European Journal of Emerging Cloud and Quantum Computing
ISSN: Applied Publication Frequency: Halfyearly Open Access | International Journal | Double Blinded Peer Reviewed Journal
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The Integration of Quantum Computing and Cloud Platforms: Forging a New Paradigm in Digital Services
The convergence of quantum computing and cloud platforms represents a significant paradigm shift in the landscape of digital services, creating unprecedented opportunities for technological advancement. This article provides an extensive review of this integration, examining its technological underpinnings, the current state of commercial and public-sector services, emerging applications, and the persistent challenges that define its developmental trajectory. By synthesizing recent academic literature, detailed industry reports, and policy analyses, we explore the transition from theoretical concepts to practical, accessible quantum systems. We detail the progress made in the Noisy Intermediate-Scale Quantum (NISQ) era, marked by significant hardware advancements from leaders like IBM and Google, and the development of crucial error mitigation techniques that extend the utility of current processors. Key findings indicate that cloud access is the primary mechanism for democratizing quantum resources, accelerating research, and enabling the exploration of high-value use cases in sectors such as finance, pharmaceuticals, and manufacturing. Furthermore, we investigate the growing adoption by non-profit organizations to tackle complex social and environmental challenges. However, substantial hurdles related to quantum decoherence, hardware scalability, software architecture, and the quantum-classical interface remain. The discussion contextualizes these findings, highlighting the strategic importance of national policies, the industry's push towards fault-tolerant systems, and the long-term vision of hybrid quantum-classical supercomputing. We conclude that while the path to achieving full-scale, fault-tolerant quantum computing is long and complex, its integration with cloud infrastructure is the critical catalyst, paving the way for a new frontier in computational capability and societal problem-solving.
Copyright (c) 2026 Dr. Nelric J. Varnen, Dr. Kirelle A. Madswyn (Author) https://creativecommons.org/licenses/by/4.0
HealthTech, Artificial Intelligence, and Cyber-Resilient Digital Transformation: Reconfiguring Community and Organizational Resilience in the Post-Pandemic Era
The post-pandemic period has fundamentally altered the structural, technological, and ethical foundations of communities, organizations, and economic systems worldwide. The unprecedented disruption caused by COVID-19 exposed deep vulnerabilities in healthcare delivery, financial infrastructures, supply chains, and public service systems, while simultaneously accelerating the adoption of digital technologies. Against this backdrop, HealthTech, artificial intelligence, fintech innovations, cloud computing, and cybersecurity mechanisms have emerged not merely as efficiency-enhancing tools but as core pillars of resilience. This article develops a comprehensive and theoretically grounded analysis of how HealthTech and adjacent digital technologies contribute to community and organizational resilience in the post-pandemic era. Drawing strictly on the provided body of literature, the study integrates perspectives from health economics, digital transformation theory, ethical AI frameworks, cybersecurity scholarship, and financial innovation research. The article argues that HealthTech functions as a resilience amplifier by enabling adaptive healthcare delivery, data-driven decision-making, and inclusive access to services, while also reshaping governance models at the community level. Building on this foundation, the analysis situates artificial intelligence as a cross-sectoral enabler that enhances supply chain agility, financial decision-making, employee experience, and social service provision, while simultaneously introducing new ethical and cybersecurity risks. Particular attention is given to the interdependencies between HealthTech systems and digital financial infrastructures, including fintech platforms, robo-advisory services, central bank digital currencies, and emerging quantum financial concepts. These interdependencies are examined through the lens of systemic risk, trust formation, and institutional resilience. Methodologically, the article adopts a qualitative, integrative research design based on critical literature synthesis and interpretive analysis. Rather than aggregating empirical datasets, the study constructs a conceptual framework that explains how digital technologies interact across healthcare, finance, and organizational domains to produce resilience outcomes. The results demonstrate that resilience is not an automatic by-product of digitalization but a contingent outcome shaped by governance quality, ethical design, cybersecurity preparedness, and human-centered implementation. The discussion extends these findings by engaging with scholarly debates on technological determinism, digital inequality, and the limits of automation, offering nuanced rebuttals to overly optimistic narratives of digital transformation. By foregrounding HealthTech as a central node within a broader digital resilience ecosystem, this article contributes to interdisciplinary scholarship on post-pandemic recovery and long-term sustainability. It offers theoretical implications for health economics and management, practical insights for policymakers and organizational leaders, and a future research agenda focused on ethical, secure, and inclusive digital resilience strategies.
Copyright (c) 2025 Dr. Aleksander Nowak, Dr. Katarzyna Lewandowska (Author) https://creativecommons.org/licenses/by/4.0
Harnessing the Quantum Cloud: An IMRaD Review of Architectures, Challenges, and Vistas
We stand at the confluence of two technological revolutions: quantum computing and the cloud. Their fusion has created Quantum Cloud Computing (QCC), a remarkable new paradigm that is prying open the doors to the quantum realm for scientists, developers, and pioneers around the globe. This review tells the story of QCC, exploring its current landscape from the ground up. We'll journey through the fascinating world of quantum hardware, from superconducting circuits chilled to temperatures colder than deep space to individual atoms trapped by lasers. We'll navigate the complex software stacks and emerging service models, like Quantum-Function-as-a-Service (QFaaS), that make this power accessible. We then turn to the thrilling applications taking shape in chemistry, finance, and artificial intelligence. However, this journey is not without its dragons. We will confront the immense challenges of the current Noisy Intermediate-Scale Quantum (NISQ) era—the delicate nature of qubits, the disruptive chatter of system noise, and the profound security questions that arise when delegating computations to the cloud. Finally, we'll cast our eyes to the horizon, charting the future directions for this field, from tightly-knit hybrid systems to the grand vision of a globe-spanning Quantum Internet. While the ultimate promise of quantum computing is still unfolding, this review shows how the cloud has become the essential vessel for this incredible voyage of discovery.
Copyright (c) 2025 Dr. Alvion R. Kestrel, Dr. Nyra T. Velmark (Author) https://creativecommons.org/licenses/by/4.0
Governing Privacy in a Datafied World: Comparative Legal Frameworks, Compliance Architectures, and the Evolution of Data Governance Paradigms
The exponential expansion of digital technologies has redefined the contours of privacy, transforming personal data into a central asset of economic value, political power, and social governance. This transformation has simultaneously intensified regulatory scrutiny and exposed structural inadequacies in traditional legal approaches to data protection. Across jurisdictions, lawmakers and institutions have responded by developing increasingly complex data privacy laws and governance mechanisms designed to balance innovation with the protection of individual rights. This research article undertakes an extensive, theory-driven examination of the evolving global landscape of data privacy laws and data governance frameworks, with particular emphasis on comparative legal regimes, compliance strategies, and institutional governance models. Drawing on interdisciplinary scholarship from law, information systems, public administration, and data ethics, the study situates contemporary privacy regulation within broader historical, technological, and socio-political contexts. The article critically engages with global trends in data privacy regulation, highlighting the convergence and divergence between major legal instruments such as the General Data Protection Regulation and the California Consumer Privacy Act, while also examining emerging governance approaches articulated by international institutions and policy forums (World Lawyers Forum, 2022). Rather than treating privacy law as a static compliance exercise, this study conceptualizes data governance as a dynamic socio-technical system shaped by organizational practices, power relations, and normative values (Khatri and Brown, 2010; Micheli et al., 2020). Through a qualitative, interpretive methodology grounded in systematic literature analysis and doctrinal legal review, the article explores how role-based access control, encryption practices, and institutional accountability mechanisms operate as operational extensions of privacy law within organizational environments (Frontegg, 2022; Permify, 2024). The findings reveal that contemporary privacy governance is characterized by a shift from rule-centric compliance toward principle-based, risk-sensitive, and context-aware regulatory models. However, this shift also introduces new challenges related to enforcement fragmentation, organizational capacity, and equity in data rights protection, particularly in transnational data flows and sector-specific domains such as health and artificial intelligence (Janssen et al., 2020; Holly et al., 2023). By synthesizing legal theory, governance scholarship, and practical compliance literature, this article contributes a comprehensive analytical framework for understanding the future trajectory of data privacy governance. It concludes by arguing that sustainable privacy protection requires not only legal harmonization but also the institutionalization of ethical governance principles, data literacy, and adaptive regulatory learning.
Copyright (c) 2025 Dr. Alexandre Moreau, Dr. Elodie Martin, Dr. Lukas Schneider (Author) https://creativecommons.org/licenses/by/4.0
Environmental, Operational, and Systemic Determinants of Photovoltaic Performance: An Integrated Performance–Monitoring and Data-Driven Interpretation Framework
The rapid global deployment of photovoltaic (PV) systems has transformed solar energy from a niche renewable technology into a cornerstone of contemporary energy transitions. Despite this growth, persistent discrepancies between predicted and actual PV performance continue to challenge engineers, policymakers, and investors. These discrepancies arise from a complex interplay of environmental conditions, operational practices, system design choices, monitoring methodologies, and data management paradigms. This research article develops a comprehensive, publication-ready scholarly investigation into the determinants of PV system performance by synthesizing environmental, operational, analytical, and data-centric perspectives found within the existing literature. Building upon recent comprehensive reviews of PV performance factors, this study situates environmental stressors such as temperature, irradiance variability, humidity, dust accumulation, and climate-specific degradation within a broader operational and systemic context, including maintenance regimes, grid integration, monitoring standards, and data processing architectures. The article adopts an interpretive, literature-grounded methodological approach, emphasizing descriptive analytical reasoning rather than mathematical modeling or empirical experimentation. Performance indicators, degradation phenomena, and monitoring metrics are critically examined through internationally recognized standards and long-term field observations. Particular emphasis is placed on the role of standardized monitoring protocols and intelligent data interpretation frameworks in reducing uncertainty and enhancing reliability. The discussion further extends into the often-overlooked intersection between PV performance analysis and database management systems, arguing that the scalability, consistency, and semantic integrity of performance data storage directly influence analytical accuracy and decision-making. By integrating insights from energy engineering, climate-specific PV studies, monitoring standards, artificial intelligence–based prediction models, and foundational database theory, this article articulates a unified conceptual framework for understanding PV performance as a socio-technical system. The findings underscore that PV performance is not solely a function of module efficiency or environmental exposure, but rather an emergent property shaped by data quality, monitoring fidelity, and interpretive rigor. The article concludes by identifying persistent knowledge gaps, methodological limitations, and future research directions necessary for achieving resilient, high-fidelity PV performance assessment in increasingly complex energy systems (Hasan et al., 2022; Srivastava et al., 2020; IEC, 2021).
Copyright (c) 2025 Dr. Adrian Leclerc, Dr. Lukas Reinhardt (Author) https://creativecommons.org/licenses/by/4.0
Deep Neural Architectures for Thoracic Disease Identification from Chest Radiography: Interpretability, Robustness, and Clinical Integration
The rapid integration of artificial intelligence into medical imaging has transformed the landscape of thoracic disease diagnosis, particularly through the use of deep neural networks applied to chest radiography. Chest X-ray imaging remains one of the most widely used, cost-effective, and accessible diagnostic modalities for detecting pulmonary and thoracic pathologies, including pneumonia and other life-threatening conditions. However, the inherent complexity of thoracic anatomy, overlapping tissue structures, and variability in imaging protocols pose substantial challenges for accurate and reliable interpretation. In this context, deep learning-based approaches have demonstrated remarkable potential to enhance diagnostic performance by learning hierarchical representations directly from imaging data. Building upon foundational contributions in the identification of thoracic diseases using deep neural networks, this study develops a comprehensive analytical and methodological framework that synthesizes advances in convolutional neural networks, data augmentation, explainability, robustness, and clinical usability. This article presents an extensive theoretical and empirical discussion of deep learning methodologies for thoracic disease identification, grounded in a critical examination of existing literature. Particular emphasis is placed on pneumonia detection as a representative and clinically significant use case, while situating pneumonia within the broader spectrum of thoracic pathologies addressed by neural network-based systems. The methodological discourse integrates considerations of dataset construction, preprocessing, model architecture selection, training strategies, adversarial robustness, and interpretability mechanisms, drawing on established datasets and prior empirical findings. The analytical narrative interprets reported performance trends across studies, focusing on sensitivity, specificity, generalization capability, and the impact of explainable artificial intelligence techniques on clinician trust and adoption. Beyond technical performance, the article critically evaluates the epistemological and practical implications of deploying deep neural networks in clinical environments. It explores debates surrounding black-box models, concept-based explanations, data bias, and domain shift, while also addressing regulatory, ethical, and workflow integration challenges. By weaving together theoretical foundations, historical developments, comparative scholarly perspectives, and forward-looking research directions, this work aims to provide a publication-ready, in-depth contribution to the field of medical image analysis. The study ultimately argues that the successful clinical translation of deep neural network-based thoracic disease identification depends not only on accuracy gains but also on interpretability, robustness, and alignment with real-world healthcare constraints, as evidenced across the growing body of literature (Albahli et al., 2021; Siddiqi & Javaid, 2024).
Copyright (c) 2025 Dr. Alejandro Martínez Gómez, Dr. Sofia Elena Ricci, Dr. Markus Johannes Keller (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning–Driven Sentiment and Depression Analysis from Social Media Text: A Comprehensive Multilingual and Theoretical Investigation
The exponential growth of social media platforms has transformed user-generated textual data into a critical resource for understanding public opinion, emotional expression, and psychological well-being. Among the most significant and socially impactful applications of sentiment analysis is the automated detection of depressive tendencies and emotional distress expressed through online discourse. Over the past decade, computational approaches to sentiment and mental health analysis have evolved from lexicon-based and classical machine learning paradigms to sophisticated deep learning architectures capable of modeling contextual, sequential, and semantic complexities in natural language. This article presents a comprehensive, publication-ready research investigation into deep learning–based sentiment and depression analysis from social media text, with particular emphasis on recurrent and hybrid neural architectures applied to multilingual and low-resource language contexts. Grounded strictly in the provided scholarly references, the study synthesizes theoretical foundations, historical developments, methodological considerations, and critical debates surrounding sentiment analysis and depression detection using social media data. The work is theoretically anchored in prior studies on Twitter-based sentiment mining, distant supervision, and noisy text classification, while foregrounding neural network innovations such as convolutional neural networks, long short-term memory networks, bidirectional recurrent models, attention mechanisms, and hybrid CNN–LSTM architectures. Special analytical attention is given to depression analysis in the Bangla language using LSTM-based recurrent neural networks, as demonstrated in prior empirical research, which serves as a cornerstone for discussing the challenges and opportunities associated with mental health analytics in morphologically rich and underrepresented languages (Uddin et al., 2019). By situating this work within a broader ecosystem of sentiment analysis research spanning Arabic dialects, code-switched text, and multilingual social media corpora, the article critically examines how linguistic diversity, data sparsity, and sociocultural context influence model design, performance, and interpretability. Methodologically, the paper elaborates a text-based experimental framework that integrates data collection, preprocessing, representation learning, model training, and evaluation, while also acknowledging inherent limitations such as annotation subjectivity, platform bias, and ethical concerns related to mental health inference. The results section provides an interpretive synthesis of findings reported across the referenced literature, emphasizing trends in model accuracy, robustness, and generalizability rather than numerical tabulation. The discussion advances a deep theoretical interrogation of competing scholarly viewpoints, addressing debates over explainability versus performance, the validity of social media as a proxy for mental health assessment, and the risks of algorithmic bias and overgeneralization. By offering an extensive, critical, and integrative analysis, this article contributes a unified scholarly narrative that connects sentiment analysis and depression detection research across methodological traditions and linguistic settings. It underscores the necessity of context-aware, ethically grounded, and theoretically informed deep learning approaches for future advances in computational mental health and sentiment analytics.
Copyright (c) 2025 Md. Khaled Rahman, Amina El-Hassan, Jonas Müller (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning as a Socio-Technical General-Purpose Technology: Architectural Evolution, Market Dynamics, and Cross-Domain Transformations
Deep learning has emerged as one of the most transformative general-purpose technologies of the twenty-first century, reshaping not only computational practice but also economic organization, scientific inquiry, and socio-technical systems across diverse domains. From its early theoretical foundations in neural computation to its contemporary instantiations in large-scale transformer architectures, deep learning represents a convergence of algorithmic innovation, data availability, hardware acceleration, and market-driven adoption. This article presents a comprehensive and critical examination of deep learning as both a technical paradigm and an economic force, situating architectural developments within broader industrial, institutional, and societal contexts. Drawing exclusively on the provided literature, the study integrates perspectives from computer vision, natural language processing, reinforcement learning, healthcare, bioinformatics, finance, smart cities, recommendation systems, and neuromorphic computing to articulate a unified analytical narrative. Particular attention is paid to the co-evolution of model architectures and market structures, highlighting how advances such as convolutional neural networks, transformers, and reinforcement learning systems have catalyzed new applications while simultaneously being shaped by commercial incentives and infrastructural constraints. The analysis foregrounds the role of deep learning markets, including hardware, software, and services, as articulated in contemporary industry assessments, to contextualize academic innovation within real-world deployment trajectories (MarketsandMarkets, 2023). Rather than treating market growth as an external outcome, the article argues that economic forces actively influence research priorities, architectural choices, and evaluation norms within the deep learning community. Methodologically, the study adopts a qualitative, integrative research design grounded in interpretive synthesis of existing scholarly and industry sources, enabling a richly elaborated discussion without reliance on mathematical formalism or empirical experimentation. The results section presents a descriptive analysis of thematic patterns emerging across application domains, while the discussion section offers an extended theoretical interpretation that engages scholarly debates on generalization, scalability, efficiency, and ethical governance. By emphasizing depth over brevity and interpretation over summary, this article contributes a holistic, publication-ready account of deep learning’s current state and future trajectories. It positions deep learning not merely as a collection of algorithms, but as an evolving socio-technical system whose implications extend far beyond computation into the fabric of modern society (Krizhevsky et al., 2012; Brown et al., 2020; Devlin et al., 2019). In doing so, it provides a foundation for future interdisciplinary research that critically interrogates both the promises and the limitations of deep learning as a dominant technological paradigm (MarketsandMarkets, 2023).
Copyright (c) 2026 Dr. Elias Morgenstern, Dr. Sofia Lindqvist, Dr. Matteo Rinaldi (Author) https://creativecommons.org/licenses/by/4.0
AN INVESTIGATION INTO THE TRANSFORMATIVE POWER OF QUANTUM COMPUTING IN SCIENTIFIC RESEARCH
We are on the verge of a new era in computation, one that operates on the strange and wonderful rules of the quantum world. This article explores the story of quantum computing, a technology that promises to solve problems far beyond the reach of today's fastest supercomputers. We'll journey from the core ideas that make it all possible—concepts like superposition and entanglement—to the incredible machines being built in labs around the world. We'll see how these machines are being programmed and witness the first hints of their power in fields like medicine, materials science, and cybersecurity. Finally, we'll take an honest look at the immense challenges that still lie ahead on the path to building a truly powerful quantum computer. This is a story of human ingenuity pushing the boundaries of science, a tale of the immense potential and practical hurdles of the coming quantum age.
Copyright (c) 2024 European Journal of Emerging Cloud and Quantum Computing
A FRAMEWORK FOR INTEGRATING QUANTUM COMPUTING WITH MULTI-CLOUD ARCHITECTURES: ENHANCING COMPUTATIONAL EFFICIENCY AND SECURITY
The evolution of cloud computing towards multi-cloud architectures has provided significant advantages in flexibility and resilience. Concurrently, quantum computing has emerged as a new computational paradigm with the potential to solve problems intractable for classical systems. However, access to quantum resources is currently fragmented across siloed, provider-specific cloud platforms, negating the benefits of a multi-cloud strategy. This paper addresses this gap by proposing a comprehensive framework for integrating heterogeneous quantum computing resources into a unified multi-cloud architecture. The Method involves a four-layer architectural model comprising: (1) an intelligent orchestration and workload management layer with policy-driven resource selection, (2) a universal quantum gateway for interoperability featuring advanced circuit transpilation, (3) a zero-trust secure communication fabric utilizing post-quantum cryptography and quantum key distribution, and (4) the underlying heterogeneous compute infrastructure of classical and quantum processors. The anticipated Results of implementing this framework include significant, quantifiable enhancements in computational efficiency for complex optimization and simulation problems in fields like drug discovery and materials science, and a strengthened, future-proof security posture resistant to both classical and quantum threats. The Discussion interprets these results, outlining the profound economic and scientific implications. It also provides a deep analysis of the significant challenges to implementation—including quantum hardware immaturity, quantum-classical latency, and interoperability hurdles—and proposes a detailed, phased roadmap for future research. In Conclusion, this work establishes a foundational blueprint for a synergistic quantum-classical ecosystem, paving the way for a new generation of advanced, secure, and powerful cloud environments.
STRATEGIC GRID DEVELOPMENT IN THE ANDES: INTEGRATING GEOSPATIAL INTELLIGENCE FOR RESILIENT TRANSMISSION NETWORKS
This article investigates the crucial role of geospatial considerations in the strategic planning and expansion of electricity transmission networks within the unique geographical context of the Andean region. It delves into how the rugged mountainous terrain, diverse ecosystems, and susceptibility to natural hazards significantly influence the design, cost, and resilience of transmission infrastructure. The paper reviews advanced Transmission Expansion Planning (TEP) methodologies, including AC models and AI-driven approaches for managing uncertainties, and specifically highlights their adaptation to Andean realities through the integration of topographical, geological, and hydrological data. The results demonstrate that a geospatial-informed approach leads to more robust network designs, enhanced resilience against seismic events and climate change impacts, and optimized cross-border interconnections. While acknowledging challenges such as data availability and modeling complexity, the discussion emphasizes the necessity of multi-criteria decision-making and regional collaboration. Future directions include leveraging advanced AI/ML, refining climate change resilience modeling, and fostering supportive policy frameworks to ensure sustainable and adaptable electrical grids in the Andes.
IOT-POWERED BRAILLE ACCESS: A REFRESHABLE OCR SYSTEM FOR VISUALLY IMPAIRED AND DEAF-BLIND INDEPENDENCE
Visual and deaf-blind impairments present significant challenges in accessing printed information, creating a substantial barrier to education, employment, and daily independence. Traditional assistive technologies often lack real-time adaptability and comprehensive integration. This article proposes an innovative Internet of Things (IoT)-driven solution for visually impaired and deaf-blind users, combining Optical Character Recognition (OCR) with refreshable Braille displays, all facilitated by a Wireless Sensor Network (WSN). The system aims to provide seamless, real-time conversion of visual text into tactile Braille, addressing the critical need for accessible information. By leveraging the pervasive nature of IoT and the connectivity of WSNs, this technology offers a dynamic and responsive platform for navigating diverse textual environments. The refreshable Braille component overcomes the limitations of static Braille materials, enabling on-the-fly information access. This paper details the system's architecture, methodologies for OCR processing and Braille conversion, and the WSN's role in data transmission. Potential benefits include enhanced literacy, improved independence, and greater inclusion for a demographic often marginalized by inaccessible information formats.
ENHANCED SUPPORT VECTOR REGRESSION PERFORMANCE THROUGH HARRIS HAWKS OPTIMIZATION FOR PARAMETER SELECTION
Support Vector Regression (SVR) is a powerful machine learning technique widely applied in time series forecasting and various prediction tasks. However, its performance significantly hinges on the appropriate selection of crucial parameters: the regularization constant (C), the epsilon-insensitive loss function parameter (ϵ), and kernel-specific parameters such as gamma (γ) for Radial Basis Function (RBF) kernels. Traditional methods for parameter optimization, such as grid search, are computationally expensive and prone to local optima, while heuristic approaches like Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) can still face challenges in convergence speed and solution quality. This article introduces a novel approach for optimizing SVR parameters by leveraging the recently developed Harris Hawks Optimization (HHO) algorithm. HHO is a metaheuristic inspired by the cooperative behavior and hunting strategies of Harris' hawks in nature. The proposed HHO-SVR hybrid model aims to efficiently search for the optimal combination of SVR parameters, thereby enhancing its predictive accuracy and generalization capability. This paper details the theoretical foundations of SVR and HHO, the methodology for their integration, and hypothetical experimental results demonstrating the effectiveness of the HHO-SVR model compared to other established optimization techniques in improving forecasting performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The findings suggest that HHO provides a robust and efficient mechanism for fine-tuning SVR, making it a promising tool for complex regression problems.
ENHANCED DIABETES PREDICTION VIA STACKED ENSEMBLE MACHINE LEARNING
Diabetes mellitus, a pervasive chronic metabolic disorder, presents an escalating global health crisis necessitating highly accurate and timely diagnostic interventions to prevent severe long-term complications. This research comprehensively investigates the application and efficacy of a stacked ensemble machine learning paradigm for enhancing diabetes prediction capabilities. Utilizing the well-established Pima Indian Diabetes Dataset, our methodology employs a multi-tiered stacking framework. This framework synergistically combines the predictive outputs of diverse base learners, including Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Extreme Gradient Boosting. A Logistic Regression model was strategically selected to serve as the meta-learner, intelligently integrating and optimizing the collective predictions derived from these foundational models. Through rigorous evaluation against a suite of standard classification metrics—namely accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC)—the proposed stacked ensemble model consistently demonstrated superior performance when compared to its individual constituent base learners. The ensemble achieved a notable accuracy of 81.3%, precision of 76.8%, recall of 68.2%, an F1-score of 72.2%, and an impressive AUC-ROC of 0.871. These compelling results unequivocally underscore the substantial advantages of adopting ensemble learning methodologies in bolstering predictive robustness and achieving enhanced accuracy within the domain of medical diagnostics. Consequently, the developed model represents a significant advancement, offering a highly promising and practical tool for healthcare professionals. Its deployment could facilitate the early and precise identification of individuals at elevated risk of developing diabetes, thereby enabling crucial timely interventions and ultimately contributing to improved patient management strategies and public health outcomes.
COMPUTED TOMOGRAPHY IMAGE SEGMENTATION FOR ISCHEMIC STROKE LESION DELINEATION: A COMPREHENSIVE BIBLIOMETRIC ANALYSIS AND ADVANCED METHODOLOGICAL REVIEW
Ischemic stroke remains a leading cause of global morbidity and mortality, necessitating rapid and accurate diagnosis for effective treatment. Computed Tomography (CT) imaging is a primary modality for initial assessment, particularly for distinguishing ischemic stroke from hemorrhagic stroke. Accurate, automated segmentation of ischemic stroke lesion areas from CT images is crucial for guiding clinical decisions, evaluating treatment efficacy, and predicting patient outcomes. This article presents a comprehensive bibliometric analysis of research trends from 2013 to 2023 and an in-depth survey of contemporary methodologies employed for CT scan image segmentation to identify ischemic stroke lesion areas. We meticulously explore the evolution of techniques from traditional image processing to advanced deep learning approaches, highlighting key trends, challenges, and future directions. The bibliometric analysis, encompassing over 2,000 publications, reveals a surging interest in the field, with significant contributions from China, the United States, and India. The methodological review emphasizes the dominance and superior performance of deep learning models, particularly Convolutional Neural Networks, while also addressing persistent challenges such as data scarcity, lesion subtlety, and model interpretability. The analysis underscores the critical need for robust, generalizable, and automated segmentation tools to improve diagnostic workflows and ultimately enhance patient care.