RESUMO
Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine in both two types with aura or without aura and plays an essential role in studying pathophysiological differences between these two groups. Several studies indicated that the reason for migraine aura is cortical spreading depression (CSD) but did not clarify its impact on migraine without aura and lack of habituation. In this study, 22 migraine patients (MWA, N = 13), (MWoA, N = 9), and healthy controls (HC, N = 19) were the participants. Participants were exposed to the steady state of visual evoked potentials also known as (SSVEP), which are the signals for a natural response to the visual motivation at four Hz or six Hz for 2 s followed by the inter-stimulus interval that varies between 1 and 1.5 s. The order of the temporal frequencies was randomized, and each temporal frequency was shown 100 times. We recorded from 128 customized electrode locations using high-density electroencephalography (HD-EEG) and measured amplitude and habituation for the N1-P1 and P1-N2 from the first to the sixth blocks of 100 sweep features in patients and healthy controls. Using the entropy, a decrease in amplitude and SSVEP N1-P1 habituation between the first and the sixth block appeared in both MWA and MWoA (p = 0.0001, Slope = -0.4643), (p = 0.065, Slope = 0.1483), respectively, compared to HC. For SSVEP P1-N2 between the first and sixth block, it is varied in both MWA (p = 0.0029, Slope = -0.3597) and MWoA (p = 0.027, Slope = 0.2010) compared to HC. Therefore, migraine patients appear amplitude decrease and habituation deficit but with different rates between MWA, and MWoA compared to HCs. Our findings suggest this disparity between MWoA and MWA in the lack of habituation and amplitude decrease in the inter-ictal phase has a close relationship with CSD. In light of the fact that CSD manifests during the inter-ictal phase of migraine with aura, which is when migraine seizures are most likely to occur, multiple researchers have lately reached this conclusion. This investigation led us to the conclusion that CSD during the inter-ictal phase and migraine without aura are associated. In other words, even if previous research has not demonstrated it, CSD is the main contributor to both types of migraine (those with and without aura).
RESUMO
The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs.
RESUMO
Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.