The water supply chain is vulnerable to risks such as unauthorized usage and identity impersonation. Traditional solutions lack transparency, tamper resistance, and scalability, making them unsuitable for multi-stakeholder environments. To address these challenges, our paper presents BEDLAM, a Blockchain-Enabled Dual-Layer Authentication Model framework, designed to secure water supply chain operations. The framework employs two complementary authentication layers, namely, (i) a blockchain-based identity management layer that provides verifiable stakeholder authentication while leveraging Zero-Knowledge Proofs (ZKPs) and (ii) a smart contract-based verification layer that regulates access control, service allocation, and transaction validation among multiple entities. The first layer of BEDLAM is implemented on the Mina Blockchain, via the Auro Wallet, and evaluated by using Tinkercad-based circuit simulations. The second layer is implemented using smart contracts to ensure user access control. Our proposed system ensures cryptographic data verification with finality, achieving a latency of 153 ms and generating tamper-proof records. Sensor data are processed on resource-constrained IoT devices, producing compliance proofs. Multiple simulations involving batch users demonstrate linear scalability (average proof time of 26 s per user, 0.038 transactions per second) and significant stability. The success rate of transactions is 99.3% with exponential back-off retries under 40% simulated packet loss.

Crowdfunding is an important mechanism for supporting innovative projects by connecting creators with distributed contributors. Prior research has identified persistent limitations in both traditional and blockchain-based crowdfunding platforms, including limited transparency, centralized control, passive contributor roles, and inflexible fund management processes. These limitations hinder accountability, equitable participation, and effective decision-making throughout the campaign lifecycle. This paper presents a blockchain-enabled crowdfunding framework designed as a decision-support artifact for adaptive fund allocation and participatory governance. The framework enables contributors to engage in spending-request governance through Quadratic Voting, which balances influence across heterogeneous financial stakes and mitigates dominance by large contributors. To support adaptive campaign management, the framework further integrates Ethereum smart contracts with a Markov Decision Process (MDP), enabling campaign-level decisions to respond to evolving contribution patterns and campaign states. The framework is implemented and evaluated through controlled experiments on the Sepolia Ethereum test network. The evaluation includes both an internal ablation of Quadratic Voting and MDP-based adaptive support and an external comparison against representative blockchain-based baselines. The results show that the combined Quadratic Voting and MDP design achieves lower approval latency and higher throughput than partial or static variants of the framework, and that the full proposed platform outperforms the compared baseline systems under increasing campaign workload. Overall, the study demonstrates how participatory governance, adaptive decision support, and transparent smart-contract execution can be systematically integrated into crowdfunding platforms, providing practical guidance for the design of scalable, efficient, and accountable decentralized crowdfunding systems.

Infectious diseases, such as monkeypox (mpox), with overlapping clinical symptoms pose significant challenges for early and accurate diagnosis, particularly in regions with limited laboratory testing capacity. The recent emergence of mpox in multiple regions, with sustained human-to-human transmission reported as of September 15, 2025, further underscores the urgent need for rapid and reliable diagnostic tools. Early clinical diagnosis remains difficult due to symptom overlap with other rash-related illnesses. To address this, we propose XAIMD, an explainable attention-driven convolutional neural network architecture that enhances automated Mpox diagnosis through transparent human-machine synergy. The proposed model, XAIMD, incorporates a Squeeze-and-Excitation-based Attention for Feature Enhancement (SAFE) mechanism to improve attention-driven feature refinement and enable accurate localization of Mpox-infected regions. XAIMD was rigorously evaluated on the Monkeypox Skin Closed Images (MSCI) dataset and benchmarked against five state-of-the-art CNN architectures. The proposed model demonstrated superior performance, achieving an AUC and precision of 100%, an accuracy of 95%, specificity of 94%, recall of 80%, and an F1-score of 89%, highlighting its robustness and reliability in automated Mpox diagnosis. Additionally, Grad-CAM visualization was employed to improve interpretability, offering clinical insight by highlighting affected lesion areas. This attention-enhanced, interpretable approach offers a scalable, accurate, and accessible solution for Mpox diagnosis, particularly vital in settings where PCR testing is limited. XAIMD demonstrates strong potential to support early detection and containment efforts in current and future outbreaks.

Purpose To develop a lightweight and accurate computer-aided diagnostic framework for multiclass skill lesion classification with focus on Mpox that enables rapid and reliable detection during fast-spreading viral outbreaks, particularly in resource-constrained healthcare environments. Design/methodology/approach The proposed framework integrates a Lightweight Depthwise-Separable Convolutional Neural Network with an Adaptive Bayesian Boosted Learning Module (LDSCNN-ABBLM). Contrast-limited adaptive histogram equalization (CLAHE) is applied to enhance lesion visibility, while class-weighted learning mitigates data imbalance. The LDSCNN backbone performs efficient feature extraction using depthwise-separable convolutions, and ABBLM employs Bayesian optimization via Optuna to adaptively tune boosting parameters over 15 trials. The model is evaluated on the MCSI and clinically validated MSLD v2 datasets and compared against multiple weighted baseline classifiers, including Weighted Random Forest, Weighted Linear SVM, Weighted LightGBM, Weighted Extra Trees, and Weighted XGBoost. Findings The proposed model achieves validation accuracies of 0.9893 and 0.9983 on the MCSI and MSLD v2 datasets, respectively, demonstrating strong diagnostic reliability and superior generalization performance compared to all baseline models. Research limitations/implications The model focuses exclusively on image-based diagnosis and does not incorporate clinical parameters such as patient history, symptoms, or laboratory findings, which are essential for comprehensive diagnosis. Furthermore, although the LDSCNN architecture is computationally efficient, real-time deployment on low-power edge devices in clinical settings may face challenges due to hardware variability and potential latency issues. Practical implications The lightweight and scalable design enables deployment in low-resource and point-of-care settings, facilitating early Mpox detection and outbreak containment. Adherence to clinical validation and ethical standards further supports the framework's integration into real-world AI-assisted dermatological and public health decision-support systems. Originality/value This study introduces a unified lightweight diagnostic framework that synergistically combines depthwise separable convolutional feature extraction with adaptive Bayesian-optimized boosting, offering a novel balance between high diagnostic accuracy and computational efficiency for Mpox lesion classification.

The rapid expansion of the Healthcare Internet-of-Things (HIoT) has created new opportunities for delivering personalized, real-time medical intelligence. In such systems, IoT devices acquire data at the point of care, edge servers provide low-latency preprocessing and lightweight inference, cloud platforms perform large-scale model training and optimization, and user interfaces enable clinical decision support and feedback. Designing an effective HIoT framework requires addressing a multi-objective trade-off: maximizing accuracy A(0) and stability S(0) while minimizing latency L(0) and energy E(0), subject to constraints on model size |0| <= Mmax and robustness S(0) >= S. To address this challenge, we propose HiPER, a hierarchical optimization-driven HIoT framework that jointly integrates acquisition, analytics, intelligence, and user interaction. To demonstrate its practical utility, HiPER is applied to monkeypox (Mpox) detection, referred to as HiPER-Mpox. In this framework, the edge employs lightweight inference with privacy-preserving transformations, while the cloud leverages transfer learning using a NASNetMobile backbone with squeeze-and-excitation channel recalibration to enhance accuracy and generalization. The user layer provides interpretable outputs and incorporates clinician feedback, improving trust and robustness. Evaluation on the Mpox Skin Lesion Dataset (MSLD) shows that HiPER-Mpox achieves 96% accuracy, 93% precision, MCC of 0.9145, and Kappa of 0.911, with an average per-image latency of 554 ms and a compact 2.07 MB edge model. These results demonstrate that the proposed HIoT framework satisfies the formulated optimization objectives while delivering a practical, interpretable, and resource-efficient solution for emerging healthcare challenges.

With the advent of smart healthcare services, rapid and automated diagnosis from images of skin lesions is critical to combat fast-spreading viruses such as monkeypox (Mpox or MPX) and significantly improve public health. The recent cases in Thailand reporting a suspected first case on August 21, 2024, and Sweden on August 14, 2024, among others, highlight the pandemic threat of Mpox. This study presents the Deep Digital Twin Services for Personalized Treatment (D2T-PT) model, which combines transfer learning and Digital Twin (DT) technology to improve the accuracy of Mpox detection and real-time monitoring, supported by the Squeeze-and-Excitation Block (SEB) attention mechanism, which opens up new horizons for personalized healthcare. Convolutional Neural Network (CNN) models were tested on the Monkeypox Skin Lesion Dataset (MSLD), with the advanced adaptive NasNetMobile model achieving excellent results: 100% recall, 98% ROC score, 97.78% accuracy with precision of 95%. This robust model enables physicians to make early and accurate Mpox diagnoses and monitor patient response to treatment in real-time, ultimately helping to contain the spread of the virus.

Water usage serves as a key factor in boosting groundwater levels, alleviating water scarcity, and enhancing sustainable farming practices. Yet, ineffective oversight and accountability among stakeholders often undermine water resource management (WRM). To address this issue, we introduce a framework called S2WRM, which integrates a dual strategy: a game theory-based method and a blockchain-based system. In the game theory component, we apply a Stackelberg game, positioning the Water Resource Management Authority (WRMA) as the leader and industrialists and farmers as followers. Through this sequential game, dynamic pricing is determined as the leader establishes pricing strategies across various scenarios, analyzing usage trends to boost WRMA revenue. In addition, a blockchain incentive system is implemented, using ERC-20 contracts to mint and distribute tokens as rewards. This blockchain framework is built on the Ethereum platform, with Stackelberg simulations coded in Python. Analysis of different scenarios reveals key insights: first, blockchain-based incentives improve consumption oversight, fostering transparency in stakeholder supply chains second, WRMA revenue rises as blockchain reduces overhead costs tied to dynamic pricing and third, sustainability is advanced through token limits and penalties.

The use of Open Radio Access Networks (Open RAN) in vehicular networks can lead to better connectivity, reliability, and performance. However, communication in this setting is often done over an unsecured wireless network, which creates a challenge in verifying the validity of received transactions by Internet of Vehicles (IoV) due to the untrusted network. It also creates a potential for attackers to tamper with the data content and conduct different IoV-related attacks. To address these issues, a new framework called “STIoV” has been proposed for secure and trustworthy communication in IoV. The framework includes a mutual authentication scheme to register and exchange session keys among the IoV participants, and a credit-based trust management system to assign reputation scores for the vehicular devices. The latter scheme discards transactions with low credit scores. To overcome the complexity and variability of the IoV network, digital twin technology is used to map Road Side Units (RSU) servers into virtual space, which facilitates constructing the vehicular relation model. An Intrusion Detection System (IDS) based on deep learning techniques is also introduced to detect anomalies in the traffic flow. The legitimate data is further used by the blockchain scheme for transaction verification, block creation and addition. Finally, the proposed framework has been evaluated based on two network intrusion datasets, and the results show the accuracy and efficacy of STIoV in comparison to several recent state-of-the-art solutions.

School teachers, both experienced and novice, are bound to follow the predesigned K-12 curriculum focusing primarily on theoretical content knowledge. They have only limited opportunities to get acquainted with experiential teaching methods incorporating practical laboratory experiments. Deficiency of practical knowledge upskill programs predominantly affects teaching competence in subjects like basic electronics. Fostering electronics teaching competency is often ignored despite the higher significance of electronics. Further, there is a scarcity of research studies on the effectiveness of practical electronics training for school teachers. Against this backdrop, this paper explores the impact of a hands-on training cum experimentation program for school teachers organized by the IEEE Education Society (EdSoc) Kerala Chapter. Titled as ‘School Teachers' Electronics Practicals Upskilling Program (STEP-UP),‘ it envisioned upskilling school teachers of Kerala, a southern state in India. The STEP-UP was focused on basic electronics engineering for day-to-day applications. To study the impact of STEP-UP on school teachers, we used the Kirkpatrick model, an established method for evaluating training programs. The impact assessment of the training program is deliberated based on the revised Kirkpatrick model with the integration of STEP-UP keywords. It was inferred from the study that school teachers are interested in actively participating in practical skill development programs. Moreover, teachers' degree of involvement emphasizes the potential of such programs in enhancing teaching quality rooted in experiential learning. The paper ends with offering a few suggestions and recommendations in accordance with the research findings on the impact of STEP-UP.

Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability and trustworthiness of the data collected from participating entities is an important obstacle to threat detection and decision-making. To this end, we integrate smart contracts with eXplainable AI (XAI) to design a robust cybersecurity framework for ZTN. The proposed framework uses a blockchain and smart contract-enabled access control and authentication mechanism to ensure trust among the participating entities. Additionally, with the collected data, we designed Digital Twins (DTs) for simulating the attack detection operation in the ZTN environment. Specifically, to provide a platform for analysis and the development of an Intrusion Detection System (IDS), the DTs are equipped with a variety of process-aware attack scenarios. A Self Attention-based Long Short Term Memory (SALSTM) network is used to evaluate the attack detection capabilities of the proposed framework. Furthermore, the explainability of the proposed AI-based IDS is achieved using the SHapley Additive exPlanations (SHAP) tool. The experimental results using N-BaIoT and a self-generated DTs dataset confirm the superiority of the proposed framework over some baseline and state-of-the-art techniques.

Drones, also known as Unmanned Aerial vehicles(UAVs), are increasingly used in various applications, including agriculture, construction and infrastructure, environmental conservation, water resources management(WRM) etc. Multiple drones are interconnected with each other as UAV swarms. These UAV swarms offer a versatile and cost-effective tool for WRM. This paper proposes the multi-drone network for WRM known as HydroDrone. The purpose of HydroDrone is to provide valuable data and insights to support decision making and improve the resilience of water supply systems on the verge of ever-changing environmental conditions. In addition, we discussed task allocation to individual drones within the swarm. HydroDrone schedules and balances the task load for water resource management. We also introduced security to multi-drone communication with the base and core stations by incorporating blockchain technology due to its decentralized nature.

The proliferation of agricultural supply chain encompasses participants such as farmers, intermediate silos, transformation plants, and clients. Managing this agro-supply chain involves various functions related to the flow of both materials and information. Before entering the market, crop protection products and inputs undergo rigorous testing and regulatory scrutiny. Despite these measures, counterfeit products reach end-users due to insufficient transparency and the sharing of outdated information among stakeholders. To address this issue, the paper suggests a three-tiered integrated solution: the Product layer, Blockchain layer, and SSI layer. This strategy involves attaching Near-Field Communication (NFC) tags to the packages at the product layer, with the blockchain monitoring each step of the supply chain. The NFC tags can be read to verify the authenticity and other details of the product. Certifications for products, inputs, and the identities of dealers and consumers are stored as self-sovereign IDs in digital wallets. The authenticity details of producers undergo auditing by the certification authority, which is then transferred to the verification authority. The Verifier confirms these details and generates a verifiable presentation received by the consumer, enabling them to make informed purchases. This approach eliminates product tampering and the involvement of unverified producers and dealers in the supply chain. The comprehensive explanation and investigation of proposed framework state adequate guidelines to make counterfeit resistance agricultural supply chain system.

The engineering education today emphasizes the need to combine book learning with real-world application. However, much of the research done by undergraduates, which could be very valuable, is scattered and not fully used. To address this, a new tool called “AI-based Research Companion (ARC)” has been developed. ARC leverages advanced Generative AI technology, including GPT-4, to systematically organize, enhance, and offer personalized recommendations for undergraduate research projects. This platform is more than a simple tool; it aims to inspire undergraduates to dive into research by making the process approachable and engaging, thus increasing participation in research activities. Initial assessments of ARC have revealed an encouraging rise in student engagement with research, indicating a shift towards more research-oriented projects. The integration of GPT-4 within ARC stands out significantly; it precisely addresses the detailed demands of undergraduate research by providing a tailored, intelligent exploration pathway. By incorporating GPT-4's advanced features with a user-centric design, ARC emerges as an innovative platform, emphasizing the pivotal role of Generative AI in enhancing and expanding undergraduate research initiatives.

The consumer Internet of Things (IoT) applications in particular smart cities are mostly equipped with Internet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a smart city environment mostly use public channels to access and share data among different participants. This has introduced a great interest in using authentication and key agreement (AKA) mechanisms and intrusion detection systems (IDS) based on artificial intelligence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inability to explain the reasons behind the decision. In this direction, we have integrated blockchain-based AKA mechanism with explainable artificial intelligence (XAI) for securing smart city-based consumer applications. Specifically, first, the participating entities communicate with each other in a secure manner to exchange data using a blockchain-based AKA mechanism. On the other hand, we have used SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the prominent features that are most influential in the decision. The practical implementation of the proposed framework proves the efficiency over other recent state-of-the-art techniques.

The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.

The profound growth in the human population over the past two centuries has created a new issue in food security. Due to the high demand for food, there is an increasing burden on the agricultural supply chain (ASC) to satisfy the hunger of every individual. As a result, there is a possibility for spoilt or contaminated food to enter the ASC. If the end-consumer consumes these bad food products, it can lead to food poisoning and even death in certain circumstances. In order to ensure that the food delivered to the consumer is safe, it is necessary to monitor the food product as it passes through the different entities present in the ASC. The traditional ASC lacks traceability and reliability. Traceability is necessary in determining the origin of a crop, while reliability is necessary in preventing foul play by any entity. Therefore, developing a traceable and reliable system for the existing ASC model has become very important. The transparent, decentralized, and immutable qualities of Blockchain, along with the help from IoT devices, will allow us to actively trace the food from farm-to-fork as it passes through the supply chain while maintaining a high reliability between each entity. Thus, this paper proposes a novel ASC model, incorporated using Blockchain and IoT technology, to mitigate the traceability and reliability issues in the ASC.

Vehicle Road Cooperation Systems (VRCS) use next-generation Internet technologies, including 5G, edge computing, and artificial intelligence to improve mobility, comfort, and travel efficiency. Internet of Vehicles (IoV) ecosystem serves as the technological backbone for VRCS by enabling seamless communication and data exchange between vehicles, infrastructure, and traffic management centers. This enables real-time, high-speed communication, efficient data processing, and enhanced security, fostering the development of autonomous driving, smart traffic management, and seamless connectivity within the VRCS ecosystem. At the same time, cyber attacks have become more complex, persistent, organized, and weaponized in IoV network. Threat Intelligence (TI) has emerged as a prominent security approach to obtain a complete view of the dynamically growing cyber threat environment. On the other hand, modeling TI is a challenging task due to the limited labels available for different cyber threat sources. Second, most of the available designs requires a large investment of resources and use hand-crafted features, making the entire process error-prone and time-consuming. To tackle these challenges, this paper presents TIMIF, a deep-learning-based threat intelligence modeling and identification framework for Intelligent IoV and is based on three key modules: first, the proposed TIMIF adopts an Automated Pattern Extractor (APE) module to extract hidden patterns from IoV networks. Employing its output, we design a TI-Based Detection (TIBD) module to detect abnormal behavior and TI-Attack Type Identification (TIATI) module to identify attack types. Extensive experiments are carried out on three different publicly intrusion data sources namely HCRL-car hacking, ToN-IoT and CICIDS-2017 to illustrate the utility of TIMIF framework over some commonly used baselines and state-of-the-art techniques.

The integration of Unmanned Aerial Vehicles (UAVs) in smart agriculture has significantly enhanced precision farming practices, enabling real-time monitoring and data collection for improved crop management. However, the reliance on wireless communication in UAV networks poses security challenges that can compromise the integrity and confidentiality of sensitive agricultural data. This paper proposes a novel approach to address these concerns through the incorporation of blockchain technology for secure communication in UAV networks deployed for smart agriculture. The proposed system leverages the decentralized and tamper-resistant nature of blockchain to establish a trust-based communication framework. Each UAV node in the network is equipped with a blockchain-enabled communication protocol, ensuring that data exchanges are securely recorded in an immutable ledger. This not only enhances data integrity but also mitigates the risk of unauthorized access and manipulation. To facilitate secure communication, smart contracts are employed to automate and enforce predefined rules governing data transactions within the UAV network. This ensures that only authenticated and authorized entities can access and modify agricultural data, fostering a transparent and accountable ecosystem. Additionally, cryptographic techniques such as public-key encryption enhance the confidentiality of transmitted data, safeguarding sensitive information from eavesdropping and unauthorized interception. The proposed blockchain-enabled secure communication system is further enhanced by incorporating consensus mechanisms that validate and confirm the integrity of data across the network. By doing so, the trustworthiness of the entire UAV network is strengthened, reducing the likelihood of malicious activities and enhancing overall system resilience.