AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.

COMPARING DIFFERENT TECHNIQUES USED BY COMPANIES INTERGRATING AI IN LITIGATION Kevin Muriithi Mirera

Data mining is a way to extract knowledge out of generally large data sets; in other words, it is an approach to discover hidden relationships among data by using artificial intelligence methods. This has made it an important field in research. Law is one of the most important fields for applying data mining given the plethora of data from law cases stenographer data to lawsuit data. Text summarization in NLP (Natural Language Processing) is the process of summarizing the information on large texts for quicker consumption it is an extremely useful technique in NLP. Identifying law case characteristics is the first step for developing further analysis. An approach based on data mining techniques is discussed in this paper to extract important entities from law cases which are written in plain text. The process will involve different Artificial intelligence techniques including clustering or other unsupervised or supervised learning techniques.

A Study of Big Data Processing for Sentiments Analysis

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.

Use of Qualitative Research to Generate a Function for Finding the Unit Cost of Software Test Cases

In this article, we demonstrate a novel use of case research to generate an empirical function through qualitative generalization. This innovative technique applies interpretive case analysis to the problem of defining and generalizing an empirical cost function for test cases through qualitative interaction with an industry cohort of subject matter experts involved in software testing at leading technology companies. While the technique is fully generalizable, this article demonstrates this technique with an example taken from the important field of software testing. The huge amount of software development conducted in today's world makes taking its cost into account imperative. While software testing is a critical aspect of the software development process, little attention has been paid to the cost of testing code, and specifically to the cost of test cases, in comparison to the cost of developing code. Our research fills the gap by providing a function for estimating the cost of test cases.

Artificial Intelligence Applications in Nutrition and Dietetics

Artificial intelligence (AI) is a branch of computer science whose purpose is to imitate thought processes, learning abilities, and knowledge management. The increasing number of applications in experimental and clinical medicine is striking. An artificial intelligence application in the field of nutrition and dietetics is a fairly new and important field. Different apps related to nutrition are offered to the use of individuals. The importance of individual nutrition has also triggered the increase in artificial intelligence apps. It is thought that different apps such as food preferences and dietary intake can play an important role in health promotion. Researchers may have some difficulties such as remembering the frequency or amount of intake in assessment of dietary intake. Some applications used in the assessment of food consumption contribute to overcoming these difficulties. Besides, these apps facilitate the work of researchers and provide more reliable results than traditional methods. The apps to be used in the field of nutrition and dietetics should be developed by considering the disadvantages. It is thought that artificial intelligence applications will contribute to both the improvement of health and the assessment and monitoring of nutritional status.

Improving HRI with Force Sensing

In the future, in a society where robots and humans live together, HRI is an important field of research. While most human–robot-interaction (HRI) studies focus on appearance and dialogue, touch-communication has not been the focus of many studies despite the importance of its role in human–human communication. This paper investigates how and where humans touch an inorganic non-zoomorphic robot arm. Based on these results, we install touch sensors on the robot arm and conduct experiments to collect data of users’ impressions towards the robot when touching it. Our results suggest two main things. First, the touch gestures were collected with two sensors, and the collected data can be analyzed using machine learning to classify the gestures. Second, communication between humans and robots using touch can improve the user’s impression of the robots.

A Tour of Gang Ethnographies: Key Themes in Contemporary Studies around the World

This chapter offers a review of recently published gang ethnographies across four continents. Historically rooted in the United States, today the gang phenomena as well as gang ethnographies are subjected to processes of globalization. Europe, Latin America, and increasingly the Global South are emerging as important field sites for ethnographic research. Contemporary unprecedented levels of international migration, displacement, and deportation of people shape current gang ethnographies and have led to reconfigurations of century-old debates. Global forces also push the traditional boundaries of ethnographic field site across nation-state borders and into the online world. In the past two decades, the nationality, gender, as well as the disciplinary background of gang ethnographers has also dramatically diversified. Nonetheless, the visibility of gang ethnographies is still highly dependent of an ethnographers’ nationality and linguistic skills. Here Anglophone researchers as well as ethnographers associated with countries that are more affluent and universities still have a clear advantage over the majority of scholars of the Global South.

Coherence and Reduction

Synchronic intertheoretic reductions are an important field of research in science. Arguably, the best model able to represent the main relations occurring in this kind of scientific reduction is the Nagelian account of reduction, a model further developed by Schaffner and nowadays known as the generalized version of the Nagel–Schaffner model (GNS). In their article (2010), Dizadji-Bahmani, Frigg, and Hartmann (DFH) specified the two main desiderata of a reduction á la GNS: confirmation and coherence. DFH first and, more rigorously, Tešic (2017) later analyse the confirmatory relation between the reducing and the reduced theory in terms of Bayesian confirmation theory. The purpose of this article is to analyse and compare the degree of coherence between the two theories involved in the GNS before and after the reduction. For this reason, in the first section, I will be looking at the reduction of thermodynamics to statistical mechanics and use it as an example to describe the GNS. In the second section, I will introduce three coherence measures which will then be employed in the comparison. Finally, in the last two sections, I will compare the degrees of coherence between the reducing and the reduced theory before and after the reduction and use a few numerical examples to understand the relation between coherence and confirmation measures.

Particle energization in space plasmas: towards a multi-point, multi-scale plasma observatory

This White Paper outlines the importance of addressing the fundamental science theme “How are charged particles energized in space plasmas” through a future ESA mission. The White Paper presents five compelling science questions related to particle energization by shocks, reconnection, waves and turbulence, jets and their combinations. Answering these questions requires resolving scale coupling, nonlinearity, and nonstationarity, which cannot be done with existing multi-point observations. In situ measurements from a multi-point, multi-scale L-class Plasma Observatory consisting of at least seven spacecraft covering fluid, ion, and electron scales are needed. The Plasma Observatory will enable a paradigm shift in our comprehension of particle energization and space plasma physics in general, with a very important impact on solar and astrophysical plasmas. It will be the next logical step following Cluster, THEMIS, and MMS for the very large and active European space plasmas community. Being one of the cornerstone missions of the future ESA Voyage 2050 science programme, it would further strengthen the European scientific and technical leadership in this important field.

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.