Cloud computing offers scalable and adaptable resources on demand, and has emerged as an essential technology for contemporary enterprises. Nevertheless, it is still challenging work to efficiently handle cloud resources because of dynamic changes in load requirement. Existing forecasting approaches are unable to handle the intricate temporal symmetries and nonlinear patterns in cloud workload data, leading to degradation of prediction accuracy. In this manuscript, a Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems (MASNN-WL-RTSP-CS) is proposed. Here, the input data from the Google cluster trace dataset were preprocessed using Multi Window Savitzky–Golay Filter (MWSGF) to remove noise while preserving important data patterns and maintaining structural symmetry in time series trends. Then, the Multi-Dimensional Attention Spiking Neural Network (MASNN) effectively models symmetric patterns in workload fluctuations to predict workload and resource time series. To enhance accuracy, the Secretary Bird Optimization Algorithm (SBOA) was utilized to optimize the MASNN parameters, ensuring accurate workload and resource time series predictions. Experimental results show that the MASNN-WL-RTSP-CS method achieves 35.66%, 32.73%, and 31.43% lower Root Mean Squared Logarithmic Error (RMSLE), 25.49%, 32.77%, and 28.93% lower Mean Square Error (MSE), and 24.54%, 23.65%, and 23.62% lower Mean Absolute Error (MAE) compared with other approaches, like ICNN-WL-RP-CS, PA-ENN-WLP-CS, and DCRNN-RUP-RP-CCE, respectively. These advances emphasize the utility of MASNN-WL-RTSP-CS in achieving more accurate workload and resource forecasts, thereby facilitating effective cloud resource management.

The existing heart failure risk prediction models are developed based on machine learning predictors. The objective of this study is to identify the key risk factors that affect the survival time of heart patients and to develop a heart failure survival prediction model using the identified risk factors. A cox proportional hazard regression method is applied to generate the proposed heart failure survival model. We used the dataset from the University of California Irvine (UCI) clinical heart failure data repository. To develop the model we have used multiple risk factors such as age, anemia, creatinine phosphokinase, diabetes history, ejection fraction, presence of high blood pressure, platelet count, serum creatinine, sex, and smoking history. Among the risk factors, high blood pressure is identified as one of the novel risk factors for heart failure. We have validated the performance of the model via statistical and empirical validation. The experimental result shows that the proposed model achieved good discrimination and calibration ability with a C-index (receiver operating characteristic (ROC) of being 0.74 and a log-likelihood ratio of 81.95 using 11 degrees of freedom on the validation dataset.