Knowledge Discovery in Big Data application to arabic handwriting characters recognition and genomic

Abstract

The exponential growth of Big Data necessitates scalable knowledge discovery methodologies capable of handling massive, heterogeneous datasets. This dissertation explores distributed and parallel deep learning architectures across two critical application domains: genomics and Arabic handwriting recognition. In genomics, a novel distributed pipeline for variant calling integrates multimodal sequencing data through a hybrid Transformer-CNN architecture with attention-based fusion. The system simultaneously processes DNA sequence context via transformer encoders and read alignment evidence through three-dimensional convolutional networks, enabling accurate genomic variant classification while achieving computational efficiency through Distributed Data Parallel (DDP) training across multiple GPUs. Multi-task learning addresses variant type classification, genotype prediction, quality score estimation, and artifact detection, while class-weighted loss functions handle severe data imbalance inherent in genomic datasets. In Arabic handwriting recognition, a hybrid architecture embedding Capsule Networks within Residual Networks (Caps-ResNet) captures hierarchical features and spatial relationships essential for cursive script analysis. This specialized architecture overcomes unique challenges including ligature complexity, diacritical mark sensitivity, and significant stylistic variability across writing styles. This multidisciplinary research demonstrates how hybrid architectures, multimodal fusion mechanisms, and distributed computing strategies enable robust, accurate, and computationally efficient knowledge extraction systems applicable to diverse scientific domains. The results contribute to the development of scalable deep learning solutions that bridge theoretical innovations with real-world applications in genomics and natural language processing.