The grid company enforces high penalties for the peak power demands of cloud data centers. These penalties result in high electricity bills that can be avoided by relying on the servers’ uninterruptible power supply (UPS) batteries as a source of energy during high-demand periods. This paper proposes a resource management framework that leverages the availability of UPS batteries to reduce the data center’s peak demands. Our framework consists of: 1) a scheduler that accounts for both the stored energy and the resource slacks when making workload placement decisions and 2) a power distributor that decides which UPS battery should store energy and by how much in order to increase the amount of energy accessible for peak shaving during high-demand periods. Our framework considers two different distributed UPS topologies: server-level topology, where each server in the data center is dedicated a single UPS battery, and rack-level topology, where each group of servers is dedicated a set of UPS batteries to be used in a shared manner. Experiments based on real Google traces show that our framework reduces significantly the data center’s electricity bill and these reductions can be up to 20% when compared to existing management techniques.

Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immersive patient care experiences. These advanced technologies, coupled with the power of bioinformatics and genomic data, facilitate groundbreaking developments. The precise prediction of ACPs from complex biological sequences remains an ongoing challenge in the genomic area. Currently, conventional approaches such as chemotherapy, target therapy, radiotherapy, and surgery are widely used for cancer treatment. However, these methods fail to completely eradicate neoplastic cells or cancer stem cells and damage healthy tissues, resulting in morbidity and even mortality. To control such diseases, oncologists and drug designers highly desire to develop new preventive techniques with more efficiency and minor side effects. Therefore, this research provides an optimized computational-based framework for discriminating against ACPs. In addition, the proposed approach intelligently integrates four peptide encoding methods, namely amino acid occurrence analysis (AAOA), dipeptide occurrence analysis (DOA), tripeptide occurrence analysis (TOA), and enhanced pseudo amino acid composition (EPseAAC). To overcome the issue of bias and reduce true error, the synthetic minority oversampling technique (SMOTE) is applied to balance the samples against each class. The empirical results over two datasets, where the accuracy of the proposed model on the benchmark dataset is 97.56% and on the independent dataset is 95.00%, verify the effectiveness of our ensemble learning mechanism and show remarkable performance when compared with state-of-the-art (SOTA) methods. In addition, the application of metaverse technology in healthcare holds promise for transformative innovations, potentially enhancing patient experiences and providing novel solutions in the realm of preventive techniques and patient care.

Introduction: artificial intelligence (AI) and Machine Learning have become buzzwords lately due to technological changes and data quality testing, especially in shape and finish analysis. Lots of research has been conducted for linear regression algorithms to predict the price in different sectors for share stock, rental properties, prices of used cars etc. This study provides suitable data split ratio for optimum cost estimation based on linear regression model. In present days there is an increasing demand for having own car for every middle-class family therefore this have given opportunity to motor vehicle business to offer wide range of used vehicle for re-sale especially companies like Maruti Suzuki, Tata motors & Mahendra motors in Indian motor vehicle industries. Therefore, it is important to know the current value of your car before spending your hard-earned money on any item. Objective: the objective of this paper is finding appropriate value of cars in Metropolitans or even in state capitals. Features like model, mileage, AC, seating capacities, fuel type automatic will be taken into account when doing this. This estimate is designed to help customers find the right options to suit their needs. Method: we have used a linear regression model to estimate the value of the respective car. Results: for doing this price prediction in this paper using liner regression we have tried to find the optimum accuracy of model by varying data split ratio for training and test data set and concluded with the result that 80/20 ratio is the best ratio with optimum model accuracy for business domain analysis with labelled data set. Conclusions: the findings underscore the importance of careful consideration when selecting a data split ratio for price prediction models in the used vehicle market. The insights gleaned from this study can inform future research and contribute to the development of more accurate and reliable regression models in similar domains

The surveillance of symptoms related to pneumonia is increasingly crucial due to its widespread occurrence and similarity to symptoms exhibited in other contagious diseases such as influenza, respiratory syncytial virus (RSV), and COVID-19. The timely identification of pneumonia can significantly diminish mortality rates. To tackle this issue, a pioneering non-contact technique for monitoring pneumonia symptoms has been developed. This investigation mainly concentrates on the early detection of pneumonia, which can serve as an indicator of the onset of other persistent ailments. The technique entails an analytical approach to scrutinize symptoms such as cold, cough, chills, sore throat, altered respiratory rates, and elevated body temperature by employing depth imaging methods. The crux of this exploration lies in the utilization of a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks to classify video images in order to identify symptoms associated with pneumonia. The proposed model has showcased an impressive overall accuracy rate of 98.02% along with a significantly optimized prediction time of a mere 8.63 ms. Moreover, the study encompasses a comprehensive evaluation of various deep learning techniques in the detection of diseases exhibiting symptoms akin to pneumonia. The study introduces a pivotal advancement in medical diagnostics, emphasizing the importance and effectiveness of a fusion-based, profound learning system in the non-contact identification of pneumonia symptoms. This innovative approach has the potential to revolutionize the way pneumonia and similar diseases are diagnosed and monitored.