AI

Behaviour-Driven Development (BDD) encodes acceptance criteria in Gherkin, but hand-authoring is laborious, and it is unclear which large language model (LLM) drafts it best. We benchmark eight LLMs generating Gherkin from three requirement corpora (requirement lists, user stories, RFP excerpts) over 2960 generations, scoring validity, runner acceptance, judged coverage and quality, similarity to gold standard, stability, and cost. Validity is near the ceiling, yet only 78% of outputs load in the Cucumber runner: a fifth emits several Feature blocks per file. Two student annotators (a small, non-expert panel) calibrate the judge on 72 blinded generations. Human score levels are matched (error 0.33 versus 0.35 between the humans) but outputs are ordered far less reliably (ICC 0.47 versus 0.64; coverage 0.21 versus 0.76): magnitudes hold, but fine rankings do not. Against that gold standard, models span 66–107% of the human–human ceiling, ordering differently again. Pareto analysis leaves three of eight models non-dominated: cost varies 157×, judged quality 0.36 points. Per-model prompt tuning yields no cross-validated gain; a restrictive token budget truncates verbose models. Two newer models displace the low-cost front: tier-level findings transfer, and model names are dated quickly. We release the corpora, gold standard, and prototype. Model choice should weigh cost and runner acceptance over judged quality. Full article

Smart systems, the Industrial Internet of Things, and cyber-physical networks increasingly make decisions on the devices where data is generated, on nodes short of memory, compute, energy, and bandwidth, and exposed to real adversaries. Two research currents have grown to meet this: one makes artificial intelligence small and distributed (quantization, pruning, distillation, TinyML, federated and split learning), the other makes it safe (defenses against poisoning, backdoors, inversion, and evasion). This review argues that the two are entangled rather than parallel. Operators that shrink a model or scatter it across nodes also redraw its attack surface, each carrying a security dividend and a security liability, and because a node’s resources are finite and shared, model capacity and defense strength compete for one multi-dimensional budget. We formalize this as an efficiency-decentralization-security (EDS) design tension, explicitly a tension and not an impossibility, and show with published measurements that the coupling is non-monotonic. Around this thesis we build three artifacts, following an explicit design-science research process: an evidence-graded scoring matrix that separates each operator’s security dividend from its liability across seven axes and reports the direction of every effect separately from the confidence in the evidence behind it; a resource-aware threat model that judges attack and defense feasibility against a tiered device, gateway, network, and server budget with stated units; and a co-design framework whose decision workflow terminates in a defense-selection program and a verification step under adaptive attack. We work the framework through an industrial predictive-maintenance scenario with the resource arithmetic computed line by line, and evaluate it retrospectively against six published edge-AI systems. The result is a decision-support guide for building edge AI that is efficient, decentralized, and secure at once. Full article

Precise segmentation of the retinal microvasculature is vital for early detection and longitudinal monitoring of systemic cardiovascular and ophthalmic diseases. However, contemporary deep learning models struggle to simultaneously preserve fine-vessel branches and suppress background noise, often leading to severe under-segmentation or false-positive artifacts. To address this persistent challenge, we propose a novel, highly efficient Triple-Path Kolmogorov–Arnold Network (KAN) optimized via Deep Mutual Learning (DML). First, the proposed architecture synergistically integrates U-Net, HaarNet, and SegNet to explicitly decouple global contextual feature extraction from high-frequency spatial edge detection. Second, we introduce field-of-view spatial constraints to strictly eliminate background interference. Third, traditional linear bottlenecks are replaced with KAN blocks, which leverage dynamic, Chebyshev polynomial-based activation functions to robustly model highly nonlinear, chaotic vascular topologies. Finally, a zero-cost DML strategy is employed during training, maximizing the network’s representational capacity without introducing any computational burden during inference. Extensive evaluations demonstrate that our proposed model successfully overcomes the traditional sensitivity-specificity trade-off. Specifically, it achieves a peak sensitivity of 72.38%, a specificity of 98.66%, and an overall Dice score of 72.48%. While maintaining exceptional computational efficiency, this tightly coupled framework establishes a robust, accurate, and clinically viable solution for automated point-of-care retinal diagnostics. Full article

Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement units (IMUs). Such systems assign each repetition to a category, yet a relevant share of repetitions fall near a class boundary, where even trained raters disagree. Classifiers trained with one-hot labels collapse these borderline repetitions onto a single class and discard this ambiguity. To address this, we build on label distribution learning, which represents each repetition as a distribution over classes instead of a single label. We introduce a way to construct such distributions without a large rater pool by perturbing the thresholds of a rule-based evaluation procedure to simulate rater disagreement. We train a network to reproduce these distributions with a Kullback–Leibler objective, which we call the ambiguity approach, and compare it against a one-hot cross-entropy baseline on four IMU exercise datasets. From the predicted distribution we then determine whether a repetition is ambiguous and which classes are relevant to it. The ambiguity approach matched or exceeded the baseline classification on all four datasets and detected ambiguity and the relevant classes more reliably. Representing the label distribution in the training target therefore adds information about ambiguity at no cost to classification. Full article

Background: Smartphones and generative artificial intelligence (GenAI) are increasingly integrated into everyday life, influencing information access, digital interaction, and cognitive tasks. While problematic smartphone use and cognitive offloading are well studied, GenAI introduces newer forms of cognitive delegation that warrant comparison. Objective: This narrative review examines convergences and divergences between smartphone- and GenAI-mediated cognitive processes, focusing on cognitive offloading, attention, reliance, self-regulation, and human agency. Review approach: Interdisciplinary literature from psychology, artificial intelligence, digital technologies, and education was narratively reviewed and compared. The aim was to integrate relevant evidence rather than provide a systematic or exhaustive synthesis. Synthesis: Smartphones primarily support connectivity, information access, and digitally mediated attention, whereas GenAI extends technological assistance toward reasoning, synthesis, and content generation. Despite important differences, both may redistribute cognitive effort between individuals and digital systems, with potential implications for self-regulation and human agency. Conclusions: Smartphones and GenAI are distinct technologies with partly overlapping but also technology-specific effects. Further empirical research is needed to determine which forms of cognitive offloading and reliance are shared across technologies and their implications for human agency. Full article

The You Only Look Once (YOLO) has been widely adopted in aquaculture monitoring and management due to its real-time performance and deployment flexibility. The recently introduced YOLO26 architecture incorporates Non-Maximum Suppression (NMS)-free end-to-end inference and is optimized for deployment on resource-constrained CPU-based devices, making it particularly relevant for edge deployment in commercial aquaculture applications. Nevertheless, its performance, operational efficiency, and deployment suitability compared with previous YOLO generations remain largely unvalidated in aquaculture-specific scenarios. This study benchmarks YOLO26 against three Ultralytics predecessors (YOLOv5u, YOLOv8, and YOLO11) across nano, small, and medium model scales for the detection of fish mortality, a critical indicator of fish population health and welfare, in recirculating aquaculture systems (RAS). Twelve model variants were evaluated for detection accuracy, training efficiency across seven dataset sizes, and inference performance on both high-performance NVIDIA A100 GPUs and the resource-constrained, CPU-only Raspberry Pi 5 edge device. All models achieved comparable performance on the full dataset, with mAP50 varying by only 1.25 percentage points across three independent training runs, indicating minimal influence of architectural generation on final mortality detection accuracy when sufficient training data are available. However, notable differences emerged in data efficiency and deployment performance. YOLOv8 demonstrated the strongest training efficiency, achieving 90% mAP50 with only 400 training images, whereas YOLO26 nano and small variants required 1000 images to reach comparable accuracy. In contrast, YOLO26 exhibited advantages during edge deployment, with YOLO26n achieving the highest inference speed on the Raspberry Pi 5 at 7.84 ± 0.13 FPS across three benchmark sessions, while YOLOv5mu outperformed all contemporary medium-scale architectures on CPU-based hardware. These results demonstrate that architectural novelty alone is an insufficient criterion for model selection. The findings support a deployment-oriented framework in which training data availability, target hardware, and inference requirements collectively inform model selection for aquaculture applications. Full article

Test-time refinement aims to improve generated programs through additional inference, but its value after an initial candidate has been produced remains unclear. We conduct a controlled evaluation of one-round Self-Refine and Self-Debug across seven models and three Python code-generation benchmarks. For each model and task, both methods refine the same initial candidate, allowing us to measure refinement gain without variation in initial generation. Self-Debug produces positive refinement gain in 16 of the 21 model–benchmark combinations and no change in the remaining five, whereas Self-Refine reduces correctness in 16 combinations and improves it in only four. Decomposing refinement gain into repairs and regressions clarifies this contrast. After a diagnostic pass, Self-Debug’s candidate preservation leaves 34.1% of initial final-test failures unaddressed, but 99.95% of initially correct candidates remain correct. With regressions nearly absent, repairs after diagnostic failures translate directly into positive gain. Self-Refine also repairs initial failures, but its regression count is more than three times its repair count overall, producing predominantly negative gain. Resource analysis shows that Self-Refine uses more tokens while generally reducing correctness, whereas Self-Debug provides a more favorable gain–overhead balance, although its inference overhead per net additional pass varies across models and benchmarks. These results show that one-round refinement is not inherently beneficial and should be applied only when its expected gain justifies the additional computation and cost. Full article

This chapter offers a conceptual and philosophical analysis of the ontological, epistemological, and ethical issues associated with the design, development, and use of Agentic AI in moral and legal decision-making. Its aim is to map, classify, and bring into focus the principal philosophical problems raised by the autonomous operation of artificial agents in contexts of moral and legal judgement. The chapter contributes to the existing literature by distinguishing between two levels of AI autonomy. Advisory systems possess limited autonomy and provide recommendations that remain subject to human evaluation, whereas regulatory systems exercise a greater degree of autonomy and are entrusted with final decision-making authority. Adopting a sceptical perspective, the analysis examines the conceptual fragility and epistemological difficulties surrounding proposals to employ Agentic AI in significant domains. Particular attention is given to the risk that ostensibly advisory systems may become tacitly regulatory in practice, especially under the influence of widespread assumptions concerning the objectivity, accuracy, and effectiveness of AI. The inquiry proceeds through a step-by-step branching structure organised around three central questions concerning the moral values on the basis of which such systems should be designed and developed, the extent to which they can understand the concepts of ethics and justice, and the attribution of responsibility for their decisions and outputs. Each question opens onto alternative lines of analysis, thereby providing a systematic map of the principal philosophical challenges involved. Its purpose is to provide a synthetic overview of the relevant philosophical issues while highlighting both their breadth and their plurality. To organise these issues systematically, the chapter adopts the schema of an algorithm, offering a clear and ordered account of the possible interconnections among the problems examined and of the ways in which alternative answers generate distinct yet interrelated lines of philosophical inquiry. Full article

The detection of audio deepfakes has emerged as a significant problem in the field of voice biometrics systems, aiming to distinguish real human voices from those generated by Artificial Intelligence (AI). With synthetic voice becoming increasingly high-quality, it is more likely that such a voice will be abused for illicit purposes like identity theft and impersonation. The dual-branch CNN with shared weights architecture presented here is augmented with self-attention modules to detect audio deepfakes with greater efficiency. Convolutional operations and dual branches are used to extract complex characteristics from raw audio signals in our module in order to directly compare the unprocessed original audio with the modified audio. Afterward, residual connections improve network performance. Designed alongside these fundamental layers, self-attention modules are trained in a layered manner to detect multi-headed attention within audio frames. This feature helps the network distinguish between original and modified audio and improves feature extraction compared with the standard method. A range of audio modifications have been analyzed to assess the effectiveness of the method, and comprehensive testing across all possible audio manipulation situations has been conducted on the Controlled Singing Voice Deepfake Detection Challenge (CtrSVDD) dataset to assess its resilience. Both deep learning (DL) and machine learning (ML) models were outperformed by the proposed dual-branch CNN with shared weights. With an accuracy of 97.26%, precision of 99%, recall of 99.27%, and an F1 score of 98.88%, this model has achieved a remarkable performance. Full article

The rising number and the complexity of crises in Europe, including the COVID-19 pandemic, migration, and cybersecurity threats, have accelerated the adoption of digital transformation and artificial intelligence (AI) in public administration. This paper is a systematic literature review (SLR) of 58 academic sources examining the effect of digital transformation and AI on crisis response outcomes in European public administration. Records were identified in Scopus and Web of Science, complemented by supplementary sources, and critically appraised through a two-step procedure based on the Mixed Methods Appraisal Tool. The review integrates major themes such as the evolution of digital governance, AI-enabled decision-making, performance in crisis management, and institutional resilience. Gains in efficiency, coordination, and responsiveness are widely reported, but the appraisal shows that they rest on a thin evidential base—one quasi-experimental study, no longitudinal designs, and twenty non-empirical records—while governance, accountability, ethical, and institutional-capacity problems persist. The article reveals significant gaps in research and suggests a broad research agenda in the future with emphasis on theoretical, methodological, and policy aspects. The paper contributes to the emerging digital-era governance literature and offers recommendations for policymakers and researchers. Full article

Small, heterogeneous clinical datasets pose a challenge for whole-cohort prediction because clinically meaningful treatment or response patterns may be diluted across biologically diverse patients. We describe and evaluate NetraAI, an interpretable dynamical-systems framework for selective subgroup discovery that uses finite-iteration contraction-inspired dynamics and long-range memory (LRM) to identify stable, outcome-linked Model-Derived Subgroups (MDS). This system can abstain by assigning No Call when a stable subgroup assignment is not supported. A large language model (LLM) Strategist is outlined only as a possible future extension; it is not evaluated here and contributes nothing to the results reported. Foundation and language models asked to perform subgroup discovery directly did not recover the structure the specialized discovery step recovered. We demonstrate this framework across three retrospective clinical trial datasets: Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia (olanzapine vs. perphenazine comparative treatment-preference benchmark), Canadian Biomarker Integration Network in Depression (CAN-BIND) depression (escitalopram response), and Comprehensive Molecular Characterization of Advanced Pancreatic Ductal Adenocarcinoma for Better Treatment Selection (COMPASS) pancreatic cancer (GnP vs. FOLFIRINOX observational regimen-associated response). The benchmark is not a contest between NetraAI and competing predictors: the same eight downstream methods are evaluated with and without what NetraAI discovered. Given the full feature sets and their own selection procedures, those methods were at or near chance on all three datasets, and blind de novo searches by an independent interaction model and by a pretrained tabular foundation model did not recover an equivalent signature or subpopulation. In internal downstream evaluation, given the discovered variables alone—the same patients, the same classifiers, the full cohort and no abstention of any kind—every one of the eight methods improved on every dataset, 24 of 24 method-dataset comparisons, moving from a raw-feature range of 0.46–0.62 AUC to 0.54–0.78. Restricting further to the subpopulation in which those variables hold improved all eight methods again in CAN-BIND and in COMPASS, 16 of 16 comparisons, reaching 0.66–0.83 and 0.96–1.00, respectively; in CATIE, where the called subgroups are the least outcome-homogeneous of the three, it improved only one of eight. Taking the framework as a whole, 23 of 24 method-dataset combinations improved over the raw-feature baseline. NetraAI abstains on patients without stable subgroup structure, calling 27.7% to 40.4% of each cohort. The contribution demonstrated is therefore subgroup discovery rather than downstream prediction, and its beneficiaries are the conventional methods themselves. In COMPASS, NetraAI identified a three-SNV signature associated with regimen-linked response ranking among called patients; because the cohort was observational and the permutation test was conditional on the selected signature, this finding is exploratory. The two mechanisms are complementary rather than competing: variable discovery establishes which features carry the structure, and abstention establishes in which patients it holds. Neither mechanism replaces conventional modeling. Variable discovery improved every method on every dataset, the pretrained tabular foundation model included; identifying the population in which those variables hold conferred further benefit in two of the three datasets and not in the third. These findings support NetraAI as an exploratory system for generating compact, inspectable subgroup hypotheses that may inform future enrichment strategies after external validation, and indicate that its value lies in what it contributes to other methods rather than in competing with them. Full article

Different imaging modalities exhibit inherent discrepancies in intensity characteristics and information representation, resulting in pronounced heterogeneity among multimodal features. When these heterogeneous features are directly learned and fused within a unified representation space, feature coupling may arise, leading to mutual interference between global structural information and local fine-grained details. To address the limited differentiated modeling of structural and fine-grained information in existing methods, we propose Hierarchical Dual-Perspective Collaborative Modeling for Multimodal Image Fusion (HDPF). HDPF employs a Dual-Perspective Feature Aggregation Block (DFAB) to jointly exploit convolution-based local representation and Transformer-based global contextual modeling. Building upon this dual-perspective representation, HDPF further constructs two differentiated pathways dedicated to structural information and detail information, respectively, thereby providing differentiated representations of complementary multimodal information. The extracted features are subsequently reorganized and integrated by the decoder to reconstruct the final fused image. Extensive experiments are conducted on multiple infrared–visible image fusion (IVF) datasets as well as medical image fusion (MIF) tasks. On the TNO dataset, HDPF achieves VIF and MI scores of 0.80 and 3.50, respectively. On the MRI–PET fusion task, the VIF score reaches 0.61. The experimental results demonstrate that HDPF achieves competitive and relatively balanced fusion performance across the evaluated multimodal image fusion tasks. Full article

Agentic artificial intelligence (AI) systems are increasingly deployed across distributed cloud–edge–radio infrastructures, where autonomous agents make decisions with direct operational and economic impact. Smart contracts (SCs) enforce business rules and policies, constraining autonomous actions according to predefined operational intent. As agent autonomy expands across heterogeneous networks, ensuring accountability requires transparency, verifiability, and compliance with SC-defined requirements. To address these challenges, this paper proposes the Agent Governance Framework (AGF), which integrates SC-based governance into agentic AI systems, enabling verifiable accountability through traceable autonomous decisions. Built on European Telecommunications Standards Institute (ETSI) and TM Forum principles, AGF comprises six components: (i) a TM Forum-aligned business support system (BSS); (ii) an agentic AI-enhanced operations support system (OSS); (iii) a Network Resource Operations subsystem; (iv) an Identity and Access Role Manager; (v) a Distributed Marketplace; and (vi) a Traceability and Auditability Registry. Together, these components provide an end-to-end (E2E) traceable architecture, linking each action to the responsible agent and SC. A prototype on a local test network with the evaluation of two complementary network test cases demonstrates the framework’s feasibility, confirms its full traceability and immutability, and highlights AGF’s potential as a foundation for reliable, large-scale agent-based systems in telecommunications networks. The results demonstrate 100% traceability across the E2E governance loop and a minimal latency overhead of 2–4% due to the Traceability and Auditability registry. Full article

Zero-day network attacks pose a significant threat because their unknown signatures evade traditional detection mechanisms. This research develops an AI-enhanced intrusion detection system that aims to detect such attacks while providing interpretable outputs for security analysts. Four machine-learning models are evaluated under strict zero-day conditions using two benchmark datasets. SHAP and LIME are applied to produce instance-level explanations, and a formal stability assessment is conducted to determine their reliability. Experimental results show that the hybrid model combining anomaly-based detection with deep learning achieves the highest zero-day Recall, with statistically significant advantages over individual models in detecting previously unseen attacks, while the standalone LSTM achieves the strongest overall balance between Precision and Recall. The generated explanations consistently reveal security-relevant features, and stability analysis confirms their robustness across conditions. The study demonstrates that integrating deep learning with stable explainable AI offers a practical and trustworthy solution for zero-day intrusion detection, contributing validated evidence to an area where explanation reliability is rarely examined. Full article

Predicting emergencies caused by uncontrolled and sometimes sudden changes in methane concentration within working and adjacent zones of coal mines remains a critical and challenging task, the solution for which can greatly enhance mining safety. This study presents a hybrid machine-learning model trained on real and synthetic data for accurate methane concentration forecasting and risk-level classification. The authors propose an ensemble method comprising staged data preprocessing, generation of physically meaningful features, and weighted ensembles for both regression and classification. The system is augmented with expert rules to correct forecasts and a built-in anomaly detection mechanism based on residual analysis. Experimental evaluation confirmed the model’s high performance: for regression, the coefficient of determination reached 0.984–0.997; the classifier achieved a recall of 92.8% for the rare “Accident” class under severe data imbalance (10:1). The ensemble approach reduced error variance by 40–60% compared to baseline models. The results indicate the feasibility of pilot application for dynamic early warning, which can substantially reduce coal mine accident risks. Full article

Existing deepfake detectors often perform well on in-domain data but generalize poorly to unseen datasets or manipulation methods. This limitation is largely attributed to their reliance on dataset-specific semantic cues rather than transferable forgery patterns. To address this limitation, we propose a generalizable deepfake detection framework that combines frequency-domain enhancement with feature disentanglement. A Phase-Amplitude Frequency Enhancement (PAFE) module enhances subtle spectral artifacts introduced during deepfake generation. We then feed the enhanced representations into an asymmetric dual-branch architecture that separates content-related information from forgery-related features. The content branch models facial semantics, while the forgery branch extracts discriminative forgery features with reduced content interference. A spatial self-attention module further refines the forgery features. We optimize the framework using image-level reconstruction loss, feature-level contrastive loss, and classification loss. Together, these objectives encourage effective feature disentanglement and improve the discriminability of the learned forgery features. Extensive experiments on several widely used deepfake benchmarks show that the proposed framework achieves competitive detection performance and improved cross-domain generalization compared with existing methods. Full article

Artificial intelligence is increasingly used in sport science to classify movement, analyse tactics, estimate readiness, forecast performance and injury risk, and support rehabilitation. Predictive performance alone, however, does not establish practical usefulness, safety, or responsible use. Through purposive source selection and critical synthesis, this review integrates sport-specific evidence with guidance on prediction modelling, human–AI interaction, and AI risk management. It highlights five recurring gaps: limited data and labels; leakage-prone or temporally inappropriate validation; weak reporting of calibration and uncertainty; unclear translation from prediction to action; and insufficient attention to athlete rights and post-deployment monitoring. We propose a six-layer framework covering data and context, prediction, decision translation, individualisation, human oversight, and deployment monitoring. For each applicable layer, the framework specifies evidence to document, appraisal questions, and common failure modes. It distinguishes model development from operational decision support and links technical evaluation with workflow utility, fairness, privacy, accountability, contestability, and lifecycle governance. The framework is conceptual, not a validated scoring or certification instrument. Sport-science AI should be evaluated as a socio-technical intervention rather than an isolated algorithm. Future studies should prioritise athlete-level and temporal validation, calibrated probabilities, external and prospective evaluation, prespecified action pathways, subgroup performance, documented override procedures, and ongoing monitoring. Full article

The effective resolution of non-deterministic polynomial time hard (NP-hard) combinatorial optimization problems requires a delicate balance between global exploration and local exploitation. While Quantum-Inspired Algorithms (QIAs) leverage principles of superposition to explore vast search spaces, they often lack the fine-grained exploitation capabilities of classical heuristics. To address this limitation, we propose the Quantum-Memetic Hybrid Algorithm (QMHA), a component-based framework that synergistically integrates qubit-based global search with adaptive classical refinement. The QMHA architecture explicitly coordinates five distinct algorithmic components: (1) quantum rotation gates for exploration, (2) a problem-aware memetic operator for immediate solution refinement, (3) an adaptive learning rate schedule, (4) periodic local search, and (5) a stagnation-based population reset for diversity management. We rigorously evaluate the framework against nine established metaheuristics, including Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), MAX-MIN Ant System (MMAS), Memetic Algorithms (MA), Quantum Evolutionary Algorithm (QEA), and Harmony Search (HS), across a comprehensive benchmark suite comprising six NP-hard problem families: constrained combinatorial (Knapsack), graph-based (Max-Cut), permutation-based (TSP), constraint satisfaction (Graph Coloring), bin optimization (Bin Packing), and scheduling (Flow Shop Scheduling), as well as real-world machine learning (Feature Selection) problems and the continuous Congress on Evolutionary Computation (CEC) 2022 benchmark. Statistical analysis using Friedman tests and Nemenyi post hoc comparisons confirms that QMHA achieves a statistically significant performance advantage ($p<0.004$) and superior average rank (1.5) compared to component baselines and state-of-the-art competitors. Comprehensive analyses include computational complexity profiling, parameter sensitivity mapping, scalability testing up to $D=2000$, noise robustness evaluation, variable correlation degradation analysis, a six-component ablation study, exploration–exploitation dynamics tracking, integration mechanism comparison across five architectures, and a multi-objective extension feasibility study. The proposed framework offers a robust, verified approach to hybrid optimization without relying on biological metaphors. Full article

The standard 12-lead ECG is the gold-standard tool for cardiac diagnosis and monitoring; however, its multi-electrode configuration limits its use in prolonged monitoring and wearable devices. Existing portable ECG devices typically record only a single lead, reducing their diagnostic value. To overcome this limitation, this study proposes novel Multi-Input Multi-Output neural networks (MIMO-Nets) that reconstruct a standard 12-lead ECG from a single raw ECG lead without requiring any feature extraction techniques. Four deep learning architectures were investigated: MIMO-Conv-Net, MIMO-U-Net, Conv-Net, and U-Net. Each model was trained and evaluated using all 12 standard ECG leads with different length L as candidate inputs to determine the optimal single-lead acquisition strategy. Results show that Lead II with L = 256 consistently provides the highest reconstruction performance across all architectures, making it the most informative single-lead input. Using Lead II as input, MIMO-U-Net yielded the highest reconstruction accuracy on a processed clinical dataset (ST-Petersburg INCART), with an average correlation of 0.864 at L = 256, closely followed by U-Net (0.862). On raw, unprocessed recordings from the PTB-XL database, both models achieved lower but still competitive correlations (0.740 and 0.732, respectively). These findings demonstrate the feasibility of single-lead-to-12-lead ECG reconstruction and highlight the role of signal preprocessing in reconstruction fidelity, supporting the development of wearable ECG systems for continuous cardiac monitoring. Full article