Learning Adaptive Behaviour

2011 ◽  
pp. 104-124 ◽  
Author(s):  
Martin E. Muller
Keyword(s):  
Machine Learning ◽  
Main Part ◽  
Machine Learning Techniques ◽  
Adaptive Behaviour ◽  
Software Systems ◽  
Interactive Software ◽  
Adaptive Hypermedia ◽  
Learning Techniques ◽  
Adaptive Hypermedia Systems ◽  
Definition Of

This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described. The most important learning methods are introduced by examples, and their applicability in adaptive hypermedia is discussed. The chapter concludes with a comparison of all approaches one might consider when applying machine learning in adaptive hypermedia systems.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

scholarly journals Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Martinetti ◽  
Peter K. Chemweno ◽  
Kostas Nizamis ◽  
Eduard Fosch-Villaronga
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Learning Techniques ◽  
Standards And Regulations ◽  
Physical Impact ◽  
Definition Of

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

Improving Automated Planning with Machine Learning

2010 ◽  
pp. 599-620
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

Improving Automated Planning with Machine Learning

2012 ◽  
pp. 1355-1373
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

Compound or phrase or in between? Testing linguistic criteria for compoundhood in English

2020 ◽  
Vol 13 (2) ◽  
pp. 250-281
Author(s):  
Patrick Ziering ◽  
Lonneke van der Plas
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
European Parliament ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Nominal Compounds ◽  
Definition Of

In this paper, we present an empirical study on the definition of compounds in English, the graded nature of the phenomenon and its correlations with the commonly used linguistic criteria for compoundhood. We create a resource that includes a diverse set of nominal compounds identified by two trained independent annotators in sentences from the proceedings of the European Parliament. In addition, the annotators provide ratings on the compoundhood of the identified compounds, and ratings for the applicability of six prominent linguistic criteria of compoundhood for each item. We show the controversy of defining compounds in practice by comparing the annotations of two annotators, and the graded nature of compoundhood. By measuring the correlation between compoundhood and the six diverse linguistic criteria using machine learning techniques, we show that some linguistic criteria are stronger predictors of compoundhood than others.

scholarly journals Lung Cancer Prediction Using Machine Learning: A Systematic Review

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
K Nearest Neighbor ◽  
Cancer Prediction ◽  
Learning Techniques ◽  
Learning Machine ◽  
Comparison Table ◽  
Definition Of

One of the large spread diseases in a human being is Lung Cancer. It remains a threat to society and is the cause of thousands of deaths worldwide. Early detection cause of lung cancer is an understandable perspective to maximize the opportunity of the existence of the patients. This paper is about the observation of lung cancer. Here, Computed Tomography (CT) is used for the observation of lung cancer. Various Algorithms are used to search out lung cancer prediction correctly like K Nearest Neighbor, SVM, Decision Tree, and many more. An Aim of the introduced analysis to design a model that can reduce the likelihood of lung cancer in a patient with maximum accuracy. We began by surveying various machine learning techniques, explaining a concise definition of the most normally used classification techniques for identifying lung cancer. Then, we analyze survey representable research works utilizing learning machine classification methods in this field. Moreover, an elaborated comparison table of surveyed paper is introduced.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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