The digital transformation of higher education marketing demands more sophisticated approaches to understanding prospective students beyond traditional demographic segmentation. This study develops a machine learning-based psychographic and behavioral segmentation framework for prospective university students in Vietnam, integrating constructs from consumer choice theory and technology adoption literature. We employ established unsupervised and supervised machine learning techniques (k-means clustering, Gaussian Mixture Models, and XGBoost classification) rather than claiming novel artificial intelligence architectures. Analyzing survey data from 1,486 Grade-12 students, our hybrid methodological approach identified three distinct segments: Intrinsically-Motivated Digital Explorers (27.7%), Prestige-Driven Traditionalists (38.9%), and Undecided Ambivalents (33.4%). Supervised learning (XGBoost) achieved 87.2% accuracy in predicting segment membership, with feature importance analysis revealing intrinsic motivation, technology readiness, and risk aversion as the primary discriminators. The findings extend higher education consumer choice theory by integrating technology readiness as an independent discriminative factor and demonstrate the methodological value of combining unsupervised and supervised machine learning for market segmentation.

In the era of social media-driven communication, sarcasm poses a big challenge for the automated sentiment analysis systems, much more on platforms like Twitter, due to the brevity and often contextually ambiguous nature of the text. Misinterpretation of sarcastic content may degrade the reliability of downstream analytics, encompassing opinion mining and content moderation. To address this challenge, we propose, in this paper, a multi-modal transformer-based approach to sarcasm detection, which integrates textual and emoji information through the use of a cross-attention mechanism. The proposed model utilizes RoBERTa for the contextual processing of textual content to generate contextualized text embeddings, whereas emojis are encoded using Emoji-BERT to capture emoji-specific semantic and emotional cuing. A Gated-LSTM layer has been employed to model sequential dependencies among emojis, and a cross-attention mechanism dynamically aligns emoji representations with textual features for enhancing the sarcasm recognition capability. Later, these fused representations are passed to a fully connected classification layer for predicting sarcasm. For the evaluation of the performance of our proposed model against state-of-the-art results, standard metrics of evaluation have been considered. Experimental results demonstrate that the proposed approach outperforms several baseline and state-of-the-art models, with an accuracy of 92.5%, precision of 91.8%, recall of 93.2%, and an F1-score of 92.5%. From these results, we learn that jointly modeling textual and emoji modalities improves the performance of sarcasm detection in social media content. Also, these findings illustrate the potential of the suggested approach in improving sarcasm-aware sentiment analysis in the realm of social media analytics and automated content moderation systems.

The article proposes a process-oriented methodology for assessing enterprise information security, which serves as an integral indicator of business process security Q based on a multi-level system of mathematical models. The proposed approach combines risk-oriented analysis, stochastic modelling, fuzzy set methods, and optimisation of the distribution of protection resources, ensuring the linkage of security indicators to the enterprise's functional business processes. The simulation model allows the reproduction of the dynamics of cyberattack flows and the assessment of the impact of variable threat intensity on the stability of business processes in near real time. Experimental validation of the methodology on depersonalised incident logs and simulated attack scenarios showed that the integration of the optimisation module provides an increase in the integral security indicator Q by 12-27% depending on the intensity of threats, and also contributes to the rational redistribution of cybersecurity resources in favour of the most critical business processes. A comparative analysis with the Classical Risk Matrix, NIST SP 800-30, and ISO/IEC 27005 confirmed the proposed model's higher accuracy and adaptability in a dynamic cyber environment. Machine learning methods are used as an auxiliary adaptive mechanism to refine model parameters, rather than as the primary risk assessment tool. The results obtained demonstrate the practical applicability of the process-oriented simulation and optimisation model for improving the resilience of enterprise business processes and reducing residual cyber risk. 

The widespread adoption of Electronic Health Records (EHRs) has remarkably transformed healthcare delivery through rapid retrieval of patient information and enhancement of internal clinical decision-making. The rapid adoption of digital health infrastructures and remote patient monitoring systems has further highlighted the need for data-driven care. Although digital health infrastructures and remote patient monitoring systems support real-time care and care planning, the majority of EHR systems remain susceptible to data breaches and privacy violations. Major EHR systems also continue to lack cross-institutional interoperability and sufficient protection against data breaches and cyber-attacks. While the majority of EHR systems remain vulnerable, the centralized architectures and cloud-based systems commonly employed to support them also fail to provide the necessary protections and assurances. 
This work proposes a tailored management framework for EHR systems based on blockchain technology, incorporating automation through smart contracts. The framework employs a dual storage system in which sensitive clinical information is stored off-chain, while on-chain data pertains to access control logs, integrity proofs, and system control metadata. 
The proposed system utilizes blockchain consensus mechanisms to confirm data immutability, smart contracts for role-based and policy-secured access control, and encryption algorithms for secure data storage. Data integrity is maintained through hashing, and the distributed ledgers allow interoperable healthcare network policies to be traced and kept unmodifiable. 
Testing indicates that the proposed blockchain-based EHR system demonstrates enhanced data security, reduced access latency due to authorization, and improved resistance to cyber-attacks compared to traditional cloud-based EHR systems. The hybrid storage method effectively minimizes storage overhead while maintaining system efficiency. The system demonstrates a viable approach to integrating smart contract systems and blockchain technologies within EHR management systems.

This research proposes the implementation of the subjective and objective weighting approach (SOWA) method as a new approach in determining the criteria weights that combines subjective assessments from experts and data-driven objective calculations. The criteria weights generated from the SOWA method are then used in various multi-criteria decision-making (MCDM) methods, such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of ratio analysis (MOORA), grey relational analysis (GRA), multi-attribute utility theory (MAUT), weighted aggregated sum product assessment (WASPAS), weighted product (WP), simple multi-attribute rating technique (SMART), multi-attributive border approximation area comparison (MABAC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), to evaluate and rank alternatives. The research results show that the SOWA method is capable of producing balanced and representative weights, as well as consistent alternative rankings across MCDM methods. Sensitivity analysis of the ranking results indicates that all methods yield identical ranking results, signifying a high level of stability and reliability of the generated weights. These findings demonstrate that the SOWA method can serve as a solid foundation in decision support systems, particularly in the context of candidate selection or evaluation based on multiple criteria.

Cloud-native architectures have become essential for modern application development, offering scalability, flexibility, and cost efficiency through paradigms like microservices, serverless computing, and event-driven systems. However, performance trade-offs, resource underutilization, and operational inefficiencies persist across different architectural models. This study delivers a comparative performance evaluation of four leading cloud-native architectures—Service Mesh, Event-Driven Systems, Serverless Computing, and Polyglot Persistence across AWS and GCP platforms. Using a controlled experimental setup, key performance metrics including response time, throughput, resource utilization, and operational cost (OC) were assessed under varying workloads. Serverless computing demonstrated superior cost-efficiency and dynamic scaling, though hampered by cold-start delays, while event-driven systems struck a balance between responsiveness and cost. Optimization strategies such as cold-start mitigation, adaptive auto-scaling, and hybrid storage improvements yielded significant performance gains across all architectures. The research provides critical insights for developers and system architects, offering data-driven recommendations to guide architectural choices and optimize cloud-native deployments. The study’s significance lies in its empirical approach, bridging theoretical design with real-world implementation to advance best practices in building scalable and sustainable cloud-native applications.

Attention-Deficit Hyperactivity Disorder (ADHD) represents a challenging neurodevelopmental disorder that consistently displays three major symptoms involving inattention and hyperactivity alongside impulsivity. Traditional approaches for diagnosis use behavioral evaluations that create both wrong conclusions and delayed help timing. This research develops a complete diagnostic solution involving deep learning federated learning and blockchain security to analyze actigraphy signals originating from IoMT devices. This method first uses UMAP as well as PCA and t-SNE to reduce data dimensions before implementing a hybrid CNN-Transformer neural network to achieve improved classification results. A distributed learning method helps medical institutions run model training autonomously while satisfying privacy rules and addressing data centralization challenges. Model updates on blockchain systems gain protection through smart contracts and cryptographic hashing to stop adversarial attacks and sustain data authenticity. Laboratory tests reveal that this approach reaches 99.2% classification precision without significant performance impact, establishing its effectiveness. This presented study provides on-the-next level ADHD diagnosis features with the help of an AIbased system that ensures privacy and guarantees tampering and scalable operations. Such results allow advancing accurate medical works by real-time monitoring of ADHD and offer safe application of medical Artificial Intelligence to distributed healthcare processes. This will provide objective and credible evaluations that will exist on a global scale.

Connected autonomous vehicles (CAVs) are reshaping mobility but remain vulnerable to technical, organizational, and regulatory risks. This study develops a hybrid multi criteria decision-making framework that integrates CRITID for objective weighting, Fuzzy BWM for expert uncertainty modeling, and VIKOR Swarm for adaptive compromise ranking. To enhance realism, four scenarios were constructed: scalability focused (A1), compliance & reliability focused (A2), resilient high performance ecosystem (A3), and organizational vulnerability focused (A4). Results show that Scenario A3 consistently outperforms others, achieving the lowest group utility shortfall, smallest individual regret, and most favorable compromise measure. Shapley Value sensitivity analysis confirmed cybersecurity and scalability as dominant criteria, while expert AI validation reinforced the robustness of A3’s ranking. Monte Carlo simulations further demonstrated stability underweight perturbations, with A3 retaining its top position in over 80% of runs. The study contributes a transparent, reproducible, and scenario based methodology for vehicular risk assessment, bridging technical and organizational dimensions. Limitations include reliance on static scenario design and expert elicitation, suggesting future work should incorporate dynamic data streams and edge AI for real time risk recalibration.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. In Cloud Computing (CC) environments, efficient Task Scheduling (TS) plays a critical role in minimizing operational expenses and enhancing system reliability. This paper presents a novel task scheduling approach that uses the Coati Optimization Algorithm (COA) to address two pivotal challenges: reducing the total cost (sum of computational cost and communication cost) and minimizing Virtual Machine (VM) failure rates. Inspired by the cooperative foraging and adaptive behavior of coatis in dynamic environments, the proposed algorithm leverages intelligent exploration and exploitation strategies to identify optimal task-to-VM mappings under fluctuating workloads. The COA incorporates cost-awareness and failure probability metrics into its fitness function to ensure robust scheduling decisions that align with budgetary constraints and fault tolerance requirements. To assess the performance of the proposed model, comprehensive simulations were conducted using the CEA-Curie real-world workload. The results were compared against three state-of-the-art approaches, MoHHOTS, RTATSA2C, and TS-GWO. Experimental evaluations demonstrate that COA significantly outperforms these existing methods by achieving a 19.8% reduction in overall cost and a 22.5% decrease in VM failure rate. These findings demonstrate that COA offer a promising pathway toward sustainable, cost-effective, and resilient task execution in large-scale cloud infrastructures, particularly under diverse and realistic workload scenarios.

University–industry collaboration (UIC) has become an essential mechanism for fostering innovation and transferring knowledge across institutional boundaries. It is a powerful driver for innovation and sustainable economic development. This study investigates the role of UIC in facilitating knowledge transfer and its impact on innovation outcomes within industries. The study also identifies barriers such as organizational misalignment, lack of trust, and limited funding. A conceptual model is proposed to demonstrate the dynamics of UIC. Recommendations include policy reforms, structured collaboration frameworks, and enhanced R&D investments. Drawing from both qualitative and quantitative methods, the study investigates the nature of UIC, the influencing factors, and its outcomes in terms of innovation capabilities. The findings underscore the importance of trust, absorptive capacity, and aligned goals in enhancing knowledge transfer. The study also identifies critical enablers and barriers, offering strategic insights for optimizing collaborative frameworks.

The advent of (Internet of Things) IoT technologies has essentially transformed traditional houses into intelligent, equipped, and networked smart houses that serve to improve the quality in the lives of human beings with respect to security, energy efficiency, and comfort through massive automation, sensing, and remote control. However, with such a shift of paradigm, due to the diversity of devices, the limitation of resources, problems of interoperability, and a growing array of cyberthreats, opens up numerous avenues for security and privacy threats. This review attempts a holistic coverage of IoT-based smart home technologies and then provides a systematic classification of the security vulnerabilities from device, network, cloud, and application layers. The key threats include unauthorized access, data leakage, propagation of malware, denial of service, and exploits targeted against AI, with an analysis of their causes and occurrences in the real world. The paper undertakes a critical assessment of contemporary countermeasures, ranging from lightweight cryptographic protocols, AI-driven intrusion detection systems, blockchain-based authentication, privacy-preserving edge computing, and zero-trust frameworks. A comparative insight into each approach conversed with the views of the established literature draws out trade-offs between security efficacy, scalability, computational overheads, and user adoption. Based on a synthesis of the modern findings, continued gaps are identified, and future directions provided: including quantum-resistant encryption, interoperable standards, and user-centric security design, acting as the working platform or actionable directions for any researchers, developers, or policymakers in building of secure, resilient, and privacy preserving smart home ecosystem.

This study proposes a hybrid quantum-classical framework for depression detection from social media text, integrating a frozen DistilBERT encoder with a variational quantum circuit (VQC)-based classification layer. The motivation stems from challenges in clinical NLP, including overfitting on limited datasets and high parameter overhead in conventional deep learning classifiers. Experiments are conducted on a balanced subset of the Reddit Self-Reported Depression Diagnosis (RSDD) dataset comprising 6,000 users. The proposed model is evaluated against classical baselines, including TF-IDF with logistic regression and a fine-tuned DistilBERT model. Results indicate that the hybrid approach achieves competitive performance, with an F1-score of 0.925 (±0.009) and improved recall (0.942 ± 0.015) compared to the classical DistilBERT baseline. Additionally, the quantum classification layer requires significantly fewer trainable parameters (72) compared to the classical dense head, demonstrating improved parameter efficiency at the classification stage. While the results suggest that variational quantum circuits can serve as an alternative non-linear classifier in low-data settings, the findings are based on simulation and require further validation on real quantum hardware. This work contributes to the emerging area of quantum natural language processing by providing an empirical evaluation of hybrid architectures on a real-world clinical text dataset.