Omar Hujran, Department of Statistics and Business Analytics, United Arab Emirates University, Al Ain, United Arab Emirates
This research report provides a comprehensive analysis of Compact Composite Descriptors (CCDs) as a highly ef icient alternative to deep learning embeddings for Content-Based Image Retrieval (CBIR) in resource-constrained environments. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) of er superior semantic performance, their computational overhead and storage requirements—often exceeding 8KB per image—limit their applicability in Edge AI and IoT scenarios. In contrast, engineered descriptors such as the Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH), and Joint Composite Descriptor (JCD) utilize fuzzy inference systems to encode visual features into ultra-compact vectors ranging from 54 to 72 bytes. The study explores the algorithmic foundations of these descriptors, their implementation within the LIRE (Lucene Image Retrieval) framework, and benchmarks demonstrating their competitive retrieval accuracy against MPEG-7 standards. Finally, the report highlights the strategic utility of CCDs for privacy-preserving, low-bandwidth visual search on edge devices, proposing hybrid architectures that leverage the speed of fuzzy composites with the semantic power of neural re-ranking.
ChatGPT, Education, Conversational quality, Learning outcomes, Ethical concerns
Jesus Antonio Motta1 and Pedro David Gomez 2, 1Laval University ,Quebec (Canada), 2 Foundation University of Health Sciences, Bogota (Colombia)
In this work, we present a highly efficient machine learning method for identifying DNA sequences that code for genes. The learning process is based on Human Genome Build 38 (GRCh38) sequences extracted from various specialized databases. The sequences were then translated into amino acid sequences and used to build matrices that facilitate the extraction of features with the TF*IDF metric for the creation of the training space. The prediction functions are learned using a convolutional neural network (CNN) deep learning model. The training spaces were created using the 24 chromosomes of the human genome and approximately 36,000 genes and pseudogenes whose names were fetched from the HUGO Gene Nomenclature Committee (HGNC). Performance analysis was performed on 24 genes associated with genetic disorders, as well as the surrounding DNA regions. The metrics used were precision, recall, F_score measure, accuracy and ROC curves for the genes of interest. The results achieved exceed all our expectations and place the work at the level of the state of the art for gene prediction.
DNA, Amino-Acids, TF×IDF, CNN, Genetic Disorder, Learning Model
