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Levees are earthen structures constructed to mitigate flooding in low-lying areas. Although levee systems can reduce flood risks, they cannot completely eliminate them. Failures within flood control systems due to inadequate maintenance or strong water currents can lead to significant property damage and catastrophic loss of life, as was seen during Hurricane Katrina. Consequently, regular inspections are essential to identify and address any issues with the levees promptly. However, current inspection methods rely on manual techniques that are time-consuming, labor-intensive, and prone to human error. Therefore, this study proposes using deep learning models for more efficient and frequent assessment of levee systems. The research suggests employing deep-learning architectures to detect three primary deficiencies: cracks, sand boils, and seepages using a semantic segmentation approach. The proposed models focus on lightweight designs, incorporating transfer learning techniques and enhancing feature representation to improve the accuracy and efficiency of fault segmentation using images within flood control systems. Moreover, these models are evaluated with real-world data specific to levees, allowing for their effectiveness in handling various faults exhibiting variations and complexity. As a result, the research enables pixel-level fault detection for AI-based inspection of levee systems, providing valuable support to existing assessment procedures. Furthermore, the study's findings have the potential to extend beyond levee inspection, with implications for broader applications of deep learning in semantic segmentation for real-world scenarios. Keywords: Levee; Faults; Deep learning; Images; Detection; Segmentation

Transposable Elements (TEs) or jumping genes are the DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even promote gross genetic arrangements. Thus, the proper classification of the identified jumping genes is important to understand their genetic and evolutionary effects. While computational methods have been developed that perform either binary classification or multi-label classification of TEs, few studies have focused on their hierarchical classification. The existing methods have limited accuracy in classifying TEs. In this study, we examine the performance of a variety of machine learning (ML) methods and propose a robust augmented Stacking-based ML method, ClassifyTE, for the hierarchical classification of TEs with high accuracy.