An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

As user service demands change constantly, task scheduling becomes an extremely significant study area within the cloud environment. The goal of scheduling is distributing the tasks on available processors in order to achieve the shortest possible makespan while adhering to priority constraints. In heterogeneous cloud computing resources, task scheduling has a large influence on system performances. The various processes in the heuristic-based algorithm of scheduling will result in varied makespans when heterogeneous resources are utilized. As a result, a smart method of scheduling must be capable of establishing precedence efficacy for each task to decrease makespan time. In our study, we develop a novel efficient method of scheduling tasks according to the firefly algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. We evaluate the performance of our algorithm by putting it through three situations with changing amounts of processors and numbers of tasks. The findings of the experiment reveal that our suggested technique found optimal solutions substantially more frequently in terms of makespan time when compared with other methods

A New Hybrid Root-Finding Algorithm to Solve Transcendental Equations Using Arcsine Function

The objective of this paper is to propose a new hybrid root finding algorithms for solving non-linear equations (NLEs) or transcendental equations (TEs). The proposed algorithm is based on the trigonometrical algorithm using arcsine function to find a root. Several numerical examples are presented to illustrate the proposed algorithms, and comparisons are presented with other existing methods to show efficiency and accuracy. Implementation of the proposed algorithms is presented in a mathematical software tool Maple

Novel substitution-box generation using group theory for secure medical image encryption in E-healthcare

With the increasing need for secure transmission and storage of medical images, the development of robust encryption algorithms is of paramount importance. Securing sensitive digital medical imagery information during transmission has emerged as a critical priority in the e-Healthcare systems. Recent research has highlighted the significance of developing advanced medical image encryption algorithms to ensure secure transmission during telediagnosis and teleconsultations. In this study, we propose a novel medical image encryption algorithm which is based on a novel substitution-box generation algebraic method using a combination of a multiplicative cyclic group with an order of 256 and a permutation group with a large order. To evaluate the security performance of the proposed generated S-box, various standard security indicators are assessed and analyzed. The newly proposed medical image encryption algorithm utilizes the generated S-box, along with bit-plane slicing, circular shifting, and XOR operations, to achieve enhanced security and robustness for encrypting sensitive imagery data. In order to assess the effectiveness of the proposed encryption algorithm, a comprehensive benchmarking analyses, specifically designed for evaluating image encryption schemes, have been conducted. The results obtained from the comparison and other analyses serve to validate the optimal features and high cryptographic strength exhibited by the proposed method. Hence, the proposed algorithm demonstrates significant effectiveness and holds considerable promise in the realm of medical image encryption for secure e-Healthcare systems

Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine

Due to privacy and sensitivity of the medical images, the creation of strong medical image encryption schemes has gained the attention of cryptographers. In this study, a robust medical image encryption scheme is developed involving a fuzzy subset of integers from 1 to 256 characterized by membership values. The S-box entries are determined using a precise mathematical formulation. Various standard analyses are performed to check the reliability and security of the newly devised S-box. Furthermore, this newly devised S-box is utilized to design proposed encrypting technique. A thorough benchmarking analysis that is specifically designed for image encryption schemes has been conducted in order to assess the algorithm’s performance. The findings of these analyses obtained through the proposed scheme, in comparison to some recently developed image encryption schemes; show that the proposed approach exhibits exceptional attributes and formidable cryptographic potency. Consequently, the algorithm not only showcases remarkable effectiveness but also holds great promise in the realm of securing medical images for telemedicine

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model’s capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population