations from the medical simulation data set will be used based on the exponential survival model via the imputation techniques. In addition to that, Multiple Imputation (MI) will be compared to the left imputation via the simulation study. The result of this simulation study suggested that for more exact observations, the results are well performed and easily implemented especially for the MI method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze Big Data processing technologies as well as to develop practical guidance to installing and working with the Apache Hadoop big data analysis ecosystem for its perspective application in universities. Specialists in the field of big data are in great demand, and with knowledge of Hadoop they are irreplaceable, since this system is fundamental and simpler analogues are based on it. This article can be used as the practical guidance to familiarize students with Apache Hadoop technology, as well as to provide theoretical material on "Big Data" technology. In the course of the work, the key definitions were given, the basic principles of working with "Big data" were formulated, and detailed instructions for installing and operating "Apache Hadoop" were developed. [ABSTRACT FROM AUTHOR]