International Journal of Innovative Technology and Interdisciplinary Sciences

COVID-19: A Comprehensive Assessment of the Pandemic's Impact in Albania

Suela Hoxhaj — 2025-01-19

The COVID-19 pandemic, which began in late 2019, has had a profound impact on the political, medical, and social landscapes of countries worldwide. This study provides a comprehensive analysis of the pandemic's effects in Albania, examining the multifaceted challenges faced by the country. It begins with an in-depth assessment of the healthcare sector, addressing the early strain on medical resources, difficulties in executing rapid immunization campaigns, and the need to adapt healthcare infrastructure to unprecedented demands. The economic analysis explores shifts in employment patterns, the collapse of key sectors such as tourism and small businesses, and government interventions, including fiscal stimulus measures aimed at alleviating financial hardship. The paper also considers the social repercussions of the pandemic, including the rise of community-driven resilience initiatives, the mental health toll of extended lockdowns, and the acceleration of digital transformation in education and public services. Using Albania as a case study, this research emphasizes the importance of regional cooperation in enhancing resilience to external crises, provides practical recommendations for future crisis preparedness, and offers broader insights relevant to the Western Balkans.

Enhancing Human Activity Recognition through Machine Learning Models: A Comparative Study

Katragadda Megha Shyam — 2025-03-10

This study explores Human Activity Recognition (HAR), a machine learning technique utilized in health monitoring and human-computer interaction. HAR identifies human actions through sensor data from accelerometers and gyroscopes in smartphones and wearables. Key components of this technique include model selection, feature extraction, preprocessing, and data collection to classify activities such as standing, lying, sitting, and walking. Despite its potential, privacy concerns warrant further research for effective deployment. A comprehensive analysis of HAR techniques has been described in this research work.

Unveiling Anomalies: Leveraging Machine Learning for Internal User Behaviour Analysis – Top 10 Use Cases

Wassim Ahmad — 2025-03-11

Insider threats pose a significant risk to organizations, as traditional Security Information and Event Management (SIEM) systems struggle to detect subtle, evolving anomalies in user behaviour. While machine learning (ML) offers promise, the absence of a structured approach to prioritize and validate high-impact threat scenarios limits its practical adoption. This research addresses this gap by systematically identifying and validating the top 10 critical insider threat use cases—including data exfiltration, privilege escalation, and lateral movement—through a methodology combining MITRE ATT&CK tactics, Verizon Data Breach Investigations Report (DBIR) statistics, and related research papers. We then integrate the Random Cut Forest (RCF) algorithm into the Wazuh/OpenSearch SIEM platform, tailoring its unsupervised learning capabilities to detect these prioritized threats in real time. By correlating ML-driven anomaly scores with rule-based alerts, our solution reduces false positives by 35% and achieves a 94% true positive rate for high-risk use cases like unauthorized access. Validation in a production environment confirms the framework’s efficacy, with detection times under 3 minutes for 80% of anomalies. Beyond technical integration, this work establishes a replicable blueprint for aligning ML models with operational priorities, empowering organizations to focus resources on the most damaging insider threats.

Students' Failure in Mathematics: A Case Study of Calculus-Related Modules at a University in Johannesburg

Luke Oluwaseye Joel — 2025-03-16

Mathematics is widely recognized as a challenging subject for many students, often leading to high failure rates among university learners. This study investigates the factors contributing to student failure in two calculus-related mathematics modules at a university in Johannesburg, South Africa. Five key factors are examined: students’ attitudes toward mathematics, self-doubt, teaching methods employed by lecturers, access to textbooks and learning materials, and short attention spans. Data were collected through a Google Form questionnaire distributed to students, and the findings were analysed using statistical methods. The results indicate that neither age nor gender significantly affects students' performance in mathematics. However, the five identified factors play a substantial role in determining success or failure. These findings are supported by a Chi-Square test, yielding a statistically significant p-value of 0.000128. We also provide some valuable insights from the polarity and subjectivity analyses of the students’ responses. While the insights provided are valuable, this study acknowledges that these factors represent only part of a broader set of influences on student outcomes in mathematics.