4 research outputs found

    Exploring the Role of Social Media in Bridging Gaps and Facilitating Global Communication

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    Social media plays a pivotal role in shaping global communication dynamics, offering unprecedented opportunities for intercultural dialogue and knowledge exchange. Understanding the influence of social media on cross-cultural communication is essential in today's interconnected world. This study aims to explore the influence of social capital theory and network theory on social media's impact on global communication. Additionally, it investigates initiatives leveraging social media to promote cross-cultural dialogue and addresses challenges such as misinformation and privacy concerns while bridging digital divides. A qualitative approach, including narrative synthesis and systematic literature review methods, was employed to analyze existing literature on social media's role in global communication. Data were collected from reputable databases such as PubMed, Google Scholar, Scopus, Web of Science, and Science Direct, using specific inclusion and exclusion criteria. The findings highlight the significant role of social capital theory and network theory in understanding the impact of social media on global communication. Initiatives utilizing social media to promote cross-cultural dialogue were diverse, ranging from online communities to social media campaigns. Moreover, challenges such as misinformation, privacy concerns, digital literacy, access disparity, and regulatory hurdles were identified. Social media platforms serve as valuable tools for fostering intercultural understanding, communication, and knowledge transfer. By addressing challenges and leveraging social capital and network theories, social media can contribute to bridging digital divides and promoting inclusive global communication

    Comparative Analysis of Machine Learning Models for Data Classification: An In-Depth Exploration

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    This research delves into the realm of data classification using machine learning models, namely 'Random Forest', 'Support Vector Machine (SVM) ' and ‘Logistic Regression'. The dataset, derived from the Australian Government's Bureau of Meteorology, encompasses weather observations from 2008 to 2017, with additional columns like 'RainToday' and the target variable 'RainTomorrow.' The study employs various metrics, including Accuracy Score, 'Jaccard Index', F1-Score, Log Loss, Recall Score and Precision Score, for model evaluation. Utilizing libraries such as 'NumPy', Pandas, matplotlib and ‘sci-kit-learn', the data pre-processing involves one-hot encoding, balancing for class imbalance and creating training and test datasets. The research implements three models, Logistic Regression, SVM and Random Forest, for data classification. Results showcase the models' performance through metrics like ROC-AUC, log loss and Jaccard Score, revealing Random Forest's superior performance in terms of ROC-AUC (0.98), compared to SVM (0.89) and Logistic Regression (0.88). The analysis also includes a detailed examination of confusion matrices for each model, providing insights into their predictive accuracy. The study contributes valuable insights into the effectiveness of these models for weather prediction, with Random Forest emerging as a robust choice. The methodologies employed can be extended to other classification tasks, providing a foundation for leveraging machine learning in diverse domains

    Investigating the Adverse Effects of Social Media and Cybercrime in Higher Education: A Case Study of an Online University

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    The utilization of social media alongside the escalating occurrence of cybercrime presents significant hurdles for higher education institutions in today's digital era, prompting a comprehensive exploration of their ramifications. This study investigates the intersection of social media and cybercrime within higher education, focusing particularly on an online university environment. Its aim is to analyze patterns of social media usage, the prevalence of cybercrime, and effective strategies for addressing these challenges among online university students. Through a mixed-methods approach, data were collected from a cohort of 100 students via surveys to evaluate their social media interactions and perceptions of cybercrime. Findings reveal a diverse distribution of students across faculties, with WhatsApp and Instagram emerging as the dominant platforms. Noteworthy is the active engagement of students on social media for academic purposes, though perspectives on cyberbullying and hacking risks vary. The study emphasizes the complex dynamics of social media and cybercrime in online higher education, highlighting the importance of comprehensive risk management and student well-being. As such, it advocates for the implementation of cybersecurity training and the enhancement of social media guidelines to cultivate digital literacy and foster a secure online learning environment. This research offers valuable insights into the evolving landscape of digital technologies within educational institutions, laying the groundwork for future investigations into effective interventions and policy frameworks.Top of For

    The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction.

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    In the field of Quranic education, mobile applications have become cutting edge instruments that offer chances to improve instruction and successfully involve learners. With a thorough survey-based analysis, this study seeks to evaluate how mobile applications affect Quranic teaching. The research methodology comprised creating a structured questionnaire to collect information on a range of topics, such as application usage trends, efficacy evaluations, contributions to bettering education, perceived effects on students' comprehension and retention of Quranic knowledge, improvement of student performance, and contrasts between traditional and mobile application-based learning approaches. Students enrolled in online university programs with an emphasis on Quranic education made up the population for this study. A sample size of 60 respondents provided insightful information. Descriptive statistics and inferential techniques were included in quantitative data analysis, which allowed for a detailed investigation of survey responses and the correlations between variables. The results show that respondents place a high value on mobile applications for improving Quranic education, and the usage patterns of these applications vary widely. Participants generally held favorable opinions about the usefulness of mobile applications in raising student achievement and comprehension of Quranic knowledge. Furthermore, it became evident that mobile application-based learning was preferred over conventional techniques. The study identifies opportunities for further research in this area and emphasizes the significance of using mobile technology to enhance Quranic instruction. In general, the study advances our understanding of how mobile applications fit into Quranic education and influences pedagogical strategies used in online learning environments