scholarly journals Performance of cache placement using supervised learning techniques in mobile edge networks

IET Networks ◽  
2021 ◽  
Author(s):  
Lubna Mohammed ◽  
Alagan Anpalagan ◽  
Ahmed S. Khwaja ◽  
Muhammad Jaseemuddin
Keyword(s):  
Supervised Learning ◽  
Learning Techniques ◽  
Cache Placement ◽  
Edge Networks

scholarly journals Acquiring Sentiment from Twitter using Supervised Learning and Lexicon-based Techniques

2016 ◽  
Vol 15 (1) ◽  
pp. 63-80
Author(s):  
Jitrlada ROJRATANAVIJIT ◽  
Preecha VICHITTHAMAROS ◽  
Sukanya PHONGSUPHAP
Keyword(s):  
Supervised Learning ◽  
Mixed Method ◽  
Short Length ◽  
Writing Systems ◽  
Learning Techniques ◽  
Average Accuracy ◽  
The Difference ◽  
Tweet Classification ◽  
Source Of Information ◽  
Processing Steps

The emergence of Twitter in Thailand has given millions of users a platform to express and share their opinions about products and services, among other subjects, and so Twitter is considered to be a rich source of information for companies to understand their customers by extracting and analyzing sentiment from Tweets. This offers companies a fast and effective way to monitor public opinions on their brands, products, services, etc. However, sentiment analysis performed on Thai Tweets has challenges brought about by language-related issues, such as the difference in writing systems between Thai and English, short-length messages, slang words, and word usage variation. This research paper focuses on Tweet classification and on solving data sparsity issues. We propose a mixed method of supervised learning techniques and lexicon-based techniques to filter Thai opinions and to then classify them into positive, negative, or neutral sentiments. The proposed method includes a number of pre-processing steps before the text is fed to the classifier. Experimental results showed that the proposed method overcame previous limitations from other studies and was very effective in most cases. The average accuracy was 84.80 %, with 82.42 % precision, 83.88 % recall, and 82.97 % F-measure.

scholarly journals Ecological Interactions and the Netflix Problem

2016 ◽  
Author(s):  
Philippe Desjardins-Proulx ◽  
Idaline Laigle ◽  
Timothée Poisot ◽  
Dominique Gravel
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Random Forests ◽  
Species Interactions ◽  
Similarity Measures ◽  
Theoretical Models ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Ecological Interactions ◽  
Learning Techniques

0AbstractSpecies interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

scholarly journals A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 9597
Author(s):  
Oyeniyi Akeem Alimi ◽  
Khmaies Ouahada ◽  
Adnan M. Abu-Mahfouz ◽  
Suvendi Rimer ◽  
Kuburat Oyeranti Adefemi Alimi
Keyword(s):  
Intrusion Detection ◽  
Supervised Learning ◽  
Power Plants ◽  
Distribution Systems ◽  
Remote Access ◽  
Future Research ◽  
Monitoring And Control ◽  
Efficient Detection ◽  
Learning Techniques ◽  
Scada Systems

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

Face Authentication Using Supervised Learning Techniques

2012 ◽  
pp. 738-745
Author(s):  
Amioy Kumar ◽  
Rohan Gupta ◽  
Akshay Sharma ◽  
Bijaya Ketan Panigrahi ◽  
M. Hanmandlu
Keyword(s):  
Supervised Learning ◽  
Face Authentication ◽  
Learning Techniques

Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks

2021 ◽  
pp. 38-57
Author(s):  
José Sousa ◽  
João Barata
Keyword(s):  
Complex Networks ◽  
Supervised Learning ◽  
Enterprise Resource Planning ◽  
Information Technologies ◽  
Enterprise Systems ◽  
Resource Planning ◽  
Sociotechnical Systems ◽  
Healthcare Data ◽  
Learning Techniques ◽  
Support Decision Making

Organizations worldwide are supporting their processes and decisions with enterprise systems (ES). Large amounts of data are produced and reproduced in these increasingly complex sociotechnical systems, opening new opportunities for the adoption of self-supervised learning techniques. Complex networks are viable solutions to create models that learn from data. This chapter presents (1) a review on the possibilities of networks for self-supervised learning, (2) three cases illustrating the potential of complex networks to address the autopoietic nature of ES (adoption of enterprise resource planning, web portal development, and healthcare data analytics), and (3) a framework to mine sociotechnical patters uncovering the entanglement of human practice and information technologies. For theory, this chapter explains the potential of complex networks to assess enterprise systems dynamics. For practice, the proposed framework can assist managers in establishing a strategy to continuously learn from their data to support decision-making in self-adapting scenarios.

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