Computer and Information Science, Issue: Vol.19, No.1

Rethinking Human-Centric Cybersecurity: A Mixed-Methods Analysis of Incident Severity Determinants

Thu, 12 Feb 2026 02:00:49 +0000

This paper, part of a larger dissertation, challenges the prevailing characterization of humans as the “weakest link” in cybersecurity, a perspective that has led to significant resource misallocation and flawed defensive strategies. Hence, the study empirically investigates the relationship between specific human factors and the severity of security incidents. Employing a sequential explanatory mixed-methods design, this research integrates quantitative analysis of 237 incidents from the VERIS Community Database with qualitative insights from interviews with 12 cybersecurity professionals. The quantitative analysis reveals a critical distinction: human error is associated with a significant reduction in incident severity (odds ratio [OR] = 0.28, p < 0.001), whereas social engineering is linked to a twofold increase in severity (OR = 2.04, p = 0.039). These findings directly challenge the monolithic view of the “human element” and the assumption that initial access vectors reliably predict impact. Qualitative data further illuminate these patterns, indicating that errors are often quickly detected and contained, whereas social engineering facilitates deeper, more persistent intrusions. This study proposes an empirically grounded framework for human-centric incident severity, advocating for a strategic shift from generic awareness training to a dual focus on error-tolerant systems and advanced behavioral detection capabilities. The research offers a refined theoretical lens for understanding human factors in cybersecurity and provides actionable recommendations for optimizing security investments.

Digital Transformation and the Internationalisation of Information Science: Applied Artificial Intelligence, Emerging Trends and Future Opportunities

Thu, 12 Feb 2026 02:04:29 +0000

Digital transformation and digital humanism are reshaping knowledge and interactions across diverse fields and industries. To adapt and fit within the modern digital economy, professional patterns and career pathways require critical and vital capabilities to herald new opportunities and prospects. In light of these dynamics, the research explores the implications of applied artificial intelligence and realities to bridge career prospects in the field of information science, while focusing on digital transformations to foster the development of novel applications. It evaluates essential emerging programs and applications to bridge and advance career projects in the field through the lens of digital transformation; demonstrates how to facilitate effective integration and adoption of applied artificial intelligence technologies and applications; explores factors that hinder these emerging trends and dynamic changes; and formulates a strategic framework to leverage applied artificial intelligence and digital transformation for future opportunities. Research applied content analysis and knowledge from diverse electronic journals, books, online databases, the Internet and the World Wide Web. Research publications and articles were identified and searched using a statistical approach of preferred reporting items for systematic reviews and meta-analyses (PRISMA) strategy and scoping review methodologies. A mixed method research design incorporating quantitative and qualitative approaches was applied to collect and analyze, with concurrent and sequential triangulation used to enhance the validity of the findings. First insights indicate that integration of emerging programs, such as artificial intelligence, machine learning, deep learning, generative artificial intelligence and large language models – reflects transformative technical expertise and strategic innovation in information science, which positions digital transformation as the defining framework to foster interdisciplinary competencies, enhance employability and advance sustainable technological and socio-economic development in the global knowledge economy. Second insights demonstrate that adoption of applied artificial intelligence depends on technological, human and ethical pillars, with digital infrastructure and cloud readiness emerging as the most influential. Third insights highlight multiple interrelated factors that hinder these emerging trends and dynamic changes - inadequate preparedness and training, limited institutional support and resources, resistance to change and lack of awareness of practical AI tools. Fourth insights determine a strategic framework to leverage artificial intelligence and digital transformation for future opportunities from curriculum coordination to technical instruction, as well as AI mentorship and leadership to effectively enhance market competitions and industrial portfolios.

Comparative Evaluation of Deep Learning Models, Security Tools, and Detection Frameworks for SQL Injection Attack Detection

Fri, 17 Apr 2026 03:27:10 +0000

This study evaluated the performance of three major SQL injection (SQLi) detection categories—deep learning models, security tools, and structured detection frameworks. Experiments were conducted on a benchmark SQLi dataset derived from publicly available and synthetically augmented SQL traffic, with performance evaluated using accuracy, F1-score, AUC, latency, and false positive rate. Using this dataset containing diverse SQLi variants, the research compared hybrid CNN–LSTM–Autoencoder and Transformer-based models against widely used tools (SQLMap, Acunetix, Microsoft Defender for SQL, CodeScan Labs) and established frameworks (IDE, DIAVA, SQL Shield, ASTF). Deep learning models achieved the highest accuracy (≥0.99), followed by frameworks (0.86–0.96), while tools recorded the lowest detection capability (0.75–0.92). ANOVA results (F = 11.12, p = 0.0013) confirmed statistically significant performance differences. The findings demonstrate the superiority of deep learning—especially hybrid architectures integrating structural, sequential, and latent features—in detecting modern SQLi attacks. This comparative analysis provides empirical evidence supporting the prioritization of adaptive neural models in database security environments.

Quipu Data Structure

Fri, 13 Mar 2026 04:52:39 +0000

A quipu is an Inca device. Made up of knotted strings of varying direction and colour, some of which may be grouped, this device encodes information. Despite ongoing efforts, the meaning of such information remains largely unknown. Unlike existing work, this research does not attempt to decipher the information embedded in a quipu, but instead to apply this traditional Indigenous device to modern software development. A data structure based on the quipu is constructed. Like a quipu, the data structure is hierarchical, partially unsorted, grouped and summed. Interfaces to the data structure in the form of four classes in both C++ and Python are given. A corresponding file format is further proposed. Three applications of the data structure are presented, namely a spreadsheet, a file system and image representation. Practical examples of each of these three applications are presented. This research brings to light the importance of traditional Indigenous knowledge in modern technology.

A New Framework for Nuanced Sentiment Analysis in Political Communication: A Machine Learning Approach to the Case Study of His Majesty King Abdullah II's UN Speech

Fri, 17 Apr 2026 02:53:00 +0000

Sentiment analysis has emerged as a crucial tool for understanding public opinion across various fields, yet studies focusing specifically on political speeches remain limited. This research employs advanced computational techniques to analyze the sentiments expressed in King Abdullah II's speech at the United Nations, which addresses pressing humanitarian concerns amid the ongoing crisis in Gaza. While traditional sentiment analysis typically classifies sentiments into positive, negative, and neutral categories, this study refines this approach by introducing five nuanced classifications: Empathetic, Call to Action, Critique of Power, Reassuring, and Urgent. Utilizing state-of-the-art natural language processing methodologies, we directly analyze the speech text, employing various supervised machine-learning algorithms to assess the effectiveness of these models. Preliminary findings reveal an accuracy rate of approximately 85%, with the Support Vector Machine algorithm demonstrating exceptional performance across all sentiment categories. Notably, The analysis uncovers a predominance of empathetic and critical sentiments, underscoring the King’s deep concern for humanitarian issues and the urgent need for action. This study highlights the classifier performance through metrics such as accuracy, precision, recall, and F1-score and fills a significant gap in the literature by providing patterns into political communication strategies during crises. The findings offer valuable implications for policymakers and community leaders, enabling them to foster civic engagement and inspire citizens to contribute to national development. By enhancing our understanding of the emotional nuances within political discourse, this research serves as a model for future sentiment analysis studies, ultimately contributing to the resilience of communities in the Arab region. Limitations include the single-speaker, single-speech dataset; we discuss directions for broader validation on larger, multi-speaker corpora.

Artificial Intelligence–Driven Accelerated Diagnostic Systems for Rapid X-Ray Interpretation, Automated Blood Analysis, and Ultra-Fast Laboratory Processing

Tue, 21 Apr 2026 01:59:54 +0000

This article proposed AI-ADS, an AI-Driven Accelerated Diagnostic System that integrates automated blood analysis, rapid X-ray interpretation, and rapid laboratory orchestration into a single latency-aware architecture. A physiology-structured blood analyzer, a reliability-gated fusion module with uncertainty estimation and budgeted early departures, and a dual-path radiograph encoder with semantic and frequency cues are all part of the proposed solution. In order to translate the speed of diagnostics into actual clinical throughput, AI-ADS employs a digital-twin controller to further model laboratory operations, and constraint-aware scheduling to reduce turnaround-time tails while prioritizing uncertain or critical cases. With an AUROC (macro) of 0.962, an AUPRC (macro) of 0.931, and F1 = 92.2% (Table 1; Fig. 1), the experimental results demonstrate that multimodal fusion improves diagnostic quality in comparison to single-stream baselines. Probability trustworthiness is enhanced by calibration and uncertainty gating, as shown in Table 2 and Figure 2, where ECE = 0.021, Brier = 0.074, and error@high-confidence = 1.9%. Table 3 and Figure 3 show that AI-ADS offers real-time inference with a throughput of 18.4 cases/sec, an early-exit rate of 71%, and a P50/P95 end-to-end latency of 50/120 ms. Table 4 and Figure 4 show that the lab's critical-test compliance increased from 81.2% to 93.5% after using the digital-twin CRL scheduler, and that the median TAT decreased from 49 to 34 minutes. The 90th percentile TAT also decreased from 92 to 62 minutes. Clinical timeliness, diagnostic reliability, and accuracy can all be improved with AI-ADS, as shown here.

Analyzing Humanitarian Advocacy: A Machine Learning Approach to Sentiment Analysis of Her Majesty Queen Rania of Jordan's Speech

Fri, 17 Apr 2026 03:15:58 +0000

This study develops a natural language processing model for sentiment analysis of Queen Rania of Jordan's speeches, focusing on her advocacy for humanitarian causes. Utilizing advanced machine learning and AI techniques, we analyze her communication about refugees and vulnerable populations to identify her advocacy strategies. By integrating technology for nuanced discourse analysis and refining traditional sentiment classifications with contextual information and expert input, we identify key themes and public sentiment trends and quantify emotional responses that may not be apparent from qualitative analysis alone. The research also examines the linguistic features of her speeches, including word choice, tone and rhetorical devices, and considers the socio-political context surrounding her messages to better understand their impact on global humanitarian issues. Given the accessibility of the speeches, a manual classification phase engaged experts to identify key terms and enhance analytic accuracy. The proposed model achieved strong performance (accuracy 87%, precision 82%, recall 80%, F1-score 81%), and after incorporating expert feedback these metrics improved (accuracy 90%, precision 88%, recall 85%, F1-score 86%). Confusion-matrix analysis showed the model reliably distinguished neutral content from positive and negative sentiment, with most misclassifications occurring between neutral and affective categories. Comparison with expert classifications found five discrepancies across empathy, urgency, and hopefulness labels, typically where the model labeled emotionally charged lines as neutral. Findings illustrate the alignment of Queen Rania’s messaging with public sentiment and underscore the role of technology-driven communication in humanitarian advocacy. We identified dominant themes such as compassion and resilience and quantified emotional responses including hope and urgency. Overall, the study aims to illuminate the effectiveness of Queen Rania's communication strategies, demonstrate how sentiment analysis and NLP can deepen understanding of public engagement, and provide actionable guidance to improve humanitarian messaging in the digital age.