Anticipating surgical actions requires more than recognising motion patterns, it also demands adherence to procedural logic and the resolution of subtle ambiguities, such as distinguishing mirrored grasp–retract interactions. However, existing Transformer-based models often fall short in this domain, often producing structurally invalid step sequences and misclassifying chirally opposite actions that appear visually similar. To address these limitations, we introduce a neuro-symbolic framework centred on a Probabilistic Temporal Grammar (PTG). The grammar was constructed from a unified corpus of surgical data (Ground-Truth), by capturing procedural structure, temporal priors, and chirality-aware terminals for opposite actions (e.g., push_needle <-> pull_suture) directly into its rules. To enforce causal consistency, the PTG incorporates a Goal-conditioned Multivariate Markov Chain (GcMMC) that models evolving object-action dependencies. Our framework employs a two-stage process: a V-JEPA-powered Transformer generates raw forecasts of future actions and durations, which are then refined by a constrained parsing algorithm guided by the PTG. Candidate futures are jointly scored for structural validity, temporal plausibility, and causal grounding. By explicitly encoding surgical logic into a unified neuro-symbolic system. Experiments across three publicly available surgical datasets show that our approach outperforms state-of-the-art anticipation models. Importantly, by generating interpretable and procedurally consistent forecasts of upcoming actions, PTG establishes the predictive foundation required for proactive robotic assistance and safe human–robot collaboration in the operating room.

The offshore energy industry faces challenges in maintaining ageing infrastructure, with over half of North Sea platforms past their 25-year design life. This creates a need for scalable inspection methods beyond traditional manual reviews. We present a collaborative Human-AI framework that assists in detecting structural defects whilst preserving expert oversight under challenging marine conditions. Our two-stage system first employs a lightweight classifier to filter video frames by risk level. High-risk frames are then analysed by a modified Pyramid Attention Network that performs precise defect localisation. Experts validate the results at both stages, ensuring the system's continuous improvement. To better identify rare but critical flaws, we design Enhanced Tversky, a composite loss function to mitigate severe class imbalance by explicitly prioritising rare yet safety-critical defects like cracks. Evaluation on 5,525 classified frames and 1,013 segmented images demonstrates F2 score of 87.59% and a mean IoU of 78.73%, with crack detection reaching a crucial 89.62% F2 score. The framework reduces expert review time by 3.88 x whilst maintaining safety standards, offering a practical approach to scaling offshore inspection capabilities through Human-AI collaboration.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model.

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Roadside Unit (RSU) plays a significant role in Vehicle-to-Everything (V2X) service to accomplish adequate performance for the autonomous driving system. Haphazard RSU deployment and selection cause to diminish the frame-work efficiency and fail to meet the service capability of most vehicles. Reducing latency by timely delivering service is a phenomenal concern of edge orchestration to enhance the quality of service (QoS) with high reliability. With this motivation, this study focuses on maintaining a trade-off between RSU deployment and selection to achieve a high convergence rate of service offloading policy for effective service consolidation. An RSU-Assisted Service Consolidation (RASC) approach is proposed considering probabilities of three service demand rates, Computation-intensive service rates, and resource prediction rates based on Deep Reinforcement Learning. A resource-weight factor is designed to classify and map the arrived services to the potential RSU based on a discrete concave-convex procedure to avoid the execution hiccups for achieving a high convergence rate of service offloading policy. The average probability rate helps to select the potential RSU to fulfill the requirements of delay-sensitive service requests. The simulation results shows the RASC method improves the service reliability rate by at least 69.5%, the energy preservation rate by 27.1%, and the service offloading reduction rate by 73.08% compared to state-of-art schemes.

Role of RSUs become essential in enhancing the service reliability rate of the end vehicle to strengthen autonomous vehicle technology. Computation-intensive services are delay-sensitive, and most existingmethodsattempted comprehensively to meet the application deadline but have not reached the expectations due to classical computing. In this regard, we design a novel offloading decision-making method based on quantum theory through reinforcement learning. Grover's algorithm is employed to select a feasible device based on cost and energy usage probability ratio. Theoretical and mathematical validations and simulation outcomes confine the impact of novel decision-making methods on the statistical constraints of the heterogeneous framework.

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring measured data demands $\geq$10Gbit/s transfer rate and $\geq$1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. The cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. The simulation results agree with the targeted effectiveness of our approach, i.e., mAP($\geq$71%) with processing delay ($\leq3.5\times 10^{6}$bits/slot), and transfer delay ($\leq$3Sms). Our simulation results indicate that our approach is highly effective.

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

Abstract The internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches.

Cyber-physical systems (CPS) consist of a variety of multicore architectures, including central processing units (CPU) and graphical processing units (GPU). In general, programmers assign sequential programs to the CPU while parallel applications are assigned to the GPU. This article provides a method for mapping an OpenCL application to a heterogeneous multicore architecture using active fuzzy learning to determine the adequacy and processing capabilities of the application. During learning, subsamples are created by developing a machine learning-based device suitability classifier that predicts which processors would have excessive computational compatibility for running OpenCL programs. In addition, this study integrates an active learning model based on entropy with a fuzzification model to find nonoverlapping patterns. To minimize rule generation, the fuzzification-based weighted probabilistic technique is presented. The defuzzification process is optimized by using uncertainty values in conjunction with classification probability. In addition, 20 different features are proposed for extraction using the newly developed LLVM-based static analyzer. The correlation analysis approach is used to determine the optimal subset of features. The synthetic minority oversampling approach with and without feature selection is used to differentiate the class imbalance problem. Instead of manually modifying the machine learning classifier, a tree-based pipeline construction approach is used to determine the optimal classifier and associated hyperparameters. Experiments are then conducted on a set of benchmarks to verify the performance of the designed model. The results show that by increasing the number of training examples and including an entropy uncertainty measure, the proposed model is able to support and improve decision boundaries. We achieved a high F-measure of 0.77 and a ROC of 0.92 by optimizing and reducing the feature subsets.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.