International Journal of Innovative Technology and Interdisciplinary Sciences

Tracking Topic Drift in AI and Workforce Narratives on Reddit: A Longitudinal Visualization from 2010–2024

Indrit Baholli — 2026-01-03

This article extends previous research on Reddit-based discourse of artificial intelligence (AI) and workforce automation by adding a longitudinal analysis of thematic evolution. With a sample of 4,243 Reddit posts between 2010 and 2024, we examine how attention has evolved over time between top AI-related topics of work, reskilling, regulation, and use cases such as ChatGPT. We apply Latent Dirichlet Allocation (LDA) topic modelling for the research and use advanced visualization tools like word clouds, heatmaps, and time-series plots to estimate topic drift. Our findings indicate that while worries over job loss and ethical governance persist, discussions over certifications and upskilling have augmented steadily. Above all, interest in generative AI started to rise steeply after 2022, indicating a remarkable change in people's opinions. These results represent the development of societal problems and technological awareness over time. This research proved helpful by using the prevalence of topics for policymakers, educators, and industry players who need to understand the changed public dis-course in the role of AI within the labour market. In this respect, it points out the increasing necessity for adaptive skills strategies and evidence-based communication.

A Data-Driven Survival Analysis of Prognostic Determinants in Patients with Alcohol-Related Liver Disease: A Prospective Study

Klerida Shehu — 2026-01-05

Alcohol-related liver disease (ALD) is a leading cause of liver-related mortality in Europe, yet prospective survival data from Southeast Europe remain limited. Prognostic assessment has traditionally focused on biological disease severity, while behavioral factors particularly sustained alcohol abstinence is less consistently incorporated. It has been conducted a prospective observational cohort study of 200 adults with confirmed ALD treated at a national tertiary referral center in Albania and followed for 12 months. Sustained alcohol abstinence (≥6 months) was modelled dynamically as a time-varying exposure within an integrated biological–behavioral prognostic framework. Overall survival was evaluated using Kaplan–Meier analysis and Cox proportional hazards models, with liver transplantation treated as a censoring event; competing-risk models were applied to account for transplantation as a competing outcome. During follow-up, 44 patients (22%) died. Non-survivors had significantly higher Model for End-Stage Liver Disease (MELD) scores (21.0 ± 7.1 vs. 15.0 ± 6.2, p < 0.001) and a higher prevalence of ascites (77% vs. 46%, p = 0.002) and hepatic encephalopathy (52% vs. 19%, p < 0.001). Sustained abstinence was less frequent among non-survivors (20% vs. 46%, p = 0.013) and was associated with improved survival (log-rank p = 0.013). In multivariable Cox and competing-risk analyses, MELD, ascites, and hepatic encephalopathy independently predicted mortality, whereas time-varying abstinence demonstrated an independent protective effect. The combined biological–behavioral model showed good discrimination and calibration (optimism-corrected Harrell’s C-index 0.78–0.82; 12-month AUC ≈ 0.80). In this underrepresented Southeast European cohort, established severity markers remained dominant predictors of short-term mortality, while the dynamic incorporation of abstinence provided incremental prognostic value, supporting improved risk stratification and pragmatic ALD management in resource-limited settings.

Charting the Digital Frontier: A Comprehensive Bibliometric Analysis of E-Agriculture Research

Anila Boshnjaku — 2026-01-11

This study presents a statistically validated bibliometric analysis of e-agriculture research published between 2020 and 2025, based on 1,363 peer-reviewed articles indexed in Scopus and Web of Science, and selected according to the PRISMA 2020 guidelines. Bibliometric mapping is combined with inferential statistical analysis and network validation to examine publication dynamics, thematic evolution, citation impact, and global collaboration patterns. Results show rapid growth in research output up to 2023, followed by a contraction in 2024. Core research themes include smart farming, Internet of Things (IoT), artificial intelligence particularly deep learning and precision agriculture. While China, India, and Brazil lead in publication volume, the United States, the Netherlands, and Germany exhibit higher citation impact, indicating a divergence between productivity and influence. Inferential testing confirms these patterns: one-way ANOVA reveals significant temporal variation in citation impact (F(5,1357)=48.5, p<2×10⁻¹⁶), and network modularity analysis (Q=0.519) demonstrates a robust thematic structure. Poisson regression further shows that publication year and thematic focus jointly shape citation performance. To extend beyond descriptive bibliometrics, the study integrates an altmetric perspective, drawing on Twitter sentiment and topic analysis to capture societal engagement with digital agriculture research. Overall, the study advances bibliometric analysis in e-agriculture by combining statistical validation, network robustness assessment, and signals of societal impact.

Data-Driven Predictive Modelling of Employee Absenteeism Using Workflow Automation Platforms

Mohammed Alars — 2026-01-16

Employee absence is a critical factor affecting organizational productivity and employee well-being. This study presents a data-driven predictive framework for employee absenteeism using a newly collected enterprise dataset comprising 8,336 employees. Absenteeism is formulated as a binary classification task, distinguishing employees with more than 80 hours of annual absence from those with lower absence levels, based on demographic and occupational characteristics. The proposed approach applies gradient-boosted decision tree models, including LightGBM, XGBoost, and CatBoost, evaluated through a stratified train–test split at the employee level to approximate temporal separation between training and prediction. Feature engineering procedures are detailed, including categorical encoding and the construction of a commuting-related indicator. All models demonstrate strong predictive performance, achieving accuracy between 85% and 87%, precision ranging from 78% to 80%, recall between 76% and 79%, and AUC–ROC values of 0.92–0.93. Model interpretability is addressed using SHAP-based feature attribution, identifying age, gender, and occupational role and location as key predictors of absenteeism risk. Furthermore, a practical system architecture is outlined, integrating the predictive models within an automated workflow using the n8n orchestration platform for deployment in human resource information systems. This enables proactive identification of high-risk absenteeism cases and supports early intervention strategies with minimal human oversight. The study contributes by addressing data leakage concerns, improving feature transparency, and demonstrating a deployable and interpretable predictive system. Future research directions include multi-organizational validation, temporal modelling using sequential data, and evaluation of system-level effectiveness in real-world HR settings.

Podcasts as Tools for Communication and Lifelong Learning in School Education

Demush Bajrami — 2026-01-17

As the adoption of information and communication technology (ICT) tools in education continues to expand, podcasts have emerged as a versatile and cost-effective medium for enhancing communication, teaching, and learning across different educational levels. However, the effective integration of podcasts in early childhood, primary, and secondary education remains under-researched. To address this gap, this paper employs a systematic review methodology to examine the effectiveness of podcasts in relation to communication skills, student engagement, and the development of lifelong learning competencies. A total of 16 peer-reviewed studies were analysed following a comprehensive literature search. The findings indicate that podcast-based learning contributes to improved listening and speaking skills, enhanced teamwork, stronger critical thinking abilities, and increased information literacy. The evidence supports podcasts as a viable alternative to traditional instructional methods, offering flexible and inclusive learning opportunities. This paper provides evidence-based recommendations for policymakers, educational institutions, and educators to guide the effective integration of podcast media into learning environments, with the aim of fostering communication competencies and promoting lifelong learning from an early age.

Data-Driven Regression Modelling of Insolvency Outcomes: Judicial Efficiency, Foreign Participation, and Recovery Trends

Teuta Hoxha — 2026-01-20

This paper provides an in-depth analysis of Albania’s debt relief system over the period 1995–2020. The dataset comprises annual bankruptcy court caseloads, foreign-creditor participation rates, and asset-reclamation statistics, allowing an examination of long-term trends amid substantial fluctuations. Using segmented time-series regression, we identify significant structural turning points associated with two major legislative reforms in 2002 and 2016. Both the level and slope of annual bankruptcy filings increased markedly following these legal interventions. A fractional logistic model indicates that foreign-creditor involvement, consistently between 13% and 15%, increased notably after 2016, reflecting improved functionality of the international recognition system for cross-border operations. Asset-recovery rates, averaging 59%, were analysed using regression and Generalized Additive Models (GAMs), showing that recovery efficiency declines under heavier judicial caseloads but improves in years with greater foreign participation. Comparative analysis with Romania, Bulgaria, and Serbia demonstrates that Albania is approaching regional norms, though gaps remain between recovery performance and institutional capacity. Overall, the results highlight those judicial reforms, the use of statistical tools in administrative decision-making, and the combination of legal modernization, courtroom efficiency, and international integration are critical determinants of effective bankruptcy systems.