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AbstractGraph burning is a model for spread of information in a network. In recent times social networks play a major role in the thought process of people and influences the social movements, election and politics. The burning number is an ideal model to calculate the speed at which information is passed throughout the network. The burning number is a number which burns a graph in minimum number of steps. In this paper, a hybrid algorithm is proposed to compute the burning number of network graph based on selection of activators. The algorithm uses spanning tree, weighted centrality and uses a vertex set that are edge connected with previously burnt set. The network graph used were ca-sandiauths, ca-netscience, dolphin and mahindas. A significant improvement was found in the process of finding minimum burning number of the graph.

The 2,2,5‐trimethyl‐1,3‐dioxane‐5‐carboxylic acid (TDCA) using both theoretical and experimental methods have been studied. The sample has been subjected to XRD, FTIR, FT‐Raman, (C13 and H1) NMR, and UV–vis spectrum analysis. Then, theoretical calculations have been performed at the DFT/B3LYP/6‐311++G(d,p) higher based scale. The theoretical and experimental geometrical parameters and frequencies have been compared well. Theoretical and experimental NMR chemical shifts have been determined. Absorption wavelengths of UV–Vis spectrum were experimentally measured and compared with TD‐DFT predictions. Detailed explanations have been given for frontier molecular orbitals, low density gradient, distribution of Mulliken charges, molecular electrostatic potential (MEP), RDG, localized orbital location, and electron localized activities. Based on the studied 2D image of the Hirschfield surfaces, H···H (65.6%) and O···H/H···O (33.6%) are found as the controlling interactions. A high binding affinity of −6.5 Kcal/mol has been calculated against 4OAR protein. These theoretical findings of the molecule may be used as an anticancer drug candidate, which helps to explain the structural stability, reactivity and anticancer potential of TDCA. High drug affinity for the TDCA has been detected by in silico ADMET prediction. [ABSTRACT FROM AUTHOR]

Summary: The cloud computing environment is subject to unprecedented cyber‐attacks as its infrastructure and protocols may contain vulnerabilities and bugs. Among these, Distributed Denial of Service (DDoS) is chosen by most cyber extortionists, creating unusual traffic that drains cloud resources, making them inaccessible to customers and end users. Hence, security solutions to combat this attack are in high demand. The existing DDoS detection techniques in literature have many drawbacks, such as overfitting, delay in detection, low detection accuracy for attacks that target multiple victims, and high False Positive Rate (FPR). In this proposed study, an Artificial Neural Network (ANN) based hybrid GBS (Grey Wolf Optimizer (GWO) + Back Propagation Network (BPN) + Self Organizing Map (SOM)) Intrusion Detection System (IDS) is proposed for intrusion detection in the cloud computing environment. The base classifier, BPN, was chosen for our research after evaluating the performance of a comprehensive set of neural network algorithms on the standard benchmark UNSW‐NS 15 dataset. BPN intrusion detection performance is further enhanced by combining it with SOM and GWO. Hybrid Feature Selection (FS) is made using a correlation‐based approach and Stratified 10‐fold cross‐validation (STCV) ranking based on Weight matrix value (W). These selected features are further fine‐tuned using metaheuristic GWO hyperparameter tuning based on a fitness function. The proposed IDS technique is validated using the standard benchmark UNSW‐NS 15 dataset, which consists of 1,75,341 and 82,332 attack cases in the training and testing datasets. This study's findings demonstrate that the proposed ANN‐based hybrid GBS IDS model outperforms other existing IDS models with a higher intrusion detection accuracy of 99.40%, fewer false alarms (0.00389), less error rate (0.001), and faster prediction time (0.29 ns). [ABSTRACT FROM AUTHOR]