Femtocell plays a significant role in technologies to improve service quality and data rates for indoor mobile users. It is the recent less expensive concept that provides better indoor coverage. Therefore, handoff process from macrocell to femtocell is an open research issue. In this paper, an adaptive macro/femto handoff scheme is proposed. It takes into consideration the velocity of mobile station, load balancing and the accessing mode of the target cell. It effectively handles the indoor handoff situations by checking user velocity which has to be lower than a threshold value in order to guarantee that this user has a sufficient time to switch from macrocell to femtocell. The proposed scheme aims to achieve load balancing between macrocells and femtocells. It also minimizes the number of femtocells in the neighbor cell list. The target femtocell is predicted based on the direction of the mobile user considering the femtocells accessing mode. Numerical results show that the proposed scheme improves femtocell usage and decreases the handoff latency and the total handoff delay compared to the standard macrocell-to-femtocell handoff process and other existing schemes.
A pandemic epidemic called the coronavirus (COVID-19) has already afflicted people all across the world. Radiologists can visually detect coronavirus infection using a chest X-ray. This study examines two methods for categorizing COVID-19 patients based on chest x-rays: pure deep learning and traditional machine learning. In the first model, three deep learning classifiers' decisions are combined using two distinct decision fusion strategies (majority voting and Bayes optimal). To enhance classification performance, the second model merges the ideas of decision and feature fusion. Using the fusion procedure, feature vectors from deep learning models generate a feature set. The classification metrics of conventional machine learning classifiers were then optimized using a voting classifier. The first proposed model performs better than the second model when it concerns diagnosing binary and multiclass classification. The first model obtains an AUC of 0.998 for multi-class classification and 0.9755 for binary classification. The second model obtains a binary classification AUC of 0.9563 and a multiclass classification AUC of 0.968. The suggested models perform better than both the standard learners and state-of-the-art and state-of-the-art methods.
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