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 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

Machine learning applications for fog computing in IoT: a survey

2021 ◽  
Vol 17 (4) ◽  
pp. 293
Author(s):  
Javad Rezazadeh ◽  
Omid Ameri Sianaki ◽  
Mitra Mousavi
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Machine Learning Applications

Object Detection in Fog Computing Using Machine Learning Algorithms

2020 ◽  
pp. 90-107
Author(s):  
Peyakunta Bhargavi ◽  
Singaraju Jyothi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Object Detection ◽  
Fog Computing ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Core Network ◽  
The Core ◽  
The Moment ◽  
Algorithmic Approaches

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.

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 Empirical Insights on Cloud Services for Machine Learning Applications

2020 ◽  
Vol 26 (2) ◽  
pp. 85-90
Author(s):  
Adrian MICU ◽  
Marius GERU ◽  
Angela-Eliza MICU ◽  
Alexandru CAPATINA ◽  
Constantin AVRAM ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Services ◽  
Machine Learning Applications

scholarly journals An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243923
Author(s):  
Michael A. Beck ◽  
Chen-Yi Liu ◽  
Christopher P. Bidinosti ◽  
Christopher J. Henry ◽  
Cara M. Godee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Training Data ◽  
Automated Generation ◽  
Agriculture Sector ◽  
Agricultural Applications ◽  
Machine Learning Applications ◽  
Uniform Background ◽  
Image Segementation ◽  
Additional Image

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.

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.

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