The identification of the same objects is very important in determining the similarity between different objects. Nowadays, there are several techniques that allow us to divide objects into different groups that differ from one to another. In order to have the best separation between the clusters, it is required that the optimal determination of the number of clusters of a corpus be made in advance. In our research, the Silhouette score technique was used in order to make the optimal determination of this number of clusters. The application of such a technique was done through the Python language, and a corpus of unstructured job vacancy data was used. After determining the optimal number, at the end we present these clusters and the similarity between them, this presentation will be done in the form of a graph in a suitable format.

Addressing the growing disparity between job market demands and the availability of skilled workers, particularly in the technology sector, is a critical challenge in numerous countries. This study introduces a model that assesses the alignment between job market requirements and university curricula, primarily through textual content analysis. Initially, this research illustrates the operational framework of the model through graphical representation. Subsequently, the proposed techniques vital to the functionality of this model are delineated. Specifically, the integration of data mining techniques is employed for the automated extraction of relevant information from both labor market demands and university curricula. An integral aspect of this study involves outlining the methodology for creating the dataset. This phase is essential as it lays the groundwork for further stages, notably the implementation of code to generate comprehensive results. The findings of this study reveal significant insights into the alignment between job market demands and university curricula. Through textual content analysis and data mining techniques, patterns and discrepancies between the two domains are identified, shedding light on areas for improvement in educational provision. Conclusions drawn from this research underscore the importance of bridging the gap between workforce requirements and educational provisions. By leveraging data-driven approaches, educators and policymakers can make informed decisions to enhance curriculum development and better prepare students for the demands of the job market. The impact of this research extends to both academia and industry, offering actionable insights for curriculum alignment and workforce readiness initiatives. Ultimately, this model contributes to the advancement of educational practices and the enhancement of workforce productivity in response to evolving industry needs.

Most cybersecurity frameworks are based on three major components such as confidentiality, integrity, and availability. All these components have their parameters that are used to secure network nodes. But finding the most cyber secure node in a network needs a measurement method. The aim of the paper is to offer a model that can be used to find the most secure network nodes considering these cybersecurity components and their parameters. This is achieved by modelling numeric values of respective weights for parameters of confidentiality, integrity, and availability. The model is applied to a simulated environment where random values standing for cybersecurity parameters are given to 30 wireless network nodes that are used as an example. Then the weighted values are processed with Python programming language by giving the most secure nodes according to needed cybersecurity components. This model can be used to recommend the right network node that can be used to deploy services securely while avoiding potential vulnerabilities and cyber-attacks.

The revolution of the industrial sector in the automated one has happened with the use of the Industrial Internet of things (IIoT). They are providing unprecedented possibilities for connection, and automation. Also, the ubiquitous of IIoT has brought new cyber security challenges, putting sensitive data at risk. This research paper proposes a comprehensive model for enhancing the cyber security of IIoT systems. Our model integrates various countermeasures, including a proactive assessment of security vulnerabilities, examination of identified vulnerabilities, categorizing data, delivery of comprehensive reports, and assurance of effective countermeasures based on a cost-benefit approach, aligned with industry standards and frameworks. The proposed model aims to address the need for the development of robust and resilient cyber security solutions for IIoT environments. This research work introduces the proposed model's main functions, integration, workflow, and references. With this research, we contribute to the enhancement of cyber security in the IIoT environment by proposing a model that assists with proactive assessment, effective response, and informed decision-making. We envision that the proposed model will support industrial organizations in securing their IIoT systems against cyber threats, ultimately have stability and secure industrial operations.

Edge or fog computing are being used to reduce latency between end devices and traditional cloud computing. The latest developments that have improved the hardware aspects of wireless sensors and mobile networks, gave the opportunity for other enhancements if such technologies are integrated between them. Regarding this enhanced heterogeneous environment, the approach of placing services needs to reconsider the security aspects. We have developed a baseline method of a solution which fulfills the cybersecurity requirements converging with the proposed multi-tier integrated model, that enhances the K-means algorithm with added processing steps supplying the criteria of the CIA triad.