Expert Systems最新文献

Multiple kernel k-means clustering (MKKC) can efficiently incorporate multiple base kernels to generate an optimal kernel. Many existing MKKC methods all need two-step operation: learning clustering indicator matrix and performing clustering on it. However, the optimal clustering results of two steps are not equivalent to those of original problem. To address this issue, in this paper we propose a novel method named one-step multiple kernel k-means clustering based on block diagonal representation (OS-MKKC-BD). By imposing a block diagonal constraint on the product of indicator matrix and its transpose, this method can encourage the indicator matrix to be block diagonal. Then the indicator matrix can produce explicit clustering indicator, so as to implement one-step clustering, which avoids the disadvantage of two-step operation. Furthermore, a simple kernel weighting strategy is used to obtain an optimal kernel, which boosts the quality of optimal kernel. In addition, a three-step iterative algorithm is designed to solve the corresponding optimization problem, where the Riemann conjugate gradient iterative method is used to solve the optimization problem of the indicator matrix. Finally, by extensive experiments on eleven real data sets and comparison of clustering results with 10 MKC methods, it is concluded that OS-MKKC-BD is effective.

Multivariate time series have more complex and high-dimensional characteristics, which makes it difficult to analyze and predict the data accurately. In this paper, a new multivariate time series prediction method is proposed. This method is a generative adversarial networks (GAN) method based on Fourier transform and bi-directional gated recurrent unit (Bi-GRU). First, the Fourier transform is utilized to extend the data features, which helps the GAN to better learn the distributional features of the original data. Second, in order to guide the model to fully learn the distribution of the original time series data, Bi-GRU is introduced as the generator of GAN. To solve the problems of mode collapse and gradient vanishing that exist in GAN, Wasserstein distance is used as the loss function of GAN. Finally, the proposed method is used for the prediction of air quality, stock price and RMB exchange rate. The experimental results show that the model can effectively predict the trend of the time series compared with the other nine baseline models. It significantly improves the accuracy and flexibility of multivariate time series forecasting and provides new ideas and methods for accurate time series forecasting in industrial, financial and environmental fields.

This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO-SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO-SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state-of-the-art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real-world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta-heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO-SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non-gradient-based optimizers.

Traditionally, the design of an expert system involves acquiring knowledge, in the form of symbolic rules, directly from the expert(s), which is a complex and time-consuming task. Although expert systems approach is quite old, it is still present, especially where explicit knowledge representation and reasoning, which assure interpretability and explainability, are necessary. Therefore, machine learning methods have been devised to extract rules from data, to facilitate that task. However, those methods are quite inflexible in adapting to the application domain and provide no help in designing the expert system. In this work, we present a framework and corresponding tool, namely ACRES, for semi-automatically generating expert systems from datasets. ACRES allows for data preprocessing, which helps in structuring knowledge in the form of a tree, called rule hierarchy, which represents (possible) dependencies among data variables and is used for rule formation. This improves interpretability and explainability of the produced systems. We have also designed and evaluated alternative methods for rule extraction from data and for calculation and use of certainty factors, to represent uncertainty; CFs can be dynamically updated. Experimental results on seven well-known datasets show that the proposed rule extraction methods are comparable to other popular machine learning approaches like decision trees, CART, JRip, PART, Random Forest, and so on, for the classification task. Finally, we give insights on two applications of ACRES.

Hand gesture recognition and classification play a pivotal role in automating Human-Computer Interaction (HCI) and have garnered substantial attention in research. In this study, the focus is placed on the application of gesture recognition in surgical settings to provide valuable feedback during medical training. A tool gesture classification system based on Deep Learning (DL) techniques is proposed, specifically employing a Long Short Term Memory (LSTM)-based model with an attention mechanism. The research is structured in three key stages: data pre-processing to eliminate outliers and smooth trajectories, addressing noise from surgical instrument data acquisition; data augmentation to overcome data scarcity by generating new trajectories through controlled spatial transformations; and the implementation and evaluation of the DL-based classification strategy. The dataset used includes recordings from ten participants with varying surgical experience, covering three types of trajectories and involving both right and left arms. The proposed classifier, combined with the data augmentation strategy, is assessed for its effectiveness in classifying all acquired gestures. The performance of the proposed model is evaluated against other DL-based methodologies commonly employed in surgical gesture classification. The results indicate that the proposed approach outperforms these benchmark methods, achieving higher classification accuracy and robustness in distinguishing diverse surgical gestures.

Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well-developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high-quality open-access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease-related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning-based methods and giving the scientific community a starting point to improve CAD detection.