Journal of Advanced Artificial Intelligence, Engineering and Technology
type: channel
description: 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.
publisher: Wren Research Journals
language: en
publication name: Journal of Advanced Artificial Intelligence, Engineering and Technology
issn: 3139-4256
items: Robust Performance Assessment of a Simulink-Tuned PID Controller for a DC Motor: A Case Study in Noise Rejection
items: Montmorillonite-Reinforced Starch Films for Sustainable, Active and Intelligent Food Packaging: Structure, Performance and Food Preservation Applications
items: A Methodology for Developing Creative Thinking in Engineering Students Based on Stem Technologies
items: The Evolution and Societal Impact of Artificial Intelligence in the 21st Century
items: Clinical MLOps: A Framework for Responsible Deployment and Observability of AI Systems in Cloud-Native Healthcare Platforms
items: Neuromorphic Modular Intelligence Architecture: Overcoming Transformer Limits Toward AGI Beyond Monolithic LLM Limits
items: A Central Window Algorithm for Explicit Goldbach Representations Certified Examples Up To 10^700
items: Cybersecurity Future Threats in Artificial Intelligence: A Comprehensive Analysis
items: A Survey of Recent Graph-Based Methods for Skeleton Based Action Recognition
items: 3D Ball Trajectory from a Single Camera: A Cost-Effective Ball Estimation Technique for Cricket
items: A Novel Hybrid VANET Routing Protocol with Dynamic Power Management for Performance Enhancement
items: Protecting Healthcare from Cyber Threats: Integrating Behavioral Insights into Cybersecurity Strategies
items: Evaluation the Mechanical Behavior of SS 304L Welding Joints Designed and Fabricated According to ASME Section IX for Cryogenic Applications of Green Energy
items: Visual and Quantitative Analysis of Deep Learning Robustness to Shear in Mechanical MNIST
items: Enhancing Classroom Efficiency: Cross-National Evaluation of Lesson Planning, Quiz Generation and Grading with Notegrade.ai
items: Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks
items: The Pluralistic Future of AI: A Comprehensive Analysis of Decentralized Large Language Models
items: Identity Refined at the Quantum Gate: Framing the AI + Post-Quantum Challenge for IAM
items: The Hallyu Wave as a Strategic Soft Power Tool: South Korea’s Cultural Diplomacy in the 21st Century
items: Improving Fiscal Sustainability in South African Local Governments: A Six-Pillar Conceptual Framework Applied to Three Case Studies
items: Social Loneliness in Youth: A BERT-based Sentiment Analysis of Digital Texts
items: HeartSense: Leveraging Machine Learning to Predict Cardiovascular Risk
items: Review of Influence of Emotional Intelligence (EI) on Collaboration Among Employees from Diverse Cultural Backgrounds in the Construction Industry
items: A Short Commentary on a Portuguese Case Study Regarding a New Practical Maturity Model for Responsible Artificial Intelligence
items: A Review on Deep Learning Techniques for Medical Image Segmentation and Classification
items: Game-Theoretic Self-Supervised Defense: A Dual-Stream Neural Architecture for Secure End-to-End Communication
items: A Novel Architectural Design Paradigm: Collaborative Construction with Design Thinking Semantic Networks and Large Language Models
items: Before Writing: Epigraphic Classification of the Bosnian Pyramid Inscriptions in Comparative Context with Vinča and Runic Traditions
items: A Perspective on the Potential of Using AI Models for Predicting Injuries
items: Environmental Ionization in Enclosed Geospheres: Comparative Study of Global and Local Measurements (2018-2025)

Robust Performance Assessment of a Simulink-Tuned PID Controller for a DC Motor: A Case Study in Noise Rejection
type: item
description: This paper presents a comprehensive simulation-based analysis of a tuned Proportional-Integral-Derivative (PID) controller applied to a Direct Current (DC) motor within the MATLAB/Simulink environment. The primary objective is twofold: First, to establish a baseline performance benchmark by quantifying the transient response characteristics of the closed-loop system under ideal noise-free conditions; and second, to rigorously evaluate the controller's robustness and disturbance rejection capabilities when subjected to stochastic measurement perturbations modeled as Band-Limited White Noise. The PID controller parameters (Kp=8.3463, Ki=36.9358, Kd=0.39908, N=731.767) were derived using the automated PID Tuner tool in Simulink to achieve a desired balance between rapid reference tracking and minimal overshoot. Simulation results for the nominal case reveal a rise time of 0.243 seconds, a settling time of 0.817 seconds and an overshoot of 7.04%, with guaranteed stability margins including a phase margin of 69 degrees at 6.4 rad/s. Upon activation of the Band-Limited White Noise source configured with a noise power of 1×10⁻⁷ and a sample time of 0.001 seconds, the system maintained unconditional closed-loop stability and zero steady-state error albeit with the expected presence of high-frequency ripple on the output signal. The findings conclusively demonstrate that the tuned PID controller provides a robust and industrially viable control solution for DC motor applications, effectively preserving transient performance and stability even in the presence of realistic sensor noise artifacts.
subject: Articles
  topic: section
subject: PID controller
  topic: keywords
subject: DC motor
  topic: keywords
subject: Noise rejection
  topic: keywords
subject: Band-limited white noise
  topic: keywords
subject: Simulink
  topic: keywords
subject: Robustness
  topic: keywords
subject: Transient response
  topic: keywords
creator: Mohammad Heidar Khamsehei Fadaei
creator: Fahimeh Sadat Ghasemi
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-07-16
publication date: 2026-07-16
doi: 10.56147/aaiet.2.2.134

Montmorillonite-Reinforced Starch Films for Sustainable, Active and Intelligent Food Packaging: Structure, Performance and Food Preservation Applications
type: item
description: The increasing demand for sustainable food packaging has promoted the development of biodegradable alternatives to petroleum-based plastics. Starch-based films are renewable, low-cost and compostable, but their practical application remains limited by poor mechanical strength, high water sensitivity and insufficient barrier performance. This review discusses recent advances in Montmorillonite (MMT)-reinforced starch films, with emphasis on the relationship between nanocomposite structure, processing conditions, functional properties and food preservation performance. The reinforcing role of MMT is analyzed through intercalation, exfoliation and network formation mechanisms, which improve polymer-clay interactions and contribute to enhanced mechanical, thermal and barrier properties. The review also highlights the influence of starch source, plasticizer content, MMT modification and processing strategies on film morphology and final performance. Special attention is given to food packaging applications, where MMT-starch films have shown potential to extend shelf life by reducing moisture loss, microbial spoilage, oxidative degradation and quality deterioration in perishable products such as strawberries, paneer, chicken, cottage cheese, tomato and litchi. In addition, intelligent packaging applications based on pH-sensitive color changes are briefly discussed as promising tools for monitoring food freshness. Comparative tables summarize reported improvements in mechanical and thermal behavior as well as relevant formulation effects. Finally, the main challenges related to nanoparticle dispersion, large-scale processing, cost-efficiency, food safety and compostability are addressed. Overall, MMT-reinforced starch films represent a promising platform for sustainable, active and intelligent food packaging, provided that future studies continue optimizing formulation, processing and functional performance under real food storage conditions.
subject: Articles
  topic: section
subject: Montmorillonite
  topic: keywords
subject: Starch films
  topic: keywords
subject: Nanocomposites
  topic: keywords
subject: Biodegradable packaging
  topic: keywords
subject: Barrier properties
  topic: keywords
subject: Active packaging
  topic: keywords
creator: Oscar Danilo Guerra Ceballos
creator: Juan Felipe Grisales Mejia
creator: Liliana Ávila Martín
creator: Wilson Daniel Caicedo Chacón
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-07-13
publication date: 2026-07-13
doi: 10.56147/aaiet.2.2.131

A Methodology for Developing Creative Thinking in Engineering Students Based on Stem Technologies
type: item
description: 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.
subject: Articles
  topic: section
subject: Creative thinking
  topic: keywords
subject: STEM education
  topic: keywords
subject: Engineering pedagogy
  topic: keywords
subject: Design thinking
  topic: keywords
subject: Technology integration
  topic: keywords
subject: Higher education
  topic: keywords
subject: Innovation skills
  topic: keywords
creator: Dilshod Baratov
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-05-18
publication date: 2026-05-18
doi: 10.56147/aaiet.2.1.126

The Evolution and Societal Impact of Artificial Intelligence in the 21st Century
type: item
description: Artificial Intelligence (AI) has developed over the years from rudimentary systems of symbolic reasoning in the middle of the twentieth century to sophisticated data-driven and generative architectures, which give rise to modern society. The acceleration of machine learning, deep neural networks and large-scale computational infrastructures has turned AI into a basic technology in the economic, social and societal sectors. This paper investigates the history of the development of AI and critically discusses its influence on society in the 21st century. Following a narrative review approach, the paper summarizes interdisciplinary literature of technological innovation, economic transformation, social change, ethical governance and sustainability issues. Various findings are found in the analysis. To begin with, AI has greatly increased productivity and operational efficiency in the industry as well as redefining the labor markets and skill requirements. Second, AI-centered systems have enhanced the provision of services in the education, health, transportation and government sectors, though the issue of bias, privacy, transparency and accountability continues to be present. Third, the spread of AI to safety-critical systems highlights the value of reliability, regulation and human-oriented design. Finally, the environmental impact of large-scale AI models represents the necessity of sustainable development practices. The paper concludes that AI is an opportunity for transformation and a governance challenge. The implications to be considered in the future are the emergence of human-focused AI models, the creation of control measures and the introduction of sustainability indicators into technological change. The fair and responsible implementation of AI will be required in order to maximize the positive impacts on society and reduce the risks in the long term.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Societal impact
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Ethics
  topic: keywords
subject: Automation
  topic: keywords
subject: Governance
  topic: keywords
subject: Digital transformation
  topic: keywords
subject: 21st century
  topic: keywords
creator: Monica Khadgi
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-07-13
publication date: 2026-07-13
doi: 10.56147/aaiet.2.2.121

Clinical MLOps: A Framework for Responsible Deployment and Observability of AI Systems in Cloud-Native Healthcare Platforms
type: item
description: 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 moni…
subject: Articles
  topic: section
subject: Artificial intelligence security
  topic: keywords
subject: Adversarial machine learning
  topic: keywords
subject: AI threat landscape
  topic: keywords
subject: Model poisoning
  topic: keywords
subject: Deep learning vulnerabilities
  topic: keywords
subject: AI-powered cyberattacks
  topic: keywords
creator: Daniel Spadacini
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-05-18
publication date: 2026-05-18
doi: 10.56147/aaiet.2.1.120

Neuromorphic Modular Intelligence Architecture: Overcoming Transformer Limits Toward AGI Beyond Monolithic LLM Limits
type: item
description: Transformer-based Large Language Models (LLMs) achieve remarkable pattern matching but struggle with deep reasoning due to fixed computation depth, bounded inference complexity and limited token memory. These structural constraints (e.g., fixed-layer depth yields only O(1)-depth circuit expressivity and context windows cap memory) hinder tasks requiring iterative, algorithmic reasoning. I propose the Neuromorphic Modular Intelligence Architecture (NMIA), a brain inspired system combining multiple specialized LLM modules with persistent memory, external symbolic verification and a non-linguistic control layer. I analyze asymptotic complexity: A monolithic transformer incurs O(n2d) cost per inference (with n tokens and dimension d), while NMIA with k modules of size n/k yields approximately O(n2d/k + O(nd)), a factor-k speedup. In a simulated experiment (n=5000), NMIA (with 5 modules) significantly outperforms a monolithic LLM on a realistic reasoning task: Modular inference is faster (mean 48.6ms vs. 104.4ms, d=6.63, 95% CI (51.5, 60.2)) and more accurate (accuracy 85.4% vs. 69.4%, d=2.04, ∆ =0.16, 95% CI (0.12, 0.20)). These results support NMIA as a viable AGI path: It demonstrates that careful modular decomposition and memory can circumvent fundamental transformer limitations without invoking purely linguistic reasoning. The analysis and simulation together argue that while monolithic LLMs have formal limits on computation, a neuromorphic, multi-agent design can extend reasoning capacity and is a promising route toward general intelligence.
subject: Articles
  topic: section
subject: Artificial General Intelligence (AGI)
  topic: keywords
subject: Modular AI architecture
  topic: keywords
subject: Transformer limitations
  topic: keywords
subject: Neuromorphic systems
  topic: keywords
subject: Large Language Models (LLMs)
  topic: keywords
subject: Neuro-symbolic AI
  topic: keywords
subject: Persistent memory systems
  topic: keywords
subject: Hierarchical reasoning
  topic: keywords
subject: Computational complexity in AI
  topic: keywords
subject: Multi-agent systems
  topic: keywords
creator: Anindya Mishra
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-07-16
publication date: 2026-07-16
doi: 10.56147/aaiet.2.2.117

A Central Window Algorithm for Explicit Goldbach Representations Certified Examples Up To 10^700
type: item
description: 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.
subject: Articles
  topic: section
subject: Goldbach conjecture
  topic: keywords
subject: Large primes
  topic: keywords
subject: Central window method
  topic: keywords
subject: Sieve methods
  topic: keywords
subject: Primality testing
  topic: keywords
subject: ECPP
  topic: keywords
subject: Computational number theory
  topic: keywords
creator: Bouchaib Bahbouhi
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-03-06
publication date: 2026-03-06
doi: 10.56147/aaiet.2.1.116

Cybersecurity Future Threats in Artificial Intelligence: A Comprehensive Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial intelligence security
  topic: keywords
subject: Adversarial machine learning
  topic: keywords
subject: AI threat landscape
  topic: keywords
subject: Model poisoning
  topic: keywords
subject: Deep learning vulnerabilities
  topic: keywords
subject: AI-powered cyberattacks
  topic: keywords
creator: Joseph Foley
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-04-14
publication date: 2026-04-14
doi: 10.56147/aaiet.2.1.113

A Survey of Recent Graph-Based Methods for Skeleton Based Action Recognition
type: item
description: 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.
subject: Articles
  topic: section
subject: Skeleton-based action recognition
  topic: keywords
subject: Graph convolutional networks
  topic: keywords
subject: Dynamic topology
  topic: keywords
subject: Hierarchical graphs
  topic: keywords
subject: Information bottleneck
  topic: keywords
subject: Language supervision
  topic: keywords
subject: Temporal-channel aggregation
  topic: keywords
creator: Abdur Rahman Siam
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-03-04
publication date: 2026-03-04
doi: 10.56147/aaiet.2.1.111

3D Ball Trajectory from a Single Camera: A Cost-Effective Ball Estimation Technique for Cricket
type: item
description: Accurate Three-Dimensional (3D) tracking of a cricket ball is a fundamental requirement for Decision Review Systems (DRS), yet existing solutions rely on expensive multi-camera infrastructures that limit accessibility, particularly in resource-constrained settings. This paper presents a cost-effective framework for estimating the 3D trajectory of a cricket ball using a single calibrated camera. The proposed method combines monocular vision geometry with a physics-based motion model to reconstruct ball position in world coordinates. Initial depth recovery is obtained from image-plane measurements and camera calibration, after which a Kalman filter incorporating a ballistic motion model is employed to suppress measurement noise and enforce physically plausible trajectories. The filtered trajectory enables reliable detection of the ball bounce point and forward prediction of post-bounce motion, which are critical for Leg-Before Wicket (LBW) analysis. Experimental results on real cricket delivery sequences demonstrate that the proposed Kalman-based smoothing reduces high-frequency reconstruction jitter by approximately 55%, decreasing the root mean square deviation from 42.7 mm to 19.2 mm. Despite using only a single camera, the reconstructed trajectories exhibit stable depth estimation and consistent bounce localization, highlighting the feasibility of the approach for low-cost DRS applications. The proposed system offers a practical alternative to multi-camera solutions, making 3D ball tracking more accessible for domestic cricket, training analysis and emerging cricketing regions.
subject: Articles
  topic: section
subject: Single-camera 3D reconstruction
  topic: keywords
subject: Cricket ball tracking
  topic: keywords
subject: Monocular vision
  topic: keywords
subject: Physics-based motion model
  topic: keywords
subject: Kalman filtering
  topic: keywords
subject: Ballistic trajectory estimation
  topic: keywords
subject: Bounce point detection
  topic: keywords
subject: Decision Review System (DRS)
  topic: keywords
subject: Leg-Before-Wicket (LBW) analysis
  topic: keywords
subject: Sports computer vision
  topic: keywords
creator: Bijaya Ghimire
creator: Gunjan K Mishra
creator: Badri Raj
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-07-16
publication date: 2026-07-16
doi: 10.56147/aaiet.2.2.110

A Novel Hybrid VANET Routing Protocol with Dynamic Power Management for Performance Enhancement
type: item
description: 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.
subject: Articles
  topic: section
subject: VANET
  topic: keywords
subject: Hybrid routing
  topic: keywords
subject: AODV
  topic: keywords
subject: OLSR
  topic: keywords
subject: Adaptive power control
  topic: keywords
subject: Intelligent Transportation Systems (ITS)
  topic: keywords
creator: Burke Geceyatmaz
creator: Fatma Tansu Hocanın
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-03-04
publication date: 2026-03-04
doi: 10.56147/aaiet.1.6.102

Protecting Healthcare from Cyber Threats: Integrating Behavioral Insights into Cybersecurity Strategies
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Human factors
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Protection motivation theory
  topic: keywords
subject: Phishing
  topic: keywords
subject: Compliance
  topic: keywords
subject: Qualitative research
  topic: keywords
subject: Behavior-driven security
  topic: keywords
subject: Cyber resilience
  topic: keywords
creator: Jack Flannery
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-12-03
publication date: 2025-12-03
doi: 10.56147/aaiet.1.5.99

Evaluation the Mechanical Behavior of SS 304L Welding Joints Designed and Fabricated According to ASME Section IX for Cryogenic Applications of Green Energy
type: item
description: 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.
subject: Articles
  topic: section
subject: SS304l
  topic: keywords
subject: Welding joint
  topic: keywords
subject: Cryogenic application
  topic: keywords
subject: Toughness
  topic: keywords
subject: Tensile
  topic: keywords
subject: Mechanical behavior
  topic: keywords
subject: Metallic behavior
  topic: keywords
subject: Hydrogen
  topic: keywords
subject: Cryogenic condition
  topic: keywords
subject: Green energy
  topic: keywords
creator: Abobakr Alsufyani
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-01-16
publication date: 2026-01-16
doi: 10.56147/aaiet.1.6.98

Visual and Quantitative Analysis of Deep Learning Robustness to Shear in Mechanical MNIST
type: item
description: 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.
subject: Articles
  topic: section
subject: Convolutional neural networks
  topic: keywords
subject: Image classification
  topic: keywords
subject: Shear perturbations
  topic: keywords
subject: Mechanical MNIST
  topic: keywords
subject: Feature extraction
  topic: keywords
creator: Babatope Pele
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-12-03
publication date: 2025-12-03
doi: 10.56147/aaiet.1.5.95

Enhancing Classroom Efficiency: Cross-National Evaluation of Lesson Planning, Quiz Generation and Grading with Notegrade.ai
type: item
description: 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 de…
subject: Articles
  topic: section
subject: AI in education
  topic: keywords
subject: Lesson plan
  topic: keywords
subject: Automated quiz generation
  topic: keywords
subject: AI-based grading
  topic: keywords
subject: Cross-national
  topic: keywords
subject: Curriculum
  topic: keywords
subject: Reliability
  topic: keywords
subject: Fairness
  topic: keywords
subject: Notegrade.ai
  topic: keywords
creator: Akinwumi Fakokunde
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-09-30
publication date: 2025-09-30
doi: 10.56147/aaiet.1.5.85

Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: F1-front wing
  topic: keywords
subject: Physics-informed neural network
  topic: keywords
subject: Computational fluid dynamics
  topic: keywords
subject: Formula 1
  topic: keywords
creator: Naval Shah
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-03-05
publication date: 2026-03-05
doi: 10.56147/aaiet.1.6.84

The Pluralistic Future of AI: A Comprehensive Analysis of Decentralized Large Language Models
type: item
description: 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.
subject: Articles
  topic: section
subject: AI: Artificial Intelligence
  topic: keywords
subject: LLM: Large Language Model
  topic: keywords
subject: Centralized LLM
  topic: keywords
subject: Cloud-based large language model
  topic: keywords
subject: Decentralized LLM
  topic: keywords
subject: Locally deployed large language model
  topic: keywords
subject: Data centers
  topic: keywords
subject: Model
  topic: keywords
subject: API
  topic: keywords
creator: Nursan Omarov
creator: Alimzahn Tokushev
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-03-05
publication date: 2026-03-05
doi: 10.56147/aaiet.1.6.81

Identity Refined at the Quantum Gate: Framing the AI + Post-Quantum Challenge for IAM
type: item
description: 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.
subject: Articles
  topic: section
subject: Identity and Access Management (IAM)
  topic: keywords
subject: Post-Quantum Cryptography (PQC)
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Risk-Based Authentication (RBA)
  topic: keywords
subject: Account recovery
  topic: keywords
subject: Passkeys
  topic: keywords
subject: FIDO2
  topic: keywords
subject: WebAuthn
  topic: keywords
subject: Verifiable Credentials (VC 2.0)
  topic: keywords
subject: Machine/Non-Human Identities (NHIs)
  topic: keywords
subject: Crypto-agility
  topic: keywords
subject: Key rotation
  topic: keywords
subject: ML-KEM (Kyber)
  topic: keywords
subject: ML-DSA (Dilithium)
  topic: keywords
subject: SLH-DSA (SPHINCS+)
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Migration strategy
  topic: keywords
subject: Q-day readiness
  topic: keywords
creator: Sravanakumar Nidamanooru
rights: Copyright (c) 2026 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2026-03-04
publication date: 2026-03-04
doi: 10.56147/aaiet.1.6.78

The Hallyu Wave as a Strategic Soft Power Tool: South Korea’s Cultural Diplomacy in the 21st Century
type: item
description: 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.
subject: Articles
  topic: section
subject: Hallyu
  topic: keywords
subject: South Korea
  topic: keywords
subject: Soft power
  topic: keywords
subject: Cultural diplomacy
  topic: keywords
subject: International relations
  topic: keywords
subject: K-pop
  topic: keywords
subject: Policy analysis
  topic: keywords
subject: National branding
  topic: keywords
subject: BTS diplomacy
  topic: keywords
creator: Cristiane Vanessa Alves de Oliveira
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-12-03
publication date: 2025-12-03
doi: 10.56147/aaiet.1.5.62

Improving Fiscal Sustainability in South African Local Governments: A Six-Pillar Conceptual Framework Applied to Three Case Studies
type: item
description: 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.
subject: Articles
  topic: section
subject: South African local government
  topic: keywords
subject: Fiscal sustainability
  topic: keywords
subject: Municipal finance
  topic: keywords
subject: Financial management
  topic: keywords
subject: Revenue diversification
  topic: keywords
subject: Community participation
  topic: keywords
subject: Digital governance
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Public-private partnerships
  topic: keywords
subject: Zero-based budgeting
  topic: keywords
creator: Shandukani Tshilidzi Thenga
creator: S Arunmozhi Selvi
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-07-09
publication date: 2025-07-09
doi: 10.56147/aaiet.1.4.57

Social Loneliness in Youth: A BERT-based Sentiment Analysis of Digital Texts
type: item
description: 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.
subject: Articles
  topic: section
subject: Social loneliness
  topic: keywords
subject: Digital platforms
  topic: keywords
subject: Youth
  topic: keywords
subject: Sentiment analysis
  topic: keywords
subject: Natural language processing
  topic: keywords
subject: BERT
  topic: keywords
creator: Serpil Sevimli Deniz
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-12-03
publication date: 2025-12-03
doi: 10.56147/aaiet.1.5.50

HeartSense: Leveraging Machine Learning to Predict Cardiovascular Risk
type: item
description: 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.
subject: Articles
  topic: section
subject: Heart disease
  topic: keywords
subject: Cardiovascular
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Gradient boosting machines
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Logistic regression
  topic: keywords
subject: Hyperparameter optimization
  topic: keywords
subject: Cross-validation techniques
  topic: keywords
subject: Gradient boosting
  topic: keywords
subject: Support vector machines
  topic: keywords
creator: Justin Lee
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-06-24
publication date: 2025-06-24
doi: 10.56147/1.4.44

Review of Influence of Emotional Intelligence (EI) on Collaboration Among Employees from Diverse Cultural Backgrounds in the Construction Industry
type: item
description: 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.
subject: Articles
  topic: section
subject: Emotional Intelligence (EI)
  topic: keywords
subject: Self-awareness
  topic: keywords
subject: Empathy
  topic: keywords
subject: Relationship management
  topic: keywords
subject: Teamwork
  topic: keywords
subject: Communication
  topic: keywords
creator: Adepeju Nafisat Sanusi
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-08-08
publication date: 2025-08-08
doi: 10.56147/aaiet.1.4.39

A Short Commentary on a Portuguese Case Study Regarding a New Practical Maturity Model for Responsible Artificial Intelligence
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Portuguese Case Study
  topic: keywords
subject: Practical Maturity Model
  topic: keywords
subject: Research and Innovation
  topic: keywords
creator: Rui Miguel Frazão Dias Ferreira
creator: António Grilo
creator: Maria Maia
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-08-08
publication date: 2025-08-08
doi: 10.56147/aaiet.1.4.37

A Review on Deep Learning Techniques for Medical Image Segmentation and Classification
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Medical imaging
  topic: keywords
subject: Classification
  topic: keywords
subject: Neural network
  topic: keywords
creator: Seyedeh Sara Hosseini
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-06-24
publication date: 2025-06-24
doi: 10.56147/1.4.36

Game-Theoretic Self-Supervised Defense: A Dual-Stream Neural Architecture for Secure End-to-End Communication
type: item
description: 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.
subject: Articles
  topic: section
subject: Dual-stream neural architecture
  topic: keywords
subject: Cryptographic methods
  topic: keywords
subject: Adversarial detection
  topic: keywords
subject: Game-theoretic defense strategies
  topic: keywords
subject: Modern communication systems
  topic: keywords
subject: Learning-based anomaly detection
  topic: keywords
creator: Yulin Zhou
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-06-09
publication date: 2025-06-09
doi: 10.56147/aaiet.1.4.34

A Novel Architectural Design Paradigm: Collaborative Construction with Design Thinking Semantic Networks and Large Language Models
type: item
description: 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.
subject: Articles
  topic: section
subject: Design thinking
  topic: keywords
subject: Semantic networking
  topic: keywords
subject: Large language models
  topic: keywords
subject: Architectural design innovation
  topic: keywords
subject: Human-machine collaboration
  topic: keywords
creator: Dong Jun
creator: Wang Zi Jia
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-06-12
publication date: 2025-06-12
doi: 10.56147/aaiet.1.4.29

Before Writing: Epigraphic Classification of the Bosnian Pyramid Inscriptions in Comparative Context with Vinča and Runic Traditions
type: item
description: 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
subject: Articles
  topic: section
subject: Bosnian pyramid complex
  topic: keywords
subject: Visoko proto-script
  topic: keywords
subject: Prehistoric symbols
  topic: keywords
subject: Epigraphy
  topic: keywords
subject: Vinča script
  topic: keywords
subject: Runic traditions
  topic: keywords
subject: Ravne tunnels
  topic: keywords
subject: Proto-writing systems
  topic: keywords
creator: Sam Osmanagich
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-05-22
publication date: 2025-05-22
doi: 10.56147/aaiet.1.3.28

A Perspective on the Potential of Using AI Models for Predicting Injuries
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: AI models
  topic: keywords
subject: Injuries prediction
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Data Learning (DL)
  topic: keywords
subject: Convolutional Neural Networks (CNN)
  topic: keywords
creator: Logan Brooks
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-06-18
publication date: 2025-06-18
doi: 10.56147/aaiet.1.3.26

Environmental Ionization in Enclosed Geospheres: Comparative Study of Global and Local Measurements (2018-2025)
type: item
description: 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 co…
subject: Articles
  topic: section
subject: Negative Air Ions (NAI)
  topic: keywords
subject: Subterranean microclimate
  topic: keywords
subject: Ravne tunnel complex
  topic: keywords
subject: Environmental ionization
  topic: keywords
subject: Air ion concentration monitoring
  topic: keywords
creator: Sam Osmanagich
rights: Copyright (c) 2025 Journal of Advanced Artificial Intelligence, Engineering and Technology
date: 2025-05-13
publication date: 2025-05-13
doi: 10.56147/aaiet.1.3.24