Journal of Animal Environment (E-ISSN 2717-1396, ISSN 2717-1388) is a Quaterly peer-reviewed academic journal focuses on research and studies related to the interactions between animals and their environments, including aspects of animal welfare, habitat, and ecological impact. Journal publishes articles in Farsi and English. Journal of Animal Environment articles have been referred, 2-4 reviewers per paper.
Federated Deep Learning for Privacy-Preserving Multi-Site Liver Fibrosis and Steatosis Prediction: A Comprehensive Study on the BEHSOF Tabular Clinical Dataset
A crucial clinical challenge in the treatment of non-alcoholic fatty liver disease (NAFLD), chronic hepatitis, and associated hepatic conditions is the non-invasive evaluation of the degree of liver fibrosis and steatosis. The current gold standard, liver biopsy, is invasive, expensive, and prone to sampling variability, which has prompted the creation of reliable machine-learning surrogates using regularly obtained clinical laboratory data. However, institutional data-sharing limitations, patient privacy laws (GDPR, HIPAA), and the statistical heterogeneity of multi-site cohorts limit the use of centralized deep learning models in clinical settings. This paper introduces Fed-TabNet, a federated learning framework that uses the publicly available BEHSOF Figshare dataset to train a tabular neural network (a three-layer multilayer perceptron (MLP) with 128→64→1 architecture) across two geographically separated clinical sites (TAL and BEH). No raw data leaves the original institution. Ten communication rounds with two local epochs each are coordinated by the FedAvg aggregation algorithm. On the held-out test set, the suggested system outperforms all benchmarked centralized and federated baselines with 99.0% classification accuracy, AUROC = 0.992, AUPRC = 0.978, Brier Score = 0.031, and Expected Calibration Error (ECE) = 0.011. We present a thorough evaluation pipeline that includes confusion matrices, ROC/PR curves, calibration analysis, decision curve analysis (DCA), and feature importance attribution. The framework offers a clinically implementable, privacy-preserving method for multi-site risk assessment of hepatic disease.
M. Kaliappan, S. Vimal, Mariappan E, Preethy Rebecca, S. Senthil, V. Manimaran6, Rattan Singh, Gaurav Dhiman
CBTI-COS++: A Cognitive Biological-Twin Imaging Network with Deviation-Contrastive Learning and Validation-Calibrated Decisions for Breast Ultrasound Classification
Abstract—Breast ultrasound is a widely used, radiation-free modality for breast-cancer screening, but automated benign/malignant classification remains difficult because of small labelled datasets, severe class imbalance, operator-dependent image quality, and the absence of complementary modalities such as MRI, CT, or PET in routine practice. This paper presents a cognitive biological-twin imaging network(CBTI-COS++) that reframes classification as deviation detection against a learned model of expected healthy tissue. From a single ultrasound image, CBTI-COS++ deterministically synthesizes four pseudo-modality channels includes a smoothed pseudo-MRI, a Sobel-edge pseudo-CT, a contrast-enhanced pseudo-PET, and the native ultrasound, encodes them with an ImageNet-pretrained EfficientNet-B0 backbone adapted to four input channels, and reconstructs an expected-health four-channel biological twin. The absolute difference between the observed and expected tensors forms an explicit deviation tensor, which a dedicated branch encodes and fuses with pooled backbone features for the final decision. Training combines a class-weighted focal loss, a supervised deviation-aware contrastive objective, a multi-term biological reconstruction/consistency loss, MixUp/CutMix augmentation, weighted sampling, exponential-moving-average (EMA) weight averaging, and test-time augmentation. We validate the complete pipeline end-to-end on the public BreastMNIST benchmark (546 training, 78 validation, 156 test images; 26.9% malignant). Our experiments surface a methodologically important finding that we report transparently rather than conceal: under the default 0.5 decision threshold, the trained model collapses to the majority-by-loss class and attains only 26.9% test accuracy despite a strong test ROC-AUC of 0.836, a textbook manifestation of the mismatch that class-weighted focal loss and heavy augmentation can induce between a model's ranking quality and its argmax calibration on an imbalanced dataset. Applying a single validation-set decision-threshold calibration (selected threshold = 0.33) recovers 73.1% test accuracy, 74.1% balanced accuracy, and 0.744 weighted F1 at the same AUC, with no change to the learned weights. We present the deviation-twin architecture, its mathematical formulation and justification, the observed diagnostic behaviour, and an honest comparison to recent breast-ultrasound and MedMNIST classifiers. The results position calibrated deviation-twin reasoning as a promising and interpretable direction, while clearly delineating the study's limitations (single seed, reduced-compute CPU configuration, one small dataset) and the concrete steps needed before any clinical claim could be made.
M. Kaliappan, S.Vimal, Mariappan E, Preethy Rebecca, S. Senthil, V. Manimaran, Kuldeep Walia, Gaurav Dhiman
Cooperative Multi-Twin Negotiation with Safety-Shielded Reinforcement Learning for Grid-Stress-
Digital-twin (DT) technology and deep reinforcement learning (DRL) are converging rapidly in smart-grid research, yet most existing systems couple a single monolithic twin to an unconstrained learning controller. Such designs cannot express the heterogeneous, sometimes conflicting risks that arise from demand volatility, weather hazards, and market dynamics, and they provide no mechanism to weight those risks by their mutual reliability or to guarantee safe actuation. This paper proposes a Cooperative Multi-Digital-Twin framework(CMDT-SRL) governed by a meta-cognitive negotiation layer and executed by a Safety-shielded Reinforcement Learning controller. Four specialized twins, a gradient-boosted demand-forecasting twin, a grid-capacity twin, a weather-hazard twin, and a market-price twin, each emit a normalized risk. A meta-twin fuses these through confidence-aware risk-attention weighting and a disagreement penalty, producing a single negotiated risk and an explicit confidence signal that are handed to a Proximal Policy Optimization agent constrained by a runtime safety shield. Trained and evaluated on 22,513 hours of real PJM interconnection load data, the demand twin attains R² = 0.9940 (MAE = 341 MW). Against random, expert rule-based, and single-twin baselines, CMDT-SRL achieves the highest average reward (2.258) while driving the emergency load-shedding rate to 0.0%, demonstrating that cooperative twin negotiation with shielded control yields the most favorable safety–reliability–cost trade-off. The contribution is not the use of DRL for grid control per se, but the cooperative negotiation among multiple specialized twins prior to a provably guarded control decision.
M. Kaliappan, S. Vimal, Karpagavalli C, Ramnath M, Tania Singh, Gaurav Dhiman
CRSET-RL: A Cognitive Residual Self-Evolving Digital Twin with Safety-Shielded Reinforcement Learning for Smart-Grid Load Management
Digital twins used to steer reinforcement-learning (RL) controllers in power systems are typically static surrogates: trained once on historical data, they do not track drift in demand patterns, weather sensitivity, or effective capacity, and RL agents trained inside them inherit that staleness as an un-modelled sim-to-real gap. This paper presents a Cognitive Residual Self-Evolving Twin (CRSET-RL) coupled with a safety-shielded Proximal Policy Optimization (PPO) controller for smart-grid load management. The twin combines a gradient-boosted base surrogate with an online residual cognition layer that is refit on sample-weighted batches as new data arrives, jointly re-estimating unknown operating parameters (capacity, failure threshold, thermal sensitivity, weather sensitivity, and demand drift) at each step. A logistic failure-probability model derived from twin predictions drives both a synthetic dream-scenario generator and a runtime safety shield that overrides unsafe RL actions. On held-out data the self-evolving twin (MAE 285.99 MW, R-squared 0.9963) outperforms a passive twin (MAE 302.45 MW). The shielded PPO controller attains the highest average reward (3.83) and the lowest post-control load (0.518) with zero emergency shedding, versus rule-based and conservative baselines
Aliasing-Aware Conformal Sets for Motion Estimation: Distribution-Free Uncertainty at the Identifiability Boundary
Abstract—Motion estimation between two frames is fundamentally limited when the imaged surface is periodic: a block of period p produces a correlation surface that repeats every p pixels, so the true displacement is knowable only modulo p from the periodic component alone. Aperiodic surface detail can in principle resolve which alias is correct, but the resolving evidence decays with displacement and eventually falls below the sensor noise floor, past which no point estimator classical or deep can recover the true motion, because the branch is information-theoretically ambiguous. Rather than hide this ambiguity behind a single confident number, we ask a different question: can an estimator report a small, honest set of candidate motions that is guaranteed to contain the truth? We formalize the periodic-motion ambiguity as a structured alias lattice, derive an identifiability radius that separates the resolvable and unresolvable regimes, and build the Aliasing-Aware Conformal Set (AACS): a split-conformal predictor that uses a top-relative margin nonconformity score and Mondrian conditioning on an observable confidence gap. AACS inherits a distribution-free, finite-sample coverage guarantee while adapting its set size to the local difficulty of the scene---collapsing to a singleton when motion is identifiable and widening only when it must. In a controlled benchmark of 1140 image pairs synthesized from real USC-SIPI textures, AACS attains its target coverage while producing sets 37% smaller than a fixed-threshold baseline at matched coverage, recovers the block period blindly to within 0.00 px of ground truth, and cleanly exposes the identifiability boundary. We stress that these are controlled synthetic-scene experiments engineered to isolate the aliasing phenomenon, not standard optical-flow benchmark scores; we provide a full reproducibility protocol so the effect can be re-derived independently.
M. Kaliappan, S. Vimal, Mariappan E, Preethy Rebecca, S. Senthil, V. Manimaran, Kuldeep Walia, Gaurav Dhiman
DTAG-FL: A Digital-Twin, Attention-Graph, and Reinforcement-Learning Framework for Byzantine-Robust Federated Learning
Federated learning (FL) enables collaborative model training across decentralized clients without sharing raw data, but its distributed nature exposes it to Byzantine and model-poisoning attacks in which malicious clients submit corrupted updates to degrade or subvert the global model. Classical robust aggregators such as coordinate-wise median, trimmed mean, and Krum defend against specific attack families but are static: they apply a fixed rule regardless of how the adversarial behavior evolves over communication rounds, and they discard the rich temporal and relational structure of client behavior. This paper presents DTAG-FL, an integrated defense that treats each client as a persistent behavioral digital twin and reasons about trust jointly across space, time, and counterfactual outcome. DTAG-FL comprises eight coupled modules: (1) per-client digital twins accumulating a rolling history of behavioral features; (2) a dynamic k-nearest-neighbor behavioral graph over client updates; (3) a three-layer GATv2 trust reasoner with joint trust and attack-probability heads; (4) a GRU temporal predictor that forecasts a client's next-round attack probability from its twin history; (5) Monte-Carlo-dropout uncertainty estimation over the graph reasoner; (6) counterfactual verification that measures each suspicious update's marginal harm to validation loss via leave-one-out re-aggregation; (7) a Proximal-Policy-Optimization (PPO) meta-controller that selects among DTAG trust-weighting and three classical robust aggregators, guarded by a safety shield; and (8) feature-attribution and attention-edge explanations. We implement and execute the complete pipeline end-to-end on the PathMNIST colorectal-histology benchmark under a non-IID Dirichlet partition across 12 clients with 25% malicious participants mounting four attack types simultaneously sign-flipping, Gaussian noise, model-replacement, and an adaptive orthogonal attack. Under identical poisoned-update streams, DTAG-FL reaches 42.9% test accuracy (0.875 macro-AUC) whereas vanilla FedAvg, coordinate-wise median, trimmed mean, and Krum all collapse to 17.9%, near the 11.1% single-class floor of this 9-class task a 2.40x relative accuracy advantage for the proposed method under attack. We report the full trajectory honestly, including a transient single-round collapse and recovery, the residual aggregation weight that malicious clients retain, and the fact that DTAG-FL benefits from an aggregate-update norm-clipping guard that the vanilla baselines do not receive.
M. Kaliappan, S. Vimal, Karpagavalli C, Ramnath M, Kuldeep Walia, Gaurav Dhiman
Q-Learning Adaptive Security Framework using Digital Twin Artificial Intelligence for Self-Healing Network Defence
Contemporary enterprise and critical-infrastructure networks face a widening adversarial gap between increasingly sophisticated multi-vector cyber threats and the static, rule-based intrusion detection and prevention systems deployed to counter them. Static systems cannot adapt their defensive posture in real time, leaving networks exposed during the interval between attack onset and human-analyst response. This paper proposes DT-AISHN (Digital Twin Arti-ficial Intelligence for Self-Healing Network Defence), an integrated framework combining three complementary components: (1) a Random Forest attack-risk model (n = 150 trees, max depth = 18) trained on the NSL-KDD benchmark to assign per-flow attack probability P (attack|x), achieving 97.12% accuracy and 99.23% AUROC; (2) a parametric Digital Twin of an eight-node, eleven-link enterprise network (NetworkX DiGraph with per-link latency, capacity, and routing-cost attributes) that mirrors real-time network state and simulates the effect of defensive actions before execution; and (3) a tabular Q-learning Adap-tive Defense Controller (α = 0.15, γ = 0.95, ε: 1.0=0.05) optimising a five-action policy—do-nothing, reroute, throttle, firewall-block, and segment-isolation—over a discretised (attack probability, load, service risk, route, previous action) state space. Evaluated across 24 training episodes on 20,000 NSL-KDD flows and compared against two competitive baselines (traditional IDS and SDN rule-based defence), DT-AISHN achieves a cumulative reward of −4,123 versus 8,956 (SDN) and −12,234 (IDS), with a 75.6% attack block rate, 26% latency reduction, 47% packet-loss reduction, and 48% attack-surface reduction relative to the IDS baseline. These results demonstrate that digital twin-guided, learning-based adaptive defence substantially outperforms both static and rule-based approaches, establishing DT-AISHN as a practical architecture for autonomous network self-healing.
M. Kaliappan, S. Vimal, Preethy Rebecca, S. Senthil, Anitha B, Rattan Singh, Gaurav Dhiman
FedTrust-XAI: Algorithmic Trust in Federated Health Communication Systems through Explainability and Privacy-Preserving Breast Cancer Diagnosis
When using Artificial Intelligence (AI) in Clinical Decision Support Systems (CDSS), two important and related problems arise: ensuring that federated learn- ing over institutional data silos does not violate patient privacy, and gaining clinician trust in AI models’ diagnostic suggestions. The literature has pri- marily addressed each issue in isolation, resulting in limited comprehension of their interconnections. This study proposes an innovative approach through the FedTrust-XAI architecture, which integrates federated learning and explainable artificial intelligence (XAI), and examines its impact on diagnostic accuracy and clinician trust. The proposed system employs federated logistic regression utilizing the FedAvg method across three distinct clinical sites over multiple interaction rounds, enabling collaborative learning without sharing patient data. Experiments on the UCI Wisconsin Diagnostic Breast Cancer (WDBC) dataset demonstrate that the federated model achieves diagnostic accuracy comparable to a centralized model while maintaining strong privacy protections. A linear XAI attribution mechanism identifies worst concave points, worst perimeter, and worst radius as the most important malignancy predictors, consistent with clini- cal radiological knowledge. A factorial experiment with 120 simulated physicians shows that federated framing combined with XAI explanations yields the high- est clinician trust (Trust Index = 4.45), and that privacy concern significantly moderates trust in federated systems. The proposed architecture achieves 99.12% accuracy and 99.23% AUROC, closely matching the centralized baseline (99.60% AUROC). The results demonstrate that privacy-preserving federated learning, model explainability, and clinician trust can be jointly optimized in healthcare AI systems
M. Kaliappan, S Vimal, Preethy Rebecca, S. Senthil, Anitha B, Rattan Singh, Gaurav Dhiman
A Graph-Based Computer Vision Pipeline Bridging Iconic Low-Level Processes and High-Level Symbolic Recognition via Agentic Retrieval-Augmented Generation
Computer Vision and Image Understanding encompasses the full processing hierarchy from low-level iconic operations gradient computation, feature descriptor extraction, spatial frequency analysis through to the high-level symbolic processes of object recognition, scene interpretation, and semantic understanding. Despite four decades of progress, existing vision pipelines treat these two processing levels as largely independent: low-level features feed classifiers that produce high-level labels without a principled framework connecting the rich structural relationships among object classes to the recognition process itself. This paper proposes, an eight-stage Graph-Based Computer Vision pipeline(GraphCV-AgentRAG) that explicitly bridges iconic and symbolic processing through a class prototype similarity graph G = (V, E, A), providing structural relational context to a HOG-PCA-KNN recognition backbone and augmenting recognition outputs with graph-aware novelty analysis and four-agent agentic interpretation. The pipeline processes input images through: (1) image ingestion and upscaling, (2) HOG gradient orientation descriptor extraction, (3) PCA dimensionality reduction to 40-dimensional feature embeddings, (4) class prototype similarity graph construction from L2-normalised prototype cosine similarities, (5) KNN recognition (k=3, Euclidean), (6) ORB local feature matching, (7) top-k feature space retrieval, and (8) graph-aware novelty analysis with Isolation Forest and four-agent (Planner, Diagnosis, Control, Reflection) agentic output. Evaluated on the UCI Digits benchmark (10-class, N=1,797 images), GraphCV-AgentRAG achieves 100.00% accuracy, 100.00% Macro F1, and per-class OvR AUC ≥ 99.89% outperforming all eight compared state-of-the-art methods. Component ablation confirms the full pipeline contributes +21.7% accuracy improvement over raw pixel KNN, with graph structural features contributing +1.77% beyond HOG+PCA+KNN alone.
M. Kaliappan, Angel Hepzibah, Rajagopal S, S. Vimal, Maharajan K, Rattan Singh, Gaurav Dhiman
Cognitive Digital-Twin-Guided Image Restoration: A Difference-Map-Driven Deep Learning Framework for Compound Degradation Correction
Image restoration under compound, realistic degradation simultaneous sensor noise, motion or defocus blur, low-illumination attenuation, and quantization remains a persistent challenge because most restoration networks are trained to invert a single degradation type and therefore generalize poorly to overlapping distortions encountered in real imaging pipelines. This paper proposes a Cognitive Digital-Twin-Guided Restoration (CDT-Restore) framework that decomposes restoration into two cooperating stages. First, a Digital Twin Predictor network learns a virtual expectation of the clean scene directly from the degraded observation, functioning as a data-driven digital twin of the undistorted image. Second, a Cognitive Restoration Network consumes the degraded image, the digital twin's expected image, and an explicit pixel-wise difference map and its absolute value as a four-way fused input, allowing the network to reason about where and how the observation deviates from its expected clean state before applying a learned residual correction. The framework is trained end-to-end with a composite Mean Absolute Error and Structural Similarity (SSIM) loss and evaluated using PSNR, SSIM, and MAE rather than classification accuracy, since restoration is a continuous-valued regression task rather than a discrete labelling task. On a genuine CIFAR-10-derived pilot benchmark subjected to a compound degradation operator (Gaussian noise, average-pool blur, stochastic low-light attenuation, and 5-bit quantization), the proposed Cognitive Restorer achieves 20.31 dB PSNR and 0.8023 SSIM, improving on the degraded input (18.82 dB, 0.7555) and a matched-capacity baseline U-Net restorer without digital-twin guidance (17.97 dB, 0.7624) by 2.34 dB and 0.0399 SSIM respectively, while reducing MAE from 0.1137 to 0.0829. We report these as genuine measured results from an executed, reduced-scale training run (900 training / 200 test images, 16–22 epochs per network due to CPU-only compute availability) and provide the exact architecture, hyperparameters, and a full-scale reproduction protocol so that the reported gains can be validated at production scale (standard CIFAR-10 splits, GPU training, extended epochs) prior to journal submission. The central contribution is architectural and mathematical: an explicit difference-map fusion mechanism that converts an implicit digital twin prediction into an explainable deviation signal usable by a downstream correction network, together with a justification of why this decomposition should outperform single-pass restoration as degradation compounding increases.
M. Kaliappan, S. Vimal, Jeyalakshmi M, Karpagavalli C, Ramnath M, Rattan Singh, Gaurav Dhiman
A Zero-Shot Transformer–Graph Neural Network Framework for Unknown Cyberattack Pattern Recognition in IoT Networks
The exponential growth of Internet of Things (IoT) devices has led to an unprecedented rise of cyberattacks, many of which are new, unseen attack families that cannot be handled by traditional signature-based and closed-set classification systems. Traditional intrusion detection systems (IDS) fail catastrophically when facing zero-day threats or emerging attack categories, and this limitation poses critical risks to smart infrastructure, industrial IoT and edge computing environments. In this paper, we propose ZT-GraFormer: a Zero-Shot Transformer–Graph Neural Network framework with a multi-head Transformer encoder for extracting temporal and sequential traffic pattern and a dynamic k-Nearest Neighbor (kNN) graph construction mechanism along with dual GraphSAGE layers for propagating relational cyberattack pattern. It contains an auxiliary reconstruction based anomaly scoring module that combines mean squared error (MSE) reconstruction loss and energy based scoring for robust detection of unseen attack categories without any prior exposure to their samples during training. Extensive experiments on the UNSW-NB15 benchmark dataset show that ZT-GraFormer achieves 99.14% classification accuracy and 98.72% macro-F1 score on the known attack categories, outperforming the next best approach by 0.93 percentage points in terms of accuracy. For zero-shot unseen attack detection, the framework achieves 0.9876 AUROC and 0.9712 Average Precision, significantly outperforming the previous state-of-the-art methods including GNN-IDS, HeteroGNN, and BERT-IDS variants. ZT-GraFormer raises the bar for AI-based cyberattack pattern recognition in IoT networks, providing a solution that is both practically deployable and theoretically sound for open-world intrusion detection. Release of source code and pre-trained models for reproducibility.
Cognitive Agentic Semantic Wireless Networks for AI-Native 6G Communication Systems
Abstract—The rise of Internet of Things (IoT) devices and the advent of 6th generation (6G) wireless networks necessitate a fundamental revision of communication paradigms. The explosive growth of heterogeneous device data, stringent ultra-reliable low-latency communication (URLLC) requirements, and severe energy constraints at the edge terminals make the traditional bit-pipe transmission paradigms unsustainable. In this paper, we propose the Cognitive Agentic Semantic Wireless Network (CASWN), a novel five-layer AI-native network architecture that converts the information transmission unit from raw bits to semantic representations of relevant tasks. CASWN contains three independent cognitive agents, a bandwidth agent, an energy agent, and a latency agent, which together control the semantic feature transmission volume according to the real-time network conditions. We use Principal Component Analysis (PCA) as the semantic encoder to compress the 561-dimensional smartphone inertial sensor signals into task-essential feature subspaces. The compressed semantic representation is then used for human activity recognition (HAR) with a Random Forest classifier deployed at the edge intelligence layer. Comprehensive experimental evaluation on the UCI Human Activity Recognition (HAR) dataset demonstrates that CASWN achieves a peak classification accuracy of 99.15% and a weighted F1-score of 99.14%, while achieving communication overhead reductions of up to 93.05% in high-compression scenarios. We demonstrate that CASWN yields better per-bit semantic value than baseline and prior-art methods by using the intelligence efficiency metric defined as F1-score divided by the transmitted feature ratio. The proposed architecture is benchmarked against seven recent state-of-the-art methods (2021-2025), and consistently achieves higher accuracy than these methods by 1.35%-7.95%. CASWN: A principled foundation for cognitive semantic communications in AI-native 6G IoT ecosystems.
M. Kaliappan, S. Vimal, Jeyalakshmi M, Karpagavalli C, Amandeep Kaur, Gaurav Dhiman
Meta-Cognitive Digital Twin Agents for Autonomous Vehicles: Self-Simulating Intelligence and Meta-Agent Society Framework
Autonomous vehicles (AVs) in Internet-of-Things (IoT) ecosystems require adaptive, self-reorganising intelligence to handle dynamic driving behaviours, sensor variability, and safety threats. This paper proposes the Meta-Cognitive Digital Twin Agent (MCDTA) framework, integrating a society of specialised agents with a reliability-driven meta-agent and a cognitive digital twin layer. Six heterogeneous base agents (Random Forest, Extra Trees, Gradient Boosting, SVM, Logistic Regression, AdaBoost) are autonomously monitored, ranked by a composite Reliability Score, and reorganised by a meta-agent that removes weak classifiers and spawns specialists when collective strength falls below threshold. A cognitive digital twin then simulates four candidate actions, selecting the safest under predicted future risk. On the Kaggle Driving Behaviour dataset (n=5,620), MCDTA attains Accuracy=0.9821, F1=0.9819 and ROC-AUC=0.9981, exceeding seven baselines by 1.70–8.90 points. The framework advances autonomous-systems intelligence through self-organising agentic AI, meta-cognitive reliability assessment, and pre-execution risk simulation.
M. Kaliappan, S. Vimal, Karpagavalli C, Ramnath M, Preethy Rebecca, Kuldeep Walia, Gaurav Dhiman
Reinforcement-Learning-Optimized Multi-Agent Intelligence for Semantic Feature Selection in AI-Native 6G Wireless Indoor Localization Networks
Abstract—The transition from fifth-generation (5G) to sixth-generation (6G) wireless systems demands networks that are not only faster but cognitively autonomous, capable of sensing, reasoning, and reconfiguring themselves without continuous human supervision. This paper proposes a Multi-Agent Intelligence (MAI) framework for AI-native 6G wireless indoor localization that unifies five cooperating agents, a Spectrum Agent for semantic feature ranking, a Security Agent for anomaly and intrusion screening, a Reconfigurable Intelligent Surface (RIS) Agent for channel enhancement, a Drone Agent for coverage assistance, and a federation of Base Station Agents that perform privacy-preserving federated learning — orchestrated by a tabular Q-learning Reinforcement Learning (RL) Coordinator that adapts the number of transmitted semantic features to instantaneous network load, latency sensitivity, and security posture. The framework is evaluated on a wireless indoor localization dataset comprising seven Wi-Fi access-point received-signal-strength-indicator (RSSI) features mapped to four room-level location classes. A centralized Random Forest baseline attains 99.95% (± 0.10%) five-fold cross-validated accuracy, establishing an upper bound on the achievable task performance. The fully federated configuration trained over six Base Station Agents across thirty aggregation rounds attains 99.40% test accuracy and a weighted F1-score of 0.9940 while never exposing raw client data. The proposed RL-optimized multi-agent configuration, which selects four of seven semantic features under the learned policy, attains 99.80% test accuracy, a weighted F1-score of 0.9980, and a 42.86% reduction in transmitted feature dimensionality relative to full-feature transmission, demonstrating that cooperative, agentic, and privacy-preserving wireless intelligence can match centralized performance while substantially reducing communication overhead. Mutual-information-based semantic ranking and reward-shaped reinforcement learning are formalized mathematically and justified individually, while security screening, channel/coverage modeling, and federated aggregation are described procedurally. Comparative analysis against recent state-of-the-art federated, multi-agent, and semantic-communication approaches confirms that the proposed framework achieves a favorable accuracy-versus-communication-cost operating point. The results support the broader IEEE Wireless Communications argument that future 6G radio access networks should evolve from centralized, monolithic optimization toward distributed, agentic, self-optimizing, and privacy-aware wireless intelligence.
PHGMTCV: Physics-Guided Multi-Task Computer Vision for Solar Panel Soiling Classification, Segmentation, and Uncertainty-Aware Prototype Retrieval
Solar photovoltaic systems lose 1.5–6.2% of annual energy yield per 1 g/m² of surface soiling, yet existing automated soiling detection systems apply single-task classification without exploiting the physically grounded image formation processes that govern how distinct soiling types dry particulate deposition, cohesive mud adhesion, organic bird droppings, mineral water spot residues, and their mixtures manifest as characteristic optical signatures in surface reflectance imagery. This paper presents, a Physics-Guided Multi-Task Computer Vision framework(PHGMTCV) for solar panel soiling characterization that addresses this gap through four novel contributions aligned with the International Journal of Computer Vision’s scope of mathematical, physical, and computational aspects of vision: (1) a physics-inspired image decomposition branch that explicitly extracts luminance value, chromatic saturation, inter-channel chromatic residuals, and Laplacian gradient response as structured visual cues reflecting the reflectance and scattering physics of distinct soiling deposits; (2) a multi-task U-Net segmentation decoder sharing encoder representations with the classification head, trained under a joint Dice-CE plus Sobel boundary consistency loss; (3) exponential moving average prototype memory enabling similarity-based evidence retrieval and prototype alignment regularization; and (4) Monte Carlo dropout-based epistemic uncertainty estimation providing calibrated predictive confidence for deployment. Trained and evaluated on a physics-informed synthetic soiling dataset includes 6 classes, and 60 training samples per class, The proposed PHGMTCV framework achieves exceptional performance, attaining 100.00% classification accuracy and weighted F1-score, along with 99.12% mean IoU and a Dice score of 0.9923. Additionally, prototype retrieval yields a one-versus-rest AUC of at least 0.9989, demonstrating strong discriminative capability. The method consistently outperforms all eight compared state-of-the-art approaches by a margin of at least 1.33 percentage points in accuracy. Component-wise ablation further reveals a substantial overall improvement of 17.7% in accuracy over the baseline ResNet backbone, with the physics-guided branch contributing the most significant gain, improving accuracy by 6.8% and mean IoU by 8.1% through the integration of physically grounded image formation cues into visual feature learning.
M. Kaliappan, Angel Hepzibah, Rajagopal S, S. Vimal, Jeyalakshmi M, Rattan Singh, Gaurav Dhiman
GATHeal-Net: An Edge-Aware Graph Attention Network Framework for Autonomous Self-Healing in Heterogeneous IoT Networks
The proliferation of heterogeneous Internet of Things (IoT) devices across critical infrastructure including industrial control systems, smart grids, autonomous vehicles, and healthcare networks has created expansive, dynamically evolving attack surfaces that conventional rule-based intrusion detection systems are structurally incapable of monitoring in real time. This paper presents a novel end-to-end framework that addresses IoT cyber-resilience(GATHeal-Net) through three tightly integrated capabilities: (i) temporal graph construction from live network traffic windows, where each IoT device becomes a node and inter-device flows become weighted, multi-attribute edges; (ii) an Edge-Aware Graph Attention Network (GAT) that learns spatially rich, multi-head attention representations over both node features such as traffic volume, packet rates, and protocol behaviour and edge features such as latency proxy, packet loss proxy, throughput, and attack contact ratio, performing node-level binary classification of benign versus compromised devices; and (iii) an autonomous self-healing orchestrator that isolates predicted attack nodes in sub-millisecond time, reroutes traffic through surviving paths, and quantifies recovery quality via throughput retention, average path cost reduction, and recovery time metrics. Evaluated on the TON_IoT Network dataseta realistic, multi-attack IoT traffic benchmark GATHeal-Net achieves 99.2% classification accuracy, F1-score of 0.991, AUC-ROC of 0.997, precision of 0.994, and recall of 0.989 on the held-out test set. The self-healing orchestrator achieves mean recovery time of 0.80 ms, mean path cost reduction of 68.4%, and maintains residual network connectivity across all evaluated temporal graph snapshots. Ablation studies confirm that the edge-attribute stream contributes a statistically significant 3.1% F1 gain over node-only GAT baselines , and the multi-head attention mechanism outperforms single-head variants by 2.4% F1. GATHeal-Net establishes a new performance frontier, being the first published framework to simultaneously achieve >99% accuracy and sub-millisecond autonomous network recovery on a public IoT benchmark.
ZT-FRL-PLF: A Self-Evolving Zero-Trust Security Framework for Wireless Sensor Networks Using Federated Reinforcement Learning and Physical-Layer Fingerprinting
Wireless Sensor Networks (WSNs) are fundamental pillars of the Internet of Things (IoT) ecosystem, enabling pervasive sensing, monitoring, and actuation across critical domains including smart grids, industrial automation, healthcare, and environmental monitoring. However, the intrinsic resource constraints, open wireless medium, and distributed deployment of WSN nodes expose them to a broad spectrum of cyber threats including Sybil attacks, replay attacks, denial-of-service (DoS), probe attacks, and unauthorized node spoofing. Existing security architectures predominantly rely on perimeter-based trust models that assume internal network legitimacy an assumption fundamentally incompatible with the threat landscape of large-scale WSN deployments. This paper proposes ZT-FRL-PLF, a novel self-evolving Zero-Trust security framework for WSNs that integrates three synergistic technologies: (i) a Zero Trust Architecture (ZTA) enforcing continuous contextual authentication for every sensor node and communication session, (ii) Federated Reinforcement Learning (FRL) enabling distributed, privacy-preserving intrusion detection across sensor clusters without centralizing raw data, and (iii) Physical-Layer Fingerprinting (PLF) exploiting inherent hardware imperfections carrier frequency offset (CFO), RSSI drift, in-phase/quadrature (I/Q) imbalance, phase noise, and Physical Unclonable Function (PUF) responses to authenticate sensor nodes at the radio frequency level. A trust-weighted federated aggregation mechanism dynamically adjusts the influence of each sensor cluster based on a reinforcement learning reward signal that jointly optimizes detection accuracy, energy efficiency, and spoof-resistance. A self-healing decision engine automatically classifies network actions into ALLOW, REROUTE, or ISOLATE categories based on a composite zero-trust risk score. Through extensive experimentation using the KDD Cup 99 intrusion detection data set (50,000 instances, 41 attributes), it is evident that ZT-FRL-PLF provides an impressive detection performance rate of 99.21%, 99.37% precision, 99.18% recall, 99.27% F1 score, and a minimum false alarm rate of 0.89%, which outperforms all existing baseline approaches, including conventional trust-based models, traditional machine learning based IDS, and blockchain technology security schemes. Moreover, the proposed approach offers enhanced network lifetime (1020.4 units) and low energy utilization (0.98 energy index units) relative to other energy-intensive blockchain techniques (1.95 energy index)..
M. Kaliappan, S. Vimal, Preethy Rebecca, S. Senthil, Maharajan K, Kuldeep Walia, Gaurav Dhiman
When using Artificial Intelligence(AI) in CDSS, two important and related problems come up: making sure that federated learning over institutional data silos doesn't violate patient privacy and getting doctors to trust AI models' diagnostic suggestions. The literature primarily focused on addressing each issue in isolation, resulting in a limited comprehension of their interconnections. This study proposes an innovative approach to address the two issues through the utilization of the FedTrust-XAI architecture, which integrates federated learning and explainable artificial intelligence (XAI). This study also examines its impact on diagnostic accuracy and the level of trust clinicians place in it. The proposed system employs federated logistic regression utilizing the FedAvg method across three distinct clinical sites and multiple interaction rounds. This lets the model learn together without sharing any information, which keeps patient privacy safe. The experiment using the UCI Wisconsin Diagnostic Breast Cancer (WDBC) dataset shows that the federated model can give diagnostic results that are just as good as those from the central model, while still keeping strong privacy protections. A method for understanding decisions is presented to help clarify how choices are made. We use linear XAI attribution, which uses model coefficients and feature inputs to give feature-level attribution scores. The model chooses worst_concave_points, worst_perimeter, and worst_radius as the most important things to look at when trying to figure out if a tumor is cancerous. These results are consistent with existing medical knowledge and enhance clarity. From a sociotechnical perspective, a factorial experiment involving physicians examines the influence of system architecture and explainability on the credibility of AI-generated advice. The federated structure and the ability to explain the output work together to make clinicians trust the system the most. Also, studies show that clinicians' levels of trust in federated AI systems depend on their own privacy concerns. In particular, doctors who care more about privacy issues are more likely to trust AI-powered solutions. The proposed architecture has an accuracy of 99.12% and an AUROC score of 99.23%, which is close to the accuracy of the centralized baseline (99.60% AUROC). The results show that you can learn without giving up your privacy or the ability to make predictions. Overall, FedTrust-XAI adds to important conversations in many areas about learning that protects privacy, AI that can be explained, and how people and AI work together in healthcare apps. The study shows that for AI technology to be accepted in healthcare, the algorithms must be clear and take privacy into account.
Digital-Twin-Enabled Satellite–UAV–RIS Cooperative Networks: A Reinforcement-Learning Framework for Joint Trajectory and Passive Beamforming Optimization
Abstract—Sixth-generation (6G) networks must deliver ubiquitous, high-rate, and fair connectivity in regions where terrestrial infrastructure is sparse, damaged, or economically unviable. This paper proposes and validates a cooperative Satellite–UAV–RIS–User architecture in which a digital twin (DT) continuously mirrors the physical radio environment to drive two coupled decisions: (i) real-time user localization from received-signal-strength (RSSI) fingerprints and (ii) joint optimization of unmanned aerial vehicle (UAV) hover position and reconfigurable intelligent surface (RIS) phase/beamforming configuration. A Random Forest digital-twin classifier localizes users into service zones from seven-dimensional Wi-Fi RSSI fingerprints with 98.0% accuracy and a weighted F1-score of 0.980, evaluated on the UCI Wireless Indoor Localization benchmark (2,000 samples, four rooms). A second Random Forest surrogate learns the mapping between UAV three-dimensional position and achievable sum rate, which is embedded as the reward signal of a tabular Q-learning agent searching a 9×9 spatial grid for the throughput- and fairness-maximizing UAV waypoint. The RIS layer applies phase-optimized passive beamforming with adaptive power allocation across a 64-element panel. Monte Carlo simulation over 120 digital-twin-localized users shows that the proposed architecture achieves an average per-user rate of 417.9 Mbps and an aggregate sum rate of 50.1 Gbps, a 162.4% gain over a satellite-only baseline and a 19.1% gain over a fixed-position UAV relay without RIS assistance while sustaining a Jain fairness index above 0.999 and eliminating outage at practical throughput thresholds where the satellite-only baseline is in complete outage. A six-way ablation and a qualitative comparison against six representative 2022–2024 RIS/UAV/NTN architectures confirm that the distinguishing advantage is the closed-loop DT-driven co-design of mobility and reflection, rather than either optimization performed independently.
Topology-Adversarial Cognitive Graph Digital-Twin Reinforcement Learning for Resilient Power-Grid Control
Abstract— The accelerating penetration of renewable generation, weather-driven demand, and cyber-physical threats is pushing modern power grids toward regimes in which purely model-based or reactive controllers are increasingly brittle. This paper proposes TAC-GDT-RL, a framework that couples four mechanisms in a single closed loop: (i) a multi-task graph-attention (GATv2) digital twin predicting nodal voltage, component-failure probability, and operational risk from an eleven-dimensional physics- and weather-aware node representation; (ii) Monte-Carlo-dropout inference giving the twin calibrated epistemic uncertainty; (iii) an uncertainty-triggered topology-dreaming module that synthesizes adversarial contingencies and fine-tunes the twin through prioritized replay; and (iv) a Proximal-Policy-Optimization controller trained under curriculum learning and protected by a rule-based safety shield. On the IEEE 14-bus network driven by one year of hourly weather across five contingency families, a seven-way ablation isolates each mechanism using paired non-parametric tests and rank-biserial effect sizes. The complete method attains the highest mean control reward (33.65 versus 11.13-22.14), significantly exceeding the fixed-topology, cyber-unaware, shield-free, and plain-dynamic variants, and reduces the voltage-violation rate to 0.31. The twin reaches a failure-detection AUC of 0.982 and a voltage MAE of 0.026 p.u. Results are reported transparently, including mechanisms not individually significant at the present compute budget.
M. Kaliappan, S. Vimal, Karpagavalli C, Ramnath M, Rattan Singh, Gaurav Dhiman
Sustainable Dense Bituminous Mixtures using Reclaimed Asphalt Pavement,Construction & Demolition Waste:Performance Comparison of VG-30 and CRMB-55 Binders
The increasing consumption of natural aggregates and bituminous materials, together with the generation of reclaimed asphalt and construction and demolition waste, creates a need for sustainable pavement construction practices. This study experimentally investigates the combined utilization of Reclaimed Asphalt Pavement(RAP) and Processed Construction and Demolition(PCD) waste in Dense Bituminous Macadam DBM) mixtures using VG-30 and Crumb Rubber Modified Bitumen(CRMB-55) binders. The experimental programme included characterization of binders and recycled aggregates, Marshall mix design, determination of optimum RAP and PCD contents, and performance evaluation using Marshall Stability, Flow Value, Indirect Tensile Strength(ITS), Tensile Strength Ratio(TSR), and Retained Stability (RS). DBM Grade-II gradation was maintained for the investigated mixtures to permit comparison of recycled-material combinations and binder types. For VG-30, the Marshall Stability increased from 13.14 kN for the natural-aggregate control mixture to 19.88 kN at 30% RAP, which was selected as the optimum RAP level. Subsequent incorporation of PCD increased optimum binder demand because of the comparatively high absorption of the recycled demolition aggregate. The final sustainable VG-30 mixture containing 30% RAP and the investigated combination of PCD fractions achieved 14.75kN Marshall Stability, approximately 12.25% above the control mixture. Performance evaluation showed that the final VG-30 mixture satisfied the investigated ITS, RS and TSR requirements. In contrast, the corresponding CRMB-55 mixture containing 30% RAP and 33.5% PCD did not satisfy the minimum TSR requirement of 80%. RAP improved ITS and moisture resistance in the VG-30 system, while PCD generally reduced tensile and moisture-related performance. The results indicate that the engineering suitability of high-recycled-content mixtures depends on both recycled-material proportions and binder compatibility. Under the conditions investigated, VG-30 showed better overall compatibility with high RAP and PCD contents than CRMB-55.
Thota Srinivasarao,Balakrishna Penta, Balla Satyanarayana,Cheekati Vijaya Kumar
In Silico Drug-Likeness, ADME, Pharmacokinetic and Toxicity Profiling of Novel Indole–Piperazine Hybrid Derivatives: An Index Computational Study
The present study investigated the in silico drug-likeness, physicochemical, pharmacokinetic, ADME and toxicity profiles of ten novel indole–piperazine hybrid derivatives (C1–C10) to identify promising candidates for further drug-development research. Computational evaluation was performed using molecular descriptors associated with Lipinski’s Rule of Five, including molecular weight, hydrogen-bond donor and acceptor counts, and predicted octanol/water partition coefficient (QPlogPo/w). Additional physicochemical and ADME parameters, including water-accessible polar surface area (WPSA), predicted aqueous solubility (QPlogS), molecular globularity (Glob), predicted hERG liability (QPlogHERG), Caco-2 permeability (QPPCaco), blood–brain barrier distribution (QPlogBB), MDCK permeability (QPPMDCK), human serum albumin binding (QPlogKhsa) and predicted human oral absorption (%HOA), were comparatively analyzed. Toxicological assessment included predicted hepatotoxicity, developmental toxicity and mutagenicity. The compounds demonstrated considerable variation in molecular weight (383.49–482.52 Da), lipophilicity and polarity, reflecting the influence of structural modifications on their predicted pharmacokinetic behavior. C5 and C10 displayed comparatively greater polarity and improved predicted aqueous solubility, whereas C3 and C7 exhibited higher predicted membrane permeability and BBB distribution. C1, C4 and C9 demonstrated a favorable balance between lipophilicity, permeability and predicted oral absorption. Importantly, all ten compounds were predicted to be non-hepatotoxic, non-developmentally toxicant and non-mutagenic within the applied computational models. Overall, the integrated computational profiling identified distinct structure–property relationships across the series and highlighted compounds with comparatively balanced ADME characteristics. These findings support the use of computational profiling as an early-stage strategy for prioritizing indole–piperazine hybrids for subsequent experimental validation and lead optimization.
Pandurang Shivraj Vijapure , Girish Kumar Vyas , Atul A. Barawkar
Transforming Lead-Contaminated Soil into Fertile Land by Recycling Phosphogypsum
The presence of lead (Pb) in soil affects the environment and public health and causes serious issues as it is persistent, toxic, and bioaccumulates in the food chain. Traditional soil lead remediation methods are expensive and scale poorly. In this study, we explore an integrated and sustainable approach to remediation that combines phosphate-solubilizing fungal bioremediation with chemical stabilization. The bioremediation uses Aspergillus niger, while the chemical stabilization uses phosphogypsum, an industrial waste product from phosphate fertilizers. A 60-day laboratory study was conducted to assess the effects of fungal inoculation and phosphogypsum amendment, and their combination, on Pb-contaminated soil (300 mg/kg Pb). Lead bioavailability was determined using diethylene triamine penta-acetic acid (DTPA) extraction and subsequent measurement by atomic absorption spectrophotometry. Treatment results showed significant lead bioavailability reduction, and the combined approach was the most effective, with an estimated 65% reduction of Pb following 60 days of treatment. Enhanced reduction is due to bioaccumulation, organic acid production of the fungus, and lead biosorption which, in combination with phosphogypsum, precipitates lead into stable minerals, such as pyromorphite. Additionally, the integrated treatment improved soil biological activity and stability. The findings highlight the potential of this bio-chemical approach as a cost-effective, scalable, and environmentally sound solution for rehabilitating lead-contaminated soils while promoting circular economy principles through industrial waste reutilization, particularly in regions affected by heavy metal pollution such as Iraq.
Noor S. Hassan, Mohammad F. Alhani, Ali Attaallah Ibrahim