Euro Vantage journals of Artificial intelligence
The Euro Vantage Journal of Artificial Intelligence (EVJAI) is a premier, peer-reviewed, open-access academic journal that provides a comprehensive platform for scholars, researchers, and professionals to explore, discuss, and disseminate advancements in artificial intelligence (AI). Published by EVJAI Solutions, EVJAI serves as a bridge between academia and industry, offering a deep insight into the latest developments in AI, machine learning, deep learning, neural networks, and computational intelligence. The main objective of EVJAI is to provide fast and reliable publication to researchers, scientists, engineers, and professionals who wish to communicate new findings, methods, and insights in artificial intelligence and its related disciplines. The journal welcomes original research articles that contribute to theoretical advancements or practical applications and also considers review papers that critically examine emerging trends and highlight future directions in the field. EVJAI covers a wide scope of topics within artificial intelligence, including machine learning, deep learning, neural networks, natural language processing, computer vision, robotics, data science, and computational intelligence. It also publishes work on generative AI, large language models, cybersecurity, and the integration of AI with smart cities, the Internet of Things (IoT), and sustainable development. Special emphasis is given to studies addressing AI ethics, governance, transparency, and responsible adoption in real-world systems. EVJAI is indexed in several major indexing databases and tries to keep an updated record of current indexing in our Indexing & Links page By maintaining this broad focus, EVJAI reflects the interdisciplinary nature of modern AI research, encouraging contributions that merge computer science, applied mathematics, cognitive science, and engineering with practical problem-solving across various sectors such as healthcare, finance, education, and industry. As an open-access journal, EVJAI provides immediate and free availability of all published content. Readers are allowed to read, download, copy, distribute, and share articles without any restrictions, provided proper acknowledgment of the original publication is given. This open-access policy reflects the journal’s mission of promoting a greater global exchange of knowledge in artificial intelligence and related disciplines. The journal is indexed in Google Scholar, which provides global visibility and accessibility for published articles. With a focus on academic excellence, open knowledge sharing, and international collaboration, the EuroVantage Journal of Artificial Intelligence (EVJAI) is committed to becoming a leading source of research and innovation in artificial intelligence. The EuroVantage Journal of Artificial Intelligence (EVJAI) citation style is available for use in EndNote. Authors and researchers can easily download the official EndNote style file from the EVJAI website and add it to their reference management software. Using the official style ensures that all references and citations are formatted correctly and consistently in accordance with the journal’s requirements. For the convenience of authors and readers, download links for citation files are provided on each article’s abstract page. These files can be directly imported into EndNote and Mendeley, allowing easy management of references and ensuring proper formatting according to the EVJAI citation style. For additional information on this journal visit our About the journal page.
Artificial Intelligence-Driven Cybersecurity Crimes: Emerging Threats, AI Bots, and Advanced Defense Strategies
The rapid advancement of Artificial Intelligence (AI) has transformed cybersecurity by enabling intelligent threat detection, automated incident response, and predictive risk assessment. However, the same technological progress has also introduced a new generation of cyber threats in which malicious actors leverage AI to automate attacks, evade traditional security mechanisms, and conduct highly targeted cybercrimes. AI-powered bots, autonomous malware, intelligent phishing campaigns, deepfake-based social engineering, and adaptive ransom ware have significantly increased the sophistication, scale, and impact of cyberattacks across governments, financial institutions, healthcare organizations, educational sectors, and critical infrastructure. This research investigates the evolution of Artificial Intelligence-driven cybersecurity crimes, emphasizing the role of AI bots in executing autonomous attacks, reconnaissance, credential theft, distributed denial-of-service operations, and misinformation campaigns. The study further evaluates advanced AI-based cybersecurity defense strategies, including machine learning-based intrusion detection systems, behavioral analytics, federated learning, explainable artificial intelligence, zero trust security architecture, threat intelligence platforms, and autonomous incident response mechanisms. A comprehensive experimental framework was developed using benchmark cybersecurity datasets to compare traditional machine learning algorithms with advanced deep learning models in detecting AI-generated cyber threats. Experimental findings demonstrate that hybrid deep learning architectures significantly outperform conventional approaches in identifying sophisticated AI-assisted attacks while reducing false-positive rates and improving detection speed.
Artificial Intelligence, Cybersecurity, AI Bots, Cybercrime, Machine Learning, Deep Learning, Autonomous Malware, Intelligent Phishing, Deepfake Attacks, Intrusion Detection Systems, Zero Trust Security, Threat Intelligence, Explainable AI, Federated Learning, Cyber Defense.
Enterprise Operational Intelligence Platforms: The Future of AI-Driven Business Infrastructure
Enterprise Operational Intelligence Platforms are rapidly transforming the architecture of modern business infrastructure through the integration of artificial intelligence, cloud computing, predictive analytics, and intelligent automation. This article examines the growing significance of AI-driven operational intelligence platforms in enhancing enterprise efficiency, operational resilience, strategic decision-making, and digital transformation across complex organizational environments. The study explores how enterprises increasingly leverage machine learning, real-time analytics, cloud-native architectures, and automated decision-support systems to optimize operations, improve cybersecurity, strengthen business continuity, and enable scalable innovation. Furthermore, the article investigates the role of AI-enabled enterprise architecture in supporting agile infrastructures, intelligent workflow orchestration, and data-driven governance models. While these technologies provide substantial opportunities for competitive advantage and operational excellence, the research also highlights critical challenges related to data governance, interoperability, privacy, ethical AI deployment, and organizational adaptation. The study concludes that enterprise operational intelligence platforms represent a foundational shift toward autonomous and intelligent business ecosystems capable of sustaining long-term digital resilience and innovation in highly dynamic economic environments.
Innovative Approaches to Developing Intelligent Accounting and Tax Management Systems for Enhanced Financial Performance
Intelligent accounting and tax management systems have become critical components of modern financial operations, enabling organizations to improve efficiency, accuracy, and regulatory compliance in an increasingly digital business environment. This paper explores innovative approaches to developing advanced accounting and tax systems that enhance financial performance and decision-making capabilities. The study examines the integration of emerging technologies such as artificial intelligence (AI), machine learning (ML), predictive analytics, cloud computing, and automated compliance monitoring within accounting frameworks. Particular attention is given to data-driven financial reporting, real-time tax assessment, fraud detection, and intelligent audit support mechanisms. The paper provides insights into the design, implementation, and optimization of next-generation accounting systems capable of managing complex financial data while ensuring transparency and compliance with evolving tax regulations. Through practical scenarios and industry-oriented applications, the study demonstrates how intelligent accounting and tax management platforms can improve operational effectiveness, reduce administrative costs, and support strategic financial governance in modern enterprises.
Intelligent Accounting and Tax Management Systems: The Role of Artificial Intelligence in Financial Process Automation
In the era of digital transformation, Artificial Intelligence (AI) is revolutionizing accounting and tax management systems by enabling higher levels of automation, accuracy, and operational efficiency. Traditional accounting processes often face challenges related to data complexity, compliance requirements, manual errors, and delayed financial reporting. This paper investigates the role of AI in modern accounting and tax systems, focusing on its application in automated bookkeeping, tax compliance monitoring, financial reporting, audit support, and fraud detection. By integrating machine learning, predictive analytics, and intelligent decision-support mechanisms, organizations can streamline financial operations, improve regulatory compliance, and enhance the quality of financial information. The study examines key AI technologies, implementation frameworks, and practical applications within accounting environments, demonstrating how intelligent accounting systems contribute to more effective tax administration, reduced operational costs, and improved financial transparency. The findings highlight the growing importance of AI-driven accounting solutions in supporting strategic financial management and sustainable organizational growth.
Multimodal AI for Early Detection of Depression and Anxiety Disorders
Depression and anxiety disorders are among the most common mental health conditions globally, and if left untreated, they can have major emotional, social and economic impacts. The major methods of diagnosis are the clinical interview, self-report instruments, and observation evaluation; all of which can be subject to subjective bias and/or delayed reporting of symptoms. The recent developments of artificial intelligence (AI) have engendered novel possibilities for improved mental health screening by bringing together multimodal data sources. By combining information from textual content, speech patterns, facial expressions, physiological signals, and behavioral indicators, multimodal AI systems can capture a more comprehensive representation of an individual's psychological state. This research focuses on the utilization of multimodal artificial intelligence (AI) methods in the detection of depression and anxiety disorders early. It summarizes the important data modalities, feature extraction techniques, fusion techniques, and deep learning architectures used in modern mental health assessment systems. The efficacy of multimodal models is also assessed in comparison to single-modality models, in particular in the improvement of predictive accuracy, robustness and diagnostic reliability. Further, some of the major problems with data privacy, model interpretability, dataset heterogeneity, and clinical deployment are covered. The results suggest significant potential for multimodal AI frameworks to assist clinicians in the objective, scalable and continuous monitoring of mental health. The authors' findings suggest that the adoption of these sophisticated multimodal learning methods, explainable AI systems, and personalized analytics can substantially impact the success of these EI strategies and potentially drive better mental healthcare outcomes.
Machine Learning-Based Intrusion Detection Systems for Enterprise Security Architectures
The rapid expansion of enterprise networks, coupled with the increasing sophistication of cyber threats, has rendered traditional signature-based intrusion detection systems (IDS) largely inadequate for modern security architectures. This paper explores the integration of machine learning (ML) techniques into intrusion detection frameworks specifically designed for large-scale enterprise environments. We examine supervised, unsupervised, and semi-supervised learning models—including random forests, support vector machines, autoencoders, and deep neural networks—and evaluate their efficacy in detecting zero-day attacks, polymorphic malware, and insider threats. A comparative analysis of ML-based IDS against conventional systems is presented, focusing on key performance metrics such as detection rate, false positive rate, computational overhead, and adaptability to evolving attack vectors. Furthermore, the paper addresses architectural challenges unique to enterprises, including data imbalance, real-time processing constraints, and integration with existing security information and event management (SIEM) systems. Findings indicate that hybrid ML models, particularly those combining anomaly detection with ensemble learning, significantly enhance detection accuracy while maintaining acceptable latency for high-throughput enterprise networks. We conclude with a set of best practices for deploying ML-based IDS within defense-in-depth strategies and propose future research directions in adversarial machine learning and federated learning for distributed enterprise architectures.
AI-Based Detection of Deepfakes and Misinformation on Social Media
Artificial Intelligence and Generative AI technologies have ramped up the generation and dissemination of deepfakes and misinformation on social media platforms. Manipulated videos, images, and synthetic audio, commonly known as ‘deepfake content,’ have become significant threats to digital trust, cybersecurity, political stability, public perception, and authenticity of information. The rapid dissemination of information on social media and the growing sophistication of techniques for generating synthetic media have created a very fertile ground for AI-powered misinformation operations.AI-powered misinformation campaigns are extremely effective and are well-suited to the current speed of information spread on social media, and the evolving techniques for creating synthetic media. Thus, the need for intelligent and automated detection systems is paramount for detecting manipulated content and mitigating its societal effect has become more acute. This study presents a machine learning, deep learning and multimodal approach for identifying deepfakes and misinformation in social media. This article explores several tools and models for content detection, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer-based models, and hybrid AI systems for real-time content verification and moderation. Further, the study examines the system architectures of automated monitoring, feature extraction, behavioral analysis, and explainable AI mechanisms to enhance detection accuracy and explainability. Potential issues of adversarial attacks and dataset limitations, model bias, computational complexity, and ethical concerns are also examined. The research also identifies future research avenues such as federated learning, blockchain based auditing of media, explainable AI, and multi-platform misinformation detection tools. In summary, the study highlights the critical role of comprehensive AI-supported systems in mitigating the spread of deepfakes and ensuring the integrity of information in today's digital communication landscape.
Digital Welfare and Social Equity in a Connected World: Challenges, Opportunities, and Policy Implications
In an increasingly interconnected and technology-driven world, digital welfare systems have become a central mechanism for delivering social support and promoting social equity. This paper examines the evolving role of digital welfare initiatives in addressing inequality and improving access to essential public services. It analyzes both the opportunities and challenges associated with the digitalization of welfare systems, with particular emphasis on their implications for inclusive development and social justice. The study explores key issues such as unequal access to digital infrastructure, variations in digital literacy, and disparities in the ability of individuals and communities to effectively engage with online welfare platforms. These digital divides often limit the reach and effectiveness of welfare programs, potentially reinforcing existing social and economic inequalities. At the same time, digital welfare systems offer significant opportunities to enhance service efficiency, improve targeting of social assistance, reduce administrative costs, and expand the accessibility of public services to underserved populations. The paper also evaluates the effectiveness of current digital welfare programs and highlights structural and policy-related limitations that hinder equitable outcomes. It further investigates emerging strategies, including inclusive digital design, mobile-based welfare delivery, data-driven policy targeting, and community-based digital support systems, which can help bridge existing gaps in access and participation. By integrating insights from social policy, digital governance, and development studies, the research emphasizes the importance of comprehensive policy frameworks that prioritize equity, accessibility, and transparency. The findings underscore that ensuring social equity in a digital welfare era requires coordinated efforts between governments, technology providers, and civil society to ensure that digital transformation benefits all segments of society fairly and inclusively.
The Impact of AI-Powered Automation on Workforce Transformation and Sustainable Economic Growth
Artificial Intelligence (AI)-powered automation is rapidly reshaping global industries by transforming traditional work environments, operational processes, and economic structures. The increasing adoption of intelligent automation technologies across sectors such as manufacturing, healthcare, finance, logistics, and information technology has introduced significant changes in workforce dynamics and organizational productivity. This study examines the impact of AI-driven automation on labor markets, employment patterns, workforce adaptability, and long-term economic growth. The paper explores how AI technologies, including machine learning, robotic process automation, intelligent analytics, and autonomous systems, enhance operational efficiency, reduce manual intervention, and support innovation-driven business models. Furthermore, the research investigates the dual impact of automation on employment, highlighting both the displacement of repetitive and routine jobs and the emergence of new opportunities in technology-oriented and data-driven professions. The study also analyzes the influence of AI-powered automation on productivity growth, industrial competitiveness, cost optimization, and digital economic expansion. In addition, critical challenges associated with large-scale automation adoption are discussed, including workforce reskilling requirements, income inequality, labor market polarization, ethical concerns, and policy adaptation. The paper further examines strategies for achieving balanced and inclusive economic transformation through education reform, digital skill development, human-AI collaboration models, and adaptive labor policies.
Leveraging Business Intelligence for Data-Driven Strategic Planning and Organizational Performance Enhancement
Business Intelligence (BI) has become an essential strategic asset for organizations seeking to improve decision-making processes, optimize operational activities, and strengthen competitive performance in dynamic business environments. This study explores the role of Business Intelligence technologies and analytical frameworks in supporting strategic planning and organizational performance management. The paper examines how BI systems facilitate data-driven decision-making by transforming large volumes of organizational data into meaningful insights that support forecasting, resource optimization, and performance evaluation. It further analyzes the application of BI tools in monitoring key performance indicators (KPIs), identifying market trends, improving operational transparency, and enhancing managerial responsiveness. In addition, the research discusses major challenges associated with BI implementation, including data integration complexity, scalability limitations, data quality management, organizational resistance, and cybersecurity concerns. The study also highlights best practices for successful BI adoption, such as establishing strong data governance frameworks, investing in employee training, and aligning BI initiatives with organizational objectives.
Digital Twin-Based Fault Diagnosis and Resilience Monitoring for Smart Grid Transformer Systems
Smart grid transformer systems are critical nodes in national power infrastructure whose failure can cascade into widespread outages, economic losses, and public safety risks. Traditional condition monitoring approaches rely on periodic offline inspections and threshold-based alarms that are ill-suited to the dynamic, real-time nature of modern grid operations. This paper proposes a comprehensive digital twin (DT) framework for fault diagnosis and resilience monitoring of high-voltage power transformers. The architecture integrates physics-based electromagnetic and thermal models, IoT sensor data streams, and machine learning classifiers within a synchronized virtual-physical environment. Five canonical fault modes—partial discharge, winding deformation, insulation degradation, oil contamination, and core saturation—are modeled and validated against historical field data. Experimental results demonstrate an overall fault detection accuracy of 97.3%, a mean time-to-diagnosis of 4.2 seconds, and a false positive rate below 1.8%. The proposed framework reduces unplanned outage risk by an estimated 43% and improves grid resilience indices by 31% compared to conventional SCADA monitoring. The study also discusses integration pathways with CISA cybersecurity mandates and the U.S. Department of Energy's grid modernization initiatives, establishing the framework as a scalable solution for national critical infrastructure protection.
Assessing the Effectiveness of Cybersecurity Program Management Frameworks and Adversarial AI Defense Mechanisms in Medium and Large Organizations
Cybersecurity and Artificial Intelligence (AI) have become central to organizational resilience in medium and large enterprises. This study experimentally evaluates the effectiveness of cybersecurity program management frameworks—ISO/IEC 27001 and NIST CSF—and assesses AI system vulnerability to adversarial attacks, including evasion, poisoning, and privacy attacks. Simulated organizational environments were employed to implement frameworks and deploy AI models for threat detection, with adversarial attacks executed to quantify vulnerabilities. Defense mechanisms, such as adversarial training and ensemble modeling, were integrated within frameworks to evaluate combined effectiveness. Results indicate that flexible frameworks like NIST CSF provide superior incident response and control effectiveness, while hybrid defense strategies significantly enhance AI robustness. The study offers actionable guidance for integrating cybersecurity governance with AI defense mechanisms, supporting organizational resilience against emerging 2026 cyber threats.
An Integrated Cybersecurity Program Management Framework for Mitigating Adversarial AI Threats in Medium and Large Organizations
The increasing adoption of artificial intelligence (AI) in organizational cybersecurity introduces both advanced defensive capabilities and new vulnerabilities, particularly from adversarial attacks targeting machine learning models. Traditional cybersecurity program management frameworks, while effective for conventional threats, often lack mechanisms to address AI-specific challenges, leaving organizations exposed to sophisticated attacks. This study proposes an integrated, multi-layered cybersecurity framework that combines governance, risk management, real-time anomaly detection, and adversarial resilience strategies. Using an Isolation Forest–based anomaly detection model implemented in a Python environment, the framework is evaluated on synthetic datasets simulating adversarial behaviors. Experimental results demonstrate high detection accuracy (96.8%), low false positive rates (3.2%), and robust response times suitable for real-world deployment. Comparative analysis with baseline models confirms superior performance and resilience under adversarial scenarios. The proposed framework provides organizations with a scalable, adaptive, and intelligent approach to manage cybersecurity risks in AI-driven environments.
Blockchain-Supported Intelligent Systems for Privacy-Preserving Data Analytics
The rapid growth of intelligent systems and data-driven technologies has increased the demand for secure and privacy-preserving data analytics. Organizations increasingly rely on machine learning and artificial intelligence to extract insights from massive datasets, yet the sensitive nature of modern data—such as healthcare records, financial transactions, and personal behavioral information—creates significant privacy challenges. Traditional centralized analytics frameworks often require data sharing among multiple entities, which increases risks of unauthorized access, data leakage, and privacy violations. Blockchain technology has emerged as a promising solution to these challenges due to its decentralized architecture, immutable ledger, and cryptographic security mechanisms. This research investigates a blockchain-supported intelligent system framework designed to enable privacy-preserving data analytics across distributed environments. The proposed architecture integrates blockchain infrastructure with advanced privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy to allow collaborative analytics without exposing raw data. Blockchain acts as a secure coordination layer that records transactions, ensures data integrity, and enforces trust among participants.
Copyright (c) 2024 Euro Vantage journals of Artificial intelligence
Enhancing Transparency and Security in Intelligent Systems through Blockchain-Based Governance Models
The rapid adoption of intelligent systems across domains such as finance, healthcare, transportation, and smart infrastructure has created new opportunities for automation, efficiency, and data-driven decision making. However, these systems often operate in complex digital ecosystems where transparency, trust, and data integrity remain significant concerns. Traditional centralized governance frameworks struggle to provide verifiable accountability and secure collaboration among multiple stakeholders. Blockchain technology, with its decentralized ledger structure and cryptographic validation mechanisms, offers a promising solution to these challenges by enabling transparent and tamper-resistant governance models for intelligent systems. This study proposes a blockchain-based governance architecture designed to enhance the transparency, reliability, and security of intelligent systems operating in distributed environments. The framework integrates decentralized consensus mechanisms, smart contract-based policy enforcement, and secure data sharing protocols to establish trustworthy governance across autonomous agents and data providers.
A Trust-Aware Intelligent Systems Framework for Secure Data Exchange Using Blockchain
Secure data exchange has become a critical requirement for modern intelligent systems that operate across distributed and heterogeneous environments. As artificial intelligence applications increasingly rely on collaborative data sharing among organizations, devices, and platforms, ensuring trust, integrity, and privacy in the exchanged information becomes a fundamental challenge. Traditional centralized security mechanisms often fail to provide sufficient transparency and tamper resistance, especially when multiple stakeholders with varying trust levels are involved. Blockchain technology, with its decentralized ledger architecture and cryptographic validation mechanisms, offers a promising solution to address these issues. This research proposes a trust-aware intelligent systems framework that leverages blockchain technology to facilitate secure and reliable data exchange across distributed intelligent environments. The framework integrates trust evaluation models with blockchain-based distributed ledgers to ensure that data transactions are verified, immutable, and traceable.
The Rise of Agentic Workflows: Why Businesses Are Adopting Them
Agentic workflows represent the next evolutionary step beyond traditional robotic process automation (RPA) and simple rule-based orchestration. Unlike conventional automation tools that execute predefined scripts, agentic workflows employ autonomous AI agents capable of reasoning, planning, multi-step decision-making, tool use, and self-correction in dynamic environments. This paper examines the technological, economic, and organizational drivers behind the rapid enterprise adoption of agentic systems in 2024–2025. Through industry case studies and adoption surveys, we demonstrate that organizations deploying agentic workflows are achieving 3–8× higher automation rates in knowledge-work processes previously considered “unautomatable.” The shift is driven by dramatic improvements in large language models, the maturation of agent frameworks, falling inference costs, and the urgent need to counter shrinking workforces and rising labor costs.
Copyright (c) 2025 Euro Vantage journals of Artificial intelligence
Workflow Automation with LLMs: Practical Guide for 2025
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have emerged as transformative tools for automating workflows across various industries. This practical guide explores how LLMs can streamline processes, enhance efficiency, and drive innovation in 2025. By delving into the fundamentals, tools, design strategies, real-world applications, and potential challenges, the paper provides actionable insights for professionals seeking to implement LLM-driven automation. Emphasizing a balance between technological capabilities and ethical considerations, it aims to equip readers with the knowledge to build robust, scalable systems that adapt to dynamic business needs. Through detailed explanations and examples, this guide underscores the potential of LLMs to revolutionize traditional workflows, reducing manual intervention while amplifying human creativity and decision-making.
Metaheuristic Optimization-Assisted LSTM-DBN Model for Predictive Healthcare Analytics and Biomedical Informatics
The integration of artificial intelligence (AI) with healthcare informatics has led to significant advancements in disease prediction, patient risk assessment, and medical decision support. However, healthcare data is highly heterogeneous, dynamic, and prone to imbalance and uncertainty—posing challenges for accurate temporal modeling and feature extraction. This research presents a Metaheuristic Optimization-Assisted LSTM-DBN Model, a hybrid deep learning framework designed to enhance predictive healthcare analytics by fusing Long Short-Term Memory (LSTM) networks for temporal modeling with Deep Belief Networks (DBN) for hierarchical feature abstraction. To optimize performance, Tree Growth Optimization (TGO) and Tunicate Swarm Optimization (TSO) algorithms are employed for dynamic hyperparameter tuning. The model is tested on benchmark biomedical datasets, including physiological signal sequences, patient vital trends, and disease progression records. Experimental outcomes demonstrate superior prediction accuracy, stability, and interpretability compared to traditional deep models. This optimized hybrid architecture enables precise temporal analysis of patient health trajectories, facilitating early diagnosis, clinical decision support, and efficient biomedical data modeling—paving the way for intelligent, data-driven healthcare systems.
A Unified Deep Learning Framework for Predictive Healthcare Analytics Using TJO-Based Feature Selection and Tree Growth Optimized Graph-Temporal Modeling
The intersection of healthcare and biomedical informatics has witnessed exponential data growth through electronic health records (EHRs), wearable devices, and multi-modal biomedical sensors. However, extracting actionable intelligence from this complex, high-dimensional, and temporally evolving data remains a major challenge. This paper proposes a unified deep learning framework that integrates Tree Growth Optimization (TGO)-based feature selection with a Tree Growth Optimized Long Short-Term Memory (TGO-LSTM) and graph-based modeling for predictive healthcare analytics. The framework efficiently handles temporal dependencies and inter-patient relationships by combining graph and sequential learning paradigms. TGO plays a dual role—selecting the most informative biomedical features and tuning LSTM hyperparameters for optimal predictive accuracy. The proposed model was validated using benchmark healthcare datasets such as MIMIC-III and PhysioNet, demonstrating superior disease prediction accuracy, interpretability, and computational efficiency compared to existing CNN-LSTM and GNN-RNN models. Experimental results confirm that integrating Metaheuristic optimization with graph-temporal architectures enhances the predictive power of healthcare systems while maintaining scalability and transparency—key requirements for real-world clinical deployment.
Blockchain-Based Cybersecurity Frameworks for Secure Government Communications
Government communication systems form the backbone of national security, governance, and public service delivery. These systems are frequently targeted by cyber adversaries seeking to intercept, manipulate, or disrupt sensitive information flows. Traditional security mechanisms, while effective to a degree, struggle with issues of centralization, traceability, and resilience against insider or state-sponsored threats. Blockchain technology, with its distributed ledger, immutability, and consensus mechanisms, provides a compelling foundation for reimagining secure government communications. This paper explores blockchain-based cybersecurity frameworks tailored for government contexts, focusing on confidentiality, integrity, authenticity, and resilience. It outlines architectural approaches that integrate blockchain with secure messaging protocols, decentralized identity management, and smart contract-driven access control. Key challenges, including scalability, interoperability, latency, and governance, are analyzed in detail, alongside strategies for hybrid architectures that balance performance with security. The study demonstrates that blockchain-enabled communication networks can enhance trust, accountability, and auditability across inter-agency and cross-border operations, ultimately reinforcing sovereignty and national resilience.
Automated Machine Learning (AutoML): The Path to Self-Evolving Models
The increasing complexity of machine learning (ML) model design has created a growing demand for automation in the end-to-end ML pipeline. Automated Machine Learning (AutoML) has emerged as a revolutionary paradigm that democratizes ML development by automating tasks such as data preprocessing, feature selection, model selection, and hyperparameter optimization. AutoML bridges the gap between expert data scientists and domain practitioners by enabling the creation of high-performing models with minimal human intervention. This paper explores the evolution of AutoML as a pathway toward self-evolving models—systems capable of autonomously improving their performance through continuous feedback and adaptation. It examines the core techniques underlying AutoML, including Bayesian optimization, meta-learning, neural architecture search (NAS), and reinforcement learning-based tuning strategies. Furthermore, the paper discusses the integration of AutoML with emerging technologies such as cloud computing and federated learning, highlighting its potential in building scalable, adaptive, and privacy-aware AI systems. Finally, it identifies key challenges in interpretability, computational cost, and fairness, concluding with insights into how AutoML will shape the future of autonomous, self-optimizing artificial intelligence.
Enhancing Cybersecurity Defenses Against AI-Generated Deepfake Videos: A Framework for Real-Time Detection and Mitigation
Artificial intelligence has increased quickly, allowing the production of highly realistic deepfake videos, which are extremely dangerous to cybersecurity, privacy, and putting trust in people. Conventional detector control systems do not necessarily work in real-time and systems are susceptible to malicious use. The research will suggest a holistic approach to the real-time detection and mitigation of AI-generated deepfake videos. The framework uses sophisticated machine learning and deep learning models, such as convolutional neural networks (CNNs) and transformer based ones, with multi-modal visual, audio, and metadata modeling. The suggested system is built to work hand in hand with the cybersecurity monitoring systems, allowing to perform automated threat detection and response, including flagging of content and verification of the source. Through the framework, experimental analysis has revealed that the framework can attain high detection ability with minimal latency, providing a scalable platform to digital environment measures against new deepfake and fraudsters. The research will help in the creation of more resilient cybersecurity defences as well as offer practical information to policymakers, platform proprietors and security experts.
Workflow Automation and Process Optimization through Sales force Platforms
The increasing complexity of business operations and the demand for efficiency have made workflow automation a crucial component of modern enterprise management. Salesforce, as a leading cloud-based customer relationship management (CRM) platform, offers robust tools for streamlining workflows, automating repetitive processes, and optimizing organizational performance. This paper examines how Salesforce platforms enable workflow automation and process optimization through features such as Salesforce Flow, Process Builder, and Einstein AI. It highlights the strategic role of automation in reducing manual effort, enhancing accuracy, and fostering data-driven decision-making. Furthermore, the study explores the challenges and best practices in implementing Salesforce automation, emphasizing scalability, adaptability, and integration with third-party systems. By leveraging Salesforce’s ecosystem, organizations can not only improve operational efficiency but also align workflows with customer-centric strategies that drive innovation and growth.
From Data to Insight: Decoding Student Cognition with Explainable AI Models
Understanding student cognition is critical for enhancing educational outcomes and creating adaptive learning environments. This paper presents a comprehensive study on the application of Explainable Artificial Intelligence (XAI) models in decoding student cognitive abilities using educational data. By integrating explainable machine learning techniques with educational data mining (EDM), the proposed framework enables accurate assessment of students’ learning behaviors while maintaining transparency and interpretability. The study employs models such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention-based neural networks to analyze diverse cognitive indicators including attention span, problem-solving skills, and knowledge retention. Experimental results on real-world educational datasets show significant improvements in both prediction accuracy and interpretability compared to traditional black-box models. The findings highlight the potential of XAI to provide educators with actionable insights, enabling personalized learning interventions while ensuring fairness, accountability, and trust in educational AI systems.
Artificial Intelligence in Pulp Vitality Assessment: A Novel Approach to Diagnostic Decision-Making
The advent of artificial intelligence (AI) is a groundbreaking technology in the field of dentistry and it is promising new opportunities to improve the precision of diagnosis and decision-making. A field that is of considerable clinical concern is pulp vitality assessment which is a foundation of endodontic diagnosis and treatment planning. The classical diagnostic techniques, such as thermal pulp, electric pulp, and laser Doppler flowmetry, and the more modern ones, such as laser Doppler flowmetry, are usually associated with issues of subjectivity, variability of patients, and limited accuracy. The new solution offered by AI-based methods is based on the use of machine learning and deep learning algorithms to process various clinical and imaging data, thus, allowing objective and reproducible assessment of pulp status. Combining multimodal data (radiograph and thermography, patient history) will enable AI to be more diagnostic and aid clinicians with real time data. Moreover, the technologies promise improvements in the diagnostic errors, treatment outcomes, and the way to predictive and preventive endodontics. Despite these benefits there are still challenges in the areas of data availability, algorithm transparency, and clinical integration. Before widespread adoption, ethical issues, especially those to do with data privacy and possible bias, need to be addressed. However, AI is a potentially useful paradigm shift in the area of pulp vitality testing, and it could significantly enhance the quality of diagnostic decisions in the field of endodontics.
AI-Powered Classification of Root Canal Irrigation Efficiency Based on Image Analysis
Root canal irrigation plays a vital role in the endodontic treatment success, since it guarantees the elimination of debris, microorganisms, and biofilm of the complex root canal system. Conventional irrigation efficiency assessment techniques tend to be subjective, laborious and low reproducibility. In this research, the authors explain how artificial intelligence (AI) and image analysis can be used to categorize the efficacy of root canal irrigation. Images of treated canals of high resolution were obtained and preprocessed to extract features. A convolutional neural network (CNN)-based model was trained to read morphological patterns and categorize the results of the irrigation as efficient or inefficient. The accuracy of the AI model during the classification process was rather high in the comparison with expert ratings, which indicates the possibility of objective and automated assessment. The results indicate that AI-based image recognition can be an effective supplement to an endodontic study and clinical practice and provide a stable and quick assessment of irrigation procedures. Additional future studies are advised to be conducted on a larger scale, in real time, and to be validated in clinical settings to improve reliability and become part of routine care.
Towards Transparent Learning Analytics: A Study on Explainable AI in Cognitive Skill Prediction
The increasing adoption of artificial intelligence (AI) in educational technology has paved the way for advanced predictive models capable of analyzing and forecasting student cognitive skills. However, the opaque nature of many high-performance AI models limits their trustworthiness and practical utility in educational settings. This paper explores the integration of Explainable AI (XAI) techniques into cognitive skill prediction to achieve transparency, interpretability, and fairness in learning analytics. By employing methods such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and attention-based neural networks, the proposed framework deciphers the factors influencing cognitive skill development while maintaining robust predictive performance. Experimental results on large-scale educational datasets reveal significant improvements in interpretability without compromising accuracy, enabling educators to make data-driven decisions for personalized learning interventions and equitable assessment practices.
Ethical AI and QA-Driven Cybersecurity Risk Mitigation for Critical Infrastructure
The critical infrastructure is becoming more dependent on artificial intelligence (AI) to monitor, detect and respond to cybersecurity threats, including energy grids, healthcare networks, and transportation systems. Nonetheless, with the implementation of AI, there are also other risks, such as the manipulation of algorithms, the manipulation of models, and the risk of privacy invasion. The study examines the application of ethical AI concepts and quality assurance (QA)-based models in enhancing cybersecurity risks mitigation of critical infrastructure. It highlights the importance of transparency, fairness, and accountability in AI driven threat detection, and strict QA testing of AI models to ensure ongoing testing, validation and monitoring. The research report focuses on an organized process of risk scoring, prioritization, and automated system response and maintaining ethical and regulatory standards. With the predictive capabilities of AI coupled with QA and supervision, such work is a roadmap on how to construct resilient, trustworthy, and ethically dedicated cybersecurity systems that will resist emerging threats to critical infrastructure.
AI-Enabled Predictive Maintenance for Industrial Equipment: Enhancing Reliability and Reducing Downtime
The growing sophistication of industrial systems and the high price of unplanned equipment breakdowns has increased the importance of sophisticated maintenance plans. Conventional methods, including reactive and preventive maintenance, tend to be ineffective, which results in excessive downtimes, wastage of resources and safety issues. Predictive maintenance is a transformative solution to artificial intelligence (AI) as it applies machine learning algorithms, sensor data, and real-time analytics to predict equipment failures before they happen. Predictive maintenance systems can be optimized, increase the asset lifespan, and decrease operational costs considerably thanks to the integration with the Industrial Internet of Things (IIoT), big data platforms, or digital twins. The paper will discuss the use of AI in predictive maintenance with its technological basis, industry use, advantages, and limitations and its future opportunities. The research underlines predictive maintenance as one of the foundations of Industry 4.0 and sustainable industrial development by showing how AI-based knowledge promotes better reliability and reduces downtimes.