1
|
Pathway of Trends and Technologies in Fall Detection: A Systematic Review. Healthcare (Basel) 2022; 10:healthcare10010172. [PMID: 35052335 PMCID: PMC8776012 DOI: 10.3390/healthcare10010172] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 01/25/2023] Open
Abstract
Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.
Collapse
|
2
|
A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment. SUSTAINABILITY 2022. [DOI: 10.3390/su14010510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.
Collapse
|
3
|
Aswad FM, Kareem AN, Khudhur AM, Khalaf BA, Mostafa SA. Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction. JOURNAL OF INTELLIGENT SYSTEMS 2021. [DOI: 10.1515/jisys-2021-0179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system.
Collapse
Affiliation(s)
- Firas Mohammed Aswad
- Computer Department, College of Basic Education, University of Diyala , 32001 , Diyala , Iraq
| | - Ali Noori Kareem
- Computer Engineering Department, Bilad Alrafidain University College , 32001 , Diyala , Iraq
| | - Ahmed Mahmood Khudhur
- Computer Engineering Department, Bilad Alrafidain University College , 32001 , Diyala , Iraq
| | - Bashar Ahmed Khalaf
- Department of Medical Instruments Engineering Techniques, Bilad Alrafidain University College , 32001 , Diyala , Iraq
| | - Salama A. Mostafa
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia , Batu Pahat 86400 , Johor , Malaysia
| |
Collapse
|
4
|
Jawad MS, Mahdin H, Mohammed Alduais NA, Hlayel M, Mostafa SA, Abd Wahab MH. Recent and Future Innovative Artificial Intelligence Services and Fields. 2021 4TH INTERNATIONAL SYMPOSIUM ON AGENTS, MULTI-AGENT SYSTEMS AND ROBOTICS (ISAMSR) 2021. [DOI: 10.1109/isamsr53229.2021.9567891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
5
|
Multi-Agent Robot System to Monitor and Enforce Physical Distancing Constraints in Large Areas to Combat COVID-19 and Future Pandemics. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167200] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of the virus. In this paper, we propose an autonomous monitoring system that is able to enforce physical distancing rules in large areas round the clock without human intervention. We present a novel system to automatically detect groups of individuals who do not comply with physical distancing constraints, i.e., maintaining a distance of 1 m, by tracking them within large areas to re-identify them in case of repetitive non-compliance and enforcing physical distancing. We used a distributed network of multiple CCTV cameras mounted to the walls of buildings for the detection, tracking and re-identification of non-compliant groups. Furthermore, we used multiple self-docking autonomous robots with collision-free navigation to enforce physical distancing constraints by sending alert messages to those persons who are not adhering to physical distancing constraints. We conducted 28 experiments that included 15 participants in different scenarios to evaluate and highlight the performance and significance of the present system. The presented system is capable of re-identifying repetitive violations of physical distancing constraints by a non-compliant group, with high accuracy in terms of detection, tracking and localization through a set of coordinated CCTV cameras. Autonomous robots in the present system are capable of attending to non-compliant groups in multiple regions of a large area and encouraging them to comply with the constraints.
Collapse
|
6
|
Chan PP, Zheng J, Liu H, Tsang E, Yeung DS. Robustness analysis of classical and fuzzy decision trees under adversarial evasion attack. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107311] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
7
|
Agent architecture of an intelligent medical system based on federated learning and blockchain technology. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021. [DOI: 10.1016/j.jisa.2021.102748] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
8
|
Ali RR, Mohamad KM. RX_myKarve carving framework for reassembling complex fragmentations of JPEG images. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2018.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
9
|
Lima BN, Balducci P, Passos RP, Novelli C, Fileni CHP, Vieira F, Camargo LBD, Vilela Junior GDB. Artificial intelligence based on fuzzy logic for the analysis of human movement in healthy people: a systematic review. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09885-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
10
|
Zhang J, Ye Z, Li K. Multi-sensor information fusion detection system for fire robot through back propagation neural network. PLoS One 2020; 15:e0236482. [PMID: 32706794 PMCID: PMC7380588 DOI: 10.1371/journal.pone.0236482] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/06/2020] [Indexed: 11/19/2022] Open
Abstract
Objective To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated. Method According to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system. Results Through the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study. Conclusion The research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest remaining fire, the results are applicable to the forests of various countries, with a wide range of applications.
Collapse
Affiliation(s)
- JunJie Zhang
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom
| | - ZiYang Ye
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom
| | - KaiFeng Li
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom
- * E-mail:
| |
Collapse
|
11
|
A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems. ENERGIES 2020. [DOI: 10.3390/en13112787] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.
Collapse
|
12
|
Cervantes JA, López S, Rodríguez LF, Cervantes S, Cervantes F, Ramos F. Artificial Moral Agents: A Survey of the Current Status. SCIENCE AND ENGINEERING ETHICS 2020; 26:501-532. [PMID: 31721023 DOI: 10.1007/s11948-019-00151-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 10/17/2019] [Indexed: 05/24/2023]
Abstract
One of the objectives in the field of artificial intelligence for some decades has been the development of artificial agents capable of coexisting in harmony with people and other systems. The computing research community has made efforts to design artificial agents capable of doing tasks the way people do, tasks requiring cognitive mechanisms such as planning, decision-making, and learning. The application domains of such software agents are evident nowadays. Humans are experiencing the inclusion of artificial agents in their environment as unmanned vehicles, intelligent houses, and humanoid robots capable of caring for people. In this context, research in the field of machine ethics has become more than a hot topic. Machine ethics focuses on developing ethical mechanisms for artificial agents to be capable of engaging in moral behavior. However, there are still crucial challenges in the development of truly Artificial Moral Agents. This paper aims to show the current status of Artificial Moral Agents by analyzing models proposed over the past two decades. As a result of this review, a taxonomy to classify Artificial Moral Agents according to the strategies and criteria used to deal with ethical problems is proposed. The presented review aims to illustrate (1) the complexity of designing and developing ethical mechanisms for this type of agent, and (2) that there is a long way to go (from a technological perspective) before this type of artificial agent can replace human judgment in difficult, surprising or ambiguous moral situations.
Collapse
Affiliation(s)
- José-Antonio Cervantes
- Department of Computer Science and Engineering, Centro Universitario de los Valles, Universidad de Guadalajara, Carretera Guadalajara - Ameca Km. 45.5, 46600, Ameca, Mexico.
| | - Sonia López
- Department of Computer Science and Engineering, Centro Universitario de los Valles, Universidad de Guadalajara, Carretera Guadalajara - Ameca Km. 45.5, 46600, Ameca, Mexico
| | | | - Salvador Cervantes
- Department of Computer Science and Engineering, Centro Universitario de los Valles, Universidad de Guadalajara, Carretera Guadalajara - Ameca Km. 45.5, 46600, Ameca, Mexico
| | - Francisco Cervantes
- Department of Electronics, Systems and Informatics, Instituto Tecnológico y de Estudios Superiores de Occidente, Tlaquepaque, Mexico
| | - Félix Ramos
- Department of Computer Science, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Guadalajara, Mexico
| |
Collapse
|
13
|
Mostafa SA, Mustapha A, Mohammed MA, Hamed RI, Arunkumar N, Abd Ghani MK, Jaber MM, Khaleefah SH. Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
14
|
Mutlag AA, Abd Ghani MK, Arunkumar N, Mohammed MA, Mohd O. Enabling technologies for fog computing in healthcare IoT systems. FUTURE GENERATION COMPUTER SYSTEMS 2019; 90:62-78. [DOI: 10.1016/j.future.2018.07.049] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
15
|
K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft comput 2018. [DOI: 10.1007/s00500-018-3618-7] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
16
|
Hassan MH, Mostafa SA, Mustapha A, Abd Wahab MH, Md Nor D. A Survey of Multi-Agent System Approach in Risk Assessment. 2018 INTERNATIONAL SYMPOSIUM ON AGENT, MULTI-AGENT SYSTEMS AND ROBOTICS (ISAMSR) 2018. [DOI: 10.1109/isamsr.2018.8540551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|