Jin-Ghoo Choi

The integration of Vehicular Ad Hoc Networks (VANETs) has changed intelligent transportation systems by making it possible for vehicles, roadside units (RSUs), and traffic management infrastructure to talk to each other in real time. This feature makes the roads safer, more convenient for drivers, and more efficient for traffic, but it also makes VANET ecosystems vulnerable to many types of cyberattacks, including Denial‐of‐Service (DoS) and false data injection, which can be very dangerous for safety and privacy. Conventional security solutions frequently struggle to address the highly dynamic, decentralized, and latency‐sensitive characteristics of VANET environments. Intrusion Detection Systems (IDS) powered by Artificial Intelligence (AI) have become promising solutions, but there are still issues with computational overhead, secure model updates, and data privacy in distributed vehicular networks. To address these challenges, we introduce Quantum Lightweight Federated Learning (FL), an innovative hybrid machine learning framework that integrates the exponential computational power of Quantum Computing (QC) with the decentralized, privacy‐preserving advantages of FL. The suggested method combines knowledge distillation with the FL process to create a lightweight detection model that works well on vehicle nodes with limited resources. Moreover, QKD Encryption is used to protect model parameters during federated aggregation, making sure that end‐to‐end privacy is maintained without slowing down processing. Lastly, SHAP, an Explainable AI method, is used to make sense of the choices made by the proposed model. Using the CICDDoS‐2019 dataset for experimental validation shows that the proposed model is strong, with an accuracy of 99.36%, a high recall rate of 99.53%, and a precision rate of 99.38% across different attack scenarios.

Heart disease remains a leading cause of global morbidity and mortality, emphasizing the need for early and accurate prediction. Traditional models, howeve

The rapid proliferation of 6G-enabled Consumer Electronics (CE) has introduced significant security and privacy challenges. Traditional security mechanisms often fall short in addressing these issues due to the unique characteristics of CE networks. To enhance Intrusion Detection Systems (IDS), data-driven Artificial Intelligence (AI) approaches have gained considerable attention. Nonetheless, AI-based IDS face challenges related to scalability, privacy preservation, and high computational demands—particularly when handling high-dimensional and complex data. To overcome these limitations, this paper proposes a novel framework called Blockchain and Quantum Federated Learning (BQFL). BQFL integrates blockchain technology and quantum computing with Federated Learning (FL) to provide an efficient, secure, robust, and privacy-preserving solution for intrusion detection in CE environments. Specifically, blockchain enables fault-tolerant and decentralized trust management for parameter aggregation on the Quantum FL (QFL) server. Furthermore, the framework leverages the high-speed, low-latency capabilities of 6G networks to enable real-time and secure data processing and communication among a vast number of CE devices. We validate the effectiveness of the proposed framework through extensive experiments on ToN-IoT dataset.

Cleaning and inspection of pipelines and gun barrels are crucial for ensuring safety and integrity to extend their lifespan. Existing automatic inspection approaches lack high robustness, as well as portability, and have movement restrictions and complexity. This study presents the design and development of a scalable, comprehensive automated inspection, cleaning, and evaluation mechanism (CAICEM) for large‐sized pipelines and barrels with diameters in the range of 105 mm–210 mm. The proposed system is divided into electrical and mechanical assemblies that are independently designed, tested, fabricated, integrated, and controlled with industrial grid controllers and processors. These actuators are suitably programmed to provide the desired actions through toggle switches on a simple housing subassembly. The stress analysis and material specifications are obtained using ANSYS to ensure robustness and practicability. Later, on‐ground testing and optimization are performed before industrial prototyping. The inspection system of the proposed mechanism includes barrel‐mounted and brush‐mounted cameras with sensors utilized to keep track of the pipeline deposits and monitor user activity. The experimental results demonstrate that the proposed mechanism is cost‐effective and achieves the desired objectives with minimum human efforts in the least possible time for both smooth and rifled large‐diameter pipes and barrels.

Pregnancy complications in the early months of the family process can lead to miscarriage. Miscarriage does not occur due to only one reason; many factors are involved in causing miscarriage. Deep Learning (DL) can help healthcare providers by providing advanced analysis. In this study, we have proposed two novel ensemble models, Echo Dense Inception Blending (EDI-Blend) and Dense Reservoir Inception Modular Network (DRIM-Net), for miscarriage prediction. The dataset is balanced through the use of the hybrid balancing technique NearSMOTE. As the dataset is high-dimensional, we have used the Absolute Shrinkage and Selection Operator to select essential features from the dataset that significantly impact pregnancy complications. We validate the output of our proposed EDI-Blend and DRIM-Net models using 10-Fold Cross Validation. To determine the contribution of features for miscarriage prediction two eXplainable Artificial Intelligence techniques are applied to EDI-Blend and DRIM-Net: Interpretable Model-agnostic Explanations and SHapley Additive exPlanations. We have compared our proposed EDI-Blend model with base models, and the results show that EDI-Blend model performance is more efficient, with 0.732 accuracy, 0.721 recall, 0.732 F1-score, and 0.721 Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). The DRIM-Net model is also compared with baseline models and achieves 0.768 F1-score, 0.764 precision, 0.769 accuracy, 0.769 recall, and 0.837 ROC-AUC.

The smart and sustainable networks require highly connected systems that can improve their operational performance, reduce environmental impact, and increase functional efficiency