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Pregnancy-related cardiovascular disease with stroke remains a considerable source of higher maternal morbidity and mortality occurs in periods of pregnancy, delivery, and postpartum. It is essential to counsel the mother before pregnancy by an expert cardiologist and obstetric team to discuss any event related to preexistent cardiac or past preeclampsia for estimation of maternal and fetal risks. In pregnancy, the cardiac state includes hypertensive disorders, ischemic heart disease, valvular disease, and postpartum stroke. The incidence of stroke is increasing in pregnancy, particularly in postpartum, and its strong relationship with hypertensive disorders of pregnancy (preeclampsia). The combined cardiologist and obstetrics team requires during pregnancy mainly due to the approach to the management of a cardiac disease that subsequently prevents stroke postpartum. Therefore, a general perception of cardiac disease during pregnancy, delivery, and postpartum should be a core knowledge extent for all cardiovascular and clinicians. Many studies provided linked that deregulation of microRNAs (miRNAs) in maternal circulation and placenta tissue may development of pregnancy complications including preeclampsia considered a diagnostic marker. The desire of this review provides a detailed outline of current knowledge and dealing in this field with strength on the physiological changes during pregnancy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Umme Salma et al.)

The smart city is an emerging concept that is based on the integration of various electronic devices and citizens that enhance the flow of information. IoT is an integral part for next generation wireless network infrastructure for acting as an interface of collecting data and controlling delivery of message which are using in smart cities. In this paper, an IoT-oriented relay assisted MIMO for beyond the fifth-generation wireless network system is proposed. The proposed system provides higher capacity and lower BER. The proposed system's BER results are compared with various combinations of transmission and receiving antennas at source, relay, and destination. It is found from BER performance that the developed scheme with relay does provide 1-17 dB gain with respect to direct connection. It is also found from mathematical analysis and simulation results that this scheme provides 3 to 9 b/s/Hz improvement in performance of capacity at 5 to 10 dB by adding a different combination of STBC and VBLAST. Simulation results are also presented to demonstrate the diversity and multiplexing gain that is a key to providing high data rates with reliable communication with many interferences for the IoT system. This system can also be used for massive antennas-based IoT system by raising the number of transmitting and receiving antennas with proposed encoding and decoding techniques explained in this paper., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 M.M. Kamruzzaman et al.)

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z -value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z -test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z -test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems., Competing Interests: The authors declare that they have no conflicts of interest to report regarding this study., (Copyright © 2022 Muhammad Hameed Siddiqi et al.)

In healthcare, the analysis of patients' activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumptions, which may reduce classification accuracy. Therefore, we utilized a new algorithm to relax the assumption, allowing our model to use full-covariance distribution. Also, in this work, we proved that computation wise our method has very much lower complexity against the existing methods. For the experiments, we used four publicly available standard datasets to show the performance. We utilized a 10-fold cross-validation scheme to train, assess, and compare the proposed model with the conditional learning method, hidden Markov model (HMM), and existing HCRF model which can only use diagonal-covariance Gaussian distributions. From the experiments, it is obvious that the proposed model showed a substantial improvement with p value ≤0.2 regarding the classification accuracy., Competing Interests: The authors declare there are no conflicts of interest., (Copyright © 2019 Muhammad Hameed Siddiqi et al.)