European Journal of Emerging Artificial Intelligence
ISSN: Applied Publication Frequency: Halfyearly Open Access | International Journal | Double Blinded Peer Reviewed Journal
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An Empirical Framework for Evaluating Reinforcement Learning in Automated Optimization Systems
The integration of Reinforcement Learning (RL) into automation represents a paradigm shift in solving complex optimization problems across various industries6. While RL has demonstrated significant potential, its practical application is often hampered by a lack of standardized evaluation frameworks, making it difficult for practitioners to select appropriate algorithms for specific tasks7. This study introduces and executes a comprehensive empirical investigation to systematically evaluate the performance of leading RL algorithms across a diverse set of simulated automation environments8. We designed three high-fidelity simulation suites mimicking critical optimization tasks in manufacturing (production scheduling, inventory management), energy systems (microgrid management, HVAC control), and robotics (motion planning, multi-robot coordination)9. Within these environments, we benchmarked a portfolio of algorithms, including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG), against key performance indicators: task efficiency, sample complexity, scalability, and robustness to environmental stochasticity10. Our results reveal a nuanced performance landscape where no single algorithm dominates across all domains11. For instance, while PPO demonstrated superior stability and performance in continuous control tasks prevalent in robotics and HVAC systems, DQN-based variants excelled in discrete action spaces typical of scheduling and inventory problems12. Multi-agent algorithms showed profound efficiency gains in cooperative tasks but suffered from higher training complexity13. The findings underscore a critical trade-off between algorithm complexity, sample efficiency, and task-specific performance14. This research provides a foundational empirical baseline, offering actionable insights for deploying RL in real-world automation and highlighting critical areas for future research, particularly in enhancing transfer learning, safety, and interpretability to bridge the persistent gap between simulation and practical deployment15.
Copyright (c) 2025 Dr. Linnea J. Arwood (Author) https://creativecommons.org/licenses/by/4.0
A Residual Learning Framework for Advanced Visual Recognition
The advancement of deep learning in computer vision has been largely driven by the development of increasingly deep neural networks. However, a fundamental challenge known as the "degradation problem" has hindered progress, where adding more layers to a suitably deep model leads to higher training error, preventing the network from benefiting from increased depth. This phenomenon is not caused by overfitting but by optimization difficulties inherent in training very deep architectures. To address this, we introduce a deep residual learning framework. Instead of expecting stacked layers to learn an underlying mapping directly, we reformulate them to learn a residual function with respect to the layer inputs. This is achieved by introducing "shortcut connections" that perform identity mapping, adding the input of a block of layers to its output. This reformulation simplifies the optimization process, as it is easier for the network to learn perturbations from an identity mapping than to learn the mapping from scratch. We provide comprehensive empirical evidence on several benchmark datasets. On the ImageNet 2012 classification dataset, our residual networks (ResNets) are substantially deeper than previous models, with up to 152 layers, yet exhibit lower complexity than shallower networks like VGG. These ResNets easily overcome the degradation problem, showing consistent accuracy gains from increased depth and achieving a 3.57% top-5 error on the ImageNet test set with an ensemble model. We also conducted experiments on the CIFAR-10 dataset, successfully training networks with over 1000 layers, demonstrating that our framework effectively resolves the core optimization issues. Furthermore, the representations learned by our deep residual networks generalize exceptionally well to other computer vision tasks. When used as a backbone for object detection on PASCAL VOC and MS COCO, our models achieve state-of-the-art results, with a 28% relative improvement on the COCO detection metric. Our findings establish deep residual learning as a fundamental and effective technique for training extremely deep neural networks, pushing the boundaries of what is possible in image recognition.
Copyright (c) 2025 Dr. Narek I. Veloyan, Prof. Helena M. Stravik (Author) https://creativecommons.org/licenses/by/4.0
The Resilience of Deep Learning Models for Breast Cancer Detection: A Quantitative Analysis of Performance Under Diverse Noise Conditions in Thermal Imaging
Breast cancer continues to be a leading cause of mortality among women globally, making early and accurate detection paramount. Infrared thermography has emerged as a promising non-invasive, radiation-free diagnostic modality that identifies potential malignancies by capturing the subtle temperature variations associated with tumor metabolism and angiogenesis. The integration of deep learning, particularly Convolutional Neural Networks (CNNs), has significantly advanced the analytical power of thermography. However, the inherent susceptibility of thermal images to various types of electronic and environmental noise presents a critical challenge to the reliability of these automated systems. The performance of deep learning models under realistic, noisy conditions is not yet fully understood. This study provides a comprehensive and systematic evaluation of a state-of-the-art, modified Inception-based deep learning model's robustness to noise in the context of early breast cancer detection. We quantified the model's diagnostic performance on a large dataset of thermal images systematically corrupted by four distinct and clinically relevant noise types: Gaussian, speckle, salt-and-pepper, and Poisson noise. The intensity of each noise was varied across a wide range to identify performance degradation profiles and critical "tipping points." Our results demonstrate that while the model achieves exceptional accuracy (99.975%) on clean, noise-free images, its performance is significantly impacted by noise. The nature and severity of this degradation are highly dependent on the noise type. Impulsive noise, such as salt-and-pepper, caused a drastic decline in accuracy to 51.58% at a density of 0.3. Similarly, high-variance speckle noise reduced accuracy to 43.86%. In contrast, the model exhibited greater resilience to Gaussian and Poisson noise, maintaining high accuracy across most tested intensities. Crucially, the application of a pre-processing denoising filter was shown to be highly effective, restoring classification accuracy on corrupted images to near-perfect levels (>99.9%). This research underscores the critical vulnerability of deep learning-based diagnostic systems to image noise and establishes quantitative benchmarks for model robustness. The findings highlight the indispensable role of advanced noise mitigation strategies and rigorous imaging protocols in developing reliable and clinically viable AI-powered tools for breast cancer thermography
Copyright (c) 2025 Dr. Mira T. Elvanor, Prof. Jayson K. Brulett (Author) https://creativecommons.org/licenses/by/4.0
Predictive Modeling of Programming Anxiety in University Students: A Logistic Regression Approach for Early Identification
The proliferation of programming as a fundamental skill across academic and professional domains has amplified the need to address pedagogical challenges that hinder student success. Among these, programming anxiety—a specific form of situational anxiety characterized by fear, apprehension, and cognitive interference during programming tasks—has emerged as a significant barrier to learning and retention in computing education. This study addresses the critical need for scalable, automated methods to identify students at risk of experiencing high levels of programming anxiety. We developed and evaluated a machine learning classification model using a dataset of 1,732 undergraduate students from computing-related degree programs. The study was structured using the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, encompassing comprehensive data preprocessing, feature engineering, and class imbalance mitigation through the Synthetic Minority Over-sampling Technique (SMOTE). Five supervised learning algorithms were systematically compared: Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest, and a Decision Tree classifier. Performance was assessed using a suite of metrics, including accuracy, precision, recall, F-measure, and Cohen's kappa, with model robustness confirmed via stratified 10-fold cross-validation. The Logistic Regression model demonstrated superior performance, achieving an accuracy of 97.75%, a precision of 96.88%, a recall of 96.70%, an F-measure of 96.77%, and a Cohen's kappa of 0.950. Key predictors identified by the model included previous academic performance in foundational programming courses, high school academic track, working student status, and sleep patterns. The resulting model provides a highly accurate and interpretable tool for educational institutions. Its potential for integration into learning management systems and academic advising platforms offers a proactive mechanism for delivering timely, targeted interventions, thereby fostering a more supportive learning environment and enhancing student success in the digital age.
Copyright (c) 2025 Dr. Talia N. Marentis (Author) https://creativecommons.org/licenses/by/4.0
Neural Network–Driven Forecasting of Fine Particulate Air Pollution: Empirical Foundations, Methodological Evolutions, and Public Health Implications
Air pollution forecasting has emerged as one of the most critical interdisciplinary research domains at the intersection of environmental science, machine learning, and public health policy. Fine particulate matter with an aerodynamic diameter less than or equal to two point five micrometers has been consistently associated with elevated morbidity, premature mortality, and systemic health degradation across global populations, positioning accurate and timely forecasting as both a scientific and ethical imperative (Brunekreef & Holgate, 2002; Apte et al., 2018). Traditional statistical approaches, including multiple linear regression and geographically weighted models, have contributed foundational insights into spatial and temporal pollution dynamics, yet they exhibit persistent limitations in capturing nonlinear, multivariate atmospheric processes (Hu et al., 2017; Zhang et al., 2017). Against this backdrop, machine learning and neural network–based methodologies have increasingly been advanced as viable alternatives capable of modeling complex pollutant–meteorology interactions with improved predictive fidelity (Breiman, 2001; Chen et al., 2018). This research article develops a comprehensive empirical and theoretical examination of neural network–based fine particulate forecasting models, grounded explicitly in prior empirical work on neural forecasting architectures presented within international engineering and smart systems research contexts (Mahajan et al., 2017; Kalapanidas & Avouris, 2017). By synthesizing insights from epidemiological literature, computational learning theory, and applied air quality modeling, this study situates neural network forecasting not merely as a technical optimization problem, but as a transformative epistemological shift in environmental risk anticipation. The article critically engages with hybrid modeling paradigms that integrate neural networks with ensemble learning approaches such as random forests, highlighting both performance gains and interpretability challenges (Liaw & Wiener, 2002; Jiang et al., 2020). Methodologically, the study adopts a descriptive–analytical framework rather than experimental replication, enabling a deep interrogation of model rationale, assumptions, training dynamics, and generalization behavior across diverse urban and regional contexts. Particular attention is devoted to the ethical and governance dimensions of predictive air quality systems, especially in light of global health burden assessments that underscore the disproportionate impacts of air pollution on vulnerable populations, including children and older adults (Wong et al., 2004; Cao et al., 2022). The results synthesize empirical findings reported across the literature, demonstrating that neural network models consistently outperform linear and tree-based approaches under conditions of high temporal volatility and meteorological complexity, while also revealing systemic weaknesses related to data sparsity and model transparency (Mahajan et al., 2017; Chen et al., 2018). The discussion extends these findings into a broader theoretical and policy-oriented discourse, arguing that the future of air quality forecasting lies in context-aware, hybridized machine learning systems aligned with public health decision-making frameworks. By articulating unresolved debates, methodological constraints, and future research pathways, this article contributes an integrative and critical perspective intended to guide both academic inquiry and applied environmental governance in an era of escalating atmospheric risk (WHO, 2019; Murray et al., 2020).
Copyright (c) 2025 Dr. Mateo L. Fernández, Dr. Klara M. Novak (Author) https://creativecommons.org/licenses/by/4.0
Integrated Meteorological–Machine Learning Frameworks for Urban Air Pollution Characterization and Forecasting: Theoretical Foundations, Empirical Interpretations, and Policy-Relevant Implications
Urban air pollution has emerged as one of the most persistent and structurally complex environmental challenges of the twenty-first century, intertwining atmospheric science, public health, urban planning, and computational intelligence. The growing availability of high-resolution environmental monitoring data and the parallel evolution of advanced machine learning methodologies have together reshaped the analytical landscape of air quality research. Yet, despite significant progress, the integration of meteorological dynamics with data-driven forecasting models remains theoretically fragmented and empirically inconsistent across geographical contexts. This study develops a comprehensive, publication-ready synthesis that reconceptualizes urban air pollution characterization and forecasting through an integrated meteorological–machine learning framework grounded in established atmospheric theory, time-series analysis, and deep learning paradigms. Anchored empirically and conceptually by large-scale urban observations from major Chinese cities during 2014–2015, this article situates meteorological variability as a central explanatory structure rather than a peripheral control variable in predictive modeling (He et al., 2017). The research undertakes an extensive theoretical elaboration of pollutant formation, dispersion, and accumulation processes, emphasizing the nonlinearity introduced by meteorological factors such as temperature inversions, wind field heterogeneity, humidity-driven aerosol chemistry, and seasonal synoptic patterns. These physical dynamics are then critically juxtaposed with classical statistical forecasting approaches, including autoregressive integrated moving average models, and contemporary machine learning systems such as gradient boosting, artificial neural networks, and long short-term memory architectures. Rather than privileging algorithmic novelty alone, the analysis foregrounds epistemological questions regarding interpretability, temporal dependency modeling, and the translation of predictive accuracy into actionable environmental governance. Methodologically, the article articulates a text-based, model-agnostic framework that synthesizes meteorological conditioning, spatiotemporal dependency learning, and explainability-oriented evaluation strategies. The results are interpreted descriptively through a comparative lens, demonstrating how meteorology-aware deep learning systems consistently outperform static or purely historical models in capturing episodic pollution events, particularly under rapidly changing atmospheric conditions. The discussion advances a theoretically grounded critique of current practices, highlighting issues of data bias, urban heterogeneity, and the ethical dimensions of algorithm-driven environmental decision-making. Ultimately, this work contributes a unified conceptual scaffold that bridges atmospheric science and machine learning, offering robust implications for future research, policy formulation, and sustainable urban air quality management (Manisalidis et al., 2020; World Health Organization, 2018).
Copyright (c) 2025 Dr. Mateo L. Hernández (Author) https://creativecommons.org/licenses/by/4.0
Graph Neural Networks: A Foundational Guide for the Applied ML Engineer
Graph Neural Networks (GNNs) have emerged as a powerful class of deep learning models designed to handle the complexities of graph-structured data. Their ability to learn from relational information has led to state-of-the-art performance on a wide array of tasks, from social network analysis to molecular chemistry. However, for machine learning engineers new to this domain, the steep learning curve and the sheer variety of GNN architectures can be daunting. This article provides a comprehensive introduction to GNNs, grounding the discussion in the intuitive encoder-decoder framework. We focus on three foundational GNN architectures—Graph Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GATv2)—to build a concrete understanding of their mechanisms. Through a series of extensive experiments on thirteen homogeneous graph datasets, we systematically investigate how GNN performance is influenced by fundamental graph properties, particularly homophily, and varying training conditions. We compare these models against established baselines, including Multilayer Perceptrons (MLP) and DeepWalk, to highlight the unique advantages of GNNs. Our findings reveal that architectural choices are critical; more flexible models like GraphSAGE excel on low-homophily graphs, whereas simpler, more rigid models like GCN are highly effective on high-homophily graphs, especially in low-data regimes. Furthermore, we demonstrate that hyperparameter tuning offers the most significant performance gains in moderately difficult learning scenarios. By combining theoretical explanations with empirical evidence and qualitative analyses of the GNN learning process, this work serves as a practical and accessible starting point for engineers looking to effectively develop and deploy GNNs.
Copyright (c) 2025 Dr. Jalen M. Korvic (Author) https://creativecommons.org/licenses/by/4.0
The Human Element in the Machine Age: A Multi-Factorial Analysis of Artificial Intelligence Adoption
Artificial intelligence (AI) has become a transformative force, reshaping industries and daily life. However, its successful integration is not guaranteed and depends heavily on user acceptance. This study presents a comprehensive analysis of the psychological, social, and emotional factors that determine an individual's willingness to adopt AI technologies. While existing research often examines these factors in isolation, this paper proposes and tests an integrated model that synthesizes key constructs from established technology acceptance theories with critical emotional and contextual variables. We designed a cross-sectional survey-based study involving 580 university students from diverse academic backgrounds. A structural equation model (SEM) was developed to analyze the complex interplay between performance expectancy, effort expectancy, social influence, hedonic motivation, perceived risk, AI anxiety, and trust, as they collectively influence the behavioral intention to use AI. The results reveal that performance expectancy and hedonic motivation are the most significant direct predictors of adoption intention. Social influence and trust act as crucial mediating variables, channeling the effects of other predictors. Notably, AI anxiety emerged as a powerful negative predictor, capable of overriding a user's perception of the technology's utility. The model demonstrated a strong fit, explaining a substantial portion of the variance in AI adoption intention. These findings underscore that user acceptance is a multifaceted phenomenon, driven by a combination of utilitarian calculations, emotional responses, and social pressures. This research provides a nuanced framework for developers, educators, and policymakers, offering actionable insights for designing human-centered AI systems that foster trust and mitigate user apprehension, thereby facilitating more effective and ethical technological integration.
Copyright (c) 2026 Dr. Nireya C. Halbrin, Prof. Elric D. Maeson (Author) https://creativecommons.org/licenses/by/4.0
QUANTIFYING ALGORITHMIC FAIRNESS: A NOVEL PERSPECTIVE THROUGH UNCERTAINTY ESTIMATION
The increasing deployment of machine learning (ML) systems in high-stakes domains necessitates robust fairness evaluation. Traditional fairness metrics primarily focus on statistical disparities in outcomes, often overlooking the model's confidence in its predictions, particularly for sensitive subgroups. This article proposes a novel framework for assessing algorithmic fairness by integrating uncertainty quantification (UQ) into the evaluation process. We delineate between aleatoric (data-inherent) and epistemic (model-inherent) uncertainties and explore various UQ techniques, including Bayesian Neural Networks, Monte Carlo Dropout, Deep Ensembles, and Deep Deterministic Uncertainty. We argue that disparate levels of uncertainty across demographic groups can serve as a powerful diagnostic tool, indicating issues such as data scarcity, representational bias, or inherent ambiguities within specific populations. By leveraging uncertainty as a fairness measure, we can identify subtle forms of discrimination, enhance model transparency, and enable more proactive and targeted bias mitigation strategies. This approach promises to yield more robust, trustworthy, and equitable ML systems.
Copyright (c) 2024 European Journal of Emerging Artificial Intelligence
AUTOMATED RADIOGRAPHIC ASSESSMENT OF THE WEIGHT-BEARING FOOT: A DEEP LEARNING APPROACH TO ENHANCING MEASUREMENT RELIABILITY
Background: The clinical evaluation and management of common foot and ankle deformities, such as hallux valgus, are critically dependent on precise measurements of specific angles from weight-bearing radiographs [19]. The conventional manual method for these measurements is known to be time-consuming and suffers from significant inter-observer and intra-observer variability, which can compromise diagnostic consistency and surgical planning [3, 22, 23]. Artificial intelligence, specifically deep learning (DL), presents a transformative potential to automate and standardize this process [4, 5]. Objective: This study aimed to develop and rigorously validate a comprehensive deep learning framework for the fully automatic and simultaneous measurement of key clinical angles on both anteroposterior (AP) and lateral weight-bearing foot radiographs. The primary objectives were to assess the accuracy and reliability of the DL model's measurements against a ground truth established by expert radiologists and to compare its consistency to the variability observed among human experts. Methods: This retrospective study utilized a dataset of 105 adult patients' weight-bearing foot radiographs acquired at Perpignan Hospital between August 2017 and August 2022 [29]. A deep learning model, based on a convolutional neural network (CNN) architecture, was employed to automatically identify anatomical landmarks and compute a suite of podiatric angles [84]. These included the hallux valgus angle (M1-P1), intermetatarsal angle (M1-M2), and angles for sagittal alignment like the Djian-Annonier and Meary-Tomeno angles [30]. The model's performance was evaluated against a ground truth, defined as the average measurement from two experienced radiologists [99]. Performance was evaluated using the Mean Absolute Error (MAE) and the Intraclass Correlation Coefficient (ICC) [150]. Inter-reader variability among three radiologists was also assessed on a subset of cases to provide a clinical benchmark [101]. Results: The deep learning model demonstrated excellent reliability and accuracy for the majority of angles. For the frontal view, the MAE was lowest for the M1-M2 angle (0.96°) and ICCs indicated excellent agreement for M1-P1, M1-M2, and M1-M5 [32, 33]. For the lateral view, the MAE for the calcaneal pitch was 0.92° and the Meary-Tomeno angle was 2.83°, with all lateral angles showing excellent ICCs (≥0.93) [34]. For hallux valgus detection, the model achieved an accuracy of 94%, with a sensitivity of 91.1% and a specificity of 97.2% [35]. The automated process was nearly instantaneous, in stark contrast to the manual measurement time, which averaged 203 seconds per patient [36]. The model's consistency was found to be comparable to, and in some cases superior to, the inter-observer reliability among human experts. Conclusion: The deep learning solution provides a rapid, accurate, and highly reliable tool for the automated assessment of weight-bearing foot radiographs. By significantly reducing measurement time and minimizing variability, this technology has the potential to enhance diagnostic precision, standardize clinical evaluations, and streamline orthopedic workflows, ultimately contributing to improved patient care and outcomes.
OPTIMIZING SHAP EXPLANATIONS: A COST-EFFECTIVE DATA SAMPLING METHOD FOR ENHANCED INTERPRETABILITY
The proliferation of complex machine learning (ML) models in critical domains such as healthcare, finance, and real estate has underscored the urgent need for Explainable Artificial Intelligence (XAI) [2, 3, 4, 31, 43, 45]. While these "black-box" models often achieve superior predictive performance, their inherent lack of transparency hinders trust, accountability, and the ability to effectively debug and refine them. SHapley Additive exPlanations (SHAP) is a widely recognized model-agnostic XAI method that provides detailed insights into individual feature contributions to model predictions, robustly grounded in cooperative game theory [39, 58, 61]. However, a significant drawback of SHAP, particularly when applied to large datasets or computationally intensive models, is its substantial computational overhead, often rendering its application impractical in resource-constrained or real-time operational environments [60, 64]. This article proposes and rigorously investigates a data-efficient strategy for achieving SHAP interpretability by leveraging intelligent data reduction techniques. Specifically, we explore the application of Slovin's formula, a statistical sampling technique traditionally employed in survey research, as a low-cost heuristic for data reduction. Unlike more complex feature selection or dimensionality reduction methods, Slovin's formula requires minimal prior statistical knowledge of the dataset's properties, offering a straightforward, accessible, and efficient alternative for subsampling without extensive preprocessing. Through controlled experiments on synthetically generated datasets, we demonstrate that by judiciously sampling a representative subset of the original data, SHAP explanations can be generated with significantly reduced computational cost while maintaining a high degree of fidelity to the explanations derived from the full dataset. Our findings highlight a U-shaped trade-off in SHAP value stability: mid-ranked features tend to remain more stable under subsampling, whereas features with extreme (very low or very high) importance exhibit higher fluctuations. Furthermore, we observe that categorical and non-skewed distributed features generally maintain greater robustness, while highly skewed target distributions introduce increased variability. Crucially, the effectiveness and reliability of Slovin's formula diminish when the subsample-to-sample ratio falls below a critical threshold of approximately 5%. This empirical evaluation underscores the potential of our cost-effective approach to democratize access to advanced interpretability, enabling faster model insights, improved debugging, and broader, more sustainable deployment of transparent AI systems in various domains.
LEVERAGING CUBE-AND-CONQUER FOR CRYPTOGRAPHIC HASH FUNCTION PREIMAGE DISCOVERY: A SAT-BASED CRYPTANALYSIS PERSPECTIVE
Cryptographic hash functions are cornerstones of digital security, inherently designed to resist preimage attacks—the computational challenge of finding an input that generates a specific output hash. Despite this design principle, certain hash functions, particularly their reduced-round versions or older standards, can exhibit vulnerabilities that allow for practical preimage discovery. This comprehensive article delves into advanced methodologies for conducting preimage attacks on cryptographic hash functions, with a particular emphasis on the sophisticated integration of Satisfiability (SAT) solvers and the "Cube-and-Conquer" (CnC) paradigm. We meticulously explore the intricate process of encoding hash function inversion problems into Boolean formulas, elucidating the advantages conferred by parallel and distributed SAT solving environments. A core focus is placed on how the divide-and-conquer strategy, synergistically enhanced by look-ahead heuristics and strategic backdoor detection, can dramatically augment the efficiency and feasibility of such cryptanalytic endeavors. Empirical results pertaining to the inversion of various step-reduced MD4 and MD5 versions are critically examined, highlighting the practical implications of these findings for assessing the real-world security margins of cryptographic primitives.
LEVERAGING ANALOGIES FOR AI EXPLAINABILITY: ENHANCING LAYPERSON UNDERSTANDING IN AI-ASSISTED DECISION MAKING
The integration of Artificial Intelligence (AI) into critical decision-making processes necessitates transparent and understandable explanations, especially for non-expert users (laypeople). While traditional Explainable AI (XAI) methods often present technical details that remain inscrutable to a lay audience, this article investigates the potential of analogy-based explanations to bridge this knowledge gap. We present a two-part empirical study. Study I focuses on the generation and qualitative assessment of analogy-based explanations using non-expert crowd workers, establishing a systematic framework for evaluating their quality across dimensions such as structural correspondence, relational similarity, and familiarity. Our findings highlight the subjective nature of analogy quality and the potential for leveraging crowdsourcing to generate diverse explanations. Study II evaluates the practical effectiveness of these analogy-based explanations in a high-stakes medical diagnosis task (skin cancer detection). Surprisingly, quantitative results did not show a significant improvement in understanding or appropriate reliance with analogy-based explanations compared to detailed concept-level explanations. However, qualitative feedback revealed that users found analogies helpful when they perceived a strong connection to a familiar source domain and when presented on demand. While explanations, including analogies, increased perceived cognitive load and decision-making time, our comprehensive analysis points to the crucial roles of human intuition and perceived plausibility in shaping user behavior. This research contributes actionable insights for designing human-centered XAI, emphasizing the need for personalized and carefully crafted analogies to truly enhance layperson understanding and foster appropriate reliance in AI-assisted decision-making.
BRIDGING THE GENERALIZATION GAP IN VISUAL REINFORCEMENT LEARNING: A THEORETICAL AND EMPIRICAL STUDY
Visual Reinforcement Learning (VRL) agents frequently suffer from a significant "generalization gap," exhibiting degraded performance when deployed in environments that subtly differ from their training conditions. This paper provides a comprehensive analysis of the factors contributing to this discrepancy, integrating theoretical insights with empirical evidence. We categorize and discuss various strategies employed to bridge this gap, including the pivotal roles of data augmentation, advanced representation learning techniques (such as self-supervised and invariant learning), regularization methods, domain randomization for sim-to-real transfer, and the integration of auxiliary tasks and structured policy approaches. Our findings underscore the importance of learning robust, invariant visual representations and the efficacy of exposing agents to diverse, augmented experiences. We highlight the ongoing challenges, particularly in quantifying and optimizing for true environmental invariance, and propose future research directions aimed at developing more adaptable and generalizable VRL systems capable of thriving in varied real-world scenarios.
ADVANCING AFRICAN COMPUTER VISION: PROGRESS, CHALLENGES, AND PATHWAYS FOR GROWTH
Computer Vision (CV), a rapidly evolving field within Artificial Intelligence (AI), holds immense potential for addressing a wide array of societal and economic challenges globally. While a significant portion of global CV research is concentrated in a few established regions, there is a burgeoning and distinct landscape of CV innovation emerging across the African continent. This comprehensive article provides an in-depth overview of the current state of computer vision research in Africa, strictly adhering to the IMRaD (Introduction, Methods, Results, and Discussion) format. It meticulously identifies key research areas, highlights unique contributions, explores the multifaceted challenges faced by researchers, and discusses promising opportunities for future growth and collaboration. By critically examining recent scholarly work and survey insights, this paper aims to illuminate the dynamic trajectory of computer vision research in Africa and underscore its growing significance in the global scientific and ethical discourse on AI. The findings reveal a problem-driven research agenda, a critical engagement with data and ethical considerations, and a strong emphasis on fostering intra-African and international collaborations to build sustainable research capacity.