Object detection is crucial for individuals with visual impairment, especially when waiting for a bus. In this study, we propose a lightweight and highly accurate bus detection model based on an improved version of the YOLOv5 model. We propose integrating the GhostConv and C3Ghost Modules into the YOLOv5 network to reduce the number of parameters and floating-point operations per second (FLOPs), ensuring detection accuracy while reducing the model parameters. Following that, we added the SimSPPF module to replace the SPPF in the YOLOv5 backbone for increased computational efficiency and accurate object detection capabilities. Finally, we developed a Slim scale detection model by modifying the original YOLOv5 structure in order to make the model more efficient and faster, which is critical for real-time object detection applications. According to the experimental results, the Improved-YOLOv5 outperforms the original YOLOv5 in terms of the precision, recall, and mAP@0.5. Further analysis of the model complexity reveals that the Improved-YOLOv5 is more efficient due to fewer FLOPS, with fewer parameters, less memory usage, and faster inference time capabilities. The proposed model is smaller and more feasible to implement in resource-constrained mobile devices and a promising option for bus detection systems.

Advancements in automation and artificial intelligence have significantly impacted accessibility for individuals with visual impairments, particularly in the realm of bus public transportation. Effective bus detection and bus point-of-view (POV) classification are crucial for enhancing the independence of visually impaired individuals. This study introduces the Improved-YOLOv10, a novel model designed to tackle challenges in bus identification and pov classification by integrating Coordinate Attention (CA) and Adaptive Kernel Convolution (AKConv) into the YOLOv10 framework. The Improved YOLOv10 advances the YOLOv10 architecture through the incorporation of CA, which enhances long-range dependency modeling and spatial awareness, and AKConv, which dynamically adjusts convolutional kernels for superior feature extraction. These enhancements aim to improve both detection accuracy and efficiency, essential for real-time applications in assistive technologies. Evaluation results demonstrate that the Improved-YOLOv10 offers significant improvements in detection performance, including better Accuracy, Precision and Recall compared to YOLOv10. The model also exhibits reduced computational complexity and storage requirements, highlighting its efficiency. While the classification results show some trade-offs, with slightly decreased overall F1 score, the complexity of Giga Floating Point Operations (GFLOPs), Parameters, and Weight/MB in the Improved-YOLOv10 remains advantageous for classification tasks. The model’s architectural improvements contribute to its robustness and efficiency, making it a suitable choice for real-time applications and assistive technologies.

Freshwater fish is a type of fish for consumption to substitute saltwater fish in areas far from the sea. Freshwater fish production is not as much as saltwater fish because the selection of freshwater fish is not suitable in the cultivation process. Therefore, a classification method to determine the type of freshwater fish that is good for aquaculture is proposed, one of the classification methods that can be used is the Support Vector Machine (SVM). SVM is used to process the classification of freshwater fish that have the potential to be cultivated. SVM has weaknesses in the classification process, so SVM requires an optimization method that can overcome it's weaknesses, one optimization method that can be used is Improved Eliminate Particle Swarm Optimization (IEPSO). IEPSO is used to optimize features and parameters in the SVM method. The data used in the research came from the Fish and Livestock Breeding Center of Nganjuk Regency with a percentage of 90% of training data and 10% of test data. The results of the study using SVM-IEPSO using feature selection and parameter optimization obtained 88% accuracy in the classification process to determine the type of freshwater fish. The classification results will be more optimal if there are more types of freshwater fish species used in the training process, because this study only uses data on freshwater fish species that are only found in Livestock Breeding Center of Nganjuk Regency.

Milkfish is a cultivation commodity that contributes quite significantly on a national scale. But in practice, milkfish cultivation requires a large number of production costs, especially in the provision of fish feed. This research will discuss how to optimize the composition of milkfish feed, minimize costs without lowering production yields, and still pay attention to the nutritional needs of milkfish cultivation. The optimization method, Particle Swarm Optimization (PSO) is applied to the formulation process and the composition of milkfish feed to meet the nutritional needs of fish with minimal cost. Improvements to the PSO resulted in Adaptive Particle Swarm Optimization (APSO). This improvement conducted to strengthen PSO's ability to explore broad areas of search for solutions efficiently. The results of computational analysis prove that APSO produces a better solution than PSO.