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Synthetic Data Generation and its applictaon in Supply Chain: A survey

Synthetic data generation has emerged as a powerful tool for addressing data scarcity, enhancing privacy, and enabling robust machine learning applications across various domains. In supply chain management, synthetic data plays a crucial role in optimizing operations, forecasting demand, and improving decision-making processes. By simulating realistic yet artificially generated datasets, businesses can experiment with diverse scenarios without risking sensitive information. This survey explores the landscape of synthetic data generation methods

Personalised Federated Learning

The similarity-based sorting and grouping federated learning (SiRGFL) algorithm is proposed to be a unique method used to compute the similarity of model performance across all clients. Client-specific performance data is collected through pre-training of federated learning and the iterative data is displayed in a high-dimensional space and downscaled as well as permutated to obtain a federated learning grouping ranking that balances the model performance with generalizability.

Bofinformatics

Using convolutional neural networks for denoising and phasing mixed haplotypes from nanopore sequence data in Plasmodium falciparum.

Anomaly Detection

Develop an adaptive image detection system in the robotics lab to analyze multimodal data and integrate an intelligent recognition system based on the “anomalib” library to quickly detect unknown anomalies.