The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

The principal objective of this article is to gain insight into the biases that shape decision-making in contexts of risk and uncertainty, with a particular focus on the prospect theory and its relationship with individual confidence. A sample of 376 responses to a questionnaire that is a replication of the one originally devised by Kahneman and Tversky was subjected to analysis. Firstly, the aim is to compare the results obtained with the original study. Furthermore, the Cognitive Reflection Test (CRT) will be employed to ascertain whether behavioural biases are associated with cognitive abilities. Finally, in light of the significance and contemporary relevance of the concept of overconfidence, we propose a series of questions designed to assess it, with a view to comparing the various segments of respondents and gaining insight into the profile that reflects it. The sample of respondents is divided according to gender, age group, student status, professional status as a trader, status as an occasional investor, and status as a behavioural finance expert. It can be concluded that the majority of individuals display a profile of underconfidence, and that the hypotheses formulated by Kahneman and Tversky are generally corroborated. The low frequency of overconfident individuals suggests that the results are consistent with prospect theory in all segments, despite the opposite characteristics, given the choice of the less risk-averse alternative. These findings are useful for regulators to understand how biases affect financial decision making, and for the development of financial literacy policies in the education sector.

This study employed a deductive approach to examine external HRM factors influencing job satisfaction in the post-pandemic hybrid work environment. Explores the intermediary functions of age, gender, and work experience in this particular environment. The data-gathering procedure consisted of conducting semi-structured interviews with carefully chosen 50 managers representing various sectors, industries, organizations, and professions. The applied approach was adopted to allow a systematic and unbiased investigation of the mediating variables. The study used SPSS 25 and Smart PLS 4 to analyze the model, enhancing understanding of HRM challenges in a constantly evolving workplace. The findings offer valuable insights for HR experts and businesses, highlighting the value of comprehending what methods HRM components influence job satisfaction to optimize employee well-being and productivity. The study provides applied recommendations designed for enhancing employee contentment in the AI-evolving professional atmosphere, shedding light on the importance of supportive leadership strategies, particularly during AI-triggered downsizing. Additionally, we welcome a new era to push forward in integrating and managing AI tools and technologies to automate decision-making and data processing. Results propose that Exogenous influences of human resource management (HRM) influence manager job satisfaction considerably. Specifically, downsizing caused by AI was found to have negative consequences, whereas diversity and restructuring have favorable effects. Gender was recognized as a crucial factor that influences outcomes, then age and years of experience have the most visible effect.

This study aims to examine the impact of an innovative self-directed professional development (SDPD) model on fostering teachers’ professional development and improving their ability to manage this development independently. A quantitative research method was adopted, involving 60 participants from Almaty State Humanitarian and Pedagogical College No. 2, Almaty, Kazakhstan. Descriptive and inferential statistics were used to assess the SDPD model’s effectiveness, specifically in promoting teacher engagement, adoption of new pedagogical techniques, and improvement in reflective practices. The study findings reveal that teachers, particularly in developing regions, often face challenges in accessing formal professional development programs. The implementation of the SDPD model addresses these barriers by providing teachers with the tools and strategies required for self-improvement, regardless of geographic or economic constraints. The study participants in the pilot phase showed increased engagement with new pedagogical methods, improved reflective practices, and greater adaptability to emerging educational technologies. The algorithmic aspect of the model streamlined the professional development process, while the activity-based approach ensured that learning remained practical and relevant to teachers’ everyday needs. By offering a clear framework for continuous improvement, the model addresses the gaps in formal training access and cultivates a culture of lifelong learning. These findings suggest that the SDPD model can contribute to elevating teaching standards globally, particularly in regions with limited professional development resources.

Industry 4.0 is revolutionizing businesses’ operations and relationships with the communities to which they cater. The widespread use of computing and network programs compels firms to digitize their operations and offer novel goods, solutions, and business for practice. Universities appear to be slow to adapt to the changes in the education sector. This study suggests using consolidated digital transformation sources to evaluate the level of ability that universities have achieved in the implementation of digital procedures and to compare it to that of other business sectors across all cities and provinces in Vietnam. The text outlines specific factors that universities should consider when implementing the model. Although the objective with the expectation of education from digital transformation is high, compare it with other industries. And the scores achieved in structural agility and create of benefit for the transformative goals are 3.4, but the score of benefit of technologies is 3.0 lower than. Additionally, the organizational component’s scores were primarily focused on leadership and culture, digital strategy, market digitalization, dynamic and digital capabilities, and strengthened logistics within each industry during the digital transformation. Our findings indicate that universities lag behind other industries, perhaps as a consequence of inadequate leadership and cultural shifts. This is exacerbated by a lack of innovation and inadequate financial assistance.

The 2019 Social Enterprise Promotion Act in Thailand represents a pivotal step towards promoting social enterprises by fostering self-reliance and a fair and sustainable future for the country. Despite their significance, there is a noticeable research gap focusing on the factors that motivate Thai entrepreneurs to venture into social entrepreneurship. This study seeks to fill that gap by analyzing data from 2000 respondents in Thailand, utilizing linear regression to explore whether the awareness of the United Nations Sustainable Development Goals (SDGs), the adoption of digital technologies, extrinsic motivations, such as the overall societal view of entrepreneurs, social awareness, and perceptions of entrepreneurial capabilities influence the decision to start a social enterprise. In a gender comparison, our findings reveal that the societal context plays a crucial role for both genders, although in distinct ways: Male entrepreneurs are more influenced by individualistic extrinsic values, with motivations linked to power, respect, and societal recognition. In contrast, female entrepreneurs display a collectivistic orientation, being more likely to be inspired by intrinsic motivations, such as the success and visibility of other successful startups within their society. These findings underline the need for a gender-sensitive approach by government bodies, educational institutions, and other relevant organizations aiming to boost start-up rates of enterprises who “make a difference in the world”. Tailored support and educational programs to address the unique motivations and perspectives of male and female entrepreneurs could play a crucial role in enhancing the effectiveness of strategies designed to promote social entrepreneurship in Thailand and beyond.