This paper presents an expert decision support system for time-invariant aeroacoustic source classification. The system comprises two steps: first, the calculation of acoustic properties based on spectral and spatial information; and second, the clustering of the sources based on these properties. Example data of two scaled airframe half-model wind tunnel measurements is evaluated based on deconvolved beamforming maps. A variety of aeroacoustic features are proposed that capture the characteristics and properties of the spectra. These features represent aeroacoustic properties that can be interpreted by both the machine and experts. The features are independent of absolute flow parameters such as the observed Mach numbers. This enables the proposed method to analyze data which is measured at different flow configurations. The aeroacoustic sources are clustered based on these features to determine similar or atypical behavior. For the given example data, the method results in source type clusters that correspond to human expert classification of the source types. Combined with a classification confidence and the mean feature values for each cluster, these clusters help aeroacoustic experts in classifying the identified sources and support them in analyzing their typical behavior and identifying spurious sources in-situ during measurement campaigns.

Hello Everyone, another article in the series fully explained machine learning algorithms. In this article, we will discuss the k nearest neighbor classification problem. A good article is like a flow of the story and readers get as much information in a small amount of time. So, we will discuss the supervised classification problem learning technique. The main goal is to predict the new data point based on samples near that data point.

One of the major motivations of this research is the fact that there has been an increasing focus on Deep model interpretability with the advent of more and more complex models. More is the complexity of the model, difficult it gets to have interpretability with respect to the outputs and a lot of research is going in the field of Bayesian thinking and learning. But before understanding and being able to appreciate Bayesian in deep neural models, we should be well versed and adept with Bayesian thinking in linear models for example- Bayesian Linear regression. But there are very few good materials available online in a combined fashion which can give a clear motivation and understanding of the Bayesian Linear regression. This was one of the major motivations for this blog and here I will try to give an understanding of how to approach the Linear regression from a Bayesian analysis standpoint.

"While reading this book, I joined the authors on a learning endeavor thanks to their honesty and intellectual vulnerability. Their lack of experience with Bayesian statistics helps them to be effective communicators . . . If you are interested in starting your Bayesian journey, then Bayesian Statistics for Beginners is an excellent place to begin." Therese Donovan, Wildlife Biologist, U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit, University of Vermont, USA,Ruth M. Mickey, Professor Emerita, Department of Mathematics and Statistics, University of Vermont, USA Therese Donovan is a wildlife biologist with the U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit. Based in the Rubenstein School of Environment and Natural Resources at the University of Vermont, Therese teaches graduate courses on ecological modeling and conservation biology.

This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. The original code, exercise text, and data files for this post are available here. In part four we wrapped up our implementation of logistic regression by extending our solution to handle multi-class classification and testing it on the hand-written digits data set. Using just logistic regression we were able to hit a classification accuracy of about 97.5%, which is reasonably good but pretty much maxes out what we can achieve with a linear model. In this blog post we'll again tackle the hand-written digits data set, but this time using a feed-forward neural network with backpropagation.

Apparently, the project's domain relies on the most popular liquor in the world -- Whiskey. A dark spirit coming from a great variety of grains, distilled throughout the world and arriving at quite a number of styles (Irish, Scotch, Bourbon etc) [1]. Scotland, Ireland, Canada & Japan are among the famous exporters and on an international scale, the global production almost reaches the level of $95m revenue [2]. The main scope, hereof, is to introduce in a… 'companionable' way, how helpful can the Clustering Algorithms prove to be, anytime we need to find patterns in a (large) dataset. Actually, it might be considered as a powerful expansion of the standard Exploratory Data Analysis (EDA), which is often very beneficial to try, before using Supervised Machine Learning (ML) models.

In this article, we will discuss the machine learning clustering-based algorithm that is the DBScan cluster. The approach in this cluster algorithm is density-based than another distance-based approach. The other cluster which is distance-based looks for closeness in data points but also misclassifies if the point belongs to another class. So, density-based clustering is suited in this kind of scenario. The cluster algorithms come in unsupervised learning in which we don't rely on target variables to make clusters.

"Big data is high-volume, high velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight, and decision making" (Gartner's IT Glossary). For the majority of students, analysing big data is by far the most challenging piece of academic work that they have attempted or are ever likely to try in the future. The majority of the students do agree and would have experienced the scenario. At Tutors India, we have subject matter expertise who has capability to understand the different layers of data being integrated and the level of granularity of integration to create the holistic picture. Further the team also well equipped with advanced mathematical degrees, statistics and with multiple specialist degree.

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. Here we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached a high F1 score up to 0.874 in the infected bee detection and up to 0.727 in the detection of the Varroa Destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for this purpose. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies.