The preservation of rare documents in the form of image collections presents significant challenges regarding access to their documentary content. To enable this accessibility for software agents, this article proposes a formal representation of this type of document through a semantic description layer. This layer includes a set of descriptive metadata attached to the document, alongside the minimal and strictly necessary vocabulary required to formalize the explicit textual and visual knowledge of its documentary content. To achieve this, we present a construction methodology based on a Semantic Model of Document (SMD), where a document is treated as a core documentary resource containing a set of information resources. The semantic description of these resources, aligned with RDF framework logic, produces an Ontological Core of Document (OCD) that formally describes the document's logical structure and captures its underlying semantics. Finally, we demonstrate the practical utility of these Ontological Cores through three distinct use cases—each targeting a specific dataset level (structural, administrative, and semantic)—showing how they allow software applications to move beyond simple collection searching toward intelligent, precise information extraction directly from the documentary content.

The proliferation of online misinformation demands the development of highly accurate and computationally efficient automated systems for Fake News Detection. A primary impediment to system performance is the high dimensionality of textual features derived from techniques like TF-IDF, making optimal Feature Selection a critical step. This paper presents a detailed comparative experimental study of two prominent bio-inspired evolutionary metaheuristics, the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) used as wrapper-based FS techniques for FND. The methodologies were rigorously tested across two distinct textual datasets: the complex, large-scale FakeNewsNet corpus and a moderate-scale general news dataset. The feature sets, once optimised, were evaluated using six standard Machine Learning (ML) classifiers. The GA-based FS approach, emphasising global exploration, achieved state-of-the-art accuracy of 99.91% with the Random Forest classifier on the FakeNewsNet dataset. In contrast, the PSO-based FS approach, valued for its rapid convergence, yielded a maximum accuracy of 93.29% with the Support Vector Machine (SVM) on the general news dataset. This analysis provides empirical evidence of the intrinsic trade-off between the algorithms: GA is superior for maximising accuracy in high-dimensional, complex textual spaces, while PSO offers a more efficient and practical solution for resource-constrained or moderate-scale FND tasks. The study confirms that evolutionary computation provides a robust, effective pathway for significantly enhancing ML classifier performance in this critical domain.

The COVID-19 pandemic catalyzed an unprecedented surge of misinformation on social media, frequently intertwined with emotionally charged language. Understanding both the sentiment and truthfulness of this content is critical for public health monitoring and misinformation mitigation. However, Bangla—despite being a globally prominent language—remains severely underrepresented in joint sentiment and fake news detection research, with existing studies largely restricted to single-task settings. To bridge this gap, this paper proposes a novel multi-task BanglaBERT-based framework for the simultaneous classification of sentiment and truthfulness in COVID-19 discourse. Furthermore, we introduce the first publicly available, dual-annotated Bangla corpus for this domain, comprising 35,526 textual samples aggregated from social media and news sources. Our architecture employs a shared BanglaBERT encoder with dual task-specific heads, optimized using a task-prioritized loss function that combines modified Focal Loss and weighted cross-entropy to address inherent class imbalances. Extensive experiments demonstrate that the proposed model achieves 75.1% accuracy (Macro F1: 0.707) for sentiment classification and 88.0% accuracy (Macro F1: 0.851) for truthfulness detection. Ablation studies and error analyses confirm that our tailored loss strategies significantly enhance the recognition of underrepresented and semantically ambiguous classes, particularly neutral sentiments. By releasing our dataset, code, trained models, and a Gradio-based interactive demo, this work establishes a robust benchmark for multi-task learning in low-resource Bangla NLP and provides a practical tool for fact-checking during health crises.

The aim is to design a comprehensive shelf space optimization framework that maximizes profitability, enhances sales forecasting, improves efficiency of inventory management, and supports effective decision-making in retail businesses. A robust and interactive analytical dashboard is developed that allows users to visualize critical sales metrics, analyze historical data trends, and accurately forecast product demand and supply requirements based on seasonal variations and sales performance. The work integrates three mathematical optimization paradigms like Linear Programming (LP), metaheuristic search via Genetic Algorithms (GA), and reinforcement learning using Proximal Policy Optimization (PPO) to support both static and adaptive allocation strategies. Experimental validation highlights the relative advantages of each method, with detailed evaluations based on forecast accuracy, inventory turnover efficiency, shelf utilization rate, and overall improvement in profitability. Unlike traditional static optimization models, the PPO-based framework continuously adapts allocation decisions using environmental feedback, improving flexibility in dynamic retail scenarios The paper uses multi-objective shelf optimization considering profitability, utilization, and customer demand simultaneously. The results demonstrate that the integration of predictive analytics and advanced optimization techniques significantly performs traditional shelf management approaches, offering retailers actionable insights and operational advantages.

In this research work, multiple machine learning regression techniques were used to predict the pollution and offer a comparative study to establish the optimum model for reliably predicting air quality in terms of data quantity and processing time. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used as evaluation measures to compare these regression models. Furthermore, the processing time of each algorithm was determined via standalone learning and hyper-parameter tweaking to produce the best-fit model in terms of computational time and error rate. In this paper, we have calculated the custom score which is sum of MAE, RMSE, MAPE and time processing values. The best model obtained is the custom stacked regression model has custom score of 111.41 which is very less as compared to other regression models.

There’s a lot of promise around artificial intelligence for education to personalize learning; however, there has been very little research regarding Artificial Intelligence (AI) applications in fields with very few resources for its implementation. This paper describes a proposed AI-based adaptive learning system that aims to personalize STEM education in a low-resource school environment in Kenya. This research addresses the numerous challenges associated with such a system, such as irregular internet access, limited computer hardware in situ and no previous teacher background in both AI and education. To address these issues, an adapted reinforcement learning algorithm will personalize the content shown to students, and a modified liquid neural network is used for the prediction of student success, while not being computationally expensive. As compared to traditional adaptive systems, this adaptive learning platform supports edge computing and offline updates in order to operate in a consistently low connectivity environment. In addition, by continually adjusting the difficulty, format, and rate of delivery of STEM topics to fit the style, prior knowledge, and attention level of the individual student, this platform has been seen to improve the educational experience. An 8-month case study was done with 6 Kenyan low-resource schools and 6 comparison schools located in the city. In this case, we report a 31% increase in the level of understanding of students’ key STEM subjects, 27% reduction in student drop rate from STEM topics, and 43% increase in teacher efficiency over traditional methods. We were also able to predict the level of performance of students to a 89% success while occupying a low 1.9MB memory, making it feasible to be employed on budget Android devices. This study presents evidence that the utilization of AI for personalized adaptive learning technologies in order to minimize the disparities in the provision of STEM education in low-resource settings worldwide is possible. 

Learning management systems (LMS) have become an important part of the modern higher education industry, and Moodle is one of the most popular open-source systems, which have become popular on an international level. Despite its extensive application, the majority of the existing evaluation tools are inclined to examine either performance, sustainability, or scalability separately and, thus, cannot be useful in long-term institutional planning. The current study proposes an assessment model that is a rational model of a Moodle-based LMS, as all three dimensions are included in the assessment model. The framework identifies the performance of the system, resource utilization and resource management, load-adaptive scalability metric, and an adaptable and predictable algorithmic-based assessment procedure in a variety of deployment environments. The framework applies Min–Max normalization and weighted aggregation to combine the three evaluation dimensions into a unified assessment score. To make the model more realistic, it was tested on publicly available data, for example, the Open University Learning Analytics Dataset (OULAD), which simulated actual interactions between LMS users and the utilization of cloud resource traces to analyze scalability and sustainability. Experimental evaluation using the OULAD and cloud resource datasets demonstrated approximately a 10% improvement in performance under medium workload conditions, an 18% reduction in sustainability due to increased resource utilization, and a 20% improvement in scalability as workload increased. These findings demonstrate that the proposed framework provides a systematic and data-driven approach for evaluating Moodle-based learning management systems and supports informed institutional decision-making.

This study investigates the impact of AI-powered digital assistants on students’ feelings, engagement, and academic success within higher education environments. The study aims to investigate post-adoption behaviour, emphasizing how service experiences, functional attributes, information quality, and ease of interaction influence emotional and behavioural results. A structured survey was used to gather data from 431 respondents in higher education at Exploits university, Malawi, and the study utilized a quantitative approach. Measurement scales were adapted from validated studies in the AI adoption and educational technology literature and contextualized for the higher education setting. Partial Least Squares Structural Equation Modelling (PLS-SEM) version 4.1.1.8 was utilized to examine the connections between variables. Common method bias was assessed using the full collinearity approach, and all VIF values were below the recommended threshold, indicating that common method bias was not a significant concern. The results indicate that Service experience leads to Positive emotions (β = 0.205, p = 0.002) and Student engagement (β = 0.242, p < 0.001), validating H1a and H1b. Quality of information → Positive feelings (β = 0.161, p = 0.005), backing H3a, whereas Functional characteristics → Student involvement (β = 0.409, p < 0.001), supporting H4b. Positive emotions → Student involvement (β = 0.190, p < 0.001) and Academic achievement (β = 0.460, p < 0.001), and Student involvement → Academic achievement (β = 0.310, p < 0.001), confirming H5–H7. Contextualization × Positive emotions → Academic performance was noteworthy (β = 0.063, p = 0.038), reinforcing H8a. Nonetheless, H2a, H2b, H3b, H4a, and H8b received no support (p > 0.05). The research advances theoretical understanding by broadening AI adoption literature to include emotional and behavioural effects, while also enhancing practical implications by highlighting service quality and system efficiency. Suggestions emphasize the importance of focusing on contextual, high-quality AI resources to enhance student engagement, emotional well-being, and educational achievement. The results demonstrate that service experience is the most influential antecedent of both emotional and behavioural outcomes, whereas the effects of functional features and information quality vary across the examined relationships.