| 1. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS
- 2019
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Ingår i: International Conference on Modern Intelligent Systems Concepts MISC'18. - : CESER Publications. ; 17:2, s. 154-167
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Konferensbidrag (refereegranskat)abstract
- This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%.
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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A vision-based indoor navigation system for individuals with visual impairment
- 2019
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Ingår i: International Journal of Artificial Intelligence. - : CESER Publications. - 0974-0635. ; 17:2, s. 188-201
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Tidskriftsartikel (refereegranskat)abstract
- Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.
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| 3. |
- Karatzas, Kostas, et al.
(författare)
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Allergy medication dose and biological weather.
- 2012
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Ingår i: International Journal of Artificial Intelligence. - 0974-0635. ; 9:12A, s. 90-105
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Tidskriftsartikel (refereegranskat)abstract
- Biological weather factors like airborne pollen have been associated with allergic symptoms in sensitized individuals, whereas atmospheric pollution aggravates the impact on the overall quality of life. In this study, we investigate the relationship between the Defined Daily Dose (DDD) given to patients in cases of allergy reactions, and the different levels of pollen types, air pollutants, and meteorological conditions. In this way, specialists may be able to define the need for medication concerning individuals suffering from pollen allergy, not only according to their personal medical record but also in relation to atmospheric quality observations. Results indicate some interesting interrelationships between the use of medication and atmospheric quality conditions and show that the forecasting of daily medication use is possible with the aid of proper algorithms.
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