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A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.
Mujeeb Rehman; Arslan Shafique; Sohail Khalid; Maha Driss; Saeed Rubaiee. Future Forecasting of COVID-19: A Supervised Learning Approach. Sensors 2021, 21, 3322 .
AMA StyleMujeeb Rehman, Arslan Shafique, Sohail Khalid, Maha Driss, Saeed Rubaiee. Future Forecasting of COVID-19: A Supervised Learning Approach. Sensors. 2021; 21 (10):3322.
Chicago/Turabian StyleMujeeb Rehman; Arslan Shafique; Sohail Khalid; Maha Driss; Saeed Rubaiee. 2021. "Future Forecasting of COVID-19: A Supervised Learning Approach." Sensors 21, no. 10: 3322.
In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a "sandwich encryption." The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme’s performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible.
Arslan Shafique; Jameel Ahmed; Mujeeb Ur Rehman; Mohammad Mazyad Hazzazi. Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain. IEEE Access 2021, 9, 59108 -59130.
AMA StyleArslan Shafique, Jameel Ahmed, Mujeeb Ur Rehman, Mohammad Mazyad Hazzazi. Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain. IEEE Access. 2021; 9 (99):59108-59130.
Chicago/Turabian StyleArslan Shafique; Jameel Ahmed; Mujeeb Ur Rehman; Mohammad Mazyad Hazzazi. 2021. "Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain." IEEE Access 9, no. 99: 59108-59130.
The advancement in wireless communication has encouraged the process of data transferring through the Internet. The process of data sharing via the Internet is prone to several attacks. The sensitive information can be protected from hackers with the help of a process called Encryption. Owing to the increase in cyber-attacks, encryption has become a vital component of modern-day communication. In this article, an image encryption algorithm is suggested using dynamic substitution and chaotic systems. The suggested scheme is based upon the chaotic logistic map, chaotic sine maps and the dynamical substitution boxes (S-boxes). In the proposed scheme, the S-box selection is according to the generated sequence by deploying the chaotic sine map. To evaluate the robustness and security of the proposed encryption scheme, different security analysis like correlation analysis, information entropy, energy, histogram investigation, and mean square error are performed. The keyspace and entropy values of the enciphered images generated through the proposed encryption scheme are over 2278 and 7.99 respectively. Moreover, the correlation values are closer to zero after comparison with the other existing schemes. The unified average change intensity (UACI) and the number of pixel change rate (NPCR) for the suggested scheme are greater than 33, 99.50% respectively. The simulation outcomes and the balancing with state-of-the-art algorithms justify the security and efficiency of the suggested scheme.
Mujeeb Ur Rehman; Arslan Shafique; Sohail Khalid; Iqtadar Hussain. Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps. IEEE Access 2021, 9, 52277 -52291.
AMA StyleMujeeb Ur Rehman, Arslan Shafique, Sohail Khalid, Iqtadar Hussain. Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps. IEEE Access. 2021; 9 (99):52277-52291.
Chicago/Turabian StyleMujeeb Ur Rehman; Arslan Shafique; Sohail Khalid; Iqtadar Hussain. 2021. "Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps." IEEE Access 9, no. 99: 52277-52291.
This paper presents the synthesis and design of the multi-mode dual-band bandstop filter (MM-DBBSF). A highly selective multi-mode dual-band bandstop response is obtained using a quarter wavelength coupled line structure. It has been shown that by increasing the coupled line’s order, the selectivity and the transmission zeros are increased in the desired stopband. Moreover, a step impedance resonator (SIR) is used between two coupled lines to achieve more transmission poles for better out of band selectivity. The paper show a detailed theoretical synthesis of the coupled line and SIR structure. In order to validate the theoretical model, ideal and microstrip topologies are designed and simulated. Furthermore, a high-frequency substrate is used to fabricate four prototypes. The simulated and measured results show good concurrence.
Muhammad Faisal; Sohail Khalid; Mujeeb Ur Rehman; Muhammad Abdul Rehman. Synthesis and Design of Highly Selective Multi-Mode Dual-Band Bandstop Filter. IEEE Access 2021, 9, 43316 -43323.
AMA StyleMuhammad Faisal, Sohail Khalid, Mujeeb Ur Rehman, Muhammad Abdul Rehman. Synthesis and Design of Highly Selective Multi-Mode Dual-Band Bandstop Filter. IEEE Access. 2021; 9 ():43316-43323.
Chicago/Turabian StyleMuhammad Faisal; Sohail Khalid; Mujeeb Ur Rehman; Muhammad Abdul Rehman. 2021. "Synthesis and Design of Highly Selective Multi-Mode Dual-Band Bandstop Filter." IEEE Access 9, no. : 43316-43323.
With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system.
Arslan Shafique; Jameel Ahmed; Wadii Boulila; Hamzah Ghandorh; Jawad Ahmad; Mujeeb Ur Rehman. Detecting the Security Level of Various Cryptosystems Using Machine Learning Models. IEEE Access 2020, 9, 9383 -9393.
AMA StyleArslan Shafique, Jameel Ahmed, Wadii Boulila, Hamzah Ghandorh, Jawad Ahmad, Mujeeb Ur Rehman. Detecting the Security Level of Various Cryptosystems Using Machine Learning Models. IEEE Access. 2020; 9 ():9383-9393.
Chicago/Turabian StyleArslan Shafique; Jameel Ahmed; Wadii Boulila; Hamzah Ghandorh; Jawad Ahmad; Mujeeb Ur Rehman. 2020. "Detecting the Security Level of Various Cryptosystems Using Machine Learning Models." IEEE Access 9, no. : 9383-9393.