Journal of Advanced Artificial Intelligence, Engineering and Technology
Journal of Advanced Artificial Intelligence, Engineering and Technology is a peer-reviewed open access journal, dedicated to the latest advancements in artificial intelligence (AI), engineering, and emerging technologies. Journal serves as a platform for researchers, academics, and industry professionals to share innovative ideas, research and practical applications that shape the future of engineering, AI and technology. Covering a broad range of topics, Journal of Advanced Artificial Intelligence, Engineering and Technology includes research on machine learning, deep learning, neural networks, robotics, natural language processing, computer vision, AI ethics, etc. Additionally, it explores AI’s applications in engineering disciplines such as automation, smart manufacturing, intelligent systems, and cybersecurity. The integration of AI with the Internet of Things (IoT), blockchain, and cloud computing is also a key area of interest. Journal of Advanced Artificial Intelligence, Engineering, and Technology maintains high academic standards by ensuring rigorous peer review processes. Contributions include original research articles, review papers, case studies, and technical reports that provide valuable insights into AI’s evolving landscape. Journal aims to bridge the gap between theoretical advancements and real-world applications, driving technological progress in various sectors, including healthcare, finance, transportation, and industrial automation. It is an essential resource for researchers, engineers, and decision-makers looking to stay at the forefront of AI-driven innovation.
A Methodology for Developing Creative Thinking in Engineering Students Based on Stem Technologies
This study presents a comprehensive methodology for developing creative thinking skills in engineering students through the integration of STEM (Science, Technology, Engineering and Mathematics) technologies. The research addresses the growing demand for innovative engineers capable of solving complex, interdisciplinary problems in an increasingly technology-driven world. A mixed-methods research design was employed, combining quantitative assessment of creative thinking abilities with qualitative analysis of student experiences. The methodology incorporates project-based learning, design thinking frameworks and hands-on experimentation with emerging technologies including 3D printing, robotics, IoT systems and computational modeling. Participants included 186 undergraduate engineering students enrolled in a semester-long interdisciplinary course. Creative thinking was measured using the Torrance Tests of Creative Thinking (TTCT) and the Creative Engineering Design Assessment (CEDA). Results indicated statistically significant improvements in all dimensions of creative thinking, including fluency, flexibility originality and elaboration. Students demonstrated enhanced problem-solving capabilities and increased confidence in tackling open-ended design challenges. The findings suggest that intentional integration of STEM technologies within a structured pedagogical framework can effectively foster creative thinking competencies essential for future engineering practice. This research contributes to the growing body of literature on STEM education by providing empirical evidence for the efficacy of technology-enhanced approaches to creativity development in engineering education.
Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
Clinical MLOps: A Framework for Responsible Deployment and Observability of AI Systems in Cloud-Native Healthcare Platforms
Machine Learning Operations (MLOps) practices have reached a notable level of maturity within general-purpose software engineering. Pipelines are standardized, monitoring is automated and deployment patterns are well rehearsed. Yet when these same practices are transferred into clinical environments, their adequacy becomes less certain. The healthcare domain introduces operational and ethical pressures that conventional MLOps frameworks were not originally designed to address. Clinical AI systems operate under constraints that extend beyond typical production settings. Stringent data protection regimes including GDPR and the EU AI Act shape how data can be processed and retained. Model drift is not merely a statistical inconvenience; it may alter clinical decisions with tangible consequences for patient outcomes. Regulatory compliance demands comprehensive, tamper-evident audit trails. At the same time, decision-critical contexts impose a clear expectation of human oversight. In other words, technical robustness alone is insufficient; governance and accountability become integral system properties. This paper introduces a Clinical MLOps framework that systematically augments conventional pipelines with healthcare-specific requirements. The framework is structured around four layered components: 1. Privacy-preserving deployment patterns, 2. clinical observability mechanisms, 3. compliance-oriented audit trail architecture and; 4. human-in-the-loop governance protocols. Each layer addresses a distinct operational risk while remaining interoperable with established cloud-native tooling. To evaluate the framework, we construct a demonstrative end-to-end pipeline using the MIMIC-IV dataset a large, de-identified electronic health record repository from Beth Israel Deaconess Medical Center. Within this pipeline, we implement a patient deterioration prediction model and apply Clinical MLOps controls systematically at every stage, from data ingestion to post-deployment monitoring. This controlled implementation enables us to examine how standard MLOps tooling behaves under clinical constraints. Our findings indicate that widely adopted MLOps practices, while technically sound, leave critical governance and compliance gaps when applied in healthcare contexts. The proposed Clinical MLOps framework addresses these deficiencies without requiring impractical infrastructure changes. Importantly, it remains compatible with cloud-native architectures, making adoption feasible within contemporary health IT ecosystems. The implications extend to healthcare AI governance more broadly, particularly in relation to the operational interpretation of the EU AI Act in clinical settings.
A Central Window Algorithm for Explicit Goldbach Representations Certified Examples Up To 10^700
This article presents a constructive and algorithmic framework for producing explicit Goldbach representations of very large even integers. The method is based on a central window strategy centered at E divided by two, combined with a sieve applied to a deviation parameter, probabilistic primality screening and deterministic certification using elliptic curve primality proving. The results reported here are punctual certified examples and not exhaustive verifications. Fully certified prime-prime Goldbach representations are exhibited for selected even integers up to 10^700. Beyond this range, the same method produces high-confidence probable prime examples. The contribution of this work is methodological. It demonstrates a scalable and reproducible approach for constructing Goldbach representations at extreme numerical scales, while clearly separating certified proofs from probabilistic evidence.
Cybersecurity Future Threats in Artificial Intelligence: A Comprehensive Analysis
The rapid integration of Artificial Intelligence (AI) systems into critical infrastructure, healthcare, finance and national security sectors has introduced unprecedented cybersecurity challenges. This paper examines emerging threats that exploit AI systems and leverage their capabilities for malicious purposes. Adversarial machine learning attacks, AI-powered cyber threats, model poisoning, privacy vulnerabilities and the weaponization of generative AI are analyzed. The current threat landscape is synthesized, attack vectors specific to AI architectures are examined and defensive strategies are evaluated. Critical gaps in existing security frameworks are identified and a multi-layered defense approach is proposed, incorporating adversarial robustness, secure AI development practices and adaptive threat detection. The findings demonstrate that traditional cybersecurity paradigms are insufficient for AI systems, necessitating novel security architectures that address the unique attack surfaces of machine learning models.
A Survey of Recent Graph-Based Methods for Skeleton Based Action Recognition
Skeleton-Based Action Recognition (SBAR) leverages 3D joint trajectories to recognize human activities while offering privacy, robustness to illumination/background changes and computational efficiency. Recent progress is dominated by spatio-temporal graph neural networks (ST-GNNs) that model the human body as a graph and learn data-adaptive connectivity, hierarchical structure, compact representations, multimodal supervision and efficient temporal fusion. This survey focuses on five representative methods CTR-GCN, HD-GCN, InfoGCN, Language Supervised Training (LST) and Temporal Channel Aggregation (TCA-GCN) and positions them within the broader SBAR literature. We analyze modeling assumptions, architectural choices, training objectives and empirical results on NTU RGB+D 60/120 and North western UCLA. We additionally contextualize the trajectory from dynamic topology learning to emerging foundation and sequence models reported in 2024-2025. Finally, we summarize open challenges and provide research directions for scalable, robust and semantically grounded SBAR.
A Novel Hybrid VANET Routing Protocol with Dynamic Power Management for Performance Enhancement
This research presents a Proposed Hybrid routing protocol for Vehicular Ad-hoc Networks (VANETs), designed to address the performance trade-offs inherent in purely reactive Ad hoc On Demand Distance Vector (AODV) and proactive Optimized Link State Routing (OLSR) routing paradigms. The purpose of the research is to seamlessly integrate the strengths of AODV and OLSR into a single, context-aware framework. A significant finding is the co-design of a dynamic transmission power control mechanism that works in concert with the routing logic to adapt to fluctuating vehicle densities in real-time, effectively mitigating intermittent connectivity and the high latency characteristic of large-scale Intelligent Transportation Systems (ITS). Rigorous evaluation under high-fidelity mobility scenarios (using NS-3 and SUMO with real-world traffic patterns) confirms the protocol's efficacy. The significant findings demonstrate substantial performance enhancements over established baseline protocols, consistently achieving a Packet Delivery Ratio (PDR) exceeding 90%, maintaining an end-to-end delay below the critical 40ms threshold and realizing pernode energy savings of up to 60%. The conclusion is that this work provides a validated foundation for a highly reliable and efficient communication fabric, significantly enhancing the dependability of mission-critical ITS services and promoting the development of scalable, sustainable next-generation transportation networks.
Intelligent Transportation Systems (ITS)
Protecting Healthcare from Cyber Threats: Integrating Behavioral Insights into Cybersecurity Strategies
Cybersecurity breaches in healthcare often stem from human-factor vulnerabilities such as phishing, social engineering and policy non-compliance. Despite evolving technical defenses, behavioral risk remains a critical gap. This study uses Protection Motivation Theory (PMT) to examine how healthcare cybersecurity professionals perceive and address these threats. Semi-structured interviews with ten professionals revealed five themes: (1) tension between clinical workflows and security, (2) limited impact of generic training, (3) policy inconsistencies among leadership, (4) value of mentorship and IT presence and; (5) need for behavioral design in policies and technology. Findings suggest healthcare cybersecurity must prioritize human-centered design, participatory policy-making and adaptive interventions, offering practical insights to bolster cyber resilience.
Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
Evaluation the Mechanical Behavior of SS 304L Welding Joints Designed and Fabricated According to ASME Section IX for Cryogenic Applications of Green Energy
The strength and toughness required for the weld metal differs greatly depending upon the application for which the structure is employed and according to the type of steel and welding materials, In this study, an austenitic stainless-steel pipe, type A304L was welded with gas tungsten arc with a wall thickness of (10) schedule, Low-temperature Charpy V-Notch (CVN) impact tests were performed to investigate the effect of low temperatures on the fracture toughness of the Welded Zone (WZ), Heat-Affected Zone (HAZ) and Base Metal (BM) of pipe. For design and safety reasons, it is necessary to investigate the low-temperature impact properties of weld metals, because weld metals have higher susceptibility to embrittlement than their counterpart base metals. The mechanical characteristics of SS304L weldments were evaluated based on tensile, Charpy impact and hardness of the WJ and BM have been measured. The conclusions from this study are summarized as follows: Mechanical tests are carried out on cryogenic temperature to check the weld behavior of stainless steel 304L under cryogenic condition of hydrogen storage units of green energy. The weld joints were successfully produced without weld defects such as cracks or blowholes. The yield strength and fracture toughness for all specimens satisfy the structural design criteria ASME and ASTM.
Visual and Quantitative Analysis of Deep Learning Robustness to Shear in Mechanical MNIST
This study quantifies deep neural network robustness to physically induced distortions using MNIST and Mechanical MNIST (Step 5), a displacement-field variant. A custom 3-layer Convoluted Neural Network, CNN (32–256 filters) attains 98.17% accuracy on Mechanical MNIST, surpassing ResNet-18, which falls from 98.95% (MNIST) to 83.46%. Grad-CAM exposes saliency degradation under mechanical stress, notably for curved digits (‘3’, ‘7’); t-SNE and noise sensitivity analyses reveal distorted embeddings and diminished class separability. These findings highlight the fragility of deep, generic architectures in mechanically perturbed domains, advocating compact, domain-adaptive models for resilient classification in applications like tactile sensing or structural analysis.
Enhancing Classroom Efficiency: Cross-National Evaluation of Lesson Planning, Quiz Generation and Grading with Notegrade.ai
Introduction: AI or artificial intelligence, is appearing in K-16 classrooms for lesson planning, automated quizzes and grading, etc. Although gains in instructional efficiency and assessment reliability have been reported, there are still few comparative studies using a single AI across different policy, curriculum and infrastructure contexts from various countries. Purpose: Assess the influence of Notegrade.ai on 1. Lesson-planning efficiency and curriculum alignment; 2. Quiz generation quality and; 3. Grading speed, reliability and perceived fairness by users in several different national educational systems. Study design: This was a multi-site assessment using a mixed-methods concurrent triangulation design. We quantified pre/post adoption time spent on task (lesson planning, quiz writing, grading) and AI–human agreement in grading open and closed-response questions (using Cohen’s κ/ICC) to analyze agreement between AI and human graders. We examined curriculum against rubric-based ratings that were linked to outcomes and levels of Bloom’s taxonomy. Fairness was assessed through item-level Differential Item Functioning (DIF) analysis and through subgroup error analysis. Surveys based on TAM/UTAUT constructs, semi-structured interviews, classroom observations and artifact audits were used to gather teacher and student perceptions. Multilevel regression with site-level covariates of digital readiness, assessment policy, connectivity and teacher workload norms was used to model cross-national heterogeneity. Results: Teachers at all sites experienced a significant time savings while using Notegrade.ai to prepare lesson plans and quizzes which they used to focus more on feedback and differentiation. AI-based grading was found to have high inter-rater reliability for objective items, as well as moderate-to-high reliability for rubric scored answers and significantly reduced turnaround time. Rubric checks revealed that the generated quizzes perfectly aligned with the desired outcomes and included items at all levels of cognitive complexity. No systematic bias in subgroups was identified in the fairness analyses, although some variations were found in conditions with low connectivity and minimal training of teachers. National policy supports and specificity in local curriculum moderated adoptive as well as perceived useful. Discussion: If accompanied by score-matching rubrics, rater-calibration methods and some rudimentary training of teachers, Notegrade.ai may have a positive impact on classroom productivity without compromising assessment quality in diverse educational contexts. AI use must be coupled with physical and training support; developers must allow visible controls for aligning content to standards, calibrating rubrics and detecting bias. Recommendations for practice and research: The next steps should involve longitudinal studies that connect efficiencies made possible through the use of AI technology with actual student learning, studies that replicate this model in low resource environments, as well as cost-effectiveness research that account for total cost of ownership.
Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks
In response to recent FIA regulations reducing Formula 1 team wind tunnel hours (from 320hrs for last-place teams to 200 hours for championship leaders) and strict budget caps of $135 million per year, more efficient aerodynamic development tools are needed by teams. Conventional Computational Fluid Dynamics (CFD) simulations, though offering high fidelity results, require large computational resources with typical simulation durations of 8-24hrs per configuration analysis. This article proposes a Physics-Informed Neural Network (PINN) for the fast prediction of Formula 1 front wing aerodynamic coefficients. The suggested methodology combines CFD simulation data from SimScale with first principles of fluid dynamics through a hybrid loss function that constrains both data fidelity and physical adherence based on NavierStokes equations. Training on force and moment data from 12 aerodynamic features, the PINN model records coefficient of determination (R2) values of 0.968 for drag coefficient and 0.981 for lift coefficient prediction while lowering computational time. The physics-informed framework guarantees that predictions remain adherent to fundamental aerodynamic principles, offering F1 teams an efficient tool for the fast exploration of design space within regulatory constraints.
The Pluralistic Future of AI: A Comprehensive Analysis of Decentralized Large Language Models
The centralized status quo: The current landscape of Artificial Intelligence (AI), particularly concerning Large Language Models (LLMs), is dominated by a centralized paradigm. Industry leaders such as OpenAI, Anthropic and Google DeepMind deploy and manage their models on massive computational infrastructure within expansive data centers. This architecture, where a single, colossal model serves millions of users via cloud-based APIs, has enabled unprecedented scalability, high performance and the capacity for continuous updates. However, this model is not without significant drawbacks. It requires substantial financial investment in infrastructure, is entirely reliant on constant internet connectivity and introduces considerable privacy and data control concerns, as sensitive information must be processed in the cloud. The user also frequently pays for compute capacity they do not fully utilize, leading to an inefficient cost model based on subscriptions or per-token usage.
Identity Refined at the Quantum Gate: Framing the AI + Post-Quantum Challenge for IAM
Identity & Access Management (IAM) is being reshaped by two concurrent forces: (i) the use of Artificial Intelligence (AI) to turn rich telemetry into policy decisions and (ii) the migration to Post-Quantum Cryptography (PQC) across credentials, certificates and protocol touchpoints. We argue that the most consequential risks live in the seams account recovery/reset, Non-Human Identities (NHIs) and crypto-agile upgrades where attackers concentrate and operations are fragile. This paper contributes four things. First, it frames a precise problem statement that links AI decisioning pipelines (signals → models → policies → evidence) with PQC adoption realities (artifact sizes, timing, interop and governance). Second, it organizes the landscape into a literature/practice map for AI-in-IAM and PQC-in-IAM that practitioners can immediately use for scoping. Third, it reports results from three small, reproducible experiments designed for teaching and early planning: a policy-level risk simulation spanning sign-in and recovery (risk-based control stops ~81% of simulated fraud vs. ~63% for a static baseline while reducing legitimate friction from ~38% to ~25% at ~+50 ms p95 decision latency); an overhead model for PQC artifacts showing modest size-driven latency on typical enterprise links (incremental over RTT on the order of ~1–6 ms); and a micro-pilot comparing passkeys to password+OTP (median sign-in time ~7.2 s vs. ~12.1 s; completion ~97% vs. ~92%; support tickets 6 vs. 10 per 100 users). Finally, it outlines a research agenda for recovery governance, machine identity attestation and rotation, crypto-agile policy engines and explainability/appeals. All datasets are synthetic to enable easy replication without sensitive data.
The Hallyu Wave as a Strategic Soft Power Tool: South Korea’s Cultural Diplomacy in the 21st Century
This article investigates how the Republic of Korea has strategically employed its cultural production globally known as Hallyu (the Korean Wave) as a cornerstone of its soft power strategy in the 21st century. Based on a systematic literature review and analysis of official policy documents, this study explores how South Korean cultural exports, particularly K-pop, television dramas and digital content, have evolved into tools of symbolic and economic diplomacy. The methodology includes a structured review protocol detailing the databases searched, keywords and inclusion/exclusion criteria. Findings indicate that Hallyu functions not only as a cultural trend but also as a coherent state-led diplomatic project with tangible political and commercial outcomes. In particular, the study highlights the role of popular music groups such as BTS and BLACKPINK in fostering bilateral cooperation, promoting Korean identity abroad and shaping international discourse on innovation, diversity and modernity. It also critically examines regional resistance and structural vulnerabilities in the strategy, such as over-reliance on digital platforms and cultural frictions in conservative regions. The study concludes that Hallyu represents a paradigmatic model of soft power that blends cultural attractiveness, institutional support and global market dynamics.
Improving Fiscal Sustainability in South African Local Governments: A Six-Pillar Conceptual Framework Applied to Three Case Studies
South African municipalities struggle financially as a result of apartheid, rapid urban growth and an increasing need for services. Past segregation has made equality in fiscal spending and government services more difficult to achieve. Municipalities also struggle because their funding is limited and their systems are out-of-date, while citizens require more and improved infrastructure and services. The constant shortage of funds hampers sustainable growth and the wellbeing of many residents, most of all those in rural and poorly equipped areas, proving that traditional budget plans are not sufficient and need to be improved. This research provides a detailed six-pillar conceptual framework to focus on municipal financial sustainability and reform in local governance. These pillars help achieve the goals of identifying fiscal weaknesses, improving the use of resources, creating more options for raising funds, reforming fiscal policies, involving citizens and preserving online security. Case studies of three South African municipalities were used to determine how well the framework works in practice. They consist of the Alexandra Renewal Project, Mbizana and Umsobomvu local municipalities. Every case had different results and issues, providing new ideas about the framework’s strong points and where it could be improved. Although some municipalities have improved their handling of finances and the way they involve the public, others need to catch up in diversifying their activities and digital administration. The framework gives cities a clear plan to address the shortage of funds, promote transparency and restore public faith. For an application to work well, it needs strong policy support, training of staff and integration of new technology in financial management. Overall, this framework adds important support for strengthening local governments in South Africa and in other developing areas with the same financial problems.
Social Loneliness in Youth: A BERT-based Sentiment Analysis of Digital Texts
Background: Digital platforms profoundly shape how young individuals form and maintain social connections. While these platforms can enhance social engagement, they may also intensify feelings of social loneliness, particularly among youth navigating identity and peer relationships. Objective: This study aims to explore the emotional dimensions of young adults' digital experiences by using Natural Language Processing (NLP)-based sentiment analysis, focusing on the manifestation of social loneliness in online discourse. Methods: Textual data were collected through semi-structured interviews with 300 university students (ages 18–25) in Turkey regarding their digital platform usage. Sentiment classification was conducted using two models: a baseline Naive Bayes algorithm with TF-IDF vectorization and a BERT-based deep learning model (BERTurk). The models were evaluated based on accuracy, precision, recall and F1-score. Results: Findings revealed that 40% of participants expressed positive sentiment, 35% negative and 25% neutral regarding digital platform use. The BERT model significantly outperformed Naive Bayes in all performance metrics (F1-score ≈ 0.75), particularly in identifying negative emotional content. Qualitative themes highlighted a dual impact of digital media, serving both as a source of connection and emotional detachment. Conclusion: Context-sensitive deep learning models provide valuable insights into youth's digital social experiences. This research emphasizes the potential of AI-driven sentiment analysis to uncover psychosocial dynamics like social loneliness and offers implications for digital mental health interventions targeting young populations.
HeartSense: Leveraging Machine Learning to Predict Cardiovascular Risk
Heart disease, also known as cardiovascular disease, remains one of the leading causes of mortality worldwide. Accurate prediction models are critical to advance early detection and preventive measures. This research introduces a specialized machine learning framework to predict the severity of heart disease using Kaggle’s “UCI Heart Disease Data” a multivariate data set derived from the Cleveland database. This data set includes 14 predictive attributes, including clinical and demographic factors, which were used to train and evaluate various supervised learning algorithms. Notable models included logistic regression, decision trees, random forests and gradient boosting machines. The highest performing model (XGBoost) achieved an accuracy of 62.5%. The model evaluation relied on metrics such as precision, recall and F1 score, which were enhanced through systematic hyperparameter optimization. Advanced feature engineering and cross-validation techniques further refined the model’s predictive capability. This study demonstrates how machine learning can uncover nuanced patterns within medical datasets, offering actionable insights into cardiovascular health and aiding in clinical decision-making.
Review of Influence of Emotional Intelligence (EI) on Collaboration Among Employees from Diverse Cultural Backgrounds in the Construction Industry
This study investigates the impact of self-awareness, empathy and relationship management on teamwork and communication among construction workers with diverse cultural backgrounds. A review of existing research on EI across various industries formed the basis of this study. The research results demonstrate that workers with strong Emotional Intelligence (EI) skills excel at handling cultural differences and conflict resolution and building positive workplace relationships, which is especially valuable for professionals in the construction industry. The research data indicates that professionals who demonstrate strong Emotional Intelligence (EI) perform better when dealing with cultural differences and resolving interpersonal conflicts. Their emotional intelligence skills enable them to understand their own emotions and those of others, which leads to better communication and teamwork between diverse groups. The increased emotional awareness helps organizations develop more inclusive collaborative spaces that benefit the construction industry because its professionals work in multicultural teams under high-pressure situations that require strong interpersonal and conflict-resolution abilities.
A Short Commentary on a Portuguese Case Study Regarding a New Practical Maturity Model for Responsible Artificial Intelligence
Artificial Intelligence (AI) has the potential to significantly impact human life, bringing numerous benefits. However, it also poses significant risks to human rights and society. A plethora of frameworks and guidelines have been developed in recent years to help organizations deal with those risks and threats. The main objective of the present research was to understand how organizations and stakeholders involved in the development of AI, especially in the Research and Innovation (R & I) phases, in Portugal, are considering and dealing with possible ethical, legal and social issues driven from the application of AI. On a more theoretical level, it aims to assess the existing frameworks and instruments available to organizations involved in the processes of design, development and deployment of AI technologies, regarding the trustworthiness of those processes and the responsibilities of the stakeholders. Additionally, this research aims to assess the way those frameworks and instruments are suitable to the more dynamic AI developing organizations, especially small ones, startups, scaleups and research centres and to fill the identified gaps in those frameworks and instruments.
A Review on Deep Learning Techniques for Medical Image Segmentation and Classification
The intersection of Artificial Intelligence (AI) with medical imaging has advanced at an unprecedented rate over the last decade, enabling the diagnosis of disease, the planning of treatment and monitoring of patients. Deep learning, specifically convolutional neural networks and their derivatives, has been employed to interpret medical images with specialized methods and proven high accuracy in working with complex forms of medical imaging. This review summarizes the most well-known of deep learning architectures in the implementation of medical image analysis, including CNNs, U-Net, ResNet, DenseNet, GANs and transformer architecture-based models. We discuss the most common applications (e.g., tumor detection organ segmentation of medical images and image enhancement) associated with each of the architectures. Even with successful applications of AI analyses in medical imaging, specific challenges remain that limit adoption of AI tools by the clinical community that range from lack of annotated data to questions of interpretability and ultimately clinical applicability. We highlight the current work and future directions intended to mitigate main challenges, thus maximizing the potential of AI in clinical practice. This article serves as a detailed overview of AI and medical imaging and will serve as a deep resource to the reader that wishes to engage in the discussion of AI and its applications in medical imaging.
Game-Theoretic Self-Supervised Defense: A Dual-Stream Neural Architecture for Secure End-to-End Communication
We propose a novel dual-stream neural architecture for secure end-to-end communication that integrates self-supervised adversarial detection with game-theoretic defense strategies. The increasing sophistication of adversarial attacks necessitates adaptive security mechanisms that can dynamically respond to evolving threats while maintaining computational efficiency. Our framework addresses this challenge by combining a transformer-based contrastive learning module for real-time perturbation detection with a zero-sum Markov game formulation to optimize defense policies under Nash equilibrium. The self-supervised stream extracts invariant features from communication signals and computes threat probabilities, while the game-theoretic stream dynamically adjusts encryption parameters and defense actions based on adversarial behavior. The two streams are jointly optimized to achieve provable robustness against both known and unknown attack patterns. Furthermore, the system interfaces seamlessly with conventional intrusion detection and encryption modules, replacing rule-based heuristics with data-driven adaptive strategies. Experimental validation demonstrates significant improvements in detection accuracy and resilience compared to existing methods. The key innovation lies in the unification of self-supervision and game theory within a single end-to-end trainable framework, enabling scalable and adaptive security for modern communication networks. This approach not only enhances real-time threat mitigation but also provides theoretical guarantees on defense optimality under adversarial dynamics.
A Novel Architectural Design Paradigm: Collaborative Construction with Design Thinking Semantic Networks and Large Language Models
The abstraction and formalization of architectural design thinking into structured knowledge representations remains a critical gap in computational design theory. This paper proposes a conceptual framework for translating tacit design knowledge from exemplary architectural projects and expert design processes into machine interpretable semantic networks, aiming to bridge the epistemological divide between architectural practice and Large Language Models (LLMs). By synthesizing principles from design cognition theory and semantic graph formalisms, the framework establishes a Design Thinking Semantic Framework (DTSF) that constructs relational graph structures that map architectural lexicons to LLM-compatible semantic units, preserving the nuanced interplay between creative intuition and disciplinary rigor inherent to architectural problem-solving. Through ontological alignment mechanisms, the framework enables the transfer of domain-specific design intentionality such as the dialectic between form and function or the negotiation of heritage constraints into generative AI systems. This theoretical advancement provides a structured methodology for embedding architectural epistemology into AI models, fostering cross-disciplinary dialogue while addressing the limitations of current LLM applications in architecture. The work lays foundational principles for future AI-augmented design tools that respect architectural complexity without reducing it to combinatorial optimization.
Before Writing: Epigraphic Classification of the Bosnian Pyramid Inscriptions in Comparative Context with Vinča and Runic Traditions
This paper presents the first systematic classification of symbolic inscriptions discovered at the Bosnian Pyramid Complex, particularly in the Ravne tunnel system. Through detailed documentation of 18 figures including epigraphic photographs, archaeological illustrations and comparative charts this study establishes the basis for identifying a potentially independent symbolic system, herein referred to as the 'Visoko Proto-Script'. The analysis draws comparative parallels to known prehistoric symbol systems such as the Vinča civilization of Southeastern Europe and early Runic traditions found in Northern and Central Europe. Several key glyph forms such as chevrons, tridents, vertical strokes and branching symbols demonstrate formal and functional similarities across cultural contexts. The findings support the hypothesis that these inscriptions were not merely decorative, but symbolic in nature. While some interpretations suggest possible ritual or energetic functions, these remain tentative and require further empirical investigation. This research contributes to the broader discourse on pre-alphabetic writing, cultural continuity in symbolic systems and the role of sacred geometry in prehistoric epigraphy. Abbreviations: BCE: Before Common Era; BP: Before Present; EM: Electromagnetic; IRB: Institutional Review Board; GPS: Global Positioning System; LiDAR: Light Detection and Ranging; K1: Designation for the main engraved megalithic block in Ravne tunnels; RPQ: Radiometric Provenance Quality (if used in your data processing); AI: Artificial Intelligence; AAIET: Journal of Advanced Artificial Intelligence, Engineering and Technology; AAR: Archaeological Artifact Record (if referenced for cataloging); VPS: Visoko Proto-Script
A Perspective on the Potential of Using AI Models for Predicting Injuries
AI can play a prominent role in the medical field, specifically in physical therapy, by detecting injuries or areas of concern, that may lead to injury. Many athletes are more susceptible to injury than the average person. AI has the potential of detecting many of these injuries, possibly before they even occur. Supervised learning used in conjunction with machine learning can play a significant role in identifying injuries. Machines can be trained with data such as x-rays and MRIs, showing healthy body scans in juxtaposition to unhealthy or injury prone ones.
Environmental Ionization in Enclosed Geospheres: Comparative Study of Global and Local Measurements (2018-2025)
This study presents the results of a seven-year environmental monitoring campaign (2018-2025) conducted inside the Ravne tunnel complex, a prehistoric underground structure located near Visoko, Bosnia-Herzegovina. Biannual measurements were carried out using calibrated scientific instruments to assess air ion concentrations and related microclimatic parameters. The results reveal consistently elevated levels of Negative Air Ions (NAI) across all internal locations ranging from 10,000 to over 300,000 ions/cm³ with a peak value of 330,000 ions/cm³ recorded in late 2023. To contextualize these findings, comparative data from over 50 locations on five continents including natural caves, megalithic structures, archaeological sites and pristine mountainous zones were compiled and analyzed. The Ravne tunnel complex consistently exhibits negative ion concentrations that are an order of magnitude higher than those recorded in even the cleanest natural outdoor environments, which typically range between 100 and 5,000 ions/cm³. Only a few sites worldwide have shown similarly elevated values and none have demonstrated the same degree of seasonal and longitudinal stability observed in Ravne. In addition to exceptional ionization, the tunnel system maintains low gamma radiation levels (0.06-0.10 µSv/h), no detectable electromagnetic radiation (0.00 mW/cm²), consistently breathable oxygen concentrations (19.0%-20.9%) and high relative humidity (77%-88%) without artificial intervention. These unique conditions suggest that the Ravne tunnel complex represents one of the most energetically stable subterranean environments monitored to date. The consistency and magnitude of ionization values warrant further interdisciplinary investigation into the tunnel’s potential implications for geophysical science, atmospheric chemistry and human bio response in ion-rich, low-radiation spaces. Abbreviations: NAI: Negative Air Ions; PAI: Positive Air Ions; EMF: Electromagnetic Field; O2: Oxygen concentration; µSv/h: Microsieverts per hour (unit of radiation dose rate); CPM: Counts per minute (for radiation); RH: Relative Humidity; AIC: Air Ion Counter; BPS: Bosnian Pyramid of the Sun Foundation
Hierarchical Causal Validation Framework for Fair and Explainable LLM-Powered Recommendations with Reduced Computational Overhead
We propose a Hierarchical Causal Validation Framework (HCVF) to address bias mitigation in LLM-powered recommendation systems while maintaining explainability and computational efficiency. The framework introduces a dynamic stratification mechanism to identify high-impact causal relationships, which are then validated through tiered hybrid methods combining hierarchical Bayesian propensity scoring and attention-based conditional independence tests. The proposed method quantifies bias contributions using Shapley values over the causal graph, enabling transparent attribution of fairness violations to specific model components. Furthermore, mitigationaware embedding adjustments are applied selectively to highbias pathways, reducing unnecessary computational overhead compared to global debiasing approaches. A neural causal discovery layer continuously refines the underlying causal structure, ensuring adaptability to evolving data distributions. Experiments on real-world recommendation tasks demonstrate that HCVF achieves superior bias reduction while preserving recommendation accuracy, with a 40% reduction in validation costs relative to state-of-the-art baselines. The framework’s hierarchical design provides interpretable insights into bias propagation mechanisms, making it particularly valuable for high-stakes applications where fairness and transparency are critical. This work bridges the gap between causal fairness theory and practical deployment constraints in large-scale recommendation systems.
A Comparative Analysis of Machine Learning Models for URL-Based Phishing Detection
Phishing attacks pose a significant and ongoing cybersecurity threat, necessitating effective countermeasures. The challenge lies in accurately and automatically detecting malicious URLs, as traditional methods often fall short against evolving attacker techniques. This research addresses the need for improved detection by evaluating machine learning approaches applied to URL analysis. A dataset of labeled phishing and legitimate URLs, characterized by 30 distinct features encompassing lexical, host-based and content-related attributes, formed the basis of this study. Five machine learning models were trained and comparatively evaluated: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), XGBoost (XGB) and a Stacking Classifier ensemble. Performance analysis revealed that the XGBoost classifier achieved the highest accuracy, correctly classifying approximately 97.4% of URLs in the test set. This study demonstrates the effectiveness of machine learning, particularly XGBoost, for high-accuracy phishing URL detection using comprehensive feature sets and contributes a functional prototype system demonstrating the approach.
Human Action Recognition Using YOLOv11 Ultralytics: A Comprehensive Study for Real-Time Applications
Human action recognition (HAR) is a pivotal task in computer vision, with applications in surveillance, healthcare, robotics, and human-computer interaction. This study presents a novel framework for HAR using the YOLOv11 model by Ultralyt ics, a state-of-the-art object detection architecture optimized for real-time performance. We trained and evaluated the model on a custom dataset comprising 18 distinct human actions, captured in indoor environments using fisheye cameras. The actions range from everyday activities (e.g., walking, sitting) to specialized tasks (e.g., patient on stretcher, patient on wheelchair). Our results show that YOLOv11 achieves a mean Average Precision (mAP@0.5) of 0.401, with exceptional performance on actions like ”cleaning” (mAP@0.5: 0.760), ”searching” (mAP@0.5: 0.695), and ”patient on wheelchair” (mAP@0.5: 0.995). We provide an in-depth analysis of the model’s training metrics, bounding box distributions, precision-recall curves, F1-confidence curves, recall-confidence curves, and confusion matrices. Additionally, we present extensive qualitative results to demonstrate the model’s robustness in real-world scenarios. A comparison with existing methods, such as two-stream CNNs and Transformer-based models, highlights YOLOv11’s superior balance of accuracy and speed, making it a promising solution for real-time HAR applications. This study also discusses the model’s limitations and outlines directions for future research, paving the way for enhanced action recognition systems.
Leveraging Artificial Intelligence for Rural Education: A Systematic Review of Transforming Learning Opportunities and Bridging the Urban-Rural Divide
AI has immense potential in transforming rural education - bringing solutions to long-standing problems under resource constraints, insufficient teaching or other forms of educational inequality. Delivering AI-based technologies to limited frameworks in educational systems can be particularly effective in providing tailored learning experiences, virtual classrooms with access to quality education and using Natural Language Processing (NLP) to break language barriers. This integration could greatly contribute to an equitable educational environment in which rural students have opportunities similar to those in the cities for their peers. Despite these benefits, there are a number of challenges to the widespread implementation of artificial intelligence in rural education: poor infrastructure, higher cost of implementation and lack of instructor training. Besides, the ethical implication on privacy, information security and prejudices issues in artificial intelligence needs to be handled sensibly to promote equity and inclusivity. Overcoming these is mostly the collaborative effort between governmental institutions, academic institutions and developers of the technology coupled with financial input towards infrastructure and educating the educators. The present paper discusses the role that artificial intelligence can play in bridging the gap between urban and rural education, highlights its potential applicability and discusses the challenges that should be overcome to implement it efficiently. Finally, recommendations are made for further policy development and the enhancement of AI projects in rural education to make the education of all learners more equal and effective.
Efficient Resource Optimization of 5G Networks Enabling QoS and QoE in IoT Applications
This article elaborates the recent enhancements on IoT capabilities and efficiencies. From power, coverage, cost, complexity, device density, core network protocol, spectrum efficiency perspectives, it describes a comprehensive blueprint for driving IoT optimizations. For better mobile broadband experience, enabling Gigabit-class throughput with advanced 5G network techniques, millimeter wave, massive MIMO, carrier aggregation, and LAA benefit massive IoT improvements. Supporting Gigabit-class data rates for high-performance IoT requires high power efficiency. eMTC (enhanced machine-type communication) optimizes for the broadest range of IoT applications with VoLTE and mobility. NB-IoT (Narrow Band IoT) provides optimizations for high throughput and low delay LPWAN IoT use cases.