Search

This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots. The study aims to enhance flexibility in adapting to different environments. Three scenarios were tested, and the accuracy of the proposed algorithm was validated. In the first scenario, path planning was applied in unknown environments, both stationary and mobile, yielding excellent results in terms of time to arrival and path length, with a time of 2.3 s. In the second scenario, the algorithm was applied to complex environments containing sharp corners and unknown obstacles, resulting in a time of 2.6 s, with the algorithm also performing well in terms of path length. In the final scenario, the multi-objective algorithm was tested in a warehouse environment containing fixed, mobile, and multi-targeted obstacles, achieving a result of up to 100.4 s. Based on the results and comparisons with previous work, the proposed method was found to be highly effective, efficient, and suitable for various environments. [ABSTRACT FROM AUTHOR]

Web applications are utilized everywhere these days to share services and data online. Because companies deal with sensitive data, hackers have found them attractive targets. Vulnerabilities persist despite the numerous security procedures we've created to safeguard these applications. Major security issues have been identified in web applications used by various organizations, such as banks, healthcare providers, finance companies, and retail businesses. Cross-site scripting (XSS) attacks are one of the most significant issues, according to a report from White Hat Security. These attacks enable hackers to execute harmful programs on a user's web browser, resulting in issues such as the theft of data, cookies, passwords, and credit card numbers. This study focuses on the primary weaknesses present in contemporary web applications, particularly XSS attacks. We go over the many kinds of XSS attacks, provide instances from the real world, and describe how they operate. We also examine defenses against these attacks, discussing what works and what doesn't. [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well-optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin-the-middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks. [ABSTRACT FROM AUTHOR]

Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children's healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry. [ABSTRACT FROM AUTHOR]

Most large-scale systems including self-adaptive systems utilize feature models (FMs) to represent their complex architectures and benefit from the reuse of commonalities and variability information. Self-adaptive systems (SASs) are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts. However, reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources. The process of configuration reuse can be a better alternative to some contexts to reduce computational time, effort and error-prone. Nevertheless, systems' complexity can be reduced while the development process of systems by reusing elements or components. FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components. While current FM-based modelling techniques represent, manage, and reuse elementary features to model SASs concepts, modeling and reusing configurations have not yet been considered. In this context, this study presents an extension to FMs by introducing and managing configuration features and their reuse process. Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are widely used in military applications, monitoring of environmental information, health, domestic, and industrial applications. Despite of these wide usages, the main problem of this kind of topologies is the increasing security needs. This paper has been targeted to develop a novel message identity validation protocol in WSN to provide secure data identity in order to decrease some security threads. Introduced protocol also includes key distribution, node identification, sensitivity mechanisms, to strengthen the introduced method. Moreover, there is also repeated symmetrical key update mechanism without the need to synchronization. Finally, the developed protocol has also been tested in terms of various network parameters. [ABSTRACT FROM AUTHOR]