Towards robotics and artificial intelligence for the prevention of Covid 19 pandemic

Senhaji, Saloua; Faquir, Sanaa; Jamil, Mohammed Ouazzani

doi:10.1051/e3sconf/202122901035

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Feature selection and partition is the primary step of constructing DT, in which feature selection contains detailed information, which is mainly based on some indicators related to characteristics. The most common hands are gain rate and information gain and inversion (Senhaji et al, 2021 ). Feature segmentation can also be regarded as a re-segmentation method.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technology in Basketball Training Action Recognition

Liang

Xu²,

Kuang³

2022

Front. Neurorobot.

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The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level.

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technology in Basketball Training Action Recognition

Liang

Xu²,

Kuang³

2022

Front. Neurorobot.

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“…The Internet of Things (IoT) [7,8,9 ] has been identified as one of the key developments in the technology portfolio. The Internet of Things (IOT) enables the interconnection of communicating objects that are placed in various locations and may be distant from each other.…”

Section: The Internet Of Thingsmentioning

confidence: 99%

“…The system will generate large amounts of data that we will be forced to process in real time, to solve this problem we propose deep learning techniques. Deep learning is a popular approach to machine learning that has made many advances in all traditional areas of machine learning [8,10,11,12]. The Internet of Things (IoT) [3,5,7] and smart environment deployments generate large amounts of time-series sensor data that need to be analysed.…”

Section: Introductionmentioning

confidence: 99%

Real time monitoring of water Quality using IoT and Deep learning

Senhaji¹,

Hamlich²,

Jamil³

2021

E3S Web Conf.

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Access to safe drinking water is one of the most pressing issues facing many developing countries. Water must meet Environmental Protection Agency (E.P.A.) requirements. The normal method of measuring physico-chemical parameters is to take samples manually and send them to the laboratory to check the water quality. In this paper, we proposed a new intelligent design of a real-time water quality monitoring system using Deep Learning technology. This system is composed of several sensors that allow us to measure water parameters (physico-chemical parameters), bacteriological parameters and organoleptic parameters) and to detect the presence of certain substances (undesirable substances, toxic substances) and of a single-board/mobile computer module, Internet and other accessories. Water parameters are automatically detected by the single-board computer. Raspberry Pi3 model B. The single board computer receives the data from the sensors and this data is sent to the web server using the Internet module. It is able to detect the water quality situation worldwide. The data will be analysed in real time. The application of deep learning to these areas has been an important research topic. The Long-Short Term Memory (LSTM) network has been shown to be well suited for processing and predicting large events with long intervals and delays in the time series. LSTM networks have the ability to retain long-term memory.

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“…The sensor scans and maps the environment using a 3D visualization software available in the ROS environment. Authors of [22] designed and built a robot that will assist authorities in preventing the transmission of COVID-19 and its outbreaks. With biosensors and temperature detectors, the robot can check for the virus.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

Zia

Alhalabi

Yaghi

et al. 2022

Wireless Communications and Mobile Computing

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The COVID-19 pandemic has severely impacted various aspects of life, where countries closed their borders, and workplaces and educational institutions shut down their premises in response to lockdowns. This has adversely affected the lives of everyone, including millions of students worldwide, socially, mentally, and physically. Governments and educational authorities worldwide have taken preventive measures, such as social distancing and mask wearing, to control the spread of the virus. This paper proposes an AI-powered autonomous robot for deep mask-wearing detection to enforce proper mask wearing in educational settings. The system includes (1) Simultaneous Localization and Mapping framework to map and navigate the environment (i.e., laboratories and classrooms), (2) a multiclass face mask detection software, and (3) an auditory system to identify and alert improper or no mask wearing. We train our face mask detector using MobileNetV2 architecture and YOLOv2 object detector classification. The results demonstrate that our robot can navigate an educational environment while avoiding obstacles to detect violations. The proposed face mask detection and classification subsystem achieved a 91.4% average precision when tested on students in an engineering laboratory environment.

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Towards robotics and artificial intelligence for the prevention of Covid 19 pandemic

Cited by 6 publications

References 14 publications

Artificial Intelligence Technology in Basketball Training Action Recognition

Artificial Intelligence Technology in Basketball Training Action Recognition

Real time monitoring of water Quality using IoT and Deep learning

Autonomous Robots for Deep Mask-Wearing Detection in Educational Settings during Pandemics

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