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 2 ²⁷⁸ 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.

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.

Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy.

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.

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.