Search Results (452)

The incorporation of Digital Twins (DT) into the healthcare industry marks a revolution in terms of adopting a more proactive and personalized approach towards patient care. The increasing complexity of technological tools employed within the healthcare environment leads to assessing the potential impacts of these digital models in collaboration with AI and IoT for increased efficiency and improved results. In this context, this study offers a systematic review of existing research regarding DTs in the field of healthcare, with specific consideration of hospital applications. An extensive literature search was performed within the Scopus database for peer-reviewed publications during the period from 2021 to 2026. Following a demanding screening process, 70 relevant articles were found that fulfilled the selection criteria. The review shows an emerging trend towards the application of AI-based Digital Twins in the real-time monitoring, predictive maintenance of medical devices, and planning surgeries. The paper analyses several key characteristics of healthcare DTs, including their design and architecture, and the benefits they generate. It also presents the challenges related to data integration and ethics surrounding virtual health models and recommendations for future research. In conclusion, this review demonstrates the revolutionary role of AI- and IoT-enabled Digital Twins in the transformation of hospitals’ infrastructures. This paper summarizes the latest developments and gaps in this field and offers a starting point for further research in this area. Full article

Background: This paper aims to systematically review, based on a set of 125 papers, smart-home automation with IoT from the perspective of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main evidence synthesis was conducted on empirical studies published from 2024 to 2026, with background studies as a supplement. Methods: IEEE Xplore, Scopus, SpringerLink, ACM Digital Library, and other databases were used in a systematic search. There were 81 empirical studies published from January 2024 to March 2026 that were included in the systematic evidence synthesis; 44 additional foundational and background works were added separately to provide a theoretical and contextual base, resulting in a corpus of 125 works. Due to significant methodological variation, results were summarized qualitatively rather than by meta-analysis, employing a structured framework of evidence tiers. Results: Real-world validation was limited across the reviewed corpus; reported savings were typically in the 15–25% range, with the majority of reported savings from simulations, testbeds, and/or short-term evaluations. Based on our review, six major gaps are identified: seamless interoperability, lack of real-world validation, weak security and privacy, generalizability, standardized benchmarks, and user trust. Discussion: Overall, the research roadmap and recommendations are given in an action-oriented, future-looking way for both industry and policymakers. Full article

This systematic review aims to analyze Industry 5.0-enabled smart education in smart city ecosystems. A search was conducted in Scopus, Web of Science, and ERIC using a predefined search string. In total, 4631 records were identified, and 40 peer-reviewed articles published between 2022 and April 2026 were selected for descriptive and thematic analyses. Results indicate that Industry 5.0-based smart education forms an integrated ecosystem that includes human-centered approaches, intelligent technologies, stakeholders’ collaboration, and sustainable governance. The reviewed literature suggests that Industry 5.0-enabled smart education may support educational quality, human-capital development, digital inclusion, and sustainable smart-city development. The main technological enablers are AI, IoT, Digital Twins, immersive technologies, learning analytics, blockchain, and human–AI collaboration. Smart education also faces technological, organizational, ethical, and policy barriers. The review provides an integrated conceptual framework for Industry 5.0, smart education, and smart city ecosystems, and provides recommendations for researchers, educators, educational institutions, policymakers, technology providers, and other stakeholders involved in smart cities to design future-proof educational systems that prepare learners to address the challenges of sustainable development and rapidly changing societies by acquiring the competencies required in these environments. Full article

Background: Internet of Things (IoT) deployments face complex network dependencies and persistent data limitations that constrain machine learning (ML) intrusion detection systems (IDS). Objective: To synthesise peer-reviewed machine learning IDS research for IoT, focusing on dependency-driven failure propagation and chronic data scarcity, positioned against 2024–2025 EU regulatory requirements (NIS2, the Cyber Resilience Act). Eligibility criteria: Peer-reviewed empirical studies proposing or evaluating a machine learning or deep learning IoT intrusion detection method published in English from January 2018 (limited pre-2018 exceptions for seminal works). Information sources: IEEE Xplore, SpringerLink, Elsevier ScienceDirect, Scopus, Web of Science, and Google Scholar, searched on 12 February 2025. Risk of bias: Each candidate was scored against four criteria (objectives clarity, methodological soundness, reproducibility, IoT-security relevance); studies scoring at least 3 out of 4 were retained. Screening and scoring were performed by one reviewer, with a second reviewer independently checking 20 percent of records. Synthesis methods: Narrative thematic synthesis; heterogeneous metrics and incompatible datasets across studies precluded quantitative meta-analysis. Included studies: Of 427 records identified, 52 studies initially met inclusion criteria; a post hoc independently validated reconstruction of individual QA1–QA4 scores subsequently found that six did not meet the threshold or topical eligibility criteria, yielding a final 46-study corpus. Main findings: Generative adversarial networks (GANs) dominate dataset augmentation work, graph-based communication analysis addresses dependency modelling, and methods based on transformers or federated learning emerge from 2023 onward. Certainty of evidence: No formal grading (GRADE) applies to this narrative synthesis. Confidence in the corpus composition is high, following independent QA1–QA4 validation, while confidence in the thematic findings is moderate given single-reviewer screening and judgment-based classification. Conclusions: We identify three recurring gaps: real-time detection under resource constraints, dependency-aware detection, and regulatory compliance. Closing these gaps requires detection methods that treat IoT security as a networked and regulated system rather than an isolated device classification problem. Registration: Open Science Framework, 10.17605/OSF.IO/NMAK4 (registered retrospectively). No external funding supported this review. Full article

Mycotoxins are widespread contaminants in animal feeds and continue to pose serious threats to animal welfare, production efficiency, food security, and sustainable economic growth worldwide. Chickens are highly susceptible to multiple mycotoxins, such as aflatoxins (AFLs), deoxynivalenol (DON), fumonisins (FBs), zearalenone (ZEA), ochratoxin A (OTA), and T-2 toxin. Exposure to these toxins can damage intestinal integrity, compromise immune functions, impair nutrient absorption, and ultimately reduce production efficiency even at subclinical concentrations. Conventional mycotoxin detection methods, such as enzyme-linked immunosorbent assays (ELISA) and chromatographic techniques, provide accurate quantification but remain expensive, labor-intensive, time-consuming, and inefficient for large-scale screening, particularly when masked mycotoxins are present. The frequent co-occurrence of multiple mycotoxins further complicates risk assessment and effective management. Emerging analytical technologies, including hyperspectral imaging, biosensors, Internet of Things (IoT)-based platforms, and machine-learning algorithms, offer promising advancements for rapid detection and predictive risk forecasting. This review summarizes the toxicological impacts of major mycotoxins on poultry, outlines critical challenges in detection and prevention, and evaluates current artificial intelligence (AI) and machine learning (ML) approaches for mycotoxin identification, prediction, and management. A systematic literature search conducted across PubMed, ScienceDirect and Google Scholar identified 176 unique studies published between 2010 and 2026. Key knowledge gaps include limited availability of high-quality datasets and source code, inconsistent model interpretability, and poor reproducibility across production environments. By integrating conventional toxicology with data-driven approaches, this review highlights how predictive modeling can strengthen mycotoxin surveillance and support proactive mitigation strategies in modern poultry production systems. Full article

Early architectural decisions shape building energy demand and life cycle carbon, yet digital support remains fragmented across modelling, simulation and performance workflows. This systematic review examines how digital twin (DT) frameworks, tools and workflows are applied at the architectural design stage to support net-zero building performance. It investigates whether design-stage digital twins function as decision support systems or remain BIM-plus-simulation workflows carrying a twin label. Following PRISMA 2020, Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink and Taylor & Francis Online were searched, supplemented by citation searching. Searches were completed on 28 March 2026 and restricted to English-language, peer-reviewed publications meeting eligibility criteria. Thirty-three studies formed the analysed corpus, comprising 10 Tier 1 core digital twin studies, 15 Tier 2 DT-oriented studies and 8 Tier 3 DT-enabling studies, while 19 review and contextual sources supported framing. Studies were coded by DT conceptualisation, framework type, enabling technology, life cycle stage, net-zero indicator and validation approach. Findings were synthesised descriptively and thematically, and evidence maturity was assessed using a six-domain appraisal covering reporting quality, digital twin completeness, design-stage relevance, validation quality, reproducibility and architect usability. Only 11 studies, representing 33% of the corpus, were anchored in concept or schematic design. BIM, building-performance simulation and parametric or generative modelling were dominant, while IoT and AI or machine learning supported prediction, surrogate modelling and control. Energy was addressed in 26 studies and thermal comfort in 10, whereas embodied carbon, daylight, renewable generation and indoor air quality received limited attention. Eight studies were classified in the ‘Measured/large empirical’ validation class. Methodological heterogeneity and limited empirical validation precluded meta-analysis. Design-stage digital twins remain emerging rather than mature decision support systems. The review was retrospectively registered on the Open Science Framework (DOI: 10.17605/OSF.IO/N5Z8H) and received no external funding. Full article

The convergence of Large Language Models (LLMs) with the Internet of Things (IoT) is driving a transformative shift toward a continuous, context-aware smart healthcare ecosystem. Due to its novelty, existing research in this domain remains fragmented, leaving a critical gap in unified frameworks that synthesize domain applications, functional AI deployment roles, network architectures, and security boundaries. Following PRISMA 2020 guidelines, this paper presents a systematic literature review and quantitative analysis evaluating a selected corpus of 61 peer-reviewed and 14 preprint papers in this domain. Methodologically, we assess a novel hybrid article discovery strategy, finding that an AI-powered prompt-based literature search strategy achieves higher precision than traditional keyword-based Boolean queries (86% vs. 42%) on the evaluated search sample, which may reduce screening workloads. We found that the major limitation of AI-based literature search is non-determinism, which is also an inherent property of LLM-powered applications. To address this, we propose methodological guidelines for using an AI-assisted hybrid literature search strategy. Based on the selected literature, we establish a multi-layer taxonomy organizing the IoT-LLM advances in the healthcare domain across four pillars: application domain, LLM role, IoT device type, and architectural deployment pattern. Quantitative synthesis reveals a heavy research concentration in remote patient monitoring and personal health management (representing 59% of the corpus combined), primarily driven by the data accessibility of wearable sensors (64%). Cross-tabulation uncovers a distinct capability–constraint spectrum: cloud-based deployments lean on heavyweight state-of-the-art models (mainly GPT-family models) for complex semantic reasoning, whereas edge, federated, and blockchain-based hybrid systems leverage localized models (BERT and LLaMA families). Patient data privacy and reduced communication overhead were among the main reasons for choosing localized models. Crucially, our assessment reveals a pervasive neglect of LLM-specific vulnerabilities such as prompt injection and jailbreak attacks and a tendency to treat regulatory frameworks (e.g., HIPAA, GDPR) as design features rather than empirically validated compliance metrics. Finally, we propose an actionable future research agenda prioritizing multi-device system orchestration, emergency care integration, privacy-preserving LLMs, and deployment-scale clinical validation. Full article

Patient recovery prediction and hospital length of stay estimation remain critical challenges in healthcare resource allocation and clinical decision-making. Inaccurate discharge planning drives substantial avoidable hospital costs, while existing machine learning models remain limited by narrow, single-domain data that fail to capture the multimodal complexity of modern smart ward environments. This study proposes IoT-ClinXAI, an explainable multimodal framework that fuses IoT environmental data, wearable physiological signals, and clinical records for accurate and transparent patient recovery prediction in smart hospital wards. A Multi-domain Hierarchical Consensus Feature-Selection method groups features into structured domains, applies Borda–Kemeny weighted consensus within each domain, and reduces cross-domain redundancy while preserving complementary information. A Snake Optimization–tuned Random Forest Regressor optimizes predictive performance through adaptive hyperparameter search, while a multi-scale SHAP framework provides global and patient-level explanations for transparent clinical inference. Experiments were conducted on a real-world IoT-enabled smart ward dataset comprising patient data and recovery duration. The proposed framework achieved strong predictive performance on the held-out test set, with $R^2$ of 0.964, RMSE of 0.476 days, MAE of 0.342 days, and MAPE of 3.13%, yielding a 23.3% RMSE reduction over the unselected baseline and outperforming all feature-selection methods by 10.9–17.6% in RMSE. Statistical superiority was consistently confirmed across all pairwise comparisons using Wilcoxon signed-rank tests with Bonferroni correction ($p<0.001$). SHAP analysis identified ward allocation, respiratory rate, and oxygen saturation as the dominant recovery predictors, while environmental IoT variables showed minimal predictive contribution. These results highlight IoT-ClinXAI as a reliable and useful framework for supporting bed management, discharge planning, and hospital resource optimization in resource-constrained healthcare settings. Full article

The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Despite extensive research on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to provide a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks are the most common types in the literature, whereas Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation. Full article

Healthcare facilities represent some of the most operationally complex and critical built environments, in which building performance directly influences patient safety, clinical outcomes, staff wellbeing and management efficiency. The progressive digitisation of the construction and Facility Management (FM) sectors, driven by Building Information Modelling (BIM), Digital Twin (DT), the Internet of Things (IoT) and Artificial Intelligence (AI), offers transformative potential for the management of hospital infrastructure, predictive maintenance and indoor environmental monitoring. Despite a growing body of scientific literature, existing systematic reviews focus predominantly on clinical or patient-facing Digital Twin applications, leaving the built environment perspective—encompassing architectural systems, technical infrastructure and post-construction lifecycle management—largely unexplored. This Systematic Scoping Review follows the PRISMA-ScR protocol. A structured database search on Scopus yielded 194 records; after title/abstract screening and full-text review, the final corpus, defined here as the final set of included studies, comprises 166 studies (2013–2026; 2026 partial through 21 April) coded through a multidimensional thematic matrix. Results reveal a marked acceleration in publications from 2023 onwards, with BIM (56.6%) and Digital Twin (46.4%) as the dominant technologies and Facility Management as the prevailing domain (69.3%). The principal finding concerns implementation maturity: 52.4% of studies remain at a conceptual level, 38.6% are Prototype/Proof-of-Concept and 9.0% are Pilot/Testbed; no study documents a fully operational hospital deployment. Identified gaps include limited system interoperability, the absence of explicit normative references and insufficient integration of environmental sustainability. Beyond technical and sustainability dimensions, the review highlights a critical gap in the explicit linkage between built environment management and direct health outcomes for occupants: real-time digital monitoring of indoor environmental quality (IEQ) parameters remains the least operationally implemented domain (9.6% of studies), despite its documented effects on patient recovery, infection control and healthcare staff performance. Full article

Physics-informed anomaly detectors on smart city IoT gateways face a conflict that static calibration cannot solve. Higher detection accuracy (F1) costs inference latency and edge energy. The trade-off differs across urban sensor domains. PhySec-Edge showed that hybrid physics-informed edge-AI detection works in industrial IoT. Its calibration is static. Transferred to smart city verticals with different physical constraints, it degrades. EvoPhySec addresses this problem by introducing a multi-objective evolutionary calibration layer that adapts PhySec-Edge to five heterogeneous smart city verticals under simultaneous accuracy, latency, and energy constraints. We treat the Physics Validation Engine (PVE) thresholds, the Edge AI Detection Engine (EADE) ensemble weights, and the decision rule parameters as one three-objective optimization problem. It is solved with NSGA-III plus the Physics-Constraint Preservation Operator (PCPO), which keeps candidate solutions physically feasible during the search. Evaluated on a synthetic multi-domain smart city dataset (n = 47,250 samples, five urban verticals, eleven attack classes, five random seeds) and validated on BATADAL and a CIC-IoT2023-inspired benchmark, EvoPhySec achieves mean F1 = 0.847 ± 0.004 at 34.2 ms latency and 0.71 W edge power, Pareto-dominating the static baseline on all three objectives. The ablation study isolates the PCPO contribution at +0.031 HVI over unconstrained NSGA-III. Cross-domain transfer analysis identifies a three-vertical transferable cluster and confirms that critical infra-structure requires domain-specific calibration. These results are demonstrated on synthetic data; real-world multi-vertical validation remains future work. Full article

Thermal drying is a critical phase in coffee processing, significantly influencing energy consumption, moisture uniformity, storage stability, and sensory quality. Despite the superior throughput of mechanical dryers over open-sun drying, these systems often operate with limited observability and manual control. This systematic review evaluates the readiness of mechanical coffee drying for Digital Twin (DT) integration. A comprehensive search across Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified 22,859 records. Following multi-stage screening, 58 studies were retained for qualitative synthesis, categorized into a primary coffee-drying corpus and a secondary transferable corpus of methods from related food-processing applications. Findings indicate that while DT-enabling components, such as CFD models, drying-kinetics models, IoT monitoring, and non-destructive sensing, are established, they remain fragmented. No fully implemented and operationally validated DT for mechanical coffee drying was identified. Based on the evidence, a hybrid reduced-order physics-based model integrated with constrained supervisory control represents the most defensible near-term architecture. Future research should prioritize standardized datasets, uncertainty-aware soft sensors, and field validation across diverse dryer topologies and operating conditions. Full article

Intelligent methods such as machine learning, deep learning, and related reasoning-based techniques are widely credited with transforming digital forensics, yet it remains unclear where that transformation has actually taken hold. This study presents a systematic mapping review, conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR), that treats the adoption of intelligent methods not as an assumption but as a variable to be measured. Following searches across eight bibliographic databases, 82 primary studies were mapped onto six digital forensics domains and coded by intelligence method type, enabling a domain-by-method analysis of the field. The results reveal markedly uneven penetration. Intelligent methods dominate multimedia forensics (93% of included studies) and mobile and IoT forensics (77%), yet remain the exception in frameworks and governance (33%), imaging and acquisition (28%), memory forensics (25%), and data reduction (21%), where classical, deterministic techniques still prevail. Resolved onto a five-level maturity ladder, the domains differ not only in how much intelligence they have adopted but in its kind: some reach deep learning and explainable reasoning while others advance only to automation, and several skip intermediate stages entirely. These findings recast intelligence in digital forensics as a set of unevenly developed capabilities rather than a uniform pipeline, and identify where learning-based research has the furthest still to travel. Full article

Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffic samples. A previously published cross-layer federated detector, Baseline K-Means, showed that periodically merging worker statistics through a coordinator raises the detection rate, but it also exhibited a systematic side effect: after every merge, the precision decays, and the false-positive rate (FPR) climbs because the coordinator recomputes its threshold from streaming distances filtered by the closest observed anomaly, so tightens after every merge, flagging progressively more benign traffic; the threshold was also hand-tuned. We present AF-BKM, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, denoted as E1 and E2: (i) an adaptive decision threshold read from the benign Mahalanobis-distance distribution, requiring no manual percentile search and no attack labels (E1), and (ii) a robust, benignly anchored aggregation that blends worker means under quality weighting and outlier-worker filtering and recalibrates the threshold on a trusted benign anchor to a stable, anchor-referenced false-positive level, which a target-FPR rule can make operator-selectable instead of tightening it toward the nearest anomaly (E2). With MinMax scaling fit only on benign baseline data and non-IID federated streams on NSL-KDD, UNSW-NB15 and the N-BaIoT corpus of real traffic from commercial IoT devices, AF-BKM removes the merge-induced precision decay (the first-to-last-epoch precision change improves from $-0.134$ to $-0.002$ on NSL-KDD, from $-0.121$ to $-0.014$ on UNSW-NB15, and from $-0.170$ to $-0.009$ on N-BaIoT) and reduces the mean FPR by 30–$64\%$, depending on the dataset; all central improvements are significant across 10 seeds (Wilcoxon $p=0.002$, large effect sizes). AF-BKM preserves recall on NSL-KDD and N-BaIoT and, on the harder UNSW-NB15, exposes an explicit precision–recall trade-off through a benign target-FPR knob. In fp32, the deployed model serializes to $5.5$–52 KB, a packet is classified in 11–27 µs on a desktop CPU, and each merge round uploads a $d+3$-value summary (160–472 B) $94.6$–$98.3\%$ smaller than the same summary extended with the covariance upper triangle. A robustness study covering selected faulty-worker updates, contamination of the commissioning anchor, and detector-level white-box evasion reports the measured degradation patterns: fabricated threshold candidates have no direct path to the threshold, although a fabricated mean still reaches it indirectly through the blended centroid, and the anchor-referenced false-positive level remains stable under percent-level anchor contamination, while recall sensitivity is dataset-dependent and the evasion budget tracks the benign–attack margin of each dataset. We frame the contribution with a focused taxonomy that identifies merge-induced precision decay under non-IID workers as an open gap. Code is released for reproducibility. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article