European Journals of Emerging Computer Vision and Natural Language Processing
ISSN: Applied Publication Frequency: Halfyearly Open Access | International Journal | Double Blinded Peer Reviewed Journal
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Comparative Cryptographic Architectures and Performance Paradigms in Symmetric Encryption: A Comprehensive Analytical Study of DES, 3DES, and AES with Emphasis on Hardware and Algorithmic Evolution
The evolution of symmetric key cryptography has been fundamentally shaped by the persistent tension between security robustness, computational efficiency, and implementational feasibility across software and hardware platforms. Among the most influential symmetric encryption standards are the Data Encryption Standard (DES), Triple DES (3DES), and the Advanced Encryption Standard (AES), each representing distinct historical phases, design philosophies, and threat models in cryptographic engineering. This research article presents an exhaustive, theory-driven, and literature-grounded comparative analysis of DES, 3DES, and AES, with particular emphasis on architectural design principles, algorithmic transformations, hardware implementation strategies, and performance implications in modern computing environments. Drawing extensively and exclusively on the provided scholarly corpus, the study situates AES as a paradigmatic shift in cryptographic standardization, while critically examining its predecessors not merely as obsolete artifacts but as foundational frameworks that informed contemporary cryptographic thought (Aleisa, 2015). The article adopts a descriptive-analytical methodology, synthesizing findings from FPGA-based implementations, VLSI architectures, cryptographic theory, and applied performance evaluations. Rather than relying on mathematical formalism or experimental simulation, the analysis proceeds through deep interpretive reasoning grounded in established literature, enabling a nuanced understanding of encryption efficiency, error propagation, resistance to cryptanalytic attacks, and adaptability to emerging computational paradigms such as cloud infrastructures. Particular attention is paid to AES’s Rijndael structure, its substitution–permutation network, and its superiority over DES and 3DES in terms of security margin and throughput, while also addressing ongoing debates surrounding side-channel vulnerabilities and implementation complexity (Daemen & Rijmen, 2002; Patil et al., 2016). By integrating historical context, scholarly disagreement, and technological implications, this article contributes a comprehensive academic narrative that bridges classical cryptography and modern secure system design. The findings underscore that while AES currently dominates symmetric encryption practice, its continued relevance depends on implementation-aware design choices and sustained theoretical scrutiny, especially in hardware-constrained and high-throughput environments (Deshpande et al., 2009; Morioka & Satoh, 2004).
Copyright (c) 2025 Dr. Elias Van der Meer, Dr. Laurent Dubois (Author) https://creativecommons.org/licenses/by/4.0
Formal Verification of Learning-Based Neural Agents in Non-Deterministic and Hybrid Environments
The rapid integration of learning-based neural agents into safety-critical and autonomy-intensive domains has fundamentally reshaped contemporary discussions in artificial intelligence, formal methods, and systems engineering. Neural agents, particularly those based on deep learning and recurrent architectures, are increasingly deployed in environments characterized by non-determinism, partial observability, and complex continuous-discrete interactions. While such agents demonstrate remarkable empirical performance, their opaque decision-making processes and susceptibility to unforeseen environmental behaviors raise profound concerns regarding reliability, accountability, and safety. These concerns are especially pronounced in domains such as autonomous robotics, cyber-physical systems, and multiagent coordination, where failures can result in significant physical, economic, or societal harm. Consequently, formal verification has emerged as a critical research direction aimed at providing rigorous, mathematically grounded guarantees about the behavior of neural agents under all admissible conditions. This article presents an extensive theoretical and methodological examination of the formal verification of neural agents operating in non-deterministic and hybrid environments. Building upon foundational work in neural network verification, satisfiability modulo theories, and hybrid systems analysis, the article synthesizes and critically analyzes the evolution of verification techniques for learning-enabled systems. Particular emphasis is placed on the formal verification of neural agent-environment systems, including those incorporating recurrent neural networks and non-deterministic environmental dynamics, as explored in seminal studies on neural agents interacting with uncertain environments (Akintunde et al., 2020). The discussion situates this line of research within broader verification paradigms, including abstraction-based methods, reachability analysis, and game-theoretic formulations. Through a detailed methodological exposition, the article elucidates how neural agents can be modeled as components of closed-loop systems, how environmental non-determinism complicates verification objectives, and how existing tools and frameworks address these challenges. The results section offers an interpretive synthesis of verification outcomes reported across the literature, highlighting patterns of scalability, expressiveness, and limitation. The discussion section provides an in-depth theoretical interpretation, engaging with competing scholarly viewpoints and articulating unresolved tensions between expressivity and tractability. The article concludes by outlining future research trajectories, emphasizing the need for integrated verification-learning paradigms and more expressive formal specifications capable of capturing ethical, social, and safety constraints in learning-based autonomous systems.
Copyright (c) 2025 Dr. Elias Moreau (Author) https://creativecommons.org/licenses/by/4.0
Climate Change Impacts, Vulnerability, and Adaptive Capacity in Agrarian and Forest-Dependent Regions: An Integrated Socio-Ecological Analysis
Climate change has emerged as one of the most profound and complex challenges confronting contemporary societies, particularly in regions where livelihoods are deeply intertwined with climate-sensitive natural resources. Agrarian and forest-dependent regions across the Global South exhibit heightened vulnerability due to a combination of biophysical exposure, socio-economic fragility, institutional limitations, and historical patterns of development. This article presents an extensive and theoretically grounded examination of climate change impacts, vulnerability, and adaptive capacity, with particular attention to farming systems, forest ecosystems, and rural communities. Drawing strictly on the provided body of literature, the study integrates insights from global climate science, regional vulnerability assessments, indigenous adaptation practices, and socio-economic analyses to construct a comprehensive understanding of how climate change reshapes environmental and human systems. The article situates its analytical framework within the evolution of climate change research, tracing the conceptual shift from impact-focused assessments to multidimensional vulnerability and resilience perspectives. Central to this discussion is the recognition that climate change does not operate in isolation but interacts with pre-existing stresses such as poverty, land degradation, demographic pressure, and governance deficits. The work of the Intergovernmental Panel on Climate Change has been particularly influential in shaping contemporary understanding of climate impacts, adaptation, and vulnerability, emphasizing the uneven distribution of climate risks and the importance of context-specific responses (Intergovernmental Panel on Climate Change, 2007). Building on this foundation, the article critically engages with empirical studies from Africa and Asia that document the lived realities of climate variability, including altered rainfall regimes, rising temperatures, increased frequency of extreme events, and their cascading effects on agriculture, forests, and rural livelihoods. Methodologically, the article adopts a qualitative, integrative research design grounded in comparative literature analysis. Rather than generating new empirical data, it synthesizes and interprets existing studies to reveal patterns, contradictions, and knowledge gaps. Particular emphasis is placed on indigenous and emerging technologies for climate adaptation, socio-ecological resilience, and institutional responses at local, regional, and national scales. The results highlight the heterogeneity of climate impacts and adaptation strategies, demonstrating that vulnerability is not merely a function of exposure but is deeply shaped by social relations, access to resources, knowledge systems, and governance structures. The discussion section provides an in-depth theoretical interpretation of these findings, engaging with debates on adaptive capacity, uncertainty, resilience, and sustainable development. It critically examines dominant adaptation paradigms, interrogates their assumptions, and explores alternative pathways rooted in local knowledge and participatory governance. The article concludes by reflecting on the implications for policy, research, and practice, arguing that effective climate adaptation requires integrated, context-sensitive approaches that bridge global climate science and local socio-ecological realities.
Copyright (c) 2025 Dr. Samuel K. Adebenga (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Indonesian Scientific Article Management through Machine Learning and NLP
The exponential growth of scientific literature in Indonesia necessitates efficient automated systems for organizing, retrieving, and assessing the originality of scholarly articles. This paper explores the application of computational methods, specifically machine learning algorithms for classification and similarity measures, to enhance the management of Indonesian scientific journal articles. We investigate the effectiveness of Naive Bayes and Support Vector Machine (SVM) algorithms for thematic categorization and employ Cosine Similarity for identifying content proximity. The proposed framework includes data preprocessing, feature extraction using TF-IDF, and rigorous evaluation of the models. The findings demonstrate the viability of these approaches in improving the accessibility, discoverability, and integrity of the burgeoning volume of Indonesian academic publications. The Naive Bayes method, when applied to a balanced dataset, achieved an impressive F1-score of 98%, indicating high classification accuracy, with the classification process taking less than 60 minutes. Article similarity detection using the Cosine Similarity method accurately reflected the degree of similarity between concatenated titles and abstracts. This research offers a robust framework for enhancing the classification and search capabilities within national aggregator services like Garba Rujukan Digital (GARUDA).
Copyright (c) 2025 Dr. Natalie J. Freeman, Dr. Trevor M. Scott (Author) https://creativecommons.org/licenses/by/4.0
Emerging Frontiers in Computer Vision: A Critical Analysis of Deep Learning Techniques and Their Real-World Applications
Deep learning has become the cornerstone of modern computer vision, fundamentally transforming how machines perceive and interpret the visual world. This article presents a critical review of the key deep learning techniques that have driven this revolution. We trace the evolution of foundational concepts, from early neural networks to the sophisticated convolutional neural network (CNN) architectures that dominate the field today. The article is structured to provide a comprehensive overview, beginning with an introduction to the core concepts and historical context of deep learning in computer vision. We then delve into the methodologies, systematically examining influential architectures and techniques for major computer vision tasks, including image classification, object detection, semantic segmentation, and image restoration. Subsequently, we evaluate the performance and results of these methods, highlighting their groundbreaking impact on various application scenarios, from medical imaging to autonomous systems. Finally, we discuss the broader implications, current challenges such as the creation of deepfakes, and promising future directions for research and development. By synthesizing a wide array of seminal and contemporary works, this review offers a detailed landscape of the field, providing valuable insights for both new and experienced researchers.
Copyright (c) 2025 Dr. Ronith A. Velkar, Dr. Elinora S. Kavesh (Author) https://creativecommons.org/licenses/by/4.0
Enhanced EfficientNet for Imbalanced Medical Image Classification through Grey Wolf Optimization
Medical image classification plays a pivotal role in modern diagnostics and disease management by aiding in the early detection and precise identification of various pathologies. However, a significant challenge in this domain arises from the inherent class imbalance commonly observed in medical datasets, where the number of samples for healthy cases or common conditions vastly outnumbers those for rare diseases. This imbalance often leads to deep learning models that are biased towards the majority class, resulting in suboptimal performance, particularly poor sensitivity and specificity for the critical minority classes. This article proposes a novel and robust approach to mitigate this pervasive issue by enhancing the state-of-the-art EfficientNet convolutional neural network (CNN) architecture through the application of Grey Wolf Optimization (GWO). GWO, a metaheuristic algorithm inspired by the sophisticated hunting strategies and social hierarchy of grey wolves in nature, is systematically employed to optimally tune the critical hyperparameters of the EfficientNet model. The primary aim of this optimization is to achieve superior and more balanced classification performance across all classes, especially for the underrepresented classes within imbalanced medical image data. We comprehensively detail the methodology, encompassing the meticulous handling and preprocessing of imbalanced medical image datasets, the strategic integration of the EfficientNet architecture, and the sophisticated GWO-based hyperparameter search strategy. Our experimental results, derived from rigorous evaluation, robustly demonstrate that the GWO-optimized EfficientNet significantly improves key performance metrics such as macro F1-score, balanced accuracy, and recall for minority classes. This optimized approach consistently outperforms traditional deep learning approaches that rely on manual hyperparameter tuning or fixed parameter sets. This research offers a robust, automated, and highly effective framework for developing more accurate, reliable, and clinically relevant deep learning models, thereby contributing significantly to the advancement of artificial intelligence in critical medical applications and enhancing diagnostic precision.
Copyright (c) 2025 Dr. Audrey L. James, Dr. Brandon K. Morris (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Brake System Vigilance: Real-time Diagnostics for Enhanced Automotive Safety
Vehicle safety is profoundly impacted by the condition of its braking system, with worn brake pads being a significant contributor to road accidents. Traditional brake monitoring methods often fall short, providing alerts only at advanced stages of wear, thereby increasing accident risk and maintenance inefficiencies. This article proposes an intelligent brake system vigilance platform designed to enhance automotive safety through real-time monitoring and predictive diagnostics of brake pad health. The system integrates multiple sensor technologies, including wear, temperature, vibration, and pressure sensors, to collect comprehensive data. Leveraging IoT for low-latency data transmission and advanced machine learning algorithms, particularly CNN-LSTM networks, the platform performs real-time wear estimation, anomaly detection, and predictive modeling of remaining useful life (RUL) for brake pads. This proactive approach facilitates early warnings to drivers and maintenance personnel, enabling timely interventions that mitigate accident risks, optimize maintenance schedules, and reduce operational costs. Furthermore, the extensive data collected will provide invaluable insights for future automotive design and material science advancements. This intelligent system represents a significant step towards a safer, more efficient, and data-driven automotive future.
Copyright (c) 2026 Dr. Brooke A. Morgan, Dr. Xavier D. Long (Author) https://creativecommons.org/licenses/by/4.0
Fusing Pixels and Prose: The Transformative Impact of Integrating Computer Vision and Natural Language Processing on Multimedia Robotics Applications
Imagine a robot that doesn't just see the world, but can tell you about it. A machine that can follow your spoken instructions not just by rote, but with a genuine understanding of the objects and actions involved. This is the future being built at the intersection of two of artificial intelligence's most powerful fields: Computer Vision (CV) and Natural Language Processing (NLP). This article explores that exciting frontier. We'll journey through the core technologies that allow machines to see and to speak, from digital "eyes" that recognize objects to "minds" that can process language. Our main focus will be on the clever methods researchers use to weave these two abilities together, creating a shared understanding between pixels and prose. We'll look at the incredible results of this fusion: robots that can narrate a video, answer questions about a photograph, guide the visually impaired, or even learn a new task simply by watching and listening. Finally, we'll have an honest conversation about the tough puzzles that still need solving—like teaching machines true commonsense—and look ahead to a future where intelligent, collaborative robots become a beneficial part of our everyday lives.
Copyright (c) 2026 Dr. Nareen X. Veltrix, Dr. Tamos I. Drevik (Author) https://creativecommons.org/licenses/by/4.0
REAL-TIME AUDITORY GUIDANCE FOR THE VISUALLY IMPAIRED: AN F-RCNN APPROACH IN ASSISTIVE ROBOTICS
The convergence of Computer Vision (CV) and Natural Language Processing (NLP), two of the most dynamic research areas in machine learning, is charting a transformative course for the field of robotics. This article delves into the intricate integration of these two pivotal domains of artificial intelligence to enhance the capabilities of multimedia robotics applications. We explore how robots, by simultaneously interpreting visual data from their environment and comprehending human language, can achieve unprecedented levels of interaction and operational sophistication. The discussion navigates through the foundational principles of CV and NLP, highlighting the evolution of techniques from classical methods to advanced deep learning models [9]. We examine the methodologies behind fusing visual and linguistic data, focusing on architectures that enable robots to perform complex tasks such as object recognition and manipulation based on verbal commands [14]. A significant focus is placed on a practical application of this synergy: an assistive technology for visually impaired individuals, which utilizes a smartphone paired with a Faster Region Convolutional Neural Network (F-RCNN) based server to identify obstacles and provide real-time auditory guidance. This article presents an in-depth analysis of the applications, benefits, and inherent challenges of this integration, drawing upon a wide array of research. Through a comprehensive review of existing literature, we illustrate the profound impact of this synergy on creating more intelligent, autonomous, and intuitive robotic systems. The findings suggest that the continued advancement in the fusion of CV and NLP will be instrumental in realizing the full potential of social and industrial robots in our society [1].
Copyright (c) 2024 European Journals of Emerging Computer Vision and Natural Language Processing
SYNERGIES OF SIGHT AND LANGUAGE: A JOURNEY INTO HOW MACHINES LEARN TO SEE AND SPEAK
For decades, we've dreamed of creating machines that can see the world as we do and talk to us about it. This once-distant dream is now a vibrant reality at the intersection of two of artificial intelligence's most ambitious fields: Computer Vision (CV) and Natural Language Processing (NLP). This article takes you on a comprehensive journey through this exciting landscape, exploring how we're teaching computers to connect pixels to prose. We'll start by exploring the fundamental questions that drive this field, like the "symbol grounding problem"—the puzzle of how words get their meaning—and use frameworks like Bloom's Taxonomy to map out what it truly means for a machine to "understand." We'll break down the core tasks of vision into the "3Rs" (Recognition, Reconstruction, Reorganization) and language into its essential layers (Syntax, Semantics, Pragmatics) to see exactly where these two worlds meet. The heart of this survey is a deep dive into the toolbox of modern AI. We'll explore how machines learn to represent the world, from the early days of handcrafted visual features to the powerful deep learning models that create today's "image embeddings" and "word embeddings." From there, we'll investigate the ingenious architectures that fuse these senses together, including shared embedding spaces, the elegant encoder-decoder models that power image captioning, the clever attention mechanisms that let models "focus" on what's important, and the modular networks that allow for compositional reasoning. We'll see these methods in action as we examine the key battlegrounds where progress is measured: tasks like generating captions for images and videos, answering complex questions about a scene (VQA), and retrieving images with a simple text query. We'll then venture into the world of robotics, where this technology is giving machines the ability to follow our commands, learn by watching us, and engage in grounded, meaningful dialogue. By weaving together insights from landmark studies, we'll paint a picture of the field's incredible achievements. Finally, we'll have a candid discussion about the tough challenges that lie ahead—the need for genuine commonsense, the subtle biases that creep into our data, and the quest for true understanding—as we look toward a future of embodied, communicative AI that promises to change our relationship with technology forever.
OPTIMIZING RANDOM FOREST REGRESSOR PERFORMANCE IN BEEF QUALITY PREDICTION THROUGH HYPERPARAMETER TUNING
The continuous escalation in global meat consumption, particularly beef, underscores the critical need for sophisticated and efficient methodologies to ascertain and predict meat quality attributes. Traditional approaches to quality assessment, often reliant on invasive and laborious laboratory analyses, present significant logistical and economic challenges within the modern meat industry. In response to these limitations, the integration of advanced machine learning paradigms, specifically ensemble learning models such as the Random Forest Regressor (RFR), has emerged as a highly promising avenue for developing rapid, non-destructive, and precise prediction models. However, the inherent complexity of these models implies that their predictive efficacy is profoundly contingent upon the meticulous configuration of their intrinsic hyperparameters. This comprehensive investigation meticulously explores the intricate relationship between hyperparameter tuning and the predictive accuracy of an RFR model specifically tailored for beef quality assessment. Leveraging a robust dataset encompassing a diverse array of physico-chemical parameters and spectral signatures obtained via Near-Infrared (NIR) spectroscopy, a rigorous and systematic hyperparameter optimization strategy was implemented. This strategy prominently featured Randomized Search Cross-Validation, a statistically efficient technique designed to traverse a wide parameter space. The specific hyperparameters subjected to optimization included the number of constituent trees (n_estimators), the criteria for feature selection at each node split (max_features), the minimum number of data samples mandated for a leaf node (min_samples_leaf), and the requisite minimum samples for an internal node to undergo splitting (min_samples_split). The empirical findings unequivocally demonstrate a marked enhancement in the RFR model's overall predictive performance post-tuning. This improvement is robustly evidenced by substantial increases in the Coefficient of Determination (R2) values across all predicted beef quality attributes, complemented by significant reductions in error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The average R2 improvement across all parameters was approximately 14% compared to models using default parameters. These compelling results accentuate the indispensable role of comprehensive hyperparameter optimization in cultivating high-performing, resilient, and accurate predictive models for the multifaceted domain of beef quality assessment.
Service Placement Strategies Across the Cloud-Fog-Edge Continuum: A Comprehensive Survey
The proliferation of Internet of Things (IoT) devices and latency-sensitive applications has driven a paradigm shift from centralized cloud computing to a more distributed continuum encompassing fog and edge computing. This integrated environment, often referred to as the Cloud-Fog-Edge continuum, brings computation and services closer to data sources, addressing critical concerns such as latency, bandwidth, and energy consumption. However, efficiently placing services within this heterogeneous and dynamic infrastructure is a complex, NP-hard problem. This survey provides a comprehensive review of service placement algorithms and strategies proposed for integrated Cloud-Fog-Edge environments. We categorize existing approaches based on their underlying methodologies, including optimization-based, heuristic and meta-heuristic, graph-based, and machine learning-driven techniques. Furthermore, we discuss key challenges, such as mobility, resource heterogeneity, multi-objective optimization, and the increasing adoption of microservices and containerization. Finally, we highlight open research directions and future trends to guide further advancements in this critical area.
Safeguarding Identity: A Comprehensive Survey of Anonymization Strategies for Behavioral Biometric Data
Behavioral biometrics, encompassing unique patterns in human actions like gait, keystroke dynamics, voice, and eye movements, offer powerful tools for authentication and interaction. However, the rich, often sensitive information embedded in this data poses significant privacy risks, as it can inadvertently reveal personal attributes such as gender, age, health conditions, or emotional states. This article presents a comprehensive survey of anonymization techniques specifically designed for behavioral biometric data. It categorizes existing approaches based on modality (voice, gait, keystroke dynamics, ECG, EEG, eye movements, and gesture recognition) and strategy (data transformation, perturbation, and generative models). The discussion highlights the inherent trade-off between achieving strong privacy guarantees and maintaining sufficient data utility for intended applications. By reviewing the state-of-the-art and identifying persistent challenges, this survey aims to inform future research and foster the development of truly privacy-enhanced behavioral biometric systems.
Predictive Water Quality Management for Arowana Aquaculture Using Hybrid Iot And Fuzzy Time Series Models
Arowana (Scleropages formosus) cultivation is a challenging endeavor, largely due to the species' sensitivity to water quality fluctuations. Traditional manual monitoring methods are often inefficient, prone to human error, and lack the foresight needed for proactive management. This article presents a novel approach to water quality management in arowana aquaculture by integrating real-time monitoring capabilities of the Internet of Things (IoT) with advanced predictive analytics using multivariate fuzzy time series (FTS) models. The proposed system continuously collects critical water parameters such as pH, dissolved oxygen (DO), temperature, turbidity, and conductivity. These real-time data streams are then fed into a sophisticated fuzzy time series model that forecasts future water conditions, enabling cultivators to anticipate and mitigate potential issues before they impact fish health. The implementation demonstrates the efficacy of a hybrid IoT-FTS framework in providing timely, data-driven insights for optimizing Arowana cultivation environments, contributing significantly to sustainable aquaculture practices and reducing economic losses associated with poor water quality. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate, outperforming traditional regression models.
Immersive Technologies for Remote Human-Robot Interaction: A Systematic Literature Review
The integration of robotic systems into diverse applications necessitates robust and intuitive remote human-robot interaction (RHRI) capabilities. Traditional teleoperation methods often fall short in providing comprehensive situational awareness, immersive feedback, and intuitive control, leading to increased cognitive load and reduced performance. Extended Reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), presents a promising paradigm shift for RHRI by offering enhanced immersion, rich sensory feedback, and natural interaction modalities. This systematic literature review comprehensively analyzes the current landscape of XR-enabled RHRI systems. We categorize existing approaches by XR technology (VR, AR, MR), examine various interaction modalities (visual, haptic, auditory, gesture, eye-tracking), and explore key application domains such as industrial manufacturing, hazardous environments, medical, and space exploration. The review highlights the significant benefits of XR in improving situational awareness, control intuition, and collaborative capabilities. Furthermore, it identifies persistent challenges, including latency, field of view limitations, usability, and hardware constraints. Finally, we discuss critical future research directions, emphasizing adaptive interfaces, standardization, enhanced multimodal feedback, and long-term human factors studies, to foster more efficient, safer, and natural human-robot interactions in remote settings.