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Bouchachi I, Reddaf A, Boudjerda M, Alhassoon K, Babes B, Alsunaydih FN, Ali E, Alsharef M, Alsaleem F. Design and performances improvement of an UWB antenna with DGS structure using a grey wolf optimization algorithm. Heliyon 2024; 10:e26337. [PMID: 38434315 PMCID: PMC10904234 DOI: 10.1016/j.heliyon.2024.e26337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024] Open
Abstract
In this article, we propose the design of a rectangular-shaped patch antenna suitable for ultra-wideband (UWB) applications and short and long-range Millimeter-Wave Communications. We begin with the design of a high-gain UWB rectangular patch antenna featuring a partial ground plane and operating within the 3.1-10.6 GHz bandwidth. Complementary Split Ring Resonators (CSRRs) are integrated on both sides of the structure to meet desired specifications. The resulting UWB antenna boasts an extended frequency bandwidth, covering 2.38-22.5 GHz (twice that of the original antenna), with a peak gain of 6.5 dBi and an 88% radiation efficiency. The grey wolf optimization technique (GWO) determines optimal structural dimensions. Validation of the antenna's performance is demonstrated through the strong agreement between measurement and simulation.
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Affiliation(s)
- Islem Bouchachi
- Ecole Nationale Supérieure des Technologies Avancées, Algiers, Algeria
| | - Abdelmalek Reddaf
- Research Center in Industrial Technologies CRTI, B.P. 64, Cheraga, Algiers, Algeria
| | - Mounir Boudjerda
- Research Center in Industrial Technologies CRTI, B.P. 64, Cheraga, Algiers, Algeria
| | - Khaled Alhassoon
- Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
| | - Badreddine Babes
- Research Center in Industrial Technologies CRTI, B.P. 64, Cheraga, Algiers, Algeria
| | - Fahad N. Alsunaydih
- Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
| | - Enas Ali
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
| | - Mohammad Alsharef
- Department of Electrical Engineering, College of Engineering, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Fahd Alsaleem
- Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
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2
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Shen T, Li Y, Liu T, Lian Y, Kong L. Association between Mycoplasma pneumoniae infection, high‑density lipoprotein metabolism and cardiovascular health (Review). Biomed Rep 2024; 20:39. [PMID: 38357242 PMCID: PMC10865299 DOI: 10.3892/br.2024.1729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 01/09/2024] [Indexed: 02/16/2024] Open
Abstract
The association between Mycoplasma pneumoniae (M. pneumoniae) infection, high-density lipoprotein metabolism and cardiovascular disease is an emerging research area. The present review summarizes the basic characteristics of M. pneumoniae infection and its association with high-density lipoprotein and cardiovascular health. M. pneumoniae primarily invades the respiratory tract and damages the cardiovascular system through various mechanisms including adhesion, invasion, secretion of metabolites, production of autoantibodies and stimulation of cytokine production. Additionally, the present review highlights the potential role of high-density lipoprotein for the development of prevention and intervention of M. pneumoniae infection and cardiovascular disease, and provides suggestions for future research directions and clinical practice. It is urgent to explore the specific mechanisms underlying the association between M. pneumoniae infection, high-density lipoprotein metabolism, and cardiovascular disease and analyze the roles of the immune system and inflammatory response.
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Affiliation(s)
- Tao Shen
- Department of Clinical Laboratory, Jincheng People's Hospital, Jincheng, Shanxi 048000, P.R. China
- Jincheng Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi 048000, P.R. China
| | - Yanfang Li
- Department of Clinical Laboratory, Jincheng People's Hospital, Jincheng, Shanxi 048000, P.R. China
- Jincheng Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi 048000, P.R. China
| | - Tingting Liu
- Department of Clinical Laboratory, Jincheng People's Hospital, Jincheng, Shanxi 048000, P.R. China
- Jincheng Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi 048000, P.R. China
| | - Yunzhi Lian
- Department of Clinical Laboratory, Jincheng People's Hospital, Jincheng, Shanxi 048000, P.R. China
- Jincheng Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi 048000, P.R. China
| | - Luke Kong
- Department of Clinical Laboratory, Jincheng People's Hospital, Jincheng, Shanxi 048000, P.R. China
- Jincheng Hospital Affiliated to Changzhi Medical College, Jincheng, Shanxi 048000, P.R. China
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Mavani NR, Mohd Ali J, Hussain M, Abd. Rahman N, Hashim H. Determining food safety in canned food using fuzzy logic based on sulphur dioxide, benzoic acid and sorbic acid concentration. Heliyon 2024; 10:e26273. [PMID: 38384537 PMCID: PMC10879013 DOI: 10.1016/j.heliyon.2024.e26273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 01/11/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
Canned food market demand has arisen due to the higher need for instant and ready-to-eat food. Food preservatives are often added to canned and processed foods to prolong their shelf life and help to sustain the quality, taste, color, and food texture. However, excessive usage of such food preservatives can lead to various diseases and health issues including palpitations, allergies, and cancer. Therefore, food preservative detection in food samples is essential for safe consumption and health well-being. This paper proposed a fuzzy logic framework to determine the safety of food products based on the concentration of sulphur dioxide (SD), benzoic acid (BA), and sorbic acid (SA) in five different food categories as referred to the Food Acts 1983 and Food Regulations 1985 in Malaysia. The fuzzy logic framework comprises of Mamdani inference system design with 90 fuzzy rules, 15 and 5 membership functions for both the input and output parameters respectively. 50 random values and 10 lab analysis results based on the industrial samples were used to validate the developed algorithms in ensuring the safety of the food products. The membership functions generated for the three inputs (SD, BA, and SA) during the fuzzification steps are based on the maximum allowable limit from the food acts. The defuzzification of fuzzy logic gave an average output value of 0.1565, 0.1350, 0.1150, 0.1100, and 0.1550 for chicken curry with potatoes, satay sauce, sardine in tomato sauce, anchovies paste, and sardine spread accordingly. Results obtained from the fuzzy logic framework concluded that all the industrial samples are safe to be eaten and comply with the Sixth Schedule, Regulation 20 in both Acts.
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Affiliation(s)
- Nidhi Rajesh Mavani
- Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Jarinah Mohd Ali
- Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - M.A. Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Norliza Abd. Rahman
- Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Haslaniza Hashim
- Department of Food Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
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Syed SA, Manickam S, Uddin M, Alsufyani H, Shorfuzzaman M, Selvarajan S, Mohammed GB. Dickson polynomial-based secure group authentication scheme for Internet of Things. Sci Rep 2024; 14:4947. [PMID: 38418484 PMCID: PMC10902399 DOI: 10.1038/s41598-024-55044-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 02/20/2024] [Indexed: 03/01/2024] Open
Abstract
Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
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Affiliation(s)
- Salman Ali Syed
- Department of Computer Science, Applied College Tabarjal, Jouf University, Sakaka, Al-Jouf Province, Kingdom of Saudi Arabia
| | - Selvakumar Manickam
- National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800, Gelugor, Penang, Malaysia
| | - Mueen Uddin
- College of Computing and IT, University of Doha for Science and Technology, 24449, Doha, Qatar
| | - Hamed Alsufyani
- Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, 11673, Riyadh, Kingdom of Saudi Arabia
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, 21944, Taif, Kingdom of Saudi Arabia
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
- Department of Computer Science and Engineering, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
| | - Gouse Baig Mohammed
- Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India
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Atimbire SA, Appati JK, Owusu E. Empirical exploration of whale optimisation algorithm for heart disease prediction. Sci Rep 2024; 14:4530. [PMID: 38402276 PMCID: PMC10894250 DOI: 10.1038/s41598-024-54990-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/19/2024] [Indexed: 02/26/2024] Open
Abstract
Heart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting the need for comprehensive evaluation metrics and utilization of different datasets in the same domain (heart disease). This research introduces a heart disease risk prediction approach by harnessing the whale optimization algorithm (WOA) for feature selection and implementing a comprehensive evaluation framework. The study leverages five distinct datasets, including the combined dataset comprising the Cleveland, Long Beach VA, Switzerland, and Hungarian heart disease datasets. The others are the Z-AlizadehSani, Framingham, South African, and Cleveland heart datasets. The WOA-guided feature selection identifies optimal features, subsequently integrated into ten classification models. Comprehensive model evaluation reveals significant improvements across critical performance metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using the same dataset, validating the effectiveness of our methodology. The comprehensive evaluation framework provides a robust assessment of the model's adaptability, underscoring the WOA's effectiveness in identifying optimal features in multiple datasets in the same domain.
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Affiliation(s)
| | | | - Ebenezer Owusu
- Department of Computer Science, University of Ghana, Accra, Ghana
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Palanisamy S, Rubini SS, Khalaf OI, Hamam H. Multi-objective hybrid split-ring resonator and electromagnetic bandgap structure-based fractal antennas using hybrid metaheuristic framework for wireless applications. Sci Rep 2024; 14:3288. [PMID: 38332219 DOI: 10.1038/s41598-024-53443-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 01/31/2024] [Indexed: 02/10/2024] Open
Abstract
Design closure and parameter optimisation are crucial in creating cutting-edge antennas. Antenna performance can be improved by fine-tuning preliminary designs created using theoretical considerations and rough dimension adjustment via supervised parameter sweeps. This paper introduces a frequency reconfigurable antenna design that can operate at 28/38 GHz frequencies to meet FCC and Ofcom standards for 5G applications and in the 18 GHz frequency band for K-band radar applications. A PIN diode is used in this design to configure multiple frequency bands. The antenna has a modified rectangular patch-like structure and two optimised plugins on either side. The study that is being presented focuses on maximising the parameters that are subject to optimisation, including length (Ls), width (Ws), strip line width (W1), and height (ht), where the antenna characteristic parameters such as directivity is tuned by a hybrid optimisation scheme called Elephant Clan Updated Grey Wolf Algorithm (ECU-GWA). Here, the performance of gain and directivity are optimally attained by considering parameters such as length, width, ground plane length, width, height, and feed offsets X and Y. The bandwidth of the proposed antenna at - 10 dB is 0.8 GHz, 1.94 GHz, and 7.92 GHz, respectively, at frequencies 18.5 GHz, 28.1 GHz, and 38.1 GHz. Also, according to the simulation results, in the 18 GHz, 28 GHz, and 38 GHz frequencies S11, the return loss is - 60.81 dB, - 56.31 dB, and - 14.19 dB, respectively. The proposed frequency reconfigurable antenna simulation results achieve gains of 4.41 dBi, 6.33 dBi, and 7.70 dBi at 18.5 GHz, 28.1 GHz, and 38.1 GHz, respectively. Also, a microstrip quarter-wave monopole antenna with an ellipsoidal-shaped complementary split-ring resonator-electromagnetic bandgap structure (ECSRR-EBG) structure has been designed based on a genetic algorithm having resonating at 2.9 GHz, 4.7 GHz, 6 GHz for WLAN applications. The gain of the suggested ECSRR metamaterial and EBG periodic structure, with and without the ECCSRR bow-tie antenna. This is done both in the lab and with numbers. The measured result shows that the ECSRR metamaterial boosts gain by 5.2 dBi at 5.9 GHz. At 5.57 GHz, the two-element MIMO antenna achieves its lowest ECC of 0.00081.
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Affiliation(s)
| | - S Saranya Rubini
- Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - Osamah Ibrahim Khalaf
- Department of Solar, Al-Nahrain Research Center for Renewable Energy, Al-Nahrain University, Jadriya, Baghdad, Iraq
| | - Habib Hamam
- Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A 3E9, Canada
- International Institute of Technology and Management (IITG), Avenue des Grandes Ecoles, 1989, Libreville, Gabon
- Bridges for Academic Excellence, Tunis 1002, Centre Ville, Tunisia
- Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, 2006, Johannesburg, South Africa
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7
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Jawed S, Faye I, Malik AS. Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG. IEEE Trans Neural Syst Rehabil Eng 2024; 32:378-390. [PMID: 38194390 DOI: 10.1109/tnsre.2024.3351694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory-convolutional neural network (LSTM-CNN), and Long-term, short-term memory-Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM-CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student's visual learning style.
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Ravikumar A, Sriraman H. DPro-SM - A distributed framework for proactive straggler mitigation using LSTM. Heliyon 2024; 10:e23567. [PMID: 38169959 PMCID: PMC10758865 DOI: 10.1016/j.heliyon.2023.e23567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/16/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
The recent advancement in deep learning with growth in big data and high-performance computing is Distributed Deep Learning. The rapid rise in the volume of data and network complexity has led to significant growth in DDL. Distribution of the network leads to high communication and computation among the nodes, which leads to high training time and lower accuracy. The primary reason for the delay in communication is the presence of straggler nodes which causes the bottleneck in communication. Due to the enormous volume of parameter transfer, Distributed Deep Learning's data parallelism incurs substantial communication costs. The newly developed model-parallel methods may minimize the communication effort; however, this results in load imbalance and severe straggler issues: the proposed model DPro-SM, a distributed framework for proactive straggler mitigation using LSTM in distributed deep learning. DPro-SM uses LSTM to predict the completion time of each worker and proactively allocates resources to reduce the overall training time. The results show that DPro-SM can significantly reduce the training time and improve the scalability and efficiency of large-scale machine learning tasks.
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Affiliation(s)
- Aswathy Ravikumar
- School of Computer Science and Engineering, VIT, Chennai, 600127, India
| | - Harini Sriraman
- School of Computer Science and Engineering, VIT, Chennai, 600127, India
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Rabie OBJ, Selvarajan S, Hasanin T, Alshareef AM, Yogesh CK, Uddin M. A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models. Sci Rep 2024; 14:386. [PMID: 38172185 PMCID: PMC10764843 DOI: 10.1038/s41598-024-51154-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 01/01/2024] [Indexed: 01/05/2024] Open
Abstract
The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.
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Affiliation(s)
- Osama Bassam J Rabie
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
- Cybersecurity Center, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
- Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia.
| | - Tawfiq Hasanin
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Abdulrhman M Alshareef
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - C K Yogesh
- School of Computer Science and Engineering, ViT Chennai Campus, Chennai, India
| | - Mueen Uddin
- College of Computing and IT, University of Doha for Science and Technology, 24449, Doha, Qatar
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Niu Q, Li H, Liu Y, Qin Z, Zhang LB, Chen J, Lyu Z. Toward the Internet of Medical Things: Architecture, trends and challenges. Math Biosci Eng 2024; 21:650-678. [PMID: 38303438 DOI: 10.3934/mbe.2024028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.
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Affiliation(s)
- Qinwang Niu
- Department of Health Services and Management, Sichuan Engineering Technical College, Deyang 618000, China
| | - Haoyue Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, China
| | - Yu Liu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, China
| | - Zhibo Qin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, China
| | - Li-Bo Zhang
- Department of Radiology, General Hospital of the Northern Theater of the Chinese People's Liberation Army, Shenyang 110004, China
| | - Junxin Chen
- School of Software, Dalian University of Technology, Dalian 116621, China
| | - Zhihan Lyu
- Department of Game Design, Faculty of Arts, Uppsala University, Uppsala, Sweden
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Abbasi SK, Hosseini SJF, Samari D. A comprehensive model for the implementation of agricultural land levelling and consolidation plan in the Abu Fazel region of Ahvaz. BRAZ J BIOL 2024; 84:e266923. [DOI: 10.1590/1519-6984.266923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 09/09/2022] [Indexed: 11/07/2022] Open
Abstract
Abstract It has been shown that land fragmentation can negatively impact the efficiency of farming. Therefore, experts recommend land consolidation process, as a logical and workable solution to solve the problems and complications caused by land fragmentation. Land levelling and consolidation is a process of land reform that changes the construction of agricultural lands which leads to rural development through reforming farm management. However, a single plan cannot be applied to different regions, even though they might be in the same country. Hence, it is vital to investigate multiple factors in a certain region to devise the perfect consolidation plan. The present study, which is a survey-exploratory research, is conducted to provide a comprehensive model to implement the plan for levelling and consolidation of agricultural lands in the Abu Fazel region of Ahvaz, Iran. This research is an applied field research which uses both library and field methods to collect the required data. The study population is in Abu Fazel in the northeast of Ahvaz in Zargan region. The results of the study show that cultural, social, economic, policy-making, educational, agricultural and managerial factors have an effect on the participation of farmers in the levelling and consolidation of agricultural lands in the study area (p≥0.01). Also, there is a strong positive relationship between these factors and the farmers' participation in levelling and consolidation of agricultural lands (p≥0.01). Among these factors, it is observed that policy is main factor. Policymakers can play an effective role in land consolidation and macro development on the one hand and agricultural and rural development. On the other, by accurately assessing the interactive effect of land consolidation and related factors, along with the effects of this process on the evolution of agronomic systems.
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Albin Ahmed A, Shaahid A, Alnasser F, Alfaddagh S, Binagag S, Alqahtani D. Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis. Sensors (Basel) 2023; 24:189. [PMID: 38203051 PMCID: PMC10781295 DOI: 10.3390/s24010189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/18/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024]
Abstract
In today's digitalized era, the usage of Android devices is being extensively witnessed in various sectors. Cybercriminals inevitably adapt to new security technologies and utilize these platforms to exploit vulnerabilities for nefarious purposes, such as stealing users' sensitive and personal data. This may result in financial losses, discredit, ransomware, or the spreading of infectious malware and other catastrophic cyber-attacks. Due to the fact that ransomware encrypts user data and requests a ransom payment in exchange for the decryption key, it is one of the most devastating types of malicious software. The implications of ransomware attacks can range from a loss of essential data to a disruption of business operations and significant monetary damage. Artificial intelligence (AI)-based techniques, namely machine learning (ML), have proven to be notable in the detection of Android ransomware attacks. However, ensemble models and deep learning (DL) models have not been sufficiently explored. Therefore, in this study, we utilized ML- and DL-based techniques to build efficient, precise, and robust models for binary classification. A publicly available dataset from Kaggle consisting of 392,035 records with benign traffic and 10 different types of Android ransomware attacks was used to train and test the models. Two experiments were carried out. In experiment 1, all the features of the dataset were used. In experiment 2, only the best 19 features were used. The deployed models included a decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), ensemble of (DT, SVM, and KNN), feedforward neural network (FNN), and tabular attention network (TabNet). Overall, the experiments yielded excellent results. DT outperformed the others, with an accuracy of 97.24%, precision of 98.50%, and F1-score of 98.45%. Whereas, in terms of the highest recall, SVM achieved 100%. The acquired results were thoroughly discussed, in addition to addressing limitations and exploring potential directions for future work.
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Affiliation(s)
- Amnah Albin Ahmed
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (A.A.A.); (A.S.); (S.B.)
| | - Afrah Shaahid
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (A.A.A.); (A.S.); (S.B.)
| | - Fatima Alnasser
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (A.A.A.); (A.S.); (S.B.)
| | - Shahad Alfaddagh
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (A.A.A.); (A.S.); (S.B.)
| | - Shadha Binagag
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (A.A.A.); (A.S.); (S.B.)
| | - Deemah Alqahtani
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia; (A.A.A.); (A.S.); (S.B.)
- SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
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13
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Niu X, Chen Y, Li Z, Guo T, Ren M, Chen Y. Study on the Properties of Multi-Walled Carbon Nanotubes (MWCNTs)/Polypropylene Fiber (PP Fiber) Cement-Based Materials. Polymers (Basel) 2023; 16:41. [PMID: 38201706 PMCID: PMC10780317 DOI: 10.3390/polym16010041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 12/12/2023] [Accepted: 12/16/2023] [Indexed: 01/12/2024] Open
Abstract
In order to improve the mechanical properties and durability of cement-based materials, a certain amount of multi-walled carbon nanotubes (MWCNTs) and polypropylene fiber (PP fiber) were incorporated into cement-based materials. The mechanical properties of the multi-walled carbon nanotubes/polypropylene fiber cement-based materials were evaluated using flexural strength tests, compressive strength tests, and splitting tensile tests. The effects of multi-walled carbon nanotubes and polypropylene fiber on the durability of cement-based materials were studied using drying shrinkage tests and freeze-thaw cycle tests. The effects of the multi-walled carbon nanotubes and polypropylene fibers on the microstructure and pore structure of the cement-based materials were compared and analyzed using scanning electron microscopy and mercury intrusion tests. The results showed that the mechanical properties and durability of cement-based materials can be significantly improved when the content of multi-walled carbon nanotubes is 0.1-0.15%. The compressive strength can be increased by 9.5% and the mass loss rate is reduced by 27.9%. Polypropylene fiber has little effect on the compressive strength of the cement-based materials, but it significantly enhances the toughness of the cement-based materials. When its content is 0.2-0.3%, it has the best effect on improving the mechanical properties and durability of the cement-based materials. The flexural strength is increased by 19.1%, and the dry shrinkage rate and water loss rate are reduced by 14.3% and 16.1%, respectively. The three-dimensional network structure formed by the polypropylene fiber in the composite material plays a role in toughening and cracking resistance, but it has a certain negative impact on the pore structure of the composite material. The incorporation of multi-walled carbon nanotubes can improve the bonding performance of the polypropylene fiber and cement matrix, make up for the internal defects caused by the polypropylene fiber, and reduce the number of harmful holes and multiple harmful holes so that the cement-based composite material not only has a significant increase in toughness but also has a denser internal structure.
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Affiliation(s)
- Xiangjie Niu
- School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China; (X.N.)
| | - Yuanzhao Chen
- School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China; (X.N.)
- Technology Innovation Center of Henan Transport Industry of Utilization of Solid Waste Resources in Traffic Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
- Henan Province Engineering Technology Research Center of Environment Friendly and High-Performance Pavement Materials, Zhengzhou 450045, China
| | - Zhenxia Li
- School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China; (X.N.)
- Technology Innovation Center of Henan Transport Industry of Utilization of Solid Waste Resources in Traffic Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
- Henan Province Engineering Technology Research Center of Environment Friendly and High-Performance Pavement Materials, Zhengzhou 450045, China
| | - Tengteng Guo
- School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China; (X.N.)
- Technology Innovation Center of Henan Transport Industry of Utilization of Solid Waste Resources in Traffic Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
- Henan Province Engineering Technology Research Center of Environment Friendly and High-Performance Pavement Materials, Zhengzhou 450045, China
| | - Meng Ren
- School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China; (X.N.)
| | - Yanyan Chen
- School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China; (X.N.)
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14
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Intelligence and Neuroscience C. Retracted: Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning. Comput Intell Neurosci 2023; 2023:9830360. [PMID: 38124854 PMCID: PMC10732718 DOI: 10.1155/2023/9830360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
[This retracts the article DOI: 10.1155/2022/4886586.].
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15
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Methods in Medicine CAM. Retracted: A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization. Comput Math Methods Med 2023; 2023:9898159. [PMID: 38124960 PMCID: PMC10733041 DOI: 10.1155/2023/9898159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/4019358.].
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16
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Intelligence and Neuroscience C. Retracted: Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences. Comput Intell Neurosci 2023; 2023:9892072. [PMID: 38124832 PMCID: PMC10732959 DOI: 10.1155/2023/9892072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/6455592.].
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17
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Methods in Medicine CAM. Retracted: A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications. Comput Math Methods Med 2023; 2023:9826259. [PMID: 38124922 PMCID: PMC10732767 DOI: 10.1155/2023/9826259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/8081276.].
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18
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Lu H, Uddin S. Embedding-based link predictions to explore latent comorbidity of chronic diseases. Health Inf Sci Syst 2023; 11:2. [PMID: 36593862 PMCID: PMC9803807 DOI: 10.1007/s13755-022-00206-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022] Open
Abstract
Purpose Comorbidity is a term used to describe when a patient simultaneously has more than one chronic disease. Comorbidity is a significant health issue that affects people worldwide. This study aims to use machine learning and graph theory to predict the comorbidity of chronic diseases. Methods A patient-disease bipartite graph is constructed based on the administrative claim data. The bipartite graph projection approach was used to create the comorbidity network. For the link prediction task, three graph machine learning embedding-based models (node2vec, graph neural networks and hand-crafted approach) with different variants were used on the comorbidity network to compare their performance. This study also considered three commonly used similarity-based link prediction approaches (Jaccard coefficient, Adamic-Adar index and Resource allocation index) for performance comparison. Results The results showed that the embedding-based hand-crafted features technique achieved outstanding performance compared with the remaining similarity-based and embedding-based models. Especially, the hand-crafted technique with the extreme gradient boosting classifier achieved the highest accuracy (91.67%), followed by the same technique with the Logistic regression classifier (90.26%). For this shallow embedding method, the Jaccard coefficient and the degree centrality of the original chronic disease were the most important features for comorbidity prediction. Conclusion The proposed framework can be used to predict the comorbidity of chronic disease at an early stage of hospital admission. Thus, the prediction outcome could be valuable for medical practice, giving healthcare providers more control over their services and lowering expenses.
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Affiliation(s)
- Haohui Lu
- School of Project Management, Faculty of Engineering, The University of Sydney, Level 2, 21 Ross Street, Forest Lodge, NSW 2037 Australia
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Level 2, 21 Ross Street, Forest Lodge, NSW 2037 Australia
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19
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Alzoubi S, Jawarneh M, Bsoul Q, Keshta I, Soni M, Khan MA. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology. Open Life Sci 2023; 18:20220764. [PMID: 38027230 PMCID: PMC10668111 DOI: 10.1515/biol-2022-0764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 10/02/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.
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Affiliation(s)
- Sharaf Alzoubi
- Information Technology Department, Amman Arab University, Amman, Jordan
| | - Malik Jawarneh
- Department of Computer Science and MIS, Oman College of Management and Technology, Muscat, Oman
| | - Qusay Bsoul
- Faculty of Information Technology, Applied Science Private University, Amman, Jordan
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Department of CSE, University Centre for Research & Development Chandigarh University, Mohali, Punjab, 140413, India
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20
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Tarek Z, Shams MY, Towfek SK, Alkahtani HK, Ibrahim A, Abdelhamid AA, Eid MM, Khodadadi N, Abualigah L, Khafaga DS, Elshewey AM. An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction. Biomimetics (Basel) 2023; 8:552. [PMID: 37999193 PMCID: PMC10669113 DOI: 10.3390/biomimetics8070552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 11/05/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.
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Affiliation(s)
- Zahraa Tarek
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt;
| | - Mahmoud Y. Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt;
| | - S. K. Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA;
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt;
| | - Hend K. Alkahtani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt;
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
| | - Marwa M. Eid
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt;
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33146, USA;
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan;
- College of Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ahmed M. Elshewey
- Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt;
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21
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Zhang Y, Li S, Wu F. Empirical insights into industrial policy's influence on phytoprotection innovation. Front Plant Sci 2023; 14:1295320. [PMID: 38034584 PMCID: PMC10682083 DOI: 10.3389/fpls.2023.1295320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023]
Abstract
Intelligent Phytoprotection is an important direction for the modern development of plant protection related disciplines, and its essence is the innovative application of new generation information technology industry, high-end equipment manufacturing industry, and digital industry related technologies in the traditional plant protection field. This article first identifies 224 International Patent Classification (IPC) Main groups in the field of intelligent phytoprotection technology based on the International Patent Classification System. And then combines with China's industrial policy practice, we explore the impact of industrial policy on the application number of invention patents in the field of intelligent phytoprotection technology using the Difference-in-difference (DID) method and the Synthetic DID method. The study results showed that the implementation of industrial policy can significantly promote the patent application activities in the intelligent phytoprotection treatment group, with an average increase of 517 invention patent applications compared to the control group that is not affected by the policy. The research conclusion of this article suggests that for countries and regions, industrial policies are an important tool for promoting the innovation and development of intelligent phytoprotection related technologies.
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Affiliation(s)
- Yangxun Zhang
- School of Finance and Economics, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Shaoqiang Li
- School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou, China
| | - Fengyun Wu
- School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou, China
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22
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Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci 2023; 17:1256351. [PMID: 38027475 PMCID: PMC10665494 DOI: 10.3389/fnins.2023.1256351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.
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Affiliation(s)
- Yanbu Wang
- School of Strength and Conditioning, Beijing Sport University, Beijing, China
| | - Linqing Liu
- Department of Physical Education, Peking University, Beijing, China
| | - Chao Wang
- Institute of Competitive Sports, Beijing Sport University, Beijing, China
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23
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Fu S, Cheng X, Liu J. Dynamics, circuit design, feedback control of a new hyperchaotic system and its application in audio encryption. Sci Rep 2023; 13:19385. [PMID: 37938603 PMCID: PMC10632480 DOI: 10.1038/s41598-023-46161-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023] Open
Abstract
In this study, a 4D hyperchaotic system is constructed based on the foundation of a 3D Lü chaotic system. The newly devised hyperchaotic system possesses a sole equilibrium point, showcasing a simplified system structure that reduces complexity. This simplification offers a clearer opportunity for in-depth analysis of dynamic behaviors in the realm of scientific research. The proposed hyperchaotic system undergoes an in-depth examination of its dynamical characteristics, including chaotic attractors, equilibrium point stability, Lyapunov exponents' spectrum, and bifurcation diagram. Numerical analysis results reveal that the attractor of this hyperchaotic system exhibits highly complex, non-periodic, and fractal structural dynamics. Its motion demonstrates extreme sensitivity and randomness, even within a wide range of variations in parameter d, affirming its hyperchaotic properties with two positive Lyapunov exponents. Hyperchaotic bifurcation diagrams typically exhibit highly intricate structures, such as fractals, branches, and period doubling characteristics, signifying that even minor parameter adjustments can lead to significant changes in system behavior, presenting diversity and unpredictability. Subsequently, to further investigate the practical utility of this hyperchaotic system, a linear feedback control strategy is implemented. Through linear feedback control, the hyperchaotic system is stabilized at its unique equilibrium point. Experimental validation is conducted using both computer software simulation Matlab, electronic circuit simulation Multisim, and embedded hardware STM32. The results of these experiments consistently align, providing theoretical support for the application of this hyperchaotic system in practical domains. Finally, leveraging the hyperchaotic keys generated by this hyperchaotic system, audio encryption is achieved using a cross-XOR algorithm, which is then realized on the embedded hardware platform STM32. The results show that the audio encryption scheme based on the hyperchaotic system is feasible, and the method is simple to implement, has nonlinear characteristics and certain algorithm complexity, which can be applied to audio encryption, image encryption, video encryption, and more.
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Affiliation(s)
- ShiMing Fu
- School of Artificial Intelligence, Chongqing University of Education, Chongqing, 400065, China
| | - XueFeng Cheng
- School of Big Data and Information Industry, Chongqing City Management College, Chongqing, 401331, China
| | - Juan Liu
- School of Artificial Intelligence, Chongqing University of Education, Chongqing, 400065, China.
- Chongqing University, Chongqing, 400044, China.
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
- Chongqing College, University of Chinese Academy of Sciences, Chongqing, 400722, China.
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Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health 2023; 11:1273253. [PMID: 38026291 PMCID: PMC10662291 DOI: 10.3389/fpubh.2023.1273253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.
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Affiliation(s)
- Mengfang Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanyuan Jiang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanzhou Zhang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haisheng Zhu
- Department of Cardiovascular Medicine, Wencheng People’s Hospital, Wencheng, China
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25
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Sharifzadegan A, Behnamnia M, Dehghan Monfared A. Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery. Sci Rep 2023; 13:18525. [PMID: 37898668 PMCID: PMC10613205 DOI: 10.1038/s41598-023-45685-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/23/2023] [Indexed: 10/30/2023] Open
Abstract
The precipitation and deposition of asphaltene on solid surfaces present a significant challenge throughout all stages of petroleum recovery, from hydrocarbon reservoirs in porous media to wellbore and transfer pipelines. A comprehensive understanding of asphaltene aggregation phenomena is crucial for controlling deposition issues. In addition to experimental studies, accurate prediction of asphaltene aggregation kinetics, which has received less attention in previous research, is essential. This study proposes an artificial intelligence-based framework for precisely predicting asphaltene particle aggregation kinetics. Different techniques were utilized to predict the asphaltene aggregate diameter as a function of pressure, temperature, oil specific gravity, and oil asphaltene content. These methods included the adaptive neuro-fuzzy interference system (ANFIS), radial basis function (RBF) neural network optimized with the Grey Wolf Optimizer (GWO) algorithm, extreme learning machine (ELM), and multi-layer perceptron (MLP) coupled with Bayesian Regularization (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) algorithms. The models were constructed using a series of published data. The results indicate the excellent correlation between predicted and experimental values using various models. However, the GWO-RBF modeling strategy demonstrated the highest accuracy among the developed models, with a determination coefficient, average absolute relative deviation percent, and root mean square error (RMSE) of 0.9993, 1.1326%, and 0.0537, respectively, for the total data.
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Affiliation(s)
- Ali Sharifzadegan
- Department of Petroleum Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr, 75169-13817, Iran
| | - Mohammad Behnamnia
- Department of Petroleum Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr, 75169-13817, Iran
| | - Abolfazl Dehghan Monfared
- Department of Petroleum Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr, 75169-13817, Iran.
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26
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Healthcare Engineering JO. Retracted: Gene Expression-Assisted Cancer Prediction Techniques. J Healthc Eng 2023; 2023:9858247. [PMID: 37860433 PMCID: PMC10584535 DOI: 10.1155/2023/9858247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/4242646.].
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27
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Healthcare Engineering JO. Retracted: An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors. J Healthc Eng 2023; 2023:9801737. [PMID: 37860393 PMCID: PMC10584630 DOI: 10.1155/2023/9801737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/1233166.].
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28
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Tiwari PK, Singh P, Rajagopal NK, Deepa K, Gulavani S, Verma A, Kumar YP. IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques. Comput Intell Neurosci 2023; 2023:5113417. [PMID: 37854640 PMCID: PMC10581845 DOI: 10.1155/2023/5113417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/19/2022] [Accepted: 04/06/2023] [Indexed: 10/20/2023]
Abstract
Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is combined with IoT. The reinforcement learning techniques created in recent years have largely focused on incorporating deep learning technology to improve the generalization skills of the algorithm while ignoring the issue of detecting and taking full advantage of the dilemma. To increase the effectiveness of exploration, a deep reinforcement algorithm based on computational intelligence is proposed in this study, using intelligent sensors and the Bayesian approach. In addition, the technique for computing the posterior distribution of parameters in Bayesian linear regression is expanded to nonlinear models such as artificial neural networks. The Bayesian Bootstrap Deep Q-Network (BBDQN) algorithm is created by combining the bootstrapped DQN with the recommended computing technique. Finally, tests in two scenarios demonstrate that, when faced with severe exploration problems, BBDQN outperforms DQN and bootstrapped DQN in terms of exploration efficiency.
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Affiliation(s)
| | - Pooja Singh
- School of Computing Science & Engineering, Department of CSE, Galgotias University, Greater Noida, UP, India
| | | | - K. Deepa
- Department of Computer Science and Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, Karur, Tamilnadu, India
| | - Sampada Gulavani
- Department of MCA, Bharati Vidyapeeth (Deemed to Be University) Institute of Management, Kolhapur, Maharashtra, India
| | - Amit Verma
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University Gharuan, Mohali, Punjab, India
| | - Yekula Prasanna Kumar
- Department of Mining Engineering, College of Engineering and Technology, Bule Hora University, Blue Hora 144, Oromia Region, Ethiopia
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29
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Intelligence and Neuroscience C. Retracted: Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder. Comput Intell Neurosci 2023; 2023:9871294. [PMID: 37829878 PMCID: PMC10567214 DOI: 10.1155/2023/9871294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
[This retracts the article DOI: 10.1155/2022/8555489.].
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30
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Healthcare Engineering JO. Retracted: A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism. J Healthc Eng 2023; 2023:9848619. [PMID: 37829405 PMCID: PMC10567189 DOI: 10.1155/2023/9848619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/3688881.].
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31
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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32
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Wang Z, Chen H, Yang S, Luo X, Li D, Wang J. A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization. PeerJ Comput Sci 2023; 9:e1569. [PMID: 37810346 PMCID: PMC10557502 DOI: 10.7717/peerj-cs.1569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/14/2023] [Indexed: 10/10/2023]
Abstract
Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.
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Affiliation(s)
- Zhendong Wang
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Hui Chen
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Shuxin Yang
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Xiao Luo
- School of Electrical Engineering ang Automation, Jiangxi University of Science and Technology, Ganzhou, China
| | - Dahai Li
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Junling Wang
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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33
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Luo K. A distributed SDN-based intrusion detection system for IoT using optimized forests. PLoS One 2023; 18:e0290694. [PMID: 37647336 PMCID: PMC10468089 DOI: 10.1371/journal.pone.0290694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/15/2023] [Indexed: 09/01/2023] Open
Abstract
Along with the expansion of Internet of Things (IoT), the importance of security and intrusion detection in this network also increases, and the need for new and architecture-specific intrusion detection systems (IDS) is felt. In this article, a distributed intrusion detection system based on a software defined networking (SDN) is presented. In this method, the network structure is divided into a set of sub-networks using the SDN architecture, and intrusion detection is performed in each sub-network using a controller node. In order to detect intrusion in each sub-network, a decision tree optimized by black hole optimization (BHO) algorithm is used. Thus, the decision tree deployed in each sub-network is pruned by BHO, and the split points in its decision nodes are also determined in such a way that the accuracy of each tree in detecting sub-network attacks is maximized. The performance of the proposed method is evaluated in a simulated environment and its performance in detecting attacks using the NSLKDD and NSW-NB15 databases is examined. The results show that the proposed method can identify attacks in the NSLKDD and NSW-NB15 databases with an accuracy of 99.2% and 97.2%, respectively, which indicates an increase compared to previous methods.
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Affiliation(s)
- Ke Luo
- Fujian Vocational & Technical College of Water Conservancy & Electric Power, Fujian, China
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34
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Lu S, Weng H, Dai M, Zhang B, Xu Z, Gu H, Liu Y, Li Y, Peng K. Dynamic 3D phase-shifting profilometry based on a corner optical flow algorithm. Appl Opt 2023; 62:6447-6455. [PMID: 37706838 DOI: 10.1364/ao.494119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/06/2023] [Indexed: 09/15/2023]
Abstract
Real-time 3D reconstruction has been applied in many fields, calling for many ongoing efforts to improve the speed and accuracy of the used algorithms. Phase shifting profilometry based on the Lucas-Kanade optical flow method is a fast and highly precise method to construct and display the three-dimensional shape of objects. However, in this method, a dense optical flow calculation is required for the modulation image corresponding to the acquired deformed fringe pattern, which consumes a lot of time and affects the real-time performance of 3D reconstruction and display. Therefore, this paper proposes a dynamic 3D phase shifting profilometry based on a corner optical flow algorithm to mitigate this issue. Therein, the Harris corner algorithm is utilized to locate the feature points of the measured object, so that the optical flow needs to calculate for only the feature points which, greatly reduces the amount of calculation time. Both our experiments and simulations show that our method improves the efficiency of pixel matching by four times and 3D reconstruction by two times.
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35
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Neurology B. Retracted: Ensemble Classification Approach for Sarcasm Detection. Behav Neurol 2023; 2023:9820206. [PMID: 37593134 PMCID: PMC10432121 DOI: 10.1155/2023/9820206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023] Open
Abstract
[This retracts the article DOI: 10.1155/2021/9731519.].
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36
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Jin L, Yang Z, Zou Z, Wu T, Pan H. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia. Open Life Sci 2023; 18:20220607. [PMID: 37528885 PMCID: PMC10389679 DOI: 10.1515/biol-2022-0607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 08/03/2023] Open
Abstract
The purpose of this study is to investigate the efficacy of bilateral upper-limb training (BULT) in helping people with upper-limb impairments due to stroke or brain illness regain their previous level of function. Patients recuperating from a stroke or cerebral disease were given the option of undergoing BULT or conventional training to enhance their upper-limb function. Participants were randomly allocated to one of the several different fitness programs. Results from the action research arm test, Box and block test, Wolf motor function test, Fugal-Meyer evaluation, and any other tests administered were taken into account. Some researchers have found that exercising with BULT for just 30 min per day for 6 weeks yields significant results. There were a total of 1,411 individuals from 10 randomized controlled trials included in this meta-analysis. Meta-analysis findings revealed that biofeedback treatment outperformed conventional rehabilitation therapy in reducing lower leg muscular strain, complete spasm scale score, electromyography score, and inactive ankle joint range of motion. An analysis of the literature found that BULT improved limb use in people who had suffered a stroke and hemiplegia but it did not provide any additional benefit over unilateral training.
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Affiliation(s)
- Linna Jin
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3, Qingchun East Road, Hangzhou, Zhejiang, 310020, China
| | - Zhe Yang
- Department of Sleep Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3, Qingchun East Road, Hangzhou, Zhejiang, 310020, China
| | - Zhaojun Zou
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3, Qingchun East Road, Hangzhou, Zhejiang, 310020, China
| | - Tao Wu
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3, Qingchun East Road, Hangzhou, Zhejiang, 310020, China
| | - Hongying Pan
- Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3, Qingchun East Road, Hangzhou, Zhejiang, 310020, China
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37
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Intelligence and Neuroscience C. Retracted: Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games. Comput Intell Neurosci 2023; 2023:9785062. [PMID: 37538662 PMCID: PMC10396641 DOI: 10.1155/2023/9785062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/05/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/4674140.].
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38
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Qian L, Dou Y, Gong C, Xu X, Tan Y. Research on User Behavior Based on Higher-Order Dependency Network. Entropy (Basel) 2023; 25:1120. [PMID: 37628150 PMCID: PMC10453702 DOI: 10.3390/e25081120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023]
Abstract
In the era of the popularization of the Internet of Things (IOT), analyzing people's daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people's daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.
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Affiliation(s)
| | - Yajie Dou
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; (L.Q.); (C.G.); (X.X.); (Y.T.)
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39
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Kumar A, K K, Dahiya M, Kushwah VS, Siddiqa A, Kaur K, Rahin SA. Advances in Computational Intelligence Techniques-Based Multi-Intersection Querying Theory for Efficient QoS in the Next Generation Internet of Things. Comput Intell Neurosci 2023; 2023:1388425. [PMID: 37455765 PMCID: PMC10349678 DOI: 10.1155/2023/1388425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/27/2022] [Accepted: 10/05/2022] [Indexed: 07/18/2023]
Abstract
An environment of physically linked, technologically networked things that can be found online is known as the "Internet of Things." With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene's complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence's use in the experience replay pool and speed up the algorithm's convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections for effective quality of service in the upcoming IoT applications.
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Affiliation(s)
- Ashish Kumar
- Department of CSE, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - Kannan K
- Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India
| | - Mamta Dahiya
- Department of Computer Science and Engineering, SGT University, Gurugram, India
| | - Virendra Singh Kushwah
- School of Computing Science and Engineering (SCSE), VIT Bhopal University, Sehore, India
| | - Ayesha Siddiqa
- Artificial Intelligence and Data Science Department, Islamia Engineering College, Hyderabad, Telangana, India
| | - Kiranjeet Kaur
- Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab 140413, India
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40
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Malik A, Shabaz M, Asenso E. Machine learning based model for detecting depression during Covid-19 crisis. Sci Afr 2023; 20:e01716. [PMID: 37214195 PMCID: PMC10182866 DOI: 10.1016/j.sciaf.2023.e01716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023] Open
Abstract
Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression. Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them.
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Affiliation(s)
- Arun Malik
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology Jammu, J&K, India
| | - Evans Asenso
- Department of Agricultural Engineering, School of Engineering Sciences, University of Ghana, Accra, Ghana
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41
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Barfungpa SP, Deva Sarma HK, Samantaray L. An intelligent heart disease prediction system using hybrid deep dense Aquila network. Biomed Signal Process Control 2023; 84:104742. [DOI: 10.1016/j.bspc.2023.104742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Li Y, Guan L, Ning C, Zhang P, Zhao Y, Liu Q, Ping P, Fu S. Machine learning-based models to predict one-year mortality among Chinese older patients with coronary artery disease combined with impaired glucose tolerance or diabetes mellitus. Cardiovasc Diabetol 2023; 22:139. [PMID: 37316853 DOI: 10.1186/s12933-023-01854-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/10/2023] [Indexed: 06/16/2023] Open
Abstract
PURPOSE An accurate prediction of survival prognosis is beneficial to guide clinical decision-making. This prospective study aimed to develop a model to predict one-year mortality among older patients with coronary artery disease (CAD) combined with impaired glucose tolerance (IGT) or diabetes mellitus (DM) using machine learning techniques. METHODS A total of 451 patients with CAD combined with IGT and DM were finally enrolled, and those patients randomly split 70:30 into training cohort (n = 308) and validation cohort (n = 143). RESULTS The one-year mortality was 26.83%. The least absolute shrinkage and selection operator (LASSO) method and ten-fold cross-validation identified that seven characteristics were significantly associated with one-year mortality with creatine, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and chronic heart failure being risk factors and hemoglobin, high density lipoprotein cholesterol, albumin, and statins being protective factors. The gradient boosting machine model outperformed other models in terms of Brier score (0.114) and area under the curve (0.836). The gradient boosting machine model also showed favorable calibration and clinical usefulness based on calibration curve and clinical decision curve. The Shapley Additive exPlanations (SHAP) found that the top three features associated with one-year mortality were NT-proBNP, albumin, and statins. The web-based application could be available at https://starxueshu-online-application1-year-mortality-main-49cye8.streamlitapp.com/ . CONCLUSIONS This study proposes an accurate model to stratify patients with a high risk of one-year mortality. The gradient boosting machine model demonstrates promising prediction performance. Some interventions to affect NT-proBNP and albumin levels, and statins, are beneficial to improve survival outcome among patients with CAD combined with IGT or DM.
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Affiliation(s)
- Yan Li
- Department of Endocrinology, People's Hospital of Macheng City, Hubei, China
| | - Lixun Guan
- Hematology Department, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Chaoxue Ning
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Pei Zhang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yali Zhao
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China.
| | - Qiong Liu
- Medical Care Center, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China.
| | - Ping Ping
- General Station for Drug and Instrument Supervision and Control, Joint Logistic Support Force of Chinese People's Liberation Army, Beijing, China.
| | - Shihui Fu
- Department of Cardiology, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China.
- Department of Geriatric Cardiology, Chinese People's Liberation Army General Hospital, Beijing, China.
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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Bustos D, Cardoso R, Carvalho DD, Guedes J, Vaz M, Torres Costa J, Santos Baptista J, Fernandes RJ. Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters. Sensors (Basel) 2023; 23:s23115127. [PMID: 37299854 DOI: 10.3390/s23115127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.
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Affiliation(s)
- Denisse Bustos
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Ricardo Cardoso
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Diogo D Carvalho
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Joana Guedes
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Mário Vaz
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - José Torres Costa
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Santos Baptista
- Associated Laboratory for Energy, Transports and Aeronautics-LAETA (PROA), Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Ricardo J Fernandes
- Centre of Research, Education, Innovation and Intervention in Sport-CIFI2D, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
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Chen F, Zhao B, Gao Y, Zhang W. BTDA: Two-factor dynamic identity authentication scheme for data trading based on alliance chain. J Supercomput 2023:1-20. [PMID: 37359336 PMCID: PMC10209950 DOI: 10.1007/s11227-023-05393-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
Abstract
With the increase in the market share of data trading, the risks such as identity authentication and authority management are increasingly intensified. Aiming at the problems of centralization of identity authentication, dynamic changes of identities, and ambiguity of trading authority in data trading, a two-factor dynamic identity authentication scheme for data trading based on alliance chain (BTDA) is proposed. Firstly, the use of identity certificates is simplified to solve the problems of large calculation and difficult storage. Secondly, a two-factor dynamic authentication strategy is designed, which uses distributed ledger to achieve dynamic identity authentication throughout the data trading. Finally, a simulation experiment is carried out on the proposed scheme. The theoretical comparison and analysis with similar schemes show that the proposed scheme has lower cost, higher authentication efficiency and security, easier authority management, and can be widely used in various fields of data trading scenarios.
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Affiliation(s)
- Fengmei Chen
- School of Information Science and Engineering, Linyi University, Lanshan, Linyi, 276000 Shandong China
| | - Bin Zhao
- School of Information Science and Engineering, Linyi University, Lanshan, Linyi, 276000 Shandong China
| | - Yilong Gao
- School of Information Science and Engineering, Linyi University, Lanshan, Linyi, 276000 Shandong China
| | - Wenyin Zhang
- School of Information Science and Engineering, Linyi University, Lanshan, Linyi, 276000 Shandong China
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Hedayati N, Yaghoobi A, Salami M, Gholinezhad Y, Aghadavood F, Eshraghi R, Aarabi MH, Homayoonfal M, Asemi Z, Mirzaei H, Hajijafari M, Mafi A, Rezaee M. Impact of polyphenols on heart failure and cardiac hypertrophy: clinical effects and molecular mechanisms. Front Cardiovasc Med 2023; 10:1174816. [PMID: 37293283 PMCID: PMC10244790 DOI: 10.3389/fcvm.2023.1174816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Polyphenols are abundant in regular diets and possess antioxidant, anti-inflammatory, anti-cancer, neuroprotective, and cardioprotective effects. Regarding the inadequacy of the current treatments in preventing cardiac remodeling following cardiovascular diseases, attention has been focused on improving cardiac function with potential alternatives such as polyphenols. The following online databases were searched for relevant orginial published from 2000 to 2023: EMBASE, MEDLINE, and Web of Science databases. The search strategy aimed to assess the effects of polyphenols on heart failure and keywords were "heart failure" and "polyphenols" and "cardiac hypertrophy" and "molecular mechanisms". Our results indicated polyphenols are repeatedly indicated to regulate various heart failure-related vital molecules and signaling pathways, such as inactivating fibrotic and hypertrophic factors, preventing mitochondrial dysfunction and free radical production, the underlying causes of apoptosis, and also improving lipid profile and cellular metabolism. In the current study, we aimed to review the most recent literature and investigations on the underlying mechanism of actions of different polyphenols subclasses in cardiac hypertrophy and heart failure to provide deep insight into novel mechanistic treatments and direct future studies in this context. Moreover, due to polyphenols' low bioavailability from conventional oral and intravenous administration routes, in this study, we have also investigated the currently accessible nano-drug delivery methods to optimize the treatment outcomes by providing sufficient drug delivery, targeted therapy, and less off-target effects, as desired by precision medicine standards.
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Affiliation(s)
- Neda Hedayati
- School of Medicine, Iran University of Medical Science, Tehran, Iran
| | - Alireza Yaghoobi
- Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Marziyeh Salami
- Department of Clinical Biochemistry, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Yasaman Gholinezhad
- Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farnaz Aghadavood
- Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Reza Eshraghi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran
- Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Mohammad-Hossein Aarabi
- Department of Clinical Biochemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mina Homayoonfal
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zatollah Asemi
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamed Mirzaei
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Mohammad Hajijafari
- Department of Anesthesiology, School of Medicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Alireza Mafi
- Department of Clinical Biochemistry, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
- Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Malihe Rezaee
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
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47
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Tang R, Chen W, Wu Y, Xiong H, Yan B. A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation. Sensors (Basel) 2023; 23:3834. [PMID: 37112175 PMCID: PMC10146399 DOI: 10.3390/s23083834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/01/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.
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Affiliation(s)
- Ruixiang Tang
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Wenbing Chen
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Yousong Wu
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Hongbin Xiong
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Banfu Yan
- School of Civil Engineering, Hunan University, Changsha 410082, China
- College of Civil and Architectural Engineering, Guangxi University, Nanning 530004, China
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48
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Chaudhury S, Sau K, Khan MA, Shabaz M. Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture. Math Biosci Eng 2023; 20:10404-10427. [PMID: 37322939 DOI: 10.3934/mbe.2023457] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically determined by the type of tissue that is analyzed by the test performed by the technician (benign). The goal of this study was to automate IDC classification within breast cancer histology samples using a transfer learning technique. To improve our outcomes, we combined a Gradient Color Activation Mapping (Grad CAM) and image coloring mechanism with a discriminative fine-tuning methodology employing a one-cycle strategy using FastAI techniques. There have been lots of research studies related to deep transfer learning which use the same mechanism, but this report uses a transfer learning mechanism based on lightweight Squeeze Net architecture, a variant of CNN (Convolution neural network). This strategy demonstrates that fine-tuning on Squeeze Net makes it possible to achieve satisfactory results when transitioning generic features from natural images to medical images.
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Affiliation(s)
- Sushovan Chaudhury
- University of Engineering and Management, Kolkata, Department of Computer Science and Engineering, University Area, Plot No. III, B/5, New Town Rd, Action Area III, Newtown, Kolkata, West Bengal 700160, India
| | - Kartik Sau
- University of Engineering and Management, Kolkata, Department of Computer Science and Engineering, University Area, Plot No. III, B/5, New Town Rd, Action Area III, Newtown, Kolkata, West Bengal 700160, India
| | | | - Mohammad Shabaz
- Model Institute of Engineering and Technology Jammu, J&K, India
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49
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Cardona-Álvarez YN, Álvarez-Meza AM, Cárdenas-Peña DA, Castaño-Duque GA, Castellanos-Dominguez G. A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments. Sensors (Basel) 2023; 23:3763. [PMID: 37050823 PMCID: PMC10098804 DOI: 10.3390/s23073763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.
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Affiliation(s)
| | | | | | - Germán Albeiro Castaño-Duque
- Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
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50
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Khattab R, Abdelmaksoud IR, Abdelrazek S. Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. New Gener Comput 2023; 41:343-400. [PMID: 37229176 PMCID: PMC10071474 DOI: 10.1007/s00354-023-00213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
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Affiliation(s)
- Rana Khattab
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Islam R. Abdelmaksoud
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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