scholarly journals Information modeling and knowledge extraction for machine learning applications in industrial production systems

2020 ◽  
pp. 73-81
Author(s):  
Stefan Windmann ◽  
Christian Kühnert
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Structural Information ◽  
Semantic Annotation ◽  
Information Model ◽  
Knowledge Extraction ◽  
Information Modeling ◽  
Sensor Data ◽  
Tool Chain ◽  
Machine Learning Applications

AbstractIn this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.

scholarly journals Implications of properties and quality of indoor sensor data for building machine learning applications: Two case studies in smart campuses

2021 ◽  
pp. 108529
Author(s):  
Miia Lillstrang ◽  
Markus Harju ◽  
Guillermo del Campo ◽  
Gonzalo Calderon ◽  
Juha Röning ◽  
...  
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Sensor Data ◽  
Machine Learning Applications

Programming Language Support for Implementing Machine Learning Algorithms

2020 ◽  
pp. 402-421
Author(s):  
Anitha Elavarasi S. ◽  
Jayanthi J.
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Mathematical Optimization ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
Machine Learning Applications ◽  
And Control

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.

scholarly journals Introduction to MAchine Learning & Knowledge Extraction (MAKE)

2017 ◽  
Vol 1 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Andreas Holzinger
Keyword(s):  
Machine Learning ◽  
Mass Density ◽  
Knowledge Extraction ◽  
Future Research ◽  
Learning Approach ◽  
Explanatory Factors ◽  
Cross Domain ◽  
Machine Learning Approach ◽  
Develop Software ◽  
Machine Learning Applications

The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community.

scholarly journals Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 32 ◽  
Author(s):  
Alessandro Scirè ◽  
Fabrizio Tropeano ◽  
Aris Anagnostopoulos ◽  
Ioannis Chatzigiannakis
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Fog Computing ◽  
Cloud Services ◽  
Sensor Data ◽  
High Detection Rate ◽  
Embedded Processor ◽  
Machine Learning Applications ◽  
Active Research ◽  
Asymmetric Multicore

Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.

scholarly journals Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2390
Author(s):  
Peihuang Huang ◽  
Pei Yao ◽  
Zhendong Hao ◽  
Huihong Peng ◽  
Longkun Guo
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Side Information ◽  
Minimum Weight ◽  
Learning Technology ◽  
Accuracy Rate ◽  
Single Data Point ◽  
Machine Learning Applications ◽  
Data Points ◽  
Single Data

Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy rate despite its advantage of fast procession. In this paper, we model domain knowledge (i.e., background knowledge or side information), respecting some applications as must-link and cannot-link sets, for the sake of collaborating with k-means for better accuracy. We first propose an algorithm for constrained k-means, considering only must-links. The key idea is to consider a set of data points constrained by the must-links as a single data point with a weight equal to the weight sum of the constrained points. Then, for clustering the data points set with cannot-link, we employ minimum-weight matching to assign the data points to the existing clusters. At last, we carried out a numerical simulation to evaluate the proposed algorithms against the UCI datasets, demonstrating that our method outperforms the previous algorithms for constrained k-means as well as the traditional k-means regarding the clustering accuracy rate although with a slightly compromised practical runtime.

scholarly journals Component Detection and Evaluation Framework (CDEF): A Semantic Annotation Tool

2020 ◽  
Vol 26 (S2) ◽  
pp. 1470-1474
Author(s):  
Nathan Jessurun ◽  
Olivia Paradis ◽  
Alexandra Roberts ◽  
Navid Asadizanjani
Keyword(s):  
Machine Learning ◽  
Semantic Annotation ◽  
Semantic Segmentation ◽  
Evaluation Framework ◽  
Annotation Tool ◽  
Software Application ◽  
Image Labeling ◽  
Complex Shapes ◽  
Machine Learning Applications

AbstractImage labeling is the process of manually assigning a class to subregions within an image for machine learning applications. When these subregions are complex shapes, this process is known as semantic segmentation. We propose a new software application, the Component Detection and Evaluation Framework (CDEF), for creating such semantic labels. The benefits of CDEF over existing tools are highlighted, and further improvements are proposed.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Sign in / Sign up

Export Citation Format

Share Document