Carving of the OOXML document from volatile memory using unsupervised learning techniques

2022 ◽  
Vol 65 ◽  
pp. 103096
Author(s):  
Noor Ul Ain Ali ◽  
Waseem Iqbal ◽  
Hammad Afzal
Keyword(s):  
Unsupervised Learning ◽  
Learning Techniques ◽  
Volatile Memory

scholarly journals Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques

2021 ◽  
Vol 11 (3) ◽  
pp. 1241
Author(s):  
Sergio D. Saldarriaga-Zuluaga ◽  
Jesús M. López-Lezama ◽  
Nicolás Muñoz-Galeano
Keyword(s):  
Unsupervised Learning ◽  
Distributed Generation ◽  
Network Topology ◽  
International Electrotechnical Commission ◽  
Number Of Clusters ◽  
Learning Techniques ◽  
Topology Changes ◽  
Network Topologies ◽  
Optimal Coordination ◽  
Operational Modes

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.

scholarly journals Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-2
Author(s):  
Anke Meyer-Baese ◽  
Sylvain Lespinats ◽  
Juan Manuel Gorriz Saez ◽  
Olivier Bastien
Keyword(s):  
Unsupervised Learning ◽  
Learning Techniques

Semisupervised sentiment analysis method for online text reviews

2020 ◽  
pp. 016555152091003
Author(s):  
Gyeong Taek Lee ◽  
Chang Ouk Kim ◽  
Min Song
Keyword(s):  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Model Space ◽  
Training Dataset ◽  
Learning Approach ◽  
Learning Models ◽  
Text Data ◽  
Learning Techniques ◽  
Sentiment Dictionary

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

Classification Based on Unsupervised Learning

2011 ◽  
pp. 348-395
Author(s):  
Yu Wang
Keyword(s):  
Network Security ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Network Traffic ◽  
High Speed ◽  
Ad Hoc ◽  
Training Data ◽  
Traffic Data ◽  
Response Variable ◽  
Learning Techniques

The requirement for having a labeled response variable in training data from the supervised learning technique may not be satisfied in some situations: particularly, in dynamic, short-term, and ad-hoc wireless network access environments. Being able to conduct classification without a labeled response variable is an essential challenge to modern network security and intrusion detection. In this chapter we will discuss some unsupervised learning techniques including probability, similarity, and multidimensional models that can be applied in network security. These methods also provide a different angle to analyze network traffic data. For comprehensive knowledge on unsupervised learning techniques please refer to the machine learning references listed in the previous chapter; for their applications in network security see Carmines, Edward & McIver (1981), Lane & Brodley (1997), Herrero, Corchado, Gastaldo, Leoncini, Picasso & Zunino (2007), and Dhanalakshmi & Babu (2008). Unlike in supervised learning, where for each vector 1 2 ( , , , ) n X x x x = ? we have a corresponding observed response, Y, in unsupervised learning we only have X, and Y is not available either because we could not observe it or its frequency is too low to be fit ted with a supervised learning approach. Unsupervised learning has great meanings in practice because in many circumstances, available network traffic data may not include any anomalous events or known anomalous events (e.g., traffics collected from a newly constructed network system). While high-speed mobile wireless and ad-hoc network systems have become popular, the importance and need to develop new unsupervised learning methods that allow the modeling of network traffic data to use anomaly-free training data have significantly increased.

scholarly journals Unsupervised Learning Techniques for Trilateration: From Theory to Android APP Implementation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 134525-134538 ◽  
Author(s):  
Jeongsik Choi ◽  
Yang-Seok Choi ◽  
Shilpa Talwar
Keyword(s):  
Unsupervised Learning ◽  
Learning Techniques ◽  
Android App

Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis

2019 ◽  
Vol 128 (12) ◽  
pp. 1170-1176
Author(s):  
Abigail Walker ◽  
Pavol Surda
Keyword(s):  
Unsupervised Learning ◽  
Chronic Rhinosinusitis ◽  
Clinical Data ◽  
Traditional Approach ◽  
Health Care Research ◽  
Machine Learning Techniques ◽  
Common Symptom ◽  
Disease Phenotypes ◽  
Learning Techniques ◽  
Investigative Technique

Objectives: This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional approach to investigating CRS based upon the clinically recognized phenotypes of “with polyps” and “without polyps” and instead relies upon the application of complex mathematical models to derive subgroups which can then be further examined. This review article reports on the principles which underlie this investigative technique and some of the published examples in CRS. Methods: This review summarizes the different types of unsupervised learning techniques which have been described and briefly expounds upon their useful applications. A literature review of studies which have unsupervised learning is then presented to provide a practical guide to its uses and some of the new directions of investigations suggested by their findings. Results: The commonest unsupervised learning technique applied to rhinology research is cluster analysis, which can be further subdivided into hierarchical and non-hierarchical approaches. The mathematical principles which underpin these approaches are explained within this article. Studies which have used these techniques can be broadly divided into those which have used clinical data only and that which includes biomarkers. Studies which include biomarkers adhere closely to the established canon of CRS disease phenotypes, while those that use clinical data may diverge from the typical “polyp versus non-polyp” phenotypes and reflect subgroups of patients who share common symptom modifiers. Summary: Artificial intelligence is increasingly influential in health care research and machine learning techniques have been reported in the investigation of CRS, promising several interesting new avenues for research. However, when critically appraising studies which use this technique, the reader needs to be au fait with the limitations and appropriate uses of its application.

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