Through the technology of expert systems, the expertise of highly skilled personnel can be automated and used to assist lesser skilled personnel in the diagnosis and repair of complex machines. Expert systems that incorporate causal reasoning represent a second-generation approach to the provision of diagnostic assistance. The technology involved performs postdiction by reasoning from first principles. This article is based on research in qualitative physics and the philosophy of causality. A new implementation vehicle for causal reasoning is described, one that embodies hypothetical or counterfactual reasoning (Roach, Eichelman, and Whitehead 1985) in a language called Wif (What IF).

This article describes a common diagnostic framework used to evaluate these algorithms. These competitions, started in 2009, have significantly helped shape subsequent diagnostic algorithms. Diagnostic algorithms (DAs) (1) detect malfunctioning systems, (2) isolate the faulty component or components that cause the malfunction, and possibly (3) repair the system to restore its functionality. The fundamental challenge of diagnosis is that the system is only partially observable. Therefore, diagnostic algorithms must reason backwards from symptoms to causes.

Wind energy is a clean resource, and one that is growing worldwide, since it is the most efficient renewable energy source [1,2].

When old man winter comes to town, it's important to make sure you and your family are ready for more than just a heavy snow fall. We recently got this email from a concerned parent. Dear Dr. Manny, My kids wait about 10-15 minutes for their school bus every morning, should I be worried that they could get frostbite while they wait? Frostbite is a serious medical condition that occurs when the skin and underlying tissues literally freeze. Since kids lose more heat from their skin than adults, they are at an increased risk for developing the condition.

The Decision Tree plugin is the only plugin we know of where you can easily build a decision tree allowing you to easily present your visitors yes/no type questions and walk them down your decision tree. The Decision Tree plugin is the only plugin we know of where you can easily build a decision tree (DT). Easily create your own trees with the easy to use DT editor. The Decision Tree plugin is the only plugin we know of where you can easily build a decision tree (DT). Easily create your own trees with the easy to use DT editor.

I made a automated skin disease diagnosis DEMO website based on deep learning algorithm (Model Dermatology; http://ModelDerm.com). ResNet152 and VGG19 were used as a CNN model, around 300,000 images (179 class;176 skin disorders) were used as a trainining dataset. The training images were collected from 4 university hospitals in Korea. This CNN model is the successor to my onychomycosis model (http://nail.medicalphoto.org). The web-based test platform provides 3 differential diagnosis after analyzing image.

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.

Principal component analysis (PCA) is largely adopted for chemical process monitoring and numerous PCA-based systems have been developed to solve various fault detection and diagnosis problems. Since PCA-based methods assume that the monitored process is linear, nonlinear PCA models, such as autoencoder models and kernel principal component analysis (KPCA), has been proposed and applied to nonlinear process monitoring. However, KPCA-based methods need to perform eigen-decomposition (ED) on the kernel Gram matrix whose dimensions depend on the number of training data. Moreover, prefixed kernel parameters cannot be most effective for different faults which may need different parameters to maximize their respective detection performances. Autoencoder models lack the consideration of orthogonal constraints which is crucial for PCA-based algorithms. To address these problems, this paper proposes a novel nonlinear method, called neural component analysis (NCA), which intends to train a feedforward neural work with orthogonal constraints such as those used in PCA. NCA can adaptively learn its parameters through backpropagation and the dimensionality of the nonlinear features has no relationship with the number of training samples. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of NCA in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of NCA can be found in https://github.com/haitaozhao/Neural-Component-Analysis.git.

Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scratch those features that best allow to solve a given supervised learning problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the cost of losing a main attractive trait of decision trees: the fact that each sample is routed along a small subset of tree nodes only. We here propose a model and Expectation-Maximization training scheme for decision trees that are fully probabilistic at train time, but after a deterministic annealing process become deterministic at test time. We also analyze the learned oblique split parameters on image datasets and show that Neural Networks can be trained at each split node. In summary, we present the first end-to-end learning scheme for deterministic decision trees and present results on par with or superior to published standard oblique decision tree algorithms.

In the last Part, I have talked about the main concepts behind the Decision Tree. In this post, I will show how to use decision tree component in Power BI with the aim of Predictive analysis in the report. Decision tree able to handle both. There is a Hello world dataset in Data science world name "Titanic". This dataset has information about the passengers who survived or not from the disaster.