Class imbalanced data set is a state where each class of the given data set is not evenly distributed. When such case happens, most standard classifiers fail to recognize examples that belong to a minority class. Hence, several methods have been proposed to solve this problem such as resampling, modification on classifier optimization problem or introducing a new optimization task on top of the classifier. This work proposes a new optimization task based on genetic programming, built on top of support vector machine, in order to improve the classification rate for minority class without significant reduction on accuracy metric. The experimentation carried out on wilt disease data set shows the new classifier, support vector based on genetic programming machine, gives a more balanced accuracy between classes compared to various classification techniques in solving the imbalanced classification problem.

Large language model (LLM)-based program generation tasks are hindered by high computational demands. These challenges, along with high deployment costs, often pose a barrier to practical applications. To address these, we propose a novel data-augmented multi-LLM model routing approach that classifies prompts based on whether they should be processed on a weak LLM engine or a strong LLM. Experimental results show up to 16 times better efficiency compared to the existing cascaded approaches, while preserving the inference accuracy. Thus, the proposed method optimally allocates prompts across multiple LLMs, reducing computational costs while maintaining inference accuracy.

Small or tiny object detection has gained significant attention from researchers in recent years due to its wide range of applications across various domains. This highlights its status as a promising research area. The study employs bibliometric analysis to understand the current research status and future trends in small or tiny object detection. By analyzing documents published between 2015 and 2024 from the Scopus database, we map the quantitative structure of this research area, identify the most influential publications and key contributions, and uncover potential research directions. The contribution of this paper is to provide a comprehensive bibliometric analysis of a decade of research on small or tiny object detection, a topic that has not been previously published. A total of 1627 publications were analyzed. Our findings reveal a significant increase in the number of publications over the decade, indicating growing research interest. China leads the field, contributing the majority of publications, followed by the USA and South Korea. Foundational contributions, such as "Inside-Outside Net" and GAN-based approaches, are highlighted for their impact on detection methodologies. Key topics include deep learning, convolutional neural networks, feature extraction, and object recognition. The analysis also identifies major challenges such as data scarcity, computational efficiency, and environmental complexities like occlusion and background clutter. Emerging trends include lightweight architectures, multiscale feature fusion, and edge computing, reflecting a shift toward resource-efficient implementations for practical applications. This study provides foundational insights into the current state of the field and highlights directions for future research, aiming to guide scholars and practitioners in advancing the technology of small or tiny object detection.