On certain equidimensional polymatroidal ideals

The class of equidimensional polymatroidal ideals are studied. In particular, we show that an unmixed polymatroidal ideal is connected in codimension one if and only if it is Cohen-Macaulay. Especially a matroidal ideal is connected in codimension one precisely when it is a squarefree Veronese ideal. As a consequence we indicate that for polymatroidal ideals, the Serre's condition $(S_n)$ for some $n\geq 2$ is equivalent to Cohen-Macaulay property. We also give a classification of generalized Cohen-Macaulay polymatroidal ideals.Comment: To appear in Manuscripta Mathematic

The Pulse of News in Social Media: Forecasting Popularity

News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web

Integrating DevOps with Existing Healthcare IT Infrastructure and Processes: Challenges and Key Considerations

The DevOps is a set of practices and tools that aim to improve the collaboration, communication, and collaboration between software development and IT operations teams. In healthcare systems, DevOps has the potential to improve the performance, reliability, and scalability of IT systems while ensuring regulatory compliance and the protection of sensitive patient data. However, integrating DevOps with existing healthcare IT infrastructure and processes can present several challenges, including resistance to change, compliance and regulatory requirements, integration with legacy systems, lack of resources, and skill shortages. To overcome these challenges, healthcare organizations need to consider a number of key considerations when integrating DevOps with their existing IT infrastructure and processes. These include a clear understanding of the existing IT infrastructure and processes, engagement with stakeholders, a phased approach, automation where possible, a culture of continuous improvement, ensuring security and compliance, and fostering collaboration and communication. By following these key considerations, healthcare organizations can successfully integrate DevOps with their existing IT infrastructure and processes, unlocking the full benefits of DevOps for their healthcare systems. These benefits include improved performance, reliability, and scalability, increased collaboration and communication between IT and clinical teams, and increased efficiency and cost savings. DevOps has the potential to revolutionize healthcare IT by delivering more flexible, reliable, and scalable systems that support the delivery of better patient care. By adopting DevOps, healthcare organizations can transform their IT operations and processes, ensuring that they are well-equipped to meet the changing needs of the healthcare industry

Pretty cleanness and filter-regular sequences

Let $K$ be a field and $S=K[x_1,\ldots, x_n]$. Let $I$ be a monomial ideal of $S$ and $u_1,\ldots, u_r$ be monomials in $S$ which form a filter-regular sequence on $S/I$. We show that $S/I$ is pretty clean if and only if $S/(I,u_1,\ldots, u_r)$ is pretty clean.Comment: It will be published in Czechoslovak Mathematical Journa

Predictive Analytics in Cloud Computing: An ARIMA Model Study on Performance Metrics

Predictive analytics is a key aspect of cloud computing as it helps organizations to anticipate future events and take proactive measures to prevent issues before they occur. In this research, the goal was to perform an ARIMA (AutoRegressive Integrated Moving Average) model to predict cloud performance using various performance metrics. The study utilized ten different performance metrics, such as Response Time, Resource Utilization, Availability, Error Rate, Memory Usage, CPU Utilization, Disk I/O, Network Bandwidth and others to model cloud performance. The aim was to investigate the potential of ARIMA models to predict cloud performance by analyzing the impact of these different performance metrics on the model's accuracy. The study also used four performance criteria, namely LogL (Log Likelihood), AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and HQ (Hannan-Quinn Criterion) to evaluate the performance of the ARIMA models. The results of the study showed that the ARIMA model (2,0) and (0,2) had the lowest AIC and BIC values among all the models considered. This indicated that these models were the most suitable for predicting cloud performance, as they had the lowest information loss compared to the other models. The results of the study provided evidence that ARIMA models can effectively predict cloud performance. This research highlights the importance of predictive analytics in cloud computing and the potential for ARIMA models to predict cloud performance. The findings have implications for organizations that rely on cloud computing. However, more research is needed in this area, as the study was limited to only ten performance metrics, and more extensive research is needed to validate the findings and to determine the best approach to predict cloud performance

Proactive Fault Tolerance Through Cloud Failure Prediction Using Machine Learning

One of the crucial aspects of cloud infrastructure is fault tolerance, and its primary responsibility is to address the situations that arise when different architectural parts fail. A sizeable cloud data center must deliver high service dependability and availability while minimizing failure incidence. However, modern large cloud data centers continue to have significant failure rates owing to a variety of factors, including hardware and software faults, which often lead to task and job failures. To reduce unexpected loss, it is critical to forecast task or job failures with high accuracy before they occur. This research examines the performance of four machine learning (ML) algorithms for forecasting failure in a real-time cloud environment to increase system availability using real-time data gathered from the Google Cluster Workload Traces 2019. We applied four distinct supervised machine learning algorithms are logistic regression, KNN, SVM, decision tree, and logistic regression classifiers. Confusion matrices as well as ROC curves were used to assess the reliability and robustness of each algorithm. This study will assist cloud service providers developing a robust fault tolerance design by optimizing device selection, consequently boosting system availability and eliminating unexpected system downtime