7 research outputs found

    Empirical Assessment of ICT Impact on Teaching and Learning in High Schools: A Study in the Context of Balkh, Afghanistan

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    This study comprehensively explores ICT integration in Balkh’s high schools, covering infrastructure, teaching methodologies, student outcomes, integration challenges, and preparedness. Surveys were administered to 230 participants, including teachers and students. Descriptive statistics revealed a generally positive perception of ICT infrastructure and technology usage but identified areas for improvement. ANOVA results highlighted significant disparities in the impact of multimedia, interactive tools, and technology-assisted learning, providing nuanced insights. Regression analysis unveiled a unique correlation between teachers’ observations of academic performance and ICT integration. Chi-square tests showed a substantive association between students’ perceptions of ICT impact and learning outcomes. Addressing integration challenges, technological barriers emerged as a concern, signaling the need for targeted interventions. Positive indicators in teacher preparedness, access to technological resources, and administrative support emphasized the role of ongoing professional development. These findings offer empirically grounded insights for evidence-based decisions. While acknowledging promising strides in ICT integration, the study advocates for strategic interventions to overcome challenges. It contributes a nuanced understanding of ICT dynamics, guiding informed decision-making for educators, administrators, and policy-makers. The research emphasizes optimizing ICT integration in Balkh’s high schools for effective learning through technology

    Strengthening Digital Security: Dynamic Attack Detection with LSTM, KNN, and Random Forest

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    Digital security is an ever-escalating concern in today's interconnected world, necessitating advanced intrusion detection systems. This research focuses on fortifying digital security through the integration of Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), and Random Forest for dynamic attack detection. Leveraging a robust dataset, the models were subjected to rigorous evaluation, considering metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The LSTM model exhibited exceptional proficiency in capturing intricate sequential dependencies within network traffic, attaining a commendable accuracy of 99.11%. KNN, with its non-parametric adaptability, demonstrated resilience with a high accuracy of 99.23%. However, the Random Forest model emerged as the standout performer, boasting an accuracy of 99.63% and showcasing exceptional precision, recall, and F1-score metrics. Comparative analyses unveiled nuanced differences, guiding the selection of models based on specific security requirements. The AUC-ROC comparison reinforced the discriminative power of the models, with Random Forest consistently excelling. While all models excelled in true positive predictions, detailed scrutiny of confusion matrices offered insights into areas for refinement. In conclusion, the integration of LSTM, KNN, and Random Forest presents a robust and adaptive approach to dynamic attack detection. This research contributes valuable insights to the evolving landscape of digital security, emphasizing the significance of leveraging advanced machine learning techniques in constructing resilient defenses against cyber adversaries. The findings underscore the need for adaptive security solutions as the cyber threat landscape continues to evolve, with implications for practitioners, researchers, and policymakers in the field of cybersecurity

    Artificial Intelligence for Social Media Safety and Security: A Systematic Literature Review

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    The proliferation of social media platforms has revolutionized communication and connectivity, but it has also introduced new challenges related to safety and security. In response, researchers and practitioners have turned to artificial intelligence (AI) to develop innovative solutions for mitigating online risks. This systematic literature review explores the key applications, methodologies, benefits, limitations, ethical considerations, and future directions of AI in promoting social media safety and security. The review synthesizes findings from various scholarly articles spanning various disciplines, including computer science, engineering, and social sciences. The methodology involved searching academic databases such as PubMed, Scopus, IEEE Xplore, and Google Scholar using predefined search terms and inclusion criteria. The results reveal a diverse range of AI-driven approaches for addressing safety and security concerns on social media platforms, including enhanced threat detection, automated content moderation, and real-time response mechanisms. However, the deployment of AI in social media contexts also raises ethical challenges such as algorithm bias, privacy concerns, and lack of explain ability. The conclusion emphasizes the importance of ongoing research, collaboration, and ethical guidelines to maximize the benefits of AI while minimizing its potential risks. This review contributes to the growing body of literature on AI and social media by providing insights into current trends, challenges, and future directions in this rapidly evolving field

    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

    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

    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

    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
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