scholarly journals Interactive Anomaly Identification with Erroneous Feedback

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Takaaki Tagawa ◽  
Yukihiro Tadokoro ◽  
Takehisa Yairi
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
High Reliability ◽  
Parameter Study ◽  
Learning Tools ◽  
Vehicle Data ◽  
Learning Capabilities ◽  
Benchmark Datasets ◽  
Critical Problems ◽  
System Analyst

The difficulties in analyzing large and extensive systems necessitate the use of efficient machine-learning tools to identify unknown system anomalies in order to avoid critical problems and ensure high reliability. Given that data logged by a system include unknown anomalies, anomaly identification models aim to simultaneously identify the time of occurrence and the features that contributed to these anomalies. To maximize accuracy, it is important to utilize the data as well as the domain knowledge of the system. However, it is difficult for a system analyst to possess not only machine-learning capabilities but also domain knowledge to incorporate into the model. In this paper, we propose a new anomaly identification framework capable of utilizing feedback based on domain knowledge without requiring any machine-learning capabilities. We also propose a novel method, the so-called rank ensemble method, to improve the accuracy of anomaly identification with erroneous feedback, that is, feedback that in- cludes incorrect information. Our method enables erroneous information to be adaptively ignored by assuming consistency between the data and the user feedback. An intensive parameter study using benchmark datasets and a case study with real vehicle data demonstrate the applicability of our framework.

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency

2021 ◽  
Vol 192 ◽  
pp. 103181
Author(s):  
Jagadish Timsina ◽  
Sudarshan Dutta ◽  
Krishna Prasad Devkota ◽  
Somsubhra Chakraborty ◽  
Ram Krishna Neupane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Learning Tools ◽  
Nutrient Use ◽  
Use Efficiency

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Organizing workers and machine learning tools for a less oppressive workplace

2021 ◽  
Vol 59 ◽  
pp. 102353
Author(s):  
Amber Grace Young ◽  
Ann Majchrzak ◽  
Gerald C. Kane
Keyword(s):  
Machine Learning ◽  
Learning Tools

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

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