Authors - Areej Almazroa, Sara Albahlal, Dalia Alswailem, Dhay Altamimi, Aljoharah Aldaej, Heba Kurdi Abstract - Monitoring marine litter is essential for planetary and human survival. This study proposes a novel framework integrating satellite data and big data analytics to assess marine litter distribution in coastal and oceanic environments. Leveraging open-source imagery from COPERNICUS Sentinel-2 and LANDSAT, the framework utilizes reflectance methodologies and image processing to identify and classify marine debris, focusing on spectral bands from visible blue (490 nm) to short-wave infrared (1610 nm). A pilot case study in San Diego, California, demonstrates the approach’s feasibility. The study explores the potential of microwave radiometry and machine learning for material detection and contour analysis, showing how satellite data can support dynamic and cross-platform monitoring systems. Results validate the use of remote sensing technologies to map plastic debris, providing a replicable methodology that combines emergent (e.g., satellites, drones) and traditional (e.g., sampling) techniques. This approach contributes to a deeper understanding of plastic pollution pathways, sources, and impacts across economic sectors. By generating harmonized data on mismanaged plastic waste, the study informs sustainability strategies and circular economy practices, helping redesign systemic plastic management and supporting local and global environmental governance.
Authors - Sonali S. Gaikwad, Jyotsna S. Gaikwad Abstract - In this semi-systematic literature review, a detailed study of the role of Human-Computer Interaction (HCI) in creating game-based solutions for Attention-Deficit/Hyperactivity Disorder (ADHD) among children is conducted. Six peer-reviewed research studies were selected. The study demonstrates that HCI can serve as a major therapeutic mechanism by transforming digital platform-based cognitive training into engaging, interactive experiences. These approaches not only improve focus but also enhance the overall effectiveness of interventions. Key findings from the analyzed studies are discussed, and future research directions are proposed, including multimodal hybrid systems with adaptive personalization and accessibility features to further improve outcomes for children with ADHD.
Authors - Bharathi A, Mohan Kumar P, Subha B Abstract - Rupture of an intracranial aneurysm results in catastrophic subarachnoid hemorrhage with a 30–40% fatality rate. Although treatment decisions are guided by clinical risk scores (PHASES, ELAPSS), recent research suggests that morphological analysis and computational fluid dynamics (CFD) may offer better rupture prediction. This study looked at 92 middle cerebral artery aneurysms from the CMHA dataset, which included 71 that had ruptured and 21 that had not. We evaluated four feature sets: Clinical-Basic (13 variables), Clinical-Scores (adding PHASES and ELAPSS; 15 variables), Scores and Morphology (24 variables), and Full (28 variables). We trained logistic regression models using 5- fold cross-validation with a 20% test set. We used bootstrap validation (1000 iterations) and Bonferroni-corrected feature importance analysis to reduce overfitting. The AUC for the Clinical-Basic set was 0.891±0.063. Performance was enhanced to a maximum AUC of 0.976±0.034 by adding PHASES and ELAPSS. The Full model achieved an AUC of 0.981±0.029, with neither morphological nor hemodynamic variables giving much further improvement. Significant variance was revealed by bootstrap analysis (95% CI: 0.764-0.998). At 90% specificity, the test set's AUC was 0.933, but its sensitivity was only 14.3%. The primary contributors were ELAPSS (F=143.2, p<10⁻¹) and PHASES (F=38.4, p<10⁻¹), whereas morphological and hemodynamic characteristics did not exhibit any significant correlations. Clinical scores demonstrated strong discrimination, but CFD-derived parameters offered minimal additional value in this small, imbalanced, single-center group. The wide confidence intervals and class imbalance limit clinical recommendations. Further validation in larger, multicenter studies is necessary.
Authors - Tirupathi Rao Dockara, Manisha Malhotra Abstract - AI and data platforms are increasingly expected to deliver end-to-end business automation under rapid market and regulatory change. However, prevailing platform construction strategies remain predominantly top-down: teams standardize a generic capability stack and subsequently customize it for heterogeneous domains through code, integration glue, and service forks. This approach amplifies technical debt, fragments governance, and makes continuous adaptation expensive. This paper introduces the Inverse Vertex Pyramid (IVP), a design pattern that reverses the direction of platform derivation. IVP begins at the use-case vertex by conducting rigorous analysis of high-value specialized automation scenarios and generalizes them into explicit, machine-actionable platform descriptors (metadata models, domain ontologies, policy/workflow specifications, and capability contracts) that form a stable, reusable core. Specialization is realized primarily via declarative configuration and policy changes, rather than code rewrites. We formalize IVP as a pattern, propose a reference architecture separating control and execution planes, and provide a comparative analysis against layered architectures, domain-driven design, and microservice platforms. A proof-of-concept walkthrough in regulated claims automation illustrates the generalization mechanism and highlights how IVP can reduce re-engineering, improve governance consistency, and accelerate time-to-market. The paper concludes with limitations, threats to validity, and a research agenda for automated use-case mining, formal verification of policies, and quantitative evaluation of platform agility.
Authors - Nishant Shah, Ansh Bajpai, Shrivaths S. Nair, Manas Verma K, Sabitha S Abstract - Digital accessibility in higher education is a key requirement to ensure the inclusion of students with hearing disabilities. However, institutional plat-forms often present barriers that limit autonomy, understanding of information, and full participation. The objective of this study was to evaluate the user experience of students with hearing disabilities on the EVIRTUAL, SGA, and SIS platforms of the Technical University of Manabí, identifying perceptions, accessibility barriers, and improvement proposals. A descriptive, exploratory study with a mixed-methods approach was conducted. The population consisted of seventy-eight students with hearing disabilities registered in the Inclusion Unit, from which an intentional subsample of ten participants was selected. A structured sur-vey with Likert-type scales and a participatory observation form were applied in real interaction situations with the platforms. Quantitative analysis was carried out using descriptive statistics, while qualitative information was organized into thematic categories. The results show that half of the participants achieve full autonomy in the use of the platforms, forty percent require intermittent support, and the rest need constant assistance. Regarding clarity of information and con-tent comprehension, intermediate responses predominate, which reveals recur-rent difficulties. The main barriers identified were a confusing interface, non-intuitive navigation, insufficient visual supports, and the need for external assistance. The study proposes improvements such as customizable subtitles, step-by-step visual guides, an accessibility button, a sign language interpreter avatar, and optimization for mobile devices, aimed at strengthening autonomy and user experience.
Authors - Sabarishwaran V, Gomathi K, Andey Phani Vinay, Jagadeeswaran V, Ranjith Kumar M Abstract - The rapid expansion of digital commerce platforms has significantly transformed on- line transactional systems; however, conventional centralized architectures continue to face critical challenges related to security, transparency, data integrity, and trust management. Traditional e-commerce systems rely heavily on centralized databases, making them vulnerable to data tam- pering, unauthorized access, fraudulent transactions, and single points of failure. To address these limitations, this paper proposes a secure, scalable, and modular web-based e-commerce system that is architecturally designed for integration with blockchain technology and smart contracts. The proposed system is implemented using widely adopted web technologies, with a responsive frontend and a robust backend to support essential functionalities such as user authentication, product catalog management, shopping cart operations, order processing, inventory management, and administrative control. The architecture emphasizes separation of concerns, enabling flexibility, maintainability, and future extensibility. A key contribution of this work lies in the incorporation of a blockchain-ready framework that enables immutable transaction recording and enhanced trace- ability across the entire transaction lifecycle. Smart contracts automate transaction validation and order execution. The system also introduces an AI-based anomaly detection mechanism using a Deep Q-Network to detect fraudulent behavior. Experimental validation demonstrates reliable per- formance and scalability.
Authors - Sowmyashree N, Madhu Sunkanur, Impana M, Suchithra B S, Hemalatha P G Abstract - The existence of a growing social media has created complex cyber systems in which vast quantities of interactions constitute substantial issues regarding misinformation, privacy invasion, deception of identities, and destructive behavioural tendencies. The regularity of involvement in this type of big systems requires sophisticated systems that are able to judge the motive of the user, content validity and suspicious activities within real time. Overall interest will be to develop a universal trust calculation system that will be more secure and effective in ensuring privacy and increasing the accuracy of suspicious or malicious users in social sites. The proposed Multi-Layer Federated Trust Framework algorithm is a combination of peer-based user reputation scoring, feature-based content authenticity detection, federated trust indicators aggregation, and anomaly detection with the help of behavioural anomalies. These approaches cooperate with secure aggregation and decentralized learning in removing the uncoded information exposure and enable the computation of trust at scale. The proposed algorithm is experimentally confirmed, and the obtained results are 95.2, 94.1, 93.5, and 93.8, corresponding to a minimum latency of 65 ms and a privacy preservation score of 0.98. The general results indicate a viable and holistic response that adds to secure interactions, blocks malicious acts and encourages trust in the actual social media settings.
Authors - Md. Shahidul Islam, Hasina Islam Abstract - Cross-domain recommendations are imperative in the growing tourism industry and with the increasing means of communication. Preference drift, preference transfer, and unfamiliarity with places have an overbearing impact on recommender systems. Most approaches do not address geometric misalignment across domains, which is essential for cross-domain preference shift analysis in recommendation tasks. We propose Procrustes-Based Contextual Thompson Sampling (P-CTS) for Cross-Domain POI Recommendation, integrating adversarial domain-invariant learning, optimal geometric alignment via Procrustes transformation, and adaptive Thompson Sampling with sleeping bandit management. First, the embeddings are constructed to model the preference drift across the domains. Next, the Procrustes transformation aligns source and target embedding spaces via optimal rotation, scaling, and translation. In the last phase, we initialize Beta priors with similarity-weighted pseudo-counts derived from the aligned embeddings. The experiments on Gowalla and Foursquare across domains demonstrate 5.1% improvements in Precision@5 and 9.75% improvements in cold-start accuracy, suggesting an adaptive exploration-exploitation trade-off.
Authors - Binh Pham Nguyen Thanh, Long Duong Phi, Phung Thi-Kim Nguyen, Nhan Thi Cao Abstract - The rapid proliferation of Internet of Things (IoT) devices has significantly increased the digital attack surface, which, in turn, has raised network vulnerability to sophisticated Distributed Denial of Service (DDoS) campaigns that could reduce the effectiveness of traditional signature-based Intrusion Detection System (IDS). Furthermore, conventional Machine Learning (ML) approaches are often subject to manual feature engineering and lack the capture of complex spatial and temporal dependencies, which are essential to detect subtle, polymorphic threats. In this regard, the present work proposes a lightweight hybrid Deep Learning (DL) architecture for reliable (DDoS) detection. The proposed approach integrates spatial feature extraction using a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal correlations, further enhanced by an additive attention mechanism that underlines the importance of flow segments relevant to recognition. To mitigate issues with computational complexity, a two-phase hybrid feature selection approach, a combination of Information Gain (IG) and Dynamic Particle Swarm Optimization (PSO) would be utilized to select an optimal subset of features. The performance of the model was evaluated using the CICDDoS2019 benchmark dataset. The feature selection process was able to reduce the input space from 80 to 17 relevant features. The combined CNN-BiLSTM model, along with threshold optimization, was able to achieve an accuracy of 94.1%, which indicates a significant improvement in the reduction of false negatives and validates the effectiveness of the proposed method in a secure IoT environment.
Authors - Wani Zahidah Mohd Subari, Shuzlina Abdul-Rahman, Mohamad Faizal Ab Jabal, Sharifalillah Nordin Abstract - Role-playing games (RPGs) allow the player to take on a specific role and complete different missions during gameplay. Their diversity enables a range of ap-plications beyond entertainment, as they are often used in educational contexts. Learning content can be embedded in common components, such as game fields, tasks, objects, or non-playing characters (NPCs). The paper presents several educational RPGs with their features and characteristics, and existing models of didactic video games. It proposes a two-level metamodel for describing an educational RPG. The metamodel is divided into five main components (world, educational aspects, quest, playing character, and NPCs), and their taxonomies are presented briefly. The authors propose a conceptual model that includes the interrelationships among the components mentioned. In addition, their interpretations and significance for the development of RPG educational games are explained. An example of the metamodel is represented through a quest from a real educational RPG in the field of Chemistry. The presented RPG metamodel improves under-standing and helps to better design, develop, and integrate such games into various learning environments. The presented taxonomy can serve as a useful template for structuring design details.
