To address the critical global challenge of microplastic pollution and the limitations of alignment-dependent genomic tools in analyzing fragmented environmental data, this study introduces an alignment-free computational framework for identifying microbial bioremediation potential. We established a standardized, multi-domain dataset encompassing Bacteria, Protists, Archaea, and Fungi, integrating metadata on polymer interactions to fill existing data gaps. Utilizing tetranucleotide frequency patterns (k=4), we developed a novel analysis method to isolate predictive genomic signatures, identifying C-rich motifs such as ‘CCCC’ as primary indicators of degradation capability.A statistical scoring model was subsequently implemented to rank candidate taxa, effectively prioritizing high-value organisms from complex metagenomes derived from plastic-associated microbial assemblages. Our approach demonstrates significantly reduced computational time and more sensitive than the older methods, identifying potential degraders that alignment techniques often miss. It connects the theoretical side of detecting functional genes with real-world environmental checks, creating a flexible tool to speed up finding new microbes for tackling plastic waste and restoring ecosystems.
U. Luke, Emem Okon Abang, Etimbuk Daniel Akpan et al.· E3S Web of Conferences· 0 citations
The Internet of Medical Things (IoMT) is transforming healthcare delivery, but brings significant security and privacy challenges due to the diverse range of devices, sensitive patient data, and real-time operation requirements. Existing Intrusion Detection Systems (IDS) have improved detection and privacy through approaches such as federated learning and blockchain, yet they focus primarily on network-level attacks, overlooking device-level attacks which is the primary source of data leakage, device unavailability, and model poisoning in federated learning (FL)-based approaches. For instance, Bring Your Own Device (BYOD) introduces heterogeneity and Non-Independent and Identically Distributed data (non-IID) distributions that affect the performance of conventional FL approaches. We therefore propose an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead. To preserve privacy and handle the issue of non-IID data across heterogeneous IoMT devices, we propose an adaptive FedProx-based weighted federated learning framework. Our proposed framework achieves an overall accuracy of 99.1% on the edge with latency between 1.8ms per sample, with a mean global accuracy of 99.4% and global loss of 0.037 during convergence, making it highly suitable for real-world IoMT deployments.
Emmanuel Udok, B. Stephen, U. Luke et al.· E3S Web of Conferences· 0 citations
This paper presents a comprehensive multi-modal artificial intelligence framework for the prediction of disease from electronic health records that integrates ClinicalBERT natural language processing with graph neural networks, temporal modeling and explainability analysis. Using Synthea synthetic EHR dat with SNOMED CT codes from 1,171 patients, our approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships. The system achieves predictive performance with macro-averaged F1 score of 0.4512 and AUC of 0.9071 across six chronic conditions, demonstrating outstanding results for diabetes (F1=0.900) and hypertension (F1=0.949). Novel contributions include temporal progression forecasting over 12-month periods using LSTM-Transformer hybrid architecture and comprehensive explainability framework providing gradient-based feature importance analysis and automated clinical reasoning generation. The frameworks successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support system.
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations