Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, deterministic, and low-latency decision making under uncertainty. Although the classical Artificial Intelligence (AI) approaches are effective, they often struggle to meet the stringent constraints of robustness, timing, explainability, and safety in the MC domains. Quantum Artificial Intelligence (QAI), the fusion of artificial intelligence and quantum computing (QC), can potentially provide transformative solutions to the challenges faced by classical ML models. QAI is a broader umbrella than Quantum Machine Learning (QML) and additionally includes quantum optimization, search, and reasoning; we use QAI throughout the paper for the field at large, and QML only for learning-specific subroutines. The principal contributions of this work are: (i) a systematic survey of QAI methods analyzed through the lens of MC requirements like certification, robustness, and timing; (ii) a conceptual quantum cloud resource management and scheduling framework with deployment assumptions, complexity analysis, and failure-mode discussion; and (iii) an identification of the gaps between current QAI capabilities and MC systems requirements. We also propose a conceptual model for management of quantum resources and scheduling of applications driven by timeliness constraints. We discuss multiple challenges, including trainability limits, data access, and loading bottlenecks, verification of quantum components, and adversarial QAI. Finally, we outline future research directions toward achieving interpretable, scalable, and hardware-feasible QAI models for MC application deployment.

Generative AI has emerged as a transformative paradigm for diverse applications, yet the escalating scale of modern models exposes critical computational and memory bottlenecks in classical hardware. This paper investigates the intersection of quantum computing and generative artificial intelligence (QGAI) to address these limitations and scale modern generative models. As models grow to billions of parameters, classical systems face bottlenecks in memory, energy, and training efficiency, while quantum computing offers exponential representational benefits for high-dimensional data. The paper analyzes five core quantum generative architectures-Quantum Circuit Born Machines, Quantum Generative Adversarial Networks, Quantum Boltzmann Machines, Quantum Variational Autoencoders, and Quantum Diffusion models, highlighting their design principles, learning mechanisms, and applications. QGAI models have demonstrated significant promise in domains such as drug discovery, human-machine interaction, IoT security, and financial modelling. Despite these advances, QGAI remains constrained by qubit noise, barren plateaus, and integration challenges. We conclude by identifying ten open research challenges and propose directions for achieving scalable, interpretable, and energy-efficient quantum generative learning.

The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics termed Generative Physical Artificial Intelligence (GPAI). These agentic AI systems autonomously perceive, reason, and act in complex real-world situations. This survey comprehensively analyzes GPAI systems, focusing on their architectural foundations, current applications, and key limitations. We introduce a taxonomy of five distinct approaches: Robot Foundation Models (RFMs) for cross-platform skill transfer; Vision Language Action Models (VLAs) for end-to-end multi-modal perception and control; Large Behavior Models (LBMs) for human-like movement generation; Diffusion Policy Models (DPMs) for diffusion model-based temporally coherent action generation; and World Foundation Models (WFMs) for physics-compliant simulation and data generation. We examine how these approaches complement each other: WFMs generate training data for VLAs and DPMs, RFMs enable cross-platform deployment of learned policies, while LBMs provide motion priors for natural behavior. Through examples across autonomous vehicles, industrial automation, healthcare robotics, and humanoid systems, we identify significant performance improvements and summarize promising research directions in data-efficient learning, sim-to-real transfer, edge-compatible architectures, and safety frameworks. These insights advance embodied AI for IoT-connected environments where intelligent agents interact with networked sensors, actuators, and edge devices.

Wi-Fi Channel State Information (CSI) has become a widely studied modality for device-free sensing as it captures fine-grained wireless channel variations that can be mapped to human motion and presence while avoiding the explicit visual disclosure typical of vision-based systems. CSI-based pipelines have been explored for human activity and gesture recognition, fall detection, gait analysis, pose-related inference, and indoor localization. Despite strong results in controlled settings, practical deployment remains difficult due to measurement noise, sensitivity to environmental dynamics, multi-user interference, and system-level constraints in data acquisition and real-time processing. This article surveys machine learning methods forWi-Fi CSI sensing and analyzes more than 65 representative models, connecting algorithmic design choices with implementable end-to-end system design. We introduce a hierarchical taxonomy that organizes the literature into classical machine learning approaches, deep learning architectures, and hybrid strategies. Beyond modeling, we describe the full sensing pipeline- from hardware and network interface card (NIC) selection to software tools, antenna configuration, and signal conditioning- highlighting the design trade-offs that affect robustness and reproducibility. We further compare methods across major application domains and summarize open challenges in generalization to dynamic environments, multi-user separation, and resource-efficient inference. Finally, we outline research directions toward robust generalization, scalable deployment, and privacy-aware learning to support broader real-world adoption.

ABSTRACT Generative AI (GAI), which has become increasingly popular nowadays, can be considered a brilliant computational machine that can not only assist with simple searching and organising tasks but also possesses the capability to propose new ideas, make decisions on its own and derive better conclusions from complex inputs. Finance comprises various difficult and time‐consuming tasks that require significant human effort and are highly prone to errors, such as creating and managing financial documents and reports. Hence, incorporating GAI to simplify processes and make them hassle‐free will be consequential. Integrating GAI with finance can open new doors of possibility. With its capacity to enhance decision‐making and provide more effective personalised insights, it has the power to optimise financial procedures. In this paper, we address the research gap of the lack of a detailed study exploring the possibilities and advancements of the integration of GAI with finance. We discuss applications that include providing financial consultations to customers, making predictions about the stock market, identifying and addressing fraudulent activities, evaluating risks, and organising unstructured data. We explore real‐world examples of GAI, including Finance generative pre‐trained transformer (GPT), Bloomberg GPT, and so forth. We look closer at how finance professionals work with AI‐integrated systems and tools and how this affects the overall process. We address the challenges presented by comprehensibility, bias, resource demands, and security issues while at the same time emphasising solutions such as GPTs specialised in financial contexts. To the best of our knowledge, this is the first comprehensive paper dealing with GAI for finance.

ABSTRACT Precious metals are rare metals with high economic value. Forecasting the price volatility of precious metals is essential for investment purposes. In this work, we propose a novel hybrid model of random convolutional kernels‐based neural network model (RCK) and generalized autoregressive conditional heteroscedasticity (GARCH) model for forecasting the metal price volatilities of gold, silver, and platinum. Realized volatility calculated on logarithmic returns is used as an estimate for the volatility of prices, and data standardization is performed before feeding the price volatility data to the RCK model. RCK model applies multiple carefully designed random convolution kernels on the time series input to extract robust features for forecasting. The proportion of positive values (PPV) is extracted as features from the output of convolving convolutional kernels with time‐series inputs, which are then passed through a regressor to forecast volatility. Compared to the existing methods, the proposed method has the advantage that the weights of the random convolutional kernels need not be trained, unlike other neural network models. Further, no other work has made use of random convolutional kernels for precious metal forecasting, to the best of our knowledge. We incorporated novel learning and data augmentation strategies to achieve better performance. In particular, we used the cosine annealing learning rate strategy and Mixup data augmentation technique to improve the proposed model's performance. We have used MSE (Mean Squared Error), RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error) as metrics to compare the proposed models' performance. The proposed model decreases the MSE by 53% compared to the GARCH‐LSTM model, which is the current state‐of‐the‐art hybrid model for volatility forecasting.

Electric vehicles (EVs) are becoming more popular as people try to live more eco-friendly ways. A major challenge slowing down their widespread adoption is limited driving range and inadequate charging infrastructure, particularly in rural and highway areas where charging station deployment remains uneven. This paper proposed a blockchain-based framework for Vehicle-to-Vehicle (V2V) and Vehicle-to-Grid (V2G) energy trading to address these challenges, enabling efficient decentralized energy exchanges. The framework integrates three-game theory models, contract theory, Bayesian game theory, and evolutionary game theory to optimize trading strategies, ensure fairness, and enhance grid stability. By utilizing a lightweight blockchain architecture on Hyperledger Fabric, the proposed system ensures secure, transparent, and efficient transactions while reducing operational costs. The performance evaluations show that the proposed framework surpasses existing methods, reaching a transaction throughput of 49.8 transactions per second (TPS) for payment settlements and 44.7 TPS for energy trade proposals, with an average latency ranging from 0.09 to 0.18 seconds. Resource utilization analysis shows that peer nodes experience an average CPU usage of 23.76% and memory consumption of 169.5 MB during trade proposals. These results highlight the robustness and scalability of the framework in enabling decentralized, carbon-intelligent energy trading, offering a promising solution for advancing sustainable and intelligent EV energy ecosystems.

Historical patients’ medical data has an important impact on the healthcare industry for providing the best care to patients through intelligent health diagnosis and prediction of diseases. The existing intelligent health diagnosis systems collect data from medical institutions or laboratories and then use machine learning algorithms to predict diseases. But, in most cases, the medical institutions have incomplete medical data of the patients since a patient may consult different specialists (from various hospitals) during the treatment process. To overcome this problem, we build a smart and secure federated learning framework for intelligent health diagnosis with a blockchain-based incentive mechanism and nonfungible tokens (NFTs)-based marketplace. We make use of NFTs to develop clear demarkations on the ownership and accessibility of the data of patients. We create an NFT marketplace that manages access to the historical medical data of patients. A comprehensive incentive mechanism based on several factors, including the quality and relevance of the data, the frequency, regularity of data uploading, etc., is incorporated to encourage and penalize the patients based on their contributions to the global model. We used the Polyak-averaging technique for aggregating local models to form a global model. The extensive analysis shows that the proposed model achieves comparable performance with the centralized machine learning models while affording better security and access to better data. The results also show the efficacy of the proposed blockchain-based incentive mechanism.

Blockchain (BC) and artificial intelligence (AI) technologies have independent applications in multiple industries, including banking, finance, healthcare, construction, transportation, hospitality, manufacturing, and insurance, to name a few. Moreover, these two technologies can be integrated seamlessly, thanks to their complementary and mutually supportive features. AI algorithms can make the medical BC storage efficient by their processing algorithms, also playing the role of knowledgeable gatekeepers. BC can support AI models by providing secure, sizeable, traceable, diverse, and immutable healthcare data for the training purpose. The integration of BC and AI has multiple use cases in the healthcare industry ranging from disease prediction to pandemic management. Previously, researchers have reviewed the applications of each of these technologies in healthcare independently. Although the integration of BC and AI has been fruitful, to the best of our knowledge, there has been no work in the past reviewing the confluence of these two technologies in the healthcare sector. We have classified the works based on two different classification schemes: 1) application-based and 2) AI-training paradigm-based classification. We have also provided a compilation of tools used in the integrated systems of BC and AI for healthcare. We identified that the integration of BC and AI technologies had been applied in quite different areas of healthcare ranging from biomedical research to pandemic management. It is also noted that the supervised learning algorithms and federated learning paradigm for secure decentralized AI model training are often used in the integration. Our findings reveal that majority of the reviewed works use BC as a secure database for AI models. Furthermore, we also have pointed out the potential applications of these two technologies in healthcare.

Connected Vehicles (CVs), an integral part of the future of intelligent transportation systems, use communication and sensing technologies to communicate among vehicles and infrastructure. However, as vehicles become interconnected, the vulnerability of their components to anomalies and deliberate malicious activity increases. In both cases, it is vital to detect and exclude anomalous data from the decision-making process. While deep learning techniques are gaining popularity for anomaly detection due to their adaptability, they are computationally expensive and require long training times. To overcome this challenge, this paper uses a directed acyclic graph (DAG) based distributed ledger technique and combines it with social psychology principles of ability, integrity, and benevolence to calculate the reputation of vehicles. We introduce the probability of malevolence, a measure of quality, which is a function of the error measurements (between ground truth and reported values) and reputation metrics. We introduce various anomalies such as bias, noise, short, multi-short, drift, multi-drift, stuck-at, and parasite chain attack in the simulated data from the Intelligent Driver Module framework on road topology such as uphill, ring, on-ramp, off-ramp, and road-works to validate the efficacy of the proposed framework in identifying the anomalies. Simulation results show that the malevolence factor serves as an efficient metric for automatically determining the types of anomalies in the CV network.