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Bai X, Li C, Li C, Khan A, Zhang T, Zhang B. Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic. Quant Imaging Med Surg 2023; 13:1802-1813. [PMID: 36915326 PMCID: PMC10006103 DOI: 10.21037/qims-22-842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 01/11/2023] [Indexed: 02/15/2023]
Abstract
Background Efficiently combating with the coronavirus disease 2019 (COVID-19) has been challenging for medics, police and other service providers. To reduce human interaction, multi-robot systems are promising for performing various missions such as disinfection, monitoring, temperature measurement and delivering goods to people quarantined in prescribed homes and hotels. This paper studies the task assignment problem for multiple dispersed homogeneous robots to visit a set of prescribed hotels to perform tasks such as body temperature assessment or oropharyngeal swabs for people quarantined in the hotels while trying to minimize the robots' total operation time. Each robot can move to multiple hotels sequentially within its limited maximum operation time to provide the service. Methods The task assignment problem generalizes the multiple traveling salesman problem, which is an NP-hard problem. The main contributions of the paper are twofold: (I) a lower bound on the robots' total operation time to serve all the people has been found based on graph theory, which can be used to approximately evaluate the optimality of an assignment solution; (II) several efficient marginal-cost-based task assignment algorithms are designed to assign the hotel-serving tasks to the robots. Results In the Monte Carlo simulations where different numbers of robots need to serve the people quarantined in 30 and 90 hotels, the designed task assignment algorithms can quickly (around 30 ms) calculate near-optimal assignment solutions (within 1.15 times of the optimal value). Conclusions Numerical simulations show that the algorithms can lead to solutions that are close to the optimal compared with the competitive genetic algorithm and greedy algorithm.
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Affiliation(s)
- Xiaoshan Bai
- Shenzhen University, College of Mechatronics and Control Engineering, Shenzhen, China.,Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen, China
| | - Chang Li
- Shenzhen University, College of Mechatronics and Control Engineering, Shenzhen, China.,Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen, China
| | - Chao Li
- Shenzhen University, College of Mechatronics and Control Engineering, Shenzhen, China.,Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen, China
| | - Awais Khan
- Shenzhen University, College of Mechatronics and Control Engineering, Shenzhen, China.,Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen, China.,Shenzhen University, College of Physics and Optoelectronic Engineering, Shenzhen, China
| | - Tianwei Zhang
- Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen, China
| | - Bo Zhang
- Shenzhen University, College of Mechatronics and Control Engineering, Shenzhen, China.,Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen, China
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Almulihi A, Saleh H, Hussien AM, Mostafa S, El-Sappagh S, Alnowaiser K, Ali AA, Refaat Hassan M. Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction. Diagnostics (Basel) 2022; 12:diagnostics12123215. [PMID: 36553222 PMCID: PMC9777370 DOI: 10.3390/diagnostics12123215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the "silent killer," is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper's aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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Affiliation(s)
- Ahmed Almulihi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
- Correspondence:
| | - Ali Mohamed Hussien
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
| | - Sherif Mostafa
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 34511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Khaled Alnowaiser
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Moatamad Refaat Hassan
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
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A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection. INFORMATION 2022. [DOI: 10.3390/info13110527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real news. Fake news is a significant social barrier that has a profoundly negative impact on society. Despite the large number of studies on fake news detection, they have not yet been combined to offer coherent insight on trends and advancements in this domain. Hence, the primary objective of this study was to fill this knowledge gap. The method for selecting the pertinent articles for extraction was created using the preferred reporting items for systematic reviews and meta-analyses (PRISMA). This study reviewed deep learning, machine learning, and ensemble-based fake news detection methods by a meta-analysis of 125 studies to aggregate their results quantitatively. The meta-analysis primarily focused on statistics and the quantitative analysis of data from numerous separate primary investigations to identify overall trends. The results of the meta-analysis were reported by the spatial distribution, the approaches adopted, the sample size, and the performance of methods in terms of accuracy. According to the statistics of between-study variance high heterogeneity was found with τ2 = 3.441; the ratio of true heterogeneity to total observed variation was I2 = 75.27% with the heterogeneity chi-square (Q) = 501.34, the degree of freedom = 124, and p ≤ 0.001. A p-value of 0.912 from the Egger statistical test confirmed the absence of a publication bias. The findings of the meta-analysis demonstrated satisfaction with the effectiveness of the recommended approaches from the primary studies on fake news detection that were included. Furthermore, the findings can inform researchers about various approaches they can use to detect online fake news.
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Darwish O, Tashtoush Y, Bashayreh A, Alomar A, Alkhaza’leh S, Darweesh D. A survey of uncover misleading and cyberbullying on social media for public health. CLUSTER COMPUTING 2022; 26:1709-1735. [PMID: 36034676 PMCID: PMC9396598 DOI: 10.1007/s10586-022-03706-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 07/18/2022] [Accepted: 08/08/2022] [Indexed: 05/25/2023]
Abstract
Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.
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Affiliation(s)
- Omar Darwish
- Information Security and Applied Computing, Eastern Michigan University, 900 Oakwood St, Ypsilanti, MI 48197 USA
| | - Yahya Tashtoush
- Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110 Jordan
| | - Amjad Bashayreh
- Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110 Jordan
| | - Alaa Alomar
- Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110 Jordan
| | - Shahed Alkhaza’leh
- Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110 Jordan
| | - Dirar Darweesh
- Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110 Jordan
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Chen MY, Lai YW. Using Fuzzy Clustering with Deep Learning Models for Detection of COVID-19 Disinformation. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3548458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Since the beginning of 2020, the COVID-19 pandemic has killed millions of people around the world, leading to a worldwide panic that has fueled the rapid and widespread dissemination of COVID-19-related disinformation on social media. The phenomenon, described by the World Health Organization (WHO) as an "indodemic" presents a serious challenge to governments and public health authorities, but the spread of misinformation has made human detection less efficient than the rate of spread. While there have been many studies developing automated detection techniques for COVID-19 fake news, the results often refer to high accuracy but rarely to model detection time. This research uses fuzzy theory to extract features and uses multiple deep learning model frameworks to detect Chinese and English COVID-19 misinformation. With the reduction of text features, the detection time of the model is significantly reduced, and the model accuracy does not drop excessively. This study designs two different feature extraction methods based on fuzzy classification and compares the results with different deep learning models. BiLSTM was found to provide the best detection results for COVID-19 misinformation by directly using deep learning models, with 99% accuracy in English and 86% accuracy in Chinese. Applying fuzzy clustering to English COVID-19 fake news data features maintains 99% accuracy while reducing detection time by 10%. For Chinese misinformation, detection time is reduced by 15% at the cost of an 8% drop in accuracy.
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Affiliation(s)
- Mu-Yen Chen
- Department of Engineering Science, National Cheng Kung University
- Center for Innovative FinTech Business Models, National Cheng Kung University
| | - Yi-Wei Lai
- Department of Engineering Science, National Cheng Kung University
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