Deep learning for Chilean native flora classification: a comparative analysis

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed.

Power enterprise risk identification model based on convolutional neural network and adaptive comparison algorithm

Combined with the actual characteristics of risk identification in electric power enterprises, a convolutional neural network model suitable for load sequence data prediction is determined. Particle Swarm Optimization (PSO) algorithm is used to transform the convolutional neural network (convolutional neural network) to improve the global Optimization ability and convergence speed. Simulation results show that CNN can effectively extract sample information through its convolutional layer and pool layer. After particle swarm optimization, it also achieves good results in prediction accuracy and prediction speed. Secondly, classical interpretation combination model (ISM) is used to analyze the structure of the risk system of electric power enterprises, and the link relationship model of the risk of electric power enterprises is constructed. Through the structural analysis of risk and risk factors, the paper finds out the mutual influence relationship between risk and risk factors, and further finds out the risk chain and risk source. The classical explanatory structure model is extended to the fuzzy set, and then the influence intensity model of power enterprise risk is built. This model considers the influence of risk intensity when analyzing the risk relationship of electric power enterprises, and gives different risk link relations based on different impact intensity. Through comparative analysis, the relationship between the link relationship model and the influence intensity model of the risk of electric power enterprises is obtained. Put forward the sequence similarity matching algorithm based on adaptive search window (ADTW), average algorithm using Piecewise gathered (Piecewise Aggregate Approximation, PAA) strategy for sequence sampling sequence, low precision and low calculation precision sequence alignment of paths, and according to the change of gradient on the low precision of distance matrix forecast path deviation, expand the scope of limiting path search window; Then, the algorithm gradually improves the sequence accuracy, corrects the path in the search window, calculates the new search window, and finally realizes the fast solution of DTW distance and similarity alignment path.

Medical image management and analysis system based on web for fungal keratitis images

The medical image management and analysis system proposed in this paper is a medical software developed by the Browser/Server (B/S) architecture after investigating the workflow of the relevant departments of the hospital, which realizes the entire process of patients from consultation to printing of reports. The computer-aided diagnosis function is added based on image management. Due to the difficulty in collecting medical image data, in the computer-aided diagnosis module, this paper only uses the common fungal keratitis collected from the hospital in the laboratory. Focused microscope images are used for experiments. First, the images were trained with three convolutional neural networks, AlexNet, ZFNet, and VGG16. These models which classify fungal keratitis were obtained and integrated was performed to obtain better classification results. Finally, the model was integrated with the system designed in this paper, which realized the automatic diagnosis of Confocal Microscopy (CM) images of fungal keratitis online and provided it to medical staff for reference. The system can improve the work efficiency of the image-related departments while reducing the workload of doctors in the department to manually read the films.