This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas’s (TCGA’s) lower-grade glioma collection, our proposed approach uses a VGG19-based U-Net architecture with fixed pretrained weights. The experimental findings, which show an Area Under the Curve (AUC) of 0.9957, F1-Score of 0.9679, Dice Coefficient of 0.9679, Precision of 0.9541, Recall of 0.9821, and Intersection-over-Union (IoU) of 0.9378, show how effective the proposed framework is. According to these metrics, the VGG19-powered U-Net outperforms not only the conventional U-Net model but also other variants that were compared and used different pre-trained backbones in the U-Net encoder. Clinical trial registration Not applicable as this study utilized existing publicly available dataset and did not involve a clinical trial.

The fast advancement of technology and developers’ utilization of data centers have dramatically increased energy usage in today’s society. Thermal control is a key issue in hyper-scale cloud data centers. Hotspots form when the temperature of the host rises, increasing cooling costs and affecting dependability. Precise estimation of host temperatures is critical for optimal resource management. Thermal changes in the data center make estimating temperature a difficult challenge. Existing temperature estimating algorithms are ineffective due to their processing complexity as well as lack of accuracy. Regarding that data-driven approaches seem promising for temperature prediction, this research offers a unique efficient temperature prediction model. The model uses a combination of convolutional neural networks (CNN) and stacking multi-layer bi-directional long-term short memory (BiLSTM) for thermal prediction. The findings of the experiments reveal that the model successfully anticipates the temperature with the highest $R^2$ value of 97.15% and the lowest error rate of RMSE value of 0.2892, and an RMAE of 0.5003, which decreases the projection error as opposed to the other method.

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.