Video representation and suspicious event detection using semantic technologies

Semantic Web ◽  
2020 ◽  
pp. 1-25
Author(s):  
Ashish Singh Patel ◽  
Giovanni Merlino ◽  
Dario Bruneo ◽  
Antonio Puliafito ◽  
O.P. Vyas ◽  
...  
Keyword(s):  
Event Detection ◽  
Video Data ◽  
Machine Learning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
Video Scene ◽  
Scene Representation ◽  
Level Information ◽  
Complex Events ◽  
Suspicious Activity

Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.

scholarly journals Learning Curves: A Novel Approach for Robustness Improvement of Load Forecasting

2021 ◽  
Vol 5 (1) ◽  
pp. 38
Author(s):  
Chiara Giola ◽  
Piero Danti ◽  
Sandro Magnani
Keyword(s):  
Load Forecasting ◽  
Learning Curves ◽  
Machine Learning Techniques ◽  
Essential Information ◽  
Set Size ◽  
Novel Approach ◽  
Learning Techniques ◽  
Energy Plant ◽  
Definition Of ◽  
The Right

In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when dealing with energy management, one of the most thriving application of AI is the consumption’s optimization of energy plant generators. When designing a strategy to improve the generators’ schedule, a piece of essential information is the future energy load requested by the plant. This topic, in the literature it is referred to as load forecasting, has lately gained great popularity; in this paper authors underline the problem of estimating the correct size of data to train prediction algorithms and propose a suitable methodology. The main characters of this methodology are the Learning Curves, a powerful tool to track algorithms performance whilst data training-set size varies. At first, a brief review of the state of the art and a shallow analysis of eligible machine learning techniques are offered. Furthermore, the hypothesis and constraints of the work are explained, presenting the dataset and the goal of the analysis. Finally, the methodology is elucidated and the results are discussed.

A Novel Approach for Computer-Aided Diagnosis for Distinction Between Benign and Malignant of Lung Nodules Based on Machine Learning Techniques

2018 ◽  
pp. 281-290
Author(s):  
Shashidhara Bola
Keyword(s):  
False Positive Rate ◽  
Lung Nodule ◽  
Machine Learning Techniques ◽  
Lung Nodules ◽  
Data Set ◽  
Novel Approach ◽  
Learning Techniques ◽  
Nodule Shape ◽  
Positive Rate ◽  
Aided Diagnosis

A new method is proposed to classify the lung nodules as benign and malignant. The method is based on analysis of lung nodule shape, contour, and texture for better classification. The data set consists of 39 lung nodules of 39 patients which contain 19 benign and 20 malignant nodules. Lung regions are segmented based on morphological operators and lung nodules are detected based on shape and area features. The proposed algorithm was tested on LIDC (lung image database consortium) datasets and the results were found to be satisfactory. The performance of the method for distinction between benign and malignant was evaluated by the use of receiver operating characteristic (ROC) analysis. The method achieved area under the ROC curve was 0.903 which reduces the false positive rate.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Using Machine Learning for Decreasing State Uncertainty in Planning

2020 ◽  
Vol 69 ◽  
pp. 765-806
Author(s):  
Senka Krivic ◽  
Michael Cashmore ◽  
Daniele Magazzeni ◽  
Sandor Szedmak ◽  
Justus Piater
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Process ◽  
The State ◽  
Machine Learning Techniques ◽  
Planning Problem ◽  
Planning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
State Uncertainty

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

scholarly journals Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques

2020 ◽  
Author(s):  
Alaeddine Mihoub ◽  
Hosni Snoun ◽  
Moez Krichen ◽  
Riadh Bel Hadj Salah ◽  
Montassar Kahia
Keyword(s):  
Historical Data ◽  
Economic Indicators ◽  
Machine Learning Techniques ◽  
Factors Affecting ◽  
Novel Approach ◽  
Learning Techniques ◽  
Class Level ◽  
Feature Importance ◽  
F Measure ◽  
Relevant Factors

<p>The new so-called COVID-19 virus is unfortunately founded to be highly transmissible across the globe. In this study, we propose a novel approach for estimating the spread level of the virus for each country for three different dates between April and May 2020. Unlike previous studies, this investigation does not process any historical data of spread but rather relies on the socio-economic indicators of each country. Actually, more than 1000 socio-economic indicators and more than 190 countries were processed in this study. Concretely, data preprocessing techniques and feature selection approaches were applied to extract relevant indicators for the classification process. Countries around the globe were assigned to 4 classes of spread. To find the class level of each country, many classifiers were proposed based especially on Support Vectors Machines (SVM), Multi-Layer Perceptrons (MLP) and Random Forests (RF). Obtained results show the relevance of our approach since many classifiers succeeded in capturing the spread level, especially the RF classifier, with an F-measure equal to 93.85% for April 15th, 2020. Moreover, a feature importance study is conducted to deduce the best indicators to build robust spread level classifiers. However, as pointed out in the discussion, classifiers may face some difficulties for future dates since the huge increase of cases and the lack of other relevant factors affecting this widespread.<i></i></p>

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