scholarly journals A Real-Time Interruption Management System Based on Eye-Tracking Data

2019 ◽  
Vol 63 (1) ◽  
pp. 1306-1310
Author(s):  
Hagit Shaposhnik
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Traffic Control ◽  
Management System ◽  
Tracking Data ◽  
Machine Learning Classifiers ◽  
High Workload ◽  
Interruption Management ◽  
Boosted Tree

Interruptions in the middle of a task have considerable costs. The objective of this study was to develop a system that postpones interruptions when they occur in periods of high workload. In Experiment 1, an air traffic control (ATC) simulator was presented with varying working memory demands. Pupil data were used to train a range of machine-learning classifiers to distinguish between high and low workload moments. The Gradient Boosted Tree (GBT) provided the best predictions. In Experiment 2, this classifier was used to develop a real-time interruption management system (IMS). The role of the IMS was to predict high and low workload and to postpone interruptions to the next low workload moment. To examine the IMS’s performance, its interruptions were compared to random interruptions. Results showed that the IMS successfully identified high and low workload moments with 76% accuracy, and postponed interruptions to the next low workload moment.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals Predicting Mortality with Applied Machine Learning: Can We Get There?

2019 ◽  
Vol 8 (1) ◽  
pp. 115-119
Author(s):  
Emily S. Patterson ◽  
C.J. Hansen ◽  
Theodore T. Allen ◽  
Qiwei Yang ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Drug Administration ◽  
Patient Population ◽  
Food And Drug Administration ◽  
Hospital Environment ◽  
Clinical Settings ◽  
Applied Machine Learning

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are ‘ready yet’ for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

scholarly journals Interpretable Faraday complexity classification

2021 ◽  
Vol 38 ◽  
Author(s):  
M. J. Alger ◽  
J. D. Livingston ◽  
N. M. McClure-Griffiths ◽  
J. L. Nabaglo ◽  
O. I. Wong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
State Of The Art ◽  
Real Data ◽  
Machine Learning Classifiers ◽  
Analysis Techniques ◽  
Machine Learning Methods ◽  
Rotation Measure ◽  
Boosted Tree ◽  
Complexity Classification

Abstract Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric observation is important for processing large, polarised radio surveys. Finding simple sources lets us build rotation measure grids, and finding complex sources lets us follow these sources up with slower analysis techniques or further observations. We introduce five features that can be used to train simple, interpretable machine learning classifiers for estimating Faraday complexity. We train logistic regression and extreme gradient boosted tree classifiers on simulated polarised spectra using our features, analyse their behaviour, and demonstrate our features are effective for both simulated and real data. This is the first application of machine learning methods to real spectropolarimetry data. With 95% accuracy on simulated ASKAP data and 90% accuracy on simulated ATCA data, our method performs comparably to state-of-the-art convolutional neural networks while being simpler and easier to interpret. Logistic regression trained with our features behaves sensibly on real data and its outputs are useful for sorting polarised sources by apparent Faraday complexity.

scholarly journals Technical and Observational Challenges for Future Time-Domain Surveys

2011 ◽  
Vol 7 (S285) ◽  
pp. 165-170
Author(s):  
Joshua S. Bloom
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Domain ◽  
Scientific Process ◽  
Future Time ◽  
Traditional Role ◽  
Robotic Telescopes ◽  
Time Loop

AbstractBy the end of the last decade, robotic telescopes were established as effective alternatives to the traditional role of astronomer in planning, conducting and reducing time-domain observations. By the end of this decade, machines will play a much more central role in the discovery and classification of time-domain events observed by such robots. While this abstraction of humans away from the real-time loop (and the nightly slog of the nominal scientific process) is inevitable, just how we will get there as a community is uncertain. I discuss the importance of machine learning in astronomy today, and project where we might consider heading in the future. I will also touch on the role of people and organisations in shaping and maximising the scientific returns of the coming data deluge.

scholarly journals Segmentation of the ECG Signal by Means of a Linear Regression Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 775 ◽  
Author(s):  
Javier Aspuru ◽  
Alberto Ochoa-Brust ◽  
Ramón Félix ◽  
Walter Mata-López ◽  
Luis Mena ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Real Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Machine Learning Classifiers ◽  
Detection Approach ◽  
Electrocardiogram Ecg

The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.

scholarly journals Comparison study of machine learning classifiers to detect anomalies

2020 ◽  
Vol 10 (5) ◽  
pp. 5445
Author(s):  
Nisha P Shetty ◽  
Jayashree Shetty ◽  
Rohil Narula ◽  
Kushagra Tandona
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Credit Card ◽  
Sensitive Information ◽  
Comparison Study ◽  
Intrusion Prevention ◽  
Detection Techniques ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Real Time Detection

In this era of Internet ensuring the confidentiality, authentication and integrity of any resource exchanged over the net is the imperative. Presence of intrusion prevention techniques like strong password, firewalls etc. are not sufficient to monitor such voluminous network traffic as they can be breached easily. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database.Thus, the need for real-time detection of aberrations is observed. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database. Machine learning classifiers are implemented here to learn how the values of various fields like source bytes, destination bytes etc. in a network packet decides if the packet is compromised or not . Finally the accuracy of their detection is compared to choose the best suited classifier for this purpose. The outcome thus produced may be useful to offer real time detection while exchanging sensitive information such as credit card details.

scholarly journals Internet of Things in agriculture

2020 ◽  
pp. 32
Author(s):  
Marco Vieri ◽  
Daniele Sarri ◽  
Stefania Lombardo ◽  
Marco Rimediotti ◽  
Riccardo Lisci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Embedded Systems ◽  
Product Quality ◽  
Real Time ◽  
Management System ◽  
Farming System ◽  
High Tech ◽  
Block Chain ◽  
Definition Of

Agriculture 4.0 & High Tech Farming are strictly related to connectivity between management system and tools (devices and equipment). That is called IoT approach. The definition of Internet of things is evolving due to the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. In farming system like vineyard and tillage crops, the main applications are related to monitor soil, environment and crops but also to provide prescription maps essential to control automatic operation of devices and equipment. The systemic system of IoT permits to have augmented knowledge on the overall process that is essential to manage sustainability and product quality. IoT enhances traceability by block chain.

Role of Feature Engineering and Classifier Selection for Machine Learning Predictions

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Makarand Velankar ◽  
Vaibhav Khatavkar ◽  
Vinayak Jagtap ◽  
Parag Kulkarni
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Prediction Accuracy ◽  
Feature Engineering ◽  
Machine Learning Classifiers ◽  
Classifier Selection ◽  
Learning Classifiers ◽  
Proposed Model

Features play a crucial role in several computational tasks. Feature values are input to machine learning algorithms for the prediction. The prediction accuracy depends on various factors such as selection of dataset, features and machine learning classifiers. Various feature selection and reduction approaches are experimented with to obtain better accuracies and reduce the computational overheads. Feature engineering is designing new features suitable for a specific task with the help of domain knowledge. The challenges in feature engineering are presented for the computational music domain as a case study. The experiments are performed with different combinations of feature sets and machine learning classifiers to test the accuracy of the proposed model. Music emotion recognition is used as a case study for the experimentation. Experimental results for the task of music emotion recognition provide insights into the role of features and classifiers in prediction accuracy. Different machine learning classifiers provided varied results, and the choice of a classifier is also an important decision to be made in the proposed model. The engineered features designed with the help of domain experts improved the results. It emphasizes the need for feature engineering for different domains for prediction accuracy improvement. Approaches to design an optimized model with the appropriate feature set and classifier for machine learning tasks are presented.

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