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 NUZM 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.
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.
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.
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.
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.
Modeling the Impact of Climate Risk on Agricultural Productivity and Food Prices
Climate change has emerged as one of the most critical global challenges, with profound implications for agricultural productivity and food prices. The complexity and interconnectivity of climatic variables, such as temperature fluctuations, irregular precipitation patterns, and increased frequency of extreme weather events, necessitate sophisticated modeling approaches to capture their influence on agriculture. This study aims to model the impact of climate risk on agricultural productivity and food prices using a hybrid econometric-climate simulation framework. By integrating climate projections with agricultural yield models and price forecasting mechanisms, this research presents a comprehensive understanding of how climate risk translates into economic disruptions in the agri-food sector. Through historical data analysis, regional case studies, and machine learning-assisted regression models, the study provides empirical evidence of climate-induced yield variability and subsequent market volatility. The experimental results demonstrate that climate variability can significantly suppress crop yields, especially in vulnerable regions, which leads to sharp increases in food prices, disproportionately affecting low-income populations. The findings underscore the urgency of adaptive strategies and policy responses aimed at climate resilience and food security.
Economic Impact of Deforestation on Indigenous Communities and Ecosystem Services in the Amazon
Deforestation in the Amazon rainforest continues to be a critical global issue, significantly affecting the ecological balance, climate stability, and the livelihoods of indigenous communities. This study explores the intricate economic repercussions of Amazonian deforestation on both the native populations and the ecosystem services upon which they and the wider global community depend. It utilizes a mixed-method approach combining qualitative field research with economic valuation techniques to quantify the loss in provisioning, regulating, and cultural ecosystem services. The research further analyzes the socio-economic displacement, loss of traditional income sources, and disruption in community health that results from forest degradation. In addition to evaluating data from satellite imagery and ecological models, the study incorporates direct testimonies from Amazonian tribes affected by logging, mining, and agribusiness expansion. The findings underscore a paradox wherein short-term economic gains for external actors come at the cost of long-term ecological damage and socio-economic destabilization of native populations. The results advocate for the urgent integration of indigenous economic systems into conservation policies and highlight the critical role of sustainable forest management.
The Economics of Plastic Pollution: Policy Instruments for Reducing Single-Use Plastics
Plastic pollution has emerged as a pressing environmental and economic concern in the 21st century, particularly driven by the proliferation of single-use plastics. These items, while inexpensive and convenient, impose vast externalities on ecosystems, public health, and economic productivity. This paper critically examines the economic foundations of plastic pollution and evaluates the efficacy of various policy instruments aimed at curbing the use and production of single-use plastics. Employing an economic lens, we analyze market failures associated with plastic consumption and explore corrective mechanisms such as taxes, bans, extended producer responsibility, and subsidies for alternatives. Experimental evidence and international case studies highlight the relative performance of these policies in achieving environmental and economic efficiency. By quantifying external costs and modeling consumer and producer responses to interventions, this paper offers a comprehensive framework for designing plastic reduction policies that balance equity, efficiency, and feasibility. Our findings suggest that a combination of instruments—tailored to regional economic conditions and integrated within circular economy principles—yields the most sustainable outcomes.
Water Pricing and Allocation Policies: Economic Efficiency versus Equity in Water-Scarce Regions
Water scarcity has emerged as a formidable challenge in numerous regions across the globe, necessitating robust and well-calibrated water pricing and allocation policies. This paper investigates the intricate balance between economic efficiency and equity in the management of scarce water resources. The tension between market-based solutions that promote efficiency and regulatory or equity-oriented approaches that ensure fairness is explored using both theoretical foundations and empirical case studies. We critically examine various water pricing models, allocation mechanisms, and the role of government and community participation in policy design. Experimental simulations based on data from arid regions provide insights into how alternative strategies perform under different scarcity scenarios. The results underscore the potential trade-offs and synergies between efficiency and equity goals and suggest a path forward for integrated, adaptive water governance.
The Influence of Earnings Management on Earnings Quality and Corporate Sustainability in GCC Companies
In the dynamic and evolving economies of the Gulf Cooperation Council (GCC), financial transparency and sustainability have become paramount to long-term development goals. However, the persistent practice of earnings management threatens both the quality of reported earnings and the broader corporate sustainability agenda. This study explores the relationship between earnings management, earnings quality, and corporate sustainability in listed GCC companies. By distinguishing between accrual-based and real earnings manipulation techniques, we assess their impact on earnings informativeness, reliability, and long-term strategic performance. The findings reveal that excessive earnings management undermines earnings quality, distorts financial signals to stakeholders, and detracts from environmental, social, and governance (ESG) commitments. The paper calls for stronger governance mechanisms and harmonized regulatory frameworks to promote sustainable financial reporting across the region.
Financial Facades and Sustainable Realities: Earnings Management across Distressed and Healthy GCC Enterprises
Earnings management remains a contentious issue in financial reporting, often obscuring the true economic performance of firms. In the Gulf Cooperation Council (GCC) region, where economic diversification and sustainability are strategic goals, the practice of managing earnings presents challenges to transparency and long-term resilience. This study examines the extent and nature of earnings management across distressed and financially healthy firms in the GCC. By leveraging accrual-based and real earnings management models, and differentiating between distressed and non-distressed entities, we explore how financial pressure influences reporting behavior. The findings suggest that distressed firms are more likely to engage in opportunistic earnings manipulation, while healthy firms tend to manage earnings for signaling stability and attracting investment. These dynamics have profound implications for regulators, investors, and policymakers seeking to foster sustainable and trustworthy financial systems in the region.
High-Precision Fault Identification in Motor Bearings via Enhanced Multi-Kernel ELM Framework
Motor bearings are among the most critical components in rotating machinery, and their failure often leads to costly downtime and maintenance. Accurate and early detection of bearing faults can significantly enhance the reliability and longevity of industrial systems. This study introduces a novel Enhanced Multi-Kernel Extreme Learning Machine (EMK-ELM) framework for high-precision fault identification in motor bearings. The model leverages a dynamic integration of Gaussian and polynomial kernels to improve classification performance over traditional single-kernel methods. By capturing both linear and nonlinear relationships in vibration signal data, the EMK-ELM framework demonstrates superior generalization and faster learning capabilities. The experimental evaluation is carried out using benchmark datasets such as the Case Western Reserve University (CWRU) bearing dataset, highlighting the framework’s robustness across different fault types and severities. Performance is compared with standard machine learning classifiers, including Support Vector Machines and traditional ELM, showing that EMK-ELM offers improved diagnostic accuracy, training efficiency, and fault sensitivity. This paper provides a detailed discussion of model architecture, feature engineering, experimental methodology, and statistical evaluation, supporting its claim as a high-precision, real-time-capable solution for motor bearing fault detection.
Hybrid Kernel-Driven ELM for Intelligent Fault Detection in Motor Rolling Bearings
Motor rolling bearings are critical components in industrial machinery, and their failure can lead to significant downtime and financial losses. Therefore, developing robust and intelligent fault detection systems is essential for ensuring operational reliability. This study presents a novel approach utilizing a Hybrid Kernel-Driven Extreme Learning Machine (HK-ELM) for intelligent fault detection in motor rolling bearings. The hybridization of multiple kernel functions enhances the learning capability and generalization of ELM by capturing complex nonlinear features from vibration signals. The proposed model integrates Gaussian and polynomial kernels to construct a composite kernel function, enabling adaptive feature extraction. The experimental evaluation was conducted on publicly available bearing datasets, where the hybrid kernel model was benchmarked against traditional ELM and other standard machine learning models. Results demonstrate that the HK-ELM model outperforms its counterparts in classification accuracy, training time, and fault recognition robustness, especially in the presence of noise and overlapping classes. This paper also discusses the impact of kernel parameter tuning and the hybridization strategy on diagnostic performance, concluding that the hybrid approach offers a highly scalable and efficient solution for real-time industrial fault detection applications.
A Novel Multi-Kernel Extreme Learning Machine Model for Accurate Diagnosis of Motor Bearing Faults
Accurate fault diagnosis of motor bearings is essential for ensuring the reliability and safety of rotating machinery in various industrial applications. Traditional machine learning models often struggle to balance generalization and precision, particularly when processing complex, non-linear features present in motor vibration signals. To address this challenge, this research introduces a novel Multi-Kernel Extreme Learning Machine (MKELM) model tailored for precise diagnosis of motor bearing faults. The proposed model integrates multiple kernel functions—specifically Gaussian, polynomial, and wavelet kernels—to exploit diverse data characteristics and enhance classification performance. Experimental evaluations using the Case Western Reserve University (CWRU) bearing dataset demonstrate the effectiveness of MKELM in achieving superior accuracy, robustness, and generalization compared to conventional single-kernel ELM and other baseline classifiers such as Support Vector Machines and Random Forests. Through rigorous analysis, the study validates the hypothesis that multi-kernel learning, when fused with ELM's fast learning capabilities, provides a powerful framework for bearing fault detection in real-world scenarios.
Enhanced Fault Diagnosis of Motor Rolling Bearings Using an Improved Multi-Kernel Extreme Learning Machine Approach
Fault diagnosis of motor rolling bearings is critical for maintaining the operational efficiency and safety of rotating machinery systems. With the increasing demand for precise and rapid fault detection, traditional machine learning methods often fall short in terms of adaptability and accuracy under noisy or non-linear conditions. This study proposes an Enhanced Multi-Kernel Extreme Learning Machine (EMK-ELM) approach for robust and accurate fault diagnosis of rolling bearings in electric motors. The methodology integrates multiple kernel functions in the ELM framework to better capture complex data characteristics while mitigating the impact of outliers and noise. The proposed model is trained and validated using the Case Western Reserve University bearing dataset, and its performance is benchmarked against conventional ELM and other advanced classifiers including Support Vector Machines and Convolutional Neural Networks. Experimental results demonstrate that EMK-ELM significantly improves classification accuracy, generalization, and training speed, making it an ideal candidate for real-time fault diagnosis applications. This research lays a solid foundation for scalable, efficient, and intelligent bearing condition monitoring systems.
Self-Reflective Agents: Engineering Meta-Cognition in AI for Ethical Autonomous Decision-Making
This paper introduces a novel architecture for embedding meta-cognitive capabilities in AI agents, enabling them to audit their own reasoning and assess ethical implications before making autonomous decisions. The proposed framework employs layered transformer models to simulate ethical reflection, validated through real-time scenario simulations in autonomous vehicles and military UAV systems. By integrating ethical rule validators within the transformer architecture, the system facilitates "thought auditing," allowing AI agents to evaluate the morality and safety of potential actions. The effectiveness of this approach is measured using metrics such as self-reflection rate, ethical alignment score, and safety override triggers. The findings suggest significant improvements in ethical decision-making and compliance with safety standards, highlighting the potential for policy implications in AI regulation and defense compliance.
Bridging the Digital Divide: A Socio-Technical Framework for AI-Enabled Rural Healthcare Access in Developing Economies
This study introduces a federated, AI-enabled mobile health (mHealth) framework designed to improve healthcare accessibility in digitally underserved rural regions of developing economies. The framework is built upon an offline-first architecture that supports AI-driven clinical triage, referral guidance, and secure caching of medical records on edge devices, minimizing reliance on continuous internet connectivity. Field deployments in rural areas of Pakistan, Nigeria, and Peru provide empirical evidence of the system's viability and adaptability across varied socio-technical contexts. The study outlines key implementation challenges, including infrastructure limitations, healthcare worker training, and multilingual data processing. A comprehensive stakeholder analysis highlights the roles of public health ministries, NGOs, and telecom providers in supporting sustainable deployment. The paper also discusses ethical, regulatory, and data governance considerations essential for cross-national integration of federated learning systems in healthcare. This work contributes a scalable model for bridging the global digital divide in rural healthcare through inclusive, context-sensitive AI innovation.
Global digital divide in healthcare access
Cross-national policy and ethical guidelines
Public-private partnerships in health tech
Socio-technical systems for underserved regions.
Blending Medical Insights with Scalable AI for Predictive Healthcare and Mental Health Solutions
Bringing together artificial intelligence and clinical expertise is starting to reshape how we approach both physical and mental healthcare. With growing pressure on health systems to deliver more personalized and proactive care at scale, we set out to understand how AI can be used more meaningfully, not as a black-box solution, but as something grounded in real clinical insight. In this work, we explore a framework that combines semi-supervised learning, deep convolutional neural networks, and ensemble methods to make sense of complex health data. That includes inputs like wearable sensor readings, electronic health records, and genomic profiles. On the mental health side, we trained emotion prediction models using longitudinal behavioral data to help flag early signs of depression and anxiety. For physical health, our models performed well on conditions like skin cancer and diabetes, with an AUC of 0.94 and an F1 score of 0.91 on our test sets. Performance metrics aside, we put a lot of weight on making these models understandable and clinically relevant. We used SHAP values to explain which features were driving predictions and wove in domain expertise throughout the process, from how we prepared the data to how we assessed the models. The goal wasn’t just to make something that worked, but something that could actually inform care decisions. We also looked at the infrastructure side of things. For AI to be deployed safely and at scale, especially across populations, you need more than good algorithms. Our study points to the importance of cloud infrastructure, spatial data tools, and federated learning in supporting this kind of deployment
Credit, Code, and Consequence: How AI Is Reshaping Risk Assessment and Financial Equity
Artificial Intelligence (AI) has transformed numerous sectors, with financial services among the most significantly impacted. In particular, credit risk evaluation has witnessed a paradigm shift through the adoption of machine learning and advanced data analytics. These technologies promise improved accuracy, efficiency, and inclusivity in credit decision-making. However, the path is not without obstacles. Issues such as algorithmic bias, data privacy concerns, lack of transparency, and the uneven pace of regulatory oversight pose challenges to achieving financial justice. This article explores the multifaceted application of AI in credit risk evaluation, critically analyzing both its potential and pitfalls. Through an interdisciplinary lens, we examine how AI can democratize access to credit, mitigate systemic discrimination, and improve institutional performance—while also highlighting the governance, ethical, and infrastructural prerequisites necessary for its responsible implementation.
Beyond Neural Networks – Exploring the Next Frontier in AI Architectures
The growing complexity and opacity of modern AI models, particularly deep neural networks, have sparked significant interest in the field of Explainable Artificial Intelligence (XAI). While deep learning has yielded remarkable success across various domains, its lack of interpretability poses critical challenges in transparency, accountability, and trustworthiness. This paper delves into the emerging frontier of AI architectures that prioritize explainability by design, going beyond traditional neural networks. We investigate alternative models such as symbolic AI, neurosymbolic systems, causal inference models, and other hybrid approaches that bridge the gap between performance and transparency. The paper also evaluates the philosophical, ethical, and technical implications of explainability in AI systems and proposes a roadmap for the development of next-generation interpretable AI frameworks.
AI and Privacy – Navigating a World of Constant Surveillance
As Artificial Intelligence becomes increasingly embedded in the infrastructure of modern life, it brings with it profound challenges to personal privacy and data protection. The rise of surveillance technologies powered by AI—ranging from facial recognition and predictive policing to targeted advertising and biometric monitoring—has reshaped the boundaries of public and private space. While these systems promise greater efficiency and security, they often operate in opaque and unaccountable ways, raising serious concerns about civil liberties, autonomy, and consent. Explainable AI (XAI) plays a vital role in demystifying the decision-making processes of these systems, enabling oversight and promoting ethical accountability. This paper explores the intersection of AI and privacy, focusing on how explainability can serve as a counterbalance to the growing culture of surveillance. It investigates the technical, social, and legal implications of AI-driven monitoring, and proposes a framework for aligning technological innovation with democratic values and human rights.
Deep Learning Demystified: Architectures, Applications, and Open Problems
Deep learning, a subfield of machine learning, has fundamentally altered the landscape of artificial intelligence by enabling machines to autonomously learn hierarchical representations from vast amounts of data. Leveraging multi-layered neural networks, deep learning models can perform complex tasks such as image and speech recognition, natural language understanding, and strategic decision-making. Despite its achievements, deep learning remains shrouded in complexity, with significant challenges in interpretability, scalability, and data dependence. This paper explores the foundational architectures of deep learning, surveys its most impactful applications across domains, and critically examines the key open problems that hinder its broader adoption and safe deployment. The aim is to demystify the inner workings of deep learning systems while fostering a better understanding of their potential and limitations. By providing a comprehensive overview, this paper contributes to ongoing discourse on responsible AI development and the future trajectory of deep learning technologies.
Declarative Power: The Enduring Relevance of SQL in the Age of Big Data
Structured Query Language (SQL), a declarative language introduced in the 1970s, continues to play a critical role in modern data management, despite the emergence of various big data frameworks and NoSQL alternatives. In an era characterized by massive, heterogeneous data sources and real-time analytics, the enduring relevance of SQL lies in its simplicity, expressiveness, and capacity for abstraction. This paper explores the resurgence of SQL in big data ecosystems, particularly through its integration with distributed computing platforms such as Apache Hive, Spark SQL, and Google BigQuery. It investigates how SQL has adapted to meet new data processing challenges while maintaining its declarative philosophy. By examining case studies, technological integrations, and performance considerations, this study highlights the evolving yet foundational role of SQL as a unifying layer across traditional relational databases and modern data platforms.
Conscious Machines: A Philosophical Inquiry into Artificial Sentience
As artificial intelligence (AI) systems become increasingly sophisticated, a profound question arises: can machines attain consciousness, and if so, what does that mean for our understanding of mind, identity, and ethical responsibility? This paper explores the concept of artificial sentience from a philosophical perspective, examining theories of consciousness, the requirements for subjective experience, and the implications of creating machines that might claim to possess awareness. By evaluating computational theories of mind, functionalism, and emergentist models, alongside critiques from phenomenology and existential philosophy, the discussion centers on whether artificial systems can truly be conscious or merely simulate it. The inquiry also addresses the moral and societal consequences of attributing sentience to machines, including the potential need for rights, moral consideration, and new legal frameworks. Ultimately, the paper seeks to bridge the gap between technological advancements in AI and enduring philosophical questions about the nature of consciousness.
Compliance in SQL Databases: Enforcing GDPR and HIPAA through Schema and Policy Design
Data compliance has emerged as a cornerstone of modern data management, driven by regulations such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. SQL databases, which serve as foundational elements in storing and managing sensitive information, must adapt to enforce these regulatory requirements effectively. This paper explores how SQL schema design, policy enforcement, and access control mechanisms can be leveraged to meet GDPR and HIPAA obligations. It examines structural strategies such as data minimization, pseudonymization, encryption at rest and in transit, and audit logging. Additionally, it delves into procedural enforcement using SQL-based access control lists, row-level security, data retention policies, and automated compliance checks. Through practical design principles and real-world examples, this paper provides a comprehensive guide to embedding privacy and security requirements into the relational database layer, ensuring both legal compliance and system integrity.
Few-Shot and Zero-Shot Learning: Pushing the Boundaries of Data-Efficient AI
The rapid expansion of artificial intelligence (AI) across diverse fields has traditionally relied on the availability of large labeled datasets to train effective models. However, the collection and labeling of massive datasets are expensive, time-consuming, and often impractical in specialized domains. Few-shot and zero-shot learning have emerged as groundbreaking approaches to overcome the dependency on large datasets by enabling models to generalize from minimal or no examples. Few-shot learning focuses on training models that can learn new tasks with just a handful of examples, while zero-shot learning enables models to infer and perform tasks they have never encountered before by leveraging external knowledge or semantic information. These data-efficient paradigms are reshaping the future of AI by enhancing its adaptability, scalability, and deployment across low-resource settings. This paper explores the foundations, methodologies, applications, challenges, and future directions of few-shot and zero-shot learning, emphasizing their critical role in the evolution of intelligent, versatile systems.
AI and Machine Learning in Cybersecurity: New Frontiers in Threat Prediction and Response
The rapid evolution of cyber threats has necessitated a transformative shift in cybersecurity strategies. Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of this transformation, offering advanced capabilities in threat detection, prediction, and autonomous response. This paper explores how AI and ML technologies are redefining cybersecurity practices, enhancing threat intelligence, automating incident response, and predicting potential attacks before they occur. Through a detailed analysis of current methodologies, innovative use cases, and emerging trends, the paper highlights both the opportunities and challenges associated with the deployment of AI-driven security solutions. It concludes by outlining critical considerations for the future integration of AI and ML in cybersecurity ecosystems.
An Explainable AI Approach to Intrusion Detection Using Interpretable Machine Learning Models
Intrusion Detection Systems (IDS) are integral to cybersecurity, especially as cyber threats grow in complexity and frequency. While deep learning models have demonstrated high accuracy in identifying malicious activities, their black-box nature limits their application in sensitive domains requiring transparency. This study introduces an Explainable Artificial Intelligence (XAI) framework that leverages interpretable machine learning models to detect intrusions in network traffic. We implement and evaluate models such as Decision Trees, Random Forests with SHAP analysis, and Explainable Boosting Machines (EBMs) on benchmark datasets including NSL-KDD and CICIDS2017. Our methodology emphasizes both predictive performance and interpretability. Experimental results reveal that the proposed approach achieves a strong balance between detection accuracy and model transparency, making it suitable for operational environments where human analysts must understand and trust automated decisions. Furthermore, our analysis highlights key features influencing predictions and demonstrates how interpretability can aid in forensic analysis and compliance. This paper contributes a structured, explainable approach to intrusion detection that advances the field toward more trustworthy and accountable AI-based cybersecurity solutions.
Evolution of Learning Algorithms: A Journey from Traditional to Deep Machine Learning
The progression of machine learning algorithms from traditional methods to deep learning represents a profound evolution in artificial intelligence. This journey reflects a shift from rule-based systems and statistical models, reliant on manual feature engineering, to hierarchical neural networks capable of automatic representation learning. Traditional learning algorithms like decision trees, support vector machines, and k-nearest neighbors laid the groundwork by formalizing the process of learning from data. However, their performance was constrained by computational limits and the need for domain expertise in feature extraction. The emergence of deep learning, particularly driven by advances in neural network architectures and computational power, has enabled the development of models that excel in tasks involving unstructured data such as images, text, and audio. This paper traces the historical development of learning algorithms, highlights the milestones that enabled the transition to deep learning, and explores the comparative strengths, applications, and limitations of both paradigms in the broader landscape of machine intelligence.