Computational Intelligence最新文献

In the past few years, the swift advancement of remote sensing technology has greatly promoted its widespread application in the agricultural field. For example, remote sensing technology is used to monitor the planting area and growth status of crops, classify crops, and detect agricultural disasters. In these applications, the accuracy of image classification is of great significance in improving the efficiency and sustainability of agricultural production. However, many of the existing studies primarily rely on contrastive self-supervised learning methods, which come with certain limitations such as complex data construction and a bias towards invariant features. To address these issues, additional techniques like knowledge distillation are often employed to optimize the learned features. In this article, we propose a novel approach to enhance feature acquisition specific to remote sensing images by introducing a classification-based self-supervised auxiliary task. This auxiliary task involves performing image transformation self-supervised learning tasks directly on the remote sensing images, thereby improving the overall capacity for feature representation. In this work, we design a texture fading reinforcement auxiliary task to reinforce texture features and color features that are useful for distinguishing similar classes of remote sensing. Different auxiliary tasks are fused to form a multi-view self-supervised auxiliary task and integrated with the main task to optimize the model training in an end-to-end manner. The experimental results on several popular few-shot remote sensing image datasets validate the effectiveness of the proposed method. The performance better than many advanced algorithms is achieved with a more concise structure.

Optimizing crop production is essential for sustainable agriculture and food security. This study presents the AgriFusion Model, an advanced ensemble-based machine learning framework designed to enhance precision agriculture by offering highly accurate and low-carbon crop recommendations. By integrating Random Forest, Gradient Boosting, and LightGBM, the model combines their strengths to boost predictive accuracy, robustness, and energy efficiency. Trained on a comprehensive dataset of 2200 instances covering key parameters like nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, and crop type, the model underwent rigorous preprocessing for data integrity. The RandomizedSearchCV method was employed to do hyperparameter tuning, namely improving the number of trees in the Random Forest algorithm and the learning rates in the Gradient Boosting algorithm. This ensemble approach achieves a remarkable accuracy rate of 99.48%, optimizes computer resources, lowers carbon footprint, and responds efficiently to a variety of agricultural situations. The model's performance is confirmed using metrics including cross-validation, accuracy, precision, recall, and F1 score. This demonstrates how the model might improve agricultural decision-making, make the most use of available resources, and promote ecologically responsible farming practices.

Multivariate time series data classification has a wide range of applications in reality. With rapid development of deep learning, convolutional networks are widely used in this task and have achieved the current best performance. However, due to high difficulty and cost of collecting this type of data, labeled data is still scarce. In some tasks, the model shows overfitting, resulting in relatively poor classification performance. In order to improve the classification performance under such situation, we proposed a novel classification method based on transfer learning—similar domain knowledge transfer (call SDKT for short). Firstly, we designed a multivariate time series domain distance calculation method (call MTSDDC for short), which helped selecting the source domain that is most similar to target domain; Secondly, we used ResNet as a pre-trained classifier, transferred the parameters of the similar domain network to the target domain network and continue to fine-tune the parameters. To verify our method, we conducted experiments on several public datasets. Our study has also shown that the transfer effect from the source domain to the target domain is highly negatively correlated with the distance between them, with an average Pearson coefficient of −0.78. For the transfer of most similar source domain, compared to the ResNet model without transfer and the current best model, the average accuracy improvements on the datasets we used are 4.01% and 1.46% respectively.

The advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time-consuming. In the last decades, artificial intelligence-based computer-aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time-distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.

AI systems for decision-making have become increasingly popular in several areas. However, it is possible to identify biased decisions in many applications, which have become a concern for the computer science, artificial intelligence, and law communities. Therefore, researchers are proposing solutions to mitigate bias and discrimination among decision-makers. Some explored strategies are based on GANs to generate fair data. Others are based on adversarial learning to achieve fairness by encoding fairness constraints through an adversarial model. Moreover, it is usual for each proposal to assess its model with a specific metric, making comparing current approaches a complex task. Therefore, this work proposes a systematical benchmark procedure to assess the fair machine learning models. The proposed procedure comprises a fairness-utility trade-off metric ($��\mathrm{FU-score}�$), the utility and fairness metrics to compose this assessment, the used datasets and preparation, and the statistical test. A previous work presents some of these definitions. The present work enriches the procedure by increasing the applied datasets and statistical guarantees when comparing the models' results. We performed this benchmark evaluation for the non-generative adversarial models, analyzing the literature models from the same metric perspective. This assessment could not indicate a single model which better performs for all datasets. However, we built an understanding of how each model performs on each dataset with statistical confidence.

Spiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike-timing-dependent plasticity (STDP) is an unsupervised learning process that utilizes bio-plausibility based on the relative timing of pre/post-synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps STDP to converge synaptic weights directly based on input data features: Adaptive synaptic template (AST). AST leads synaptic weights to describe synaptic connections according to the data features. It prevents STDP from changing synaptic weights based on abnormal weights that fail to describe the proper synaptic connections. This is because it does not use the current synaptic weights that can disturb proper weight convergence. We integrate AST with an SNN and conduct experiments to compare it with a baseline (the SNN without AST) and benchmarks (previous works to improve STDP). Our experimental results show that the SNN using AST classifies various data sets with 6%–39% higher accuracy than the baseline and benchmarks.