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Almakdi S, Ishaque I, Khan M, Alshehri MS, Munir N. Key dependent information confidentiality scheme based on deoxyribonucleic acid (DNA) and circular shifting. Heliyon 2024; 10:e23572. [PMID: 38192866 PMCID: PMC10772090 DOI: 10.1016/j.heliyon.2023.e23572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 11/22/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
In this era of advanced information technology, the exploration and development of novel mechanisms to ensure information confidentiality have consistently captivated the attention of upcoming researchers. In this article, we present a pioneering approach that combines DNA sequencing with a four-dimensional (4D) hyperchaotic map to bolster the security of digital information. Our primary focus is on the design of a robust and secure scheme for encrypting color images, leveraging DNA cryptography and hyperchaos. By extracting three distinct DNA sequences, we generate encryption keys through the integration of DNA computing and 4D hyperchaotic maps. Notably, these keys are intricately linked to the plaintext and vary with any alterations in the input. Consequently, the proposed encryption method stands resilient against an array of potential cryptographic attacks. To gauge the algorithm's security, we subject it to rigorous standard statistical analysis. Our findings underscore the efficiency and robustness of the proposed framework, establishing its potential for facilitating secure communication.
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Affiliation(s)
- Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia
| | - Iqra Ishaque
- Department of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad Pakistan
| | - Majid Khan
- Department of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad Pakistan
| | - Mohammed S. Alshehri
- Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia
| | - Noor Munir
- Department of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad Pakistan
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Zhuang Z, Zhuang Z, Wang T. Medical image encryption algorithm based on a new five-dimensional multi-band multi-wing chaotic system and QR decomposition. Sci Rep 2024; 14:402. [PMID: 38172586 PMCID: PMC10764812 DOI: 10.1038/s41598-023-50661-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
In this study, we propose a medical image encryption algorithm based on a new five-dimensional (5D) multi-band multi-wing chaotic system and QR decomposition. First, we construct a new 5D multi-band multi-wing chaotic system through feedback control, which has a relatively large Lyapunov exponent. Second, we decompose the plaintext image matrix and chaotic sequence into an orthogonal matrix and upper triangular matrix using QR decomposition. We multiply the orthogonal matrix decomposed from the original image by the orthogonal matrix decomposed from the chaotic sequence. In this process, we use the chaotic sequence to control left and right multiplication. Simultaneously, we chaotically rearrange the elements in the upper triangular matrix using the improved Joseph loop and then multiply the two resulting matrices. Finally, we subject the product matrix to bit-level scrambling. From the theoretical analysis and simulation results, we observed that the key space of this method was relatively large, the key sensitivity was relatively strong, it resisted attacks of statistical analysis and gray value analysis well, and it had a good encryption effect for medical images.
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Affiliation(s)
- Zeben Zhuang
- Department of Critical Care Medicine, People's Hospital of Fengjie, Fengjie, 404600, Chongqing, China
| | - Zhiben Zhuang
- School of Mathematics and Computational Science and Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua University, Huaihua, 418000, Hunan, China.
| | - Tao Wang
- College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi, 445000, Hubei, China.
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Jafari M, Shoeibi A, Khodatars M, Bagherzadeh S, Shalbaf A, García DL, Gorriz JM, Acharya UR. Emotion recognition in EEG signals using deep learning methods: A review. Comput Biol Med 2023; 165:107450. [PMID: 37708717 DOI: 10.1016/j.compbiomed.2023.107450] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/03/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023]
Abstract
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in human interactions. Human emotions can be identified through various sources, such as facial expressions, speech, behavior (gesture/position), or physiological signals. The use of physiological signals can enhance the objectivity and reliability of emotion detection. Compared with peripheral physiological signals, electroencephalogram (EEG) recordings are directly generated by the central nervous system and are closely related to human emotions. EEG signals have the great spatial resolution that facilitates the evaluation of brain functions, making them a popular modality in emotion recognition studies. Emotion recognition using EEG signals presents several challenges, including signal variability due to electrode positioning, individual differences in signal morphology, and lack of a universal standard for EEG signal processing. Moreover, identifying the appropriate features for emotion recognition from EEG data requires further research. Finally, there is a need to develop more robust artificial intelligence (AI) including conventional machine learning (ML) and deep learning (DL) methods to handle the complex and diverse EEG signals associated with emotional states. This paper examines the application of DL techniques in emotion recognition from EEG signals and provides a detailed discussion of relevant articles. The paper explores the significant challenges in emotion recognition using EEG signals, highlights the potential of DL techniques in addressing these challenges, and suggests the scope for future research in emotion recognition using DL techniques. The paper concludes with a summary of its findings.
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Affiliation(s)
- Mahboobeh Jafari
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - David López García
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Yang N, Zhang S, Bai M, Li S. Medical Image Encryption Based on Josephus Traversing and Hyperchaotic Lorenz System. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (SCIENCE) 2022; 29:1-18. [PMID: 36588800 PMCID: PMC9791633 DOI: 10.1007/s12204-022-2555-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 05/04/2022] [Indexed: 12/27/2022]
Abstract
This study proposes a new medical image encryption scheme based on Josephus traversing and hyperchaotic Lorenz system. First, a chaotic sequence is generated through hyperchaotic system. This hyperchaotic sequence is used in the scrambling and diffusion stages of the algorithm. Second, in the scrambling process, the image is initially confused by Josephus scrambling, and then the image is further confused by Arnold map. Finally, generated hyperchaos sequence and exclusive OR operation is used for the image to carry on the positive and reverse diffusion to change the pixel value of the image and further hide the effective information of the image. In addition, the information of the plaintext image is used to generate keys used in the algorithm, which increases the ability of resisting plaintext attack. Experimental results and security analysis show that the scheme can effectively hide plaintext image information according to the characteristics of medical images, and is resistant to common types of attacks. In addition, this scheme performs well in the experiments of robustness, which shows that the scheme can solve the problem of image damage in telemedicine. It has a positive significance for the future research.
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Affiliation(s)
- Na Yang
- School of Information Engineering, Chang’an University, Xi’an, 710064 China
| | - Shuxia Zhang
- School of Information Engineering, Chang’an University, Xi’an, 710064 China
| | - Mudan Bai
- School of Information Engineering, Chang’an University, Xi’an, 710064 China
| | - Shanshan Li
- School of Information Engineering, Chang’an University, Xi’an, 710064 China
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Khaldi A, Kafi MR, Moad MS. Wrapping based curvelet transform approach for ECG watermarking in telemedicine application. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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An efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic system and Otsu threshold segmentation. Comput Biol Med 2022; 146:105542. [PMID: 35483228 DOI: 10.1016/j.compbiomed.2022.105542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/08/2022] [Accepted: 04/17/2022] [Indexed: 11/22/2022]
Abstract
This paper proposes an efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic systems and Otsu threshold segmentation. In our method, first, the stereoscopic medical image is divided into the image top, middle, and lower parts. Moreover, each part is divided into background areas and regions of interest utilizing Otsu threshold segmentation, increasing about 40% the encryption efficiency when the background area is discarded. Second, compared with existing chaotic systems, the proposed coupled chaotic system has better ergodicity and randomness, with all NIST SP800-22 test data exceeding 0.01. Third, we develop a robust watermarking algorithm based on forwarding Meyer wavelet transform and singular value decomposition. Furthermore, the watermark algorithm embedded the two-dimensional code doctor-patient information in the region of interest. Finally, the experimental results demonstrate that the proposed algorithm has appealing encryption and watermark performance, the histogram and scatter graphs are governed by approximately uniform distribution, the NPCR and UACI of plaintext sensitivity and the key sensitivity are close to 99.6094% and 33.4635%, affording robustness to noise and clipping attacks.
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Hariri W, Narin A. Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review. Soft comput 2021; 25:15345-15362. [PMID: 34456618 PMCID: PMC8382671 DOI: 10.1007/s00500-021-06137-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 01/12/2023]
Abstract
The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since the standard testing system is time-consuming and not available for everyone, alternative early screening techniques have become an urgent need. In this study, the approaches used in the detection of COVID-19 based on deep learning (DL) algorithms, which have been popular in recent years, have been comprehensively discussed. The advantages and disadvantages of different approaches used in literature are examined in detail. We further present the databases and major future challenges of DL-based COVID-19 detection. The computed tomography of the chest and X-ray images gives a rich representation of the patient's lung that is less time-consuming and allows an efficient viral pneumonia detection using the DL algorithms. The first step is the preprocessing of these images to remove noise. Next, deep features are extracted using multiple types of deep models (pretrained models, generative models, generic neural networks, etc.). Finally, the classification is performed using the obtained features to decide whether the patient is infected by coronavirus or it is another lung disease. In this study, we also give a brief review of the latest applications of cough analysis to early screen the COVID-19 and human mobility estimation to limit its spread.
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Affiliation(s)
- Walid Hariri
- Labged Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria
| | - Ali Narin
- Department of Electrical and Electronics Engineering, Zonguldak Bulent Ecevit University, Zonguldak, Turkey
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Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Khosravi A, Nahavandi S, Gholamzadeh Chofreh A, Goni FA, Klemeš JJ, Mosavi A. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. RESULTS IN PHYSICS 2021; 27:104495. [PMID: 34221854 PMCID: PMC8233414 DOI: 10.1016/j.rinp.2021.104495] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 05/17/2023]
Abstract
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
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Key Words
- ANFIS, Adaptive Network-based Fuzzy Inference System
- ANN, Artificial Neural Network
- AU, Australia
- Bi-Conv-LSTM, Bidirectional Convolutional Long Short Term Memory
- Bi-GRU, Bidirectional Gated Recurrent Unit
- Bi-LSTM, Bidirectional Long Short-Term Memory
- Bidirectional
- COVID-19 Prediction
- COVID-19, Coronavirus Disease 2019
- Conv-LSTM, Convolutional Long Short Term Memory
- Convolutional Long Short Term Memory (Conv-LSTM)
- DL, Deep Learning
- DLSTM, Delayed Long Short-Term Memory
- Deep learning
- EMRO, Eastern Mediterranean Regional Office
- ES, Exponential Smoothing
- EV, Explained Variance
- GRU, Gated Recurrent Unit
- Gated Recurrent Unit (GRU)
- IR, Iran
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- Lasso, Least Absolute Shrinkage and Selection Operator
- Long Short Term Memory (LSTM)
- MAE, Mean Absolute Error
- MAPE, Mean Absolute Percentage Error
- MERS, Middle East Respiratory Syndrome
- ML, Machine Learning
- MLP-ICA, Multi-layered Perceptron-Imperialist Competitive Calculation
- MSE, Mean Square Error
- MSLE, Mean Squared Log Error
- Machine learning
- New Cases of COVID-19
- New Deaths of COVID-19
- PRISMA, Preferred Reporting Items for Precise Surveys and Meta-Analyses
- RMSE, Root Mean Square Error
- RMSLE, Root Mean Squared Log Error
- RNN, Repetitive Neural Network
- ReLU, Rectified Linear Unit
- SARS, Serious Intense Respiratory Disorder
- SARS-COV, SARS coronavirus
- SARS-COV-2, Serious Intense Respiratory Disorder Coronavirus 2
- SVM, Support Vector Machine
- VAE, Variational Auto Encoder
- WHO, World Health Organization
- WPRO, Western Pacific Regional Office
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Affiliation(s)
- Nooshin Ayoobi
- Department of Mathematics, Savitribai Phule Pune University, Pune 411007, India
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
| | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Abdoulmohammad Gholamzadeh Chofreh
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Feybi Ariani Goni
- Department of Management, Faculty of Business and Management, Brno University of Technology - VUT Brno, Kolejní 2906/4, 612 00 Brno, Czech Republic
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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