The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K’s optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

In this research, a weather forecasting model based on machine learning is proposed for improving the accuracy and efficiency of forecasting. The aim of this research is to propose a weather prediction model for short-range prediction based on numerical data. Daily weather prediction includes the work of thousands of worldwide meteorologists and observers. Modernized computers make predictions more precise than ever, and earth-orbiting weather satellites capture pictures of clouds from space. However, in many cases, the forecast under many conditions is not accurate. Numerical weather prediction (NWP) is one of the popular methods for forecasting weather conditions. NWP is a major weather modeling tool for meteorologists which contributes to more accurate accuracy. In this research, the weather forecasting model uses the C5.0 algorithm with K-means clustering. The C5.0 is one of the better decision tree classifiers, and the decision tree is a great alternative for forecasting and prediction. The algorithm for clustering the K-means is used to combine identical data together. For this process, the clustering of K-means is initially applied to divide the dataset into the closest cluster of K. For training and testing, the meteorological data collection obtained from the database Modern-Era Historical Analysis for Research and Applications (MERRA) is used. The model's performance is assessed through MAE mean absolute error (MAE) and root mean square error (RMSE). And the proposed model is assessed with accuracy, sensitivity, and specificity for validation. The results obtained are compared with other current machine learning approaches, and the proposed model achieved predictive accuracy of 90.18%.

Venous thromboembolism (VTE) remains an important consideration within surgery, with recent evidence looking to refine clinical guidance. This review provides a contemporary update of existing clinical evidence for antithrombotic regimens for surgical patients, providing future directions for prophylaxis regimens and research. For moderate to high VTE risk patients, existing evidence supports the use of heparins for prophylaxis. Direct oral anticoagulants (DOACs) have been validated within orthopaedic surgery, although there remain few completed randomised controlled trials in other surgical specialties. Recent trials have also cast doubt on the efficacy of mechanical prophylaxis, especially when adjuvant to pharmacological prophylaxis. Despite the ongoing uncertainty in higher VTE risk patients, there remains a lack of evidence for mechanical prophylaxis in low VTE risk patients, with a recent systematic search failing to identify high-quality evidence. Future research on rigorously developed and validated risk assessment models will allow the better stratification of patients for clinical and academic use. Mechanical prophylaxis’ role in modern practice remains uncertain, requiring high-quality trials to investigate select populations in which it may hold benefit and to explore whether intermittent pneumatic compression is more effective. The validation of DOACs and aspirin in wider specialties may permit pharmacological thromboprophylactic regimens that are easier to administer.

No abstract

Earlier, Process of relevant pattern observation which is present in the database observed as a hurdle for database protection. Over the time, various approaches for hiding knowledge have emerged, mainly in the focus of Association rules and frequent item sets mining. This paper, have seen the problem in different view i.e., Knowledge hiding to the context where the data and extracted knowledge have a sequential structure. The concept of NP hardness is observed over the sequential pattern hiding. A polynomial sanitization algorithm was adopted and implemented over the spatiotemporal patterns extracted from moving objects databases. Disseminating datasets of this kind presents a considerable opportunity for knowledge patterns of interest. The developed model is kept under the attack, which exploits the knowledge of underlying road networks.