Blockchain-Enabled Federated Learning Approach for Vehicular Networks

Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system's accuracy stands at 91.92\%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.Comment: 7 page

Advanced Mutant Line Developed from Fatemadhan Shows Salinity Tolerance at both Seedling and Reproductive Stages

The generation of high-yielding rice mutants and their assessment under salt stress offers a great possibility to isolate salt tolerant line(s) with desired trait of interest. Two separate experiments were conducted at the seedling and reproductive stages of rice to assess the level of salinity tolerance of few advanced high-yielding rice mutants. In the first experiment, rice seedlings were grown under hydroponic conditions and 14-day-old seedlings were subjected to salt stress (EC=10 dS/m; 7 days). Salt stress caused significant reduction in root and shoot length and biomass and leaf chlorophyll content; however, a little reduction was found in the mutant Line-1. In contrast, a sharp increase in shoot Na+/K+ ratio was found in all the genotypes except, Binadhan-10, FL-478 and the mutant Line-1, which exhibited little increased ratio. The second experiment involved exposure of plant to salt stress (EC=10 dS/m) for three weeks at the late booting stage in a sizable plastic tub filled with field soil. Salt stress resulted in a significant decrease in yield and yield attributing traits in all the genotypes except Binadhan-10. Grain yield per panicle was found significantly positive correlation with panicle length, the number of filled grains per panicle, and 100-seed weight under both control and salt stress conditions. Based on the studied traits and stress tolerance indices, Binadhan-10 and mutant Line-1 categorized as salt tolerant and rest of the genotypes were categorized as susceptible, which is also evident from the biplot of principal component analysis. Considering the results from both of the experiments, mutant Line-1 was found tolerant genotype at both seedling and reproductive stage. However, further studies are required to determine the genetic issues controlling the salinity tolerance in mutant Line-1 and the high-yield potential of mutant Line-65 under control condition in a way to develop salt tolerant and high-yielding rice varieties, respectively

Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management