Analysis of the Performance of Different Machine Learning Techniques for the Definition of Rule-based Control Strategies in a Parallel HEV

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

2016 19th International Conference on Computer and Information Technology (ICCIT) ◽

Combining a rule-based classifier with ensemble of feature sets and machine learning techniques for sentiment analysis on microblog

10.1109/iccitechn.2016.7860214 ◽

2016 ◽

Cited By ~ 5

Author(s):

Umme Aymun Siddiqua ◽

Tanveer Ahsan ◽

Abu Nowshed Chy

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Rule Based ◽

Feature Sets ◽

Learning Techniques ◽

Rule Based Classifier

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

Frontiers in Chemical Engineering ◽

10.3389/fceng.2021.666237 ◽

2021 ◽

Vol 3 ◽

Author(s):

Alberto Martinetti ◽

Peter K. Chemweno ◽

Kostas Nizamis ◽

Eduard Fosch-Villaronga

Keyword(s):

Machine Learning ◽

Human Robot Interaction ◽

Cloud Services ◽

Physical Interaction ◽

Robot Interaction ◽

Learning Techniques ◽

Standards And Regulations ◽

Physical Impact ◽

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.

Applying Artificial Intelligence for Operating System Fingerprinting

Engineering Proceedings ◽

10.3390/engproc2021007051 ◽

2021 ◽

Vol 7 (1) ◽

pp. 51

Author(s):

Rubén Pérez-Jove ◽

Cristian R. Munteanu ◽

Alejandro Pazos Sierra ◽

José M. Vázquez-Naya

Keyword(s):

Machine Learning ◽

Operating System ◽

Computer Security ◽

Machine Learning Algorithms ◽

Reference Database ◽

Penetration Test ◽

Rule Based ◽

Learning Techniques ◽

Rule Based Approach

In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%.

Recommender System of Final Project Topic Using Rule-based and Machine Learning Techniques

10.23919/eecsi53397.2021.9624310 ◽

2021 ◽

Author(s):

Cut Fiarni ◽

Herastia Maharani ◽

Billy Lukito

Keyword(s):

Machine Learning ◽

Recommender System ◽

Rule Based ◽

Final Project ◽

Learning Techniques

Improving Automated Planning with Machine Learning

Handbook of Research on Machine Learning Applications and Trends ◽

10.4018/978-1-60566-766-9.ch029 ◽

2010 ◽

pp. 599-620

Author(s):

Susana Fernández Arregui ◽

Sergio Jiménez Celorrio ◽

Tomás de la Rosa Turbides

Keyword(s):

Machine Learning ◽

Automated Planning ◽

Advantages And Disadvantages ◽

Learning Techniques ◽

Depth Analysis ◽

Search Processes ◽

Action Models ◽

New Challenges ◽

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

Machine Learning ◽

10.4018/978-1-60960-818-7.ch510 ◽

2012 ◽

pp. 1355-1373

Author(s):

Susana Fernández Arregui ◽

Sergio Jiménez Celorrio ◽

Tomás de la Rosa Turbides

Keyword(s):

Machine Learning ◽

Automated Planning ◽

Advantages And Disadvantages ◽

Learning Techniques ◽

Depth Analysis ◽

Search Processes ◽

Action Models ◽

New Challenges ◽

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.

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽

10.3390/s20226578 ◽

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 ◽

Detection Systems ◽

Learning Techniques ◽

Iot Devices ◽

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.

Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health

BioMed Research International ◽

10.1155/2018/8193694 ◽

2018 ◽

Vol 2018 ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Ugo Cesari ◽

Giuseppe De Pietro ◽

Elio Marciano ◽

Ciro Niri ◽

Giovanna Sannino ◽

...

Keyword(s):

Machine Learning ◽

Clinical Practice ◽

Clinical Study ◽

Health System ◽

Acoustic Analysis ◽

Voice Disorder ◽

Rule Based ◽

Learning Techniques ◽

The Voice

Objectives. The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. Materials and Methods. A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. Results. Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). Conclusions. Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a “good screening tool” for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm.

Learning Adaptive Behaviour

Adaptable and Adaptive Hypermedia Systems ◽

10.4018/978-1-59140-567-2.ch006 ◽

2011 ◽

pp. 104-124 ◽

Cited By ~ 2

Author(s):

Martin E. Muller

Keyword(s):

Machine Learning ◽

Main Part ◽

Adaptive Behaviour ◽

Software Systems ◽

Interactive Software ◽

Adaptive Hypermedia ◽

Learning Techniques ◽

Adaptive Hypermedia Systems ◽

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.