In the process industry, condition monitoring systems with automated fault diagnosis methods assisthuman experts and thereby improve maintenance efficiency, process sustainability, and workplace safety.Improving the automated fault diagnosis methods using data and machine learning-based models is a centralaspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets withaccurate labels needed to train and validate models, and to transfer models trained with labeled lab datato heterogeneous process industry environments. However, fault descriptions and work-orders written bydomain experts are increasingly digitized in modern condition monitoring systems, for example in the contextof rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severitiesexists as technical language annotations in industrial datasets. Furthermore, recent advances in naturallanguage processing enable weakly supervised model optimization using natural language annotations, mostnotably in the form ofnatural language supervision(NLS). This creates a timely opportunity to developtechnical language supervision(TLS) solutions for IFD systems grounded in industrial data, for exampleas a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturityof NLS over the last two years, facilitating applications beyond natural language; a rapid development ofweak supervision methods; and transfer learning as a current trend in IFD which can benefit from thesedevelopments. Finally, we describe a framework for integration of TLS in IFD which is inspired by recentNLS innovations.

Considering the models that apply the contextual information of time-series data could improve the fault diagnosis performance, some neural network structures such as RNN, LSTM, and GRU were proposed to model the industrial process effectively. However, these models are restricted by their serial computation and hence cannot achieve high diagnostic efficiency. Also the parallel CNN is difficult to implement fault diagnosis in an efficient way because it requires larger convolution kernels or deep structure to achieve long-term feature extraction capabilities. Besides, BERT model applies absolute position embedding to introduce contextual information to the model, which would bring noise to the raw data and therefore cannot be applied to fault diagnosis directly. In order to address the above problems, a fault diagnosis model named deep parallel time-series relation network(\textit{DPTRN}) has been proposed in this paper. There are mainly three advantages for DPTRN: (1) Our proposed time relationship unit is based on full multilayer perceptron(\textit{MLP}) structure, therefore, DPTRN performs fault diagnosis in a parallel way and improves computing efficiency significantly. (2) By improving the absolute position embedding, our novel decoupling position embedding unit could be applied on the fault diagnosis directly and learn contextual information. (3) Our proposed DPTRN has obvious advantage in feature interpretability. Our model outperforms other methods on both TE and KDD-CUP99 datasets which confirms the effectiveness, efficiency and interpretability of the proposed DPTRN model.

This is how the decision tree algorithm works behind the scene:

Develop an FDD approach based on unsupervised learning methods for NPPs. A comparative study on the presented methods is conducted. PCTRAN simulation is used to test the efficiencies of the proposed approach. Nuclear power plants have proved their importance in the energy sector by generating clean and uninterrupted energy over decades. Moreover, nuclear power plants (NPPs) are large-scale and complex systems with potential radioactive release risks.

On a typical day, general practitioners (GPs) make multiple decisions when diagnosing and treating patients. They have limited access to immediate imaging diagnostics and tests and rely more on the patient's history and clinical examination than the second and tertiary stages of healthcare. To establish a diagnosis, a GP starts with the chief complaint, makes a hypothesis with a perceptual list of differential diagnoses, and asks the patient a series of targeted questions to include or exclude diagnoses. The GP then performs a clinical examination to confirm further or refute diagnoses while deciding if further diagnostic tests are needed. When the GP has reached a diagnostic conclusion, with a reasonable degree of certainty, he makes the diagnosis.

A rootkit is a program (or set of programs) that allows you to hide the presence of malware in the system. Rootkits are often part of multifunctional malware that could have multiple abilities, such as providing attackers with remote access to compromised hosts, intercepting network traffic, spying on users, recording keystrokes, stealing authentication information, or using the host as a base to mine cryptocurrencies and aid in DDoS attacks. The task of the rootkit is to mask this illegitimate activity on the compromised machine. Some rootkits, such as Necurs, Flame and DirtyMoe, are designed to combine both modes of operation and thus work at both levels. They accounted for 31% of the sample.

Decision trees represent a connecting series of tests that branch off further and further down until a specific path matches a class or label. They're kind of like a flowing chart of coin flips, if/else statements, or conditions that when met lead to an end result. Decision trees are incredibly useful for classification problems in machine learning because it allows data scientists to choose specific parameters to define their classifiers. So whether you're presented with a price cutoff or target KPI value for your data, you have the ability to sort data at multiple levels and create accurate prediction models. Now there are many, many applications that utilize decision trees but for this article, I'm going to focus on using decision trees to make business decisions.

Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections. Objective: To address these challenges, we designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data and to record and share diagnostic data in real-time with a centralized database. Design: We trained our system using multiple data sets, including US-based electronic medical records (EMRs) and open-source medical literature and developed an adaptive, general medical assistant system based on machine learning algorithms. Main outcomes and Measure: The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions. The application is unique from existing systems in that it covers a wide range of common diseases, signs, and medication typical in lower-resource countries; the application works with or without an active internet connection. Results: We have built and implemented an adaptive learning system that assists trained primary care professionals by means of an Android smartphone application, which interacts with a central database and collects real-time data. The application has been tested by dozens of primary care providers. Conclusions and Relevance: Our application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters. This application could be leveraged to automatically populate local or national EMR systems.

Graphical causal models led to the development of complete non-parametric identification theory in arbitrary structured systems, and general approaches to efficient inference. Nevertheless, graphical approaches to causal inference have not been embraced by the statistics and public health communities. In those communities causal assumptions are instead expressed in terms of potential outcomes, or responses to hypothetical interventions. Such interventions are generally conceptualized only on a limited set of variables, where the corresponding experiment could, in principle, be performed. By contrast, graphical approaches to causal inference generally assume interventions on all variables are well defined - an overly restrictive and unrealistic assumption that may have limited the adoption of these approaches in applied work in statistics and public health. In this paper, we build on a unification of graphical and potential outcomes approaches to causality exemplified by Single World Intervention Graphs (SWIGs) to define graphical models with a restricted set of allowed interventions. We give a complete identification theory for such models, and develop a complete calculus of interventions based on a generalization of the do-calculus, and axioms that govern probabilistic operations on Markov kernels. A corollary of our results is a complete identification theory for causal effects in another graphical framework with a restricted set of interventions, the decision theoretic graphical formulation of causality.

An intrinsic time-scale decomposition (ITD) based method for power transformer fault diagnosis is proposed. Dissolved gas analysis (DGA) parameters are ranked according to their skewness, and then ITD based features extraction is performed. An optimal set of PRC features are determined by an XGBoost classifier. For classification purpose, an XGBoost classifier is used to the optimal PRC features set. The proposed method's performance in classification is studied using publicly available DGA data of 376 power transformers and employing an XGBoost classifier. The Proposed method achieves more than 95% accuracy and high sensitivity and F1-score, better than conventional methods and some recent machine learning-based fault diagnosis approaches. Moreover, it gives better Cohen Kappa and F1-score as compared to the recently introduced EMD-based hierarchical technique for fault diagnosis in power transformers.