Knowledge discovery researchers have recently made great progress on techniques to learn the governing equations of dynamic systems from system data. However, many of these techniques rely on significant prior knowledge about the system in question. One strong assumption is that researchers know a basis for the vector space the governing equations are in. In this paper we propose a technique to learn a set of functions that form a complete basis when only some of the basis functions are known beforehand by the researcher. We then empirically demonstrate our technique on real dynamical systems and show it is effective in learning a basis for the vector space that the governing equations are contained in.
Generative neural networks, particularly Generative Adversarial Networks (GANs), have gained a significant attention for their ability to generate new data. Moreover, transfer learning, known for its efficient processing and promising research outcomes, has emerged as a popular approach for feature extraction. This paper explores the application of the Triple-GAN model, which combines the advantages of GANs with transfer learning, particularly in the context of image classification. The study focuses on utilizing the Triple-GAN model combined with a pre-trained ResNet101 model for image classification. The evaluation of the proposed approach is conducted on the CIFAR-100 dataset, employing specific metrics to assess the model’s efficacy. The results highlight the promising nature of the proposed model, showcasing its potential for accurate image classification. Our model exhibits a remarkable level of accuracy reaching above 71.52 % outperforming other conventional ANN models. The significance of this research lies in its potential to advance image classification techniques by employing the Triple-GAN model with transfer learning as a viable alternative to traditional classification methods.
Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences.
Path planning plays a pivotal role in the successful deployment of multi-agent systems. However, the adaptability and scalability of traditional path planning algorithms falter in dynamic, complex environments. This paper introduces the Fuzzy A* algorithm – an innovative path planning approach that combines the deterministic A* algorithm with the adaptability of fuzzy logic. The proposed algorithm modifies the cost and heuristic functions dynamically in response to local environmental variables, offering a more robust solution to the challenges faced in large-scale, dynamic environments. This paper explains the principles underpinning the Fuzzy A* algorithm and explores its potential in complex scenarios. Through benchmarking against traditional path planning algorithms, we underscore the superior adaptability, efficiency, and scalability of the proposed algorithm. The study presents this innovative algorithm as a promising future solution in path planning for scalable multi-agent systems.
In neuroimaging techniques, deep learning technologies are used to analyze brain functionalities and extract beneficial features. Specifically, magnetic resonance images (MRI) and computed tomography (CT) scans are utilized to classify neurological diseases with various static and deep learning approaches. This paper describes the architecture of the deep learning system for detecting neurological disorders from MRI images. The system has been implemented with multiple layers of deep convolutional neural networks. An optimization method, grey wolf optimization, is used for tuning the hyperparameters. Other existing models for medical image classification are compared with the system we designed, and our system outperforms all for this particular dataset. The system can successfully detect the six most common neurological disorders, including Cerebral Aneurysm, Alzheimer's disease, Parkinson's disease, brain stroke, and schizophrenia.
